\r\n\tThe objective of this book is to provide a state-of-the-art review of the use of timber in building construction from various perspectives, including manufacturing, fabrication, modeling, design, and construction of residential and other types of buildings. Of special interest will be contributions related to new developments in timber technologies, design, construction, testing, sustainability, LCA, building envelope, and the performance of timber buildings in natural and man-made hazard conditions.
",isbn:"978-1-83768-263-8",printIsbn:"978-1-83768-262-1",pdfIsbn:"978-1-83768-264-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"356565153fc7e43f1bf0cb7ba5e7b28a",bookSignature:"Prof. Ali M. Memari",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12057.jpg",keywords:"Wood, Lumber, Timber Industry, Home Building, Glue-Laminated Wood, Cross-Laminated Timber, Plywood, Fire Resistance, Sustainability, Fabrication, Panelized/Modular, Material Properties",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 31st 2022",dateEndSecondStepPublish:"June 28th 2022",dateEndThirdStepPublish:"August 27th 2022",dateEndFourthStepPublish:"November 15th 2022",dateEndFifthStepPublish:"January 14th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Memari is a Professor and Bernard and Henrietta Hankin Chair in Residential Building Construction in the Departments of Architectural Engineering and Civil and Environmental Engineering. During his 30 years of teaching in structural engineering, his research focused on the behavior of structural, architectural, and enclosure components of residential and commercial buildings under natural hazard loading and environmental conditions. He has published over 300 publications.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"252670",title:"Prof.",name:"Ali",middleName:null,surname:"M. Memari",slug:"ali-m.-memari",fullName:"Ali M. Memari",profilePictureURL:"https://mts.intechopen.com/storage/users/252670/images/system/252670.jpg",biography:"Dr. Memari is a Professor and Bernard and Henrietta Hankin Chair in Residential Building Construction in the Departments of Architectural Engineering and Civil and Environmental Engineering at Penn State, and Director of The Pennsylvania Housing Research Center. 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\n
1. Introduction
\n
According to the American Academy of Ophthalmology the medical term glaucoma is used for a group of diseases that damage the optic nerve (ON) as distinctive type of optic neuropathy characterized by structural (cupping of optic nerve head—ONH, changes in connective tissue structural elements and number of the nerve fibers in ON) and functional changes (typical visual field defects). Increased intraocular pressure (IOP) is one of the most common risk factors associated with developing and progressing of the disease, but its presence or absence does not change the above-mentioned glaucoma definition [1].
\n
Epidemiological studies found that the glaucoma at the end of twentieth century covered more than 60 million people around the world. Prognostic studies show an increasing trend of the number of affected patients, and in 2020 it is going to be approximately 80 million people, and in 2040 approximately 112 million [2, 3, 4]. Cataract and glaucoma are leading causes of blindness worldwide. Because of the reversibility of the vision after cataract extraction, the glaucoma remains the leading cause of irreversible blindness. The large number of glaucoma patients, irreversible vision loss and the impact on the life quality of the affected people are just part of the reasons for making glaucoma one of the diseases with big social influence.
\n
Glaucoma is characterized by irreversible loss of ganglion cells, which axons form the ON. Ganglion cells are localized in three retinal layers—inner plexiform layer or IPL (their dendrites), ganglion cell layer or GCL (their bodies), and retinal nerve fiber layer or RNFL (their axons). Therefore exactly the above-mentioned layers are those, which glaucoma affects accompanying typical visual field defects [5]. Chronic and progressive loss of neuroretinal tissue is cardinal feature of glaucomatous optic neuropathy (GON) and criterion for diagnosis [6].
\n
\n
1.1. Anatomical aspects of the RNFL
\n
All afferent pathways in the ON start from a layer of photoreceptors (cones and rods), which is located in the retina on area more than 1000 mm2. In ONH all fibers are concentrated on surface with approximately 2–3 mm2 area [7, 8]. From the body of each ganglion cell comes out a nerve fiber or axon, which moves toward the ONH. So that it can be called conglomerate consisted of all converging axons, which are as mentioned above part of the retina and form a layer—RNFL [9]. Nowhere else the ganglions’ nerve fibers are not so much compact as they are in ONH, and this is what determines the importance of peripapillary RNFL thickness in diagnosis and follow up of patients with GON.
Papillomacular nerve fiber bundle—it starts from ganglion cells in the foveolar region. The nerve fibers from nasal foveolar area move straight toward the temporal border of ONH, and those from the temporal part make a slight arc around the nasal nerve fibers and then join to the straight bundle.
Superior and inferior retinal arcades—they are created by later formed nerve fibers and ganglion cells originating temporal to the fovea. They arc around the macula and papillomacular bundle to enter the ONH.
Temporal raphe or seam—it extends from the fovea to the temporal part of the retina and consists of very few axons, because by rule the nerve fibers from the upper half of the retina do not pass the horizontal meridian to the arcuate course of the nerve fibers from the lower part of the retina, and vice versa.
An extremely large collection of nerve fibers in the superior and inferior quadrant of the ONH—namely these two regions are set to be more vulnerable for glaucomatous damages.
Nasal nerve fibers—they move radially toward the ONH.
Exact location of the nerve fibers in the ONH according to their position in the fundus—the more peripheral retinal location the more central ONH localization.
\n
Basic features, used to make an assessment of RNFL images [9]:
Striations of RNFL—normally RNFL can be seen as striated bright and dark lines in the areas of superior and inferior temporal blood vessels in healthy eyes. If atrophy is presented (<50 μm RNFL thickness) the striations of the background disappear and bright lines cannot be seen because of the RNFL loss [11] (see Figure 1).
Defects of the background brightness—they can be diffuse loss and localized defects (wedge-shaped and cleft-shaped). The width in the cleft-shaped defects is the same along the full length, however the width in wedge-shaped defects is different, peripherally they are wider and become narrower toward the ONH. This could be explained with convergent course of the nerve fibers. Diffuse defects have an impact over the complete RNFL thickness in the fundus and also their diagnosis is more difficult from localized defects.
Visualization of the blood vessels—normally RNFL covers retinal blood vessels. That’s why small and medium-sized blood vessels have unclear contours and look misty. When RNFL atrophy appears, then blood vessels can be seen clearly because of the less covering from the nerve fiber layer.
\n
Figure 1.
RNFL striations in superior temporal area of the fundus in healthy eye of 52 years old female.
\n
All nerve fibers are arranged in a specific way in the ONH not only in each and every human being but also in each and every of the human eyes. Equal quantity nerve fibers may look in a different way in the borders of ONHs with dissimilar disc area, depth of the lamina cribrosa, and height of the scleral canal [12]. Equal functional capacitate could be presented by different looking structures and vice versa—equal looking structures could have different functional activity [13, 14, 15].
\n
The RNFL thickness depends on: age, ethnicity, number and thickness of the nerve fibers, quantity of the glia, quantity of the blood vessels, disk area of the ONH, axial length of the eye (Ax). The thickness of the measured RNFL depends also on: the stage of peripapillary atrophy/conus myopicus, vitreoretinal tractions. The excavation (cupping) depends on: disc area, number and thickness of the nerve fibers, quantity of the glia [14]. Normally in the course of time the RNFL thickness decreases with age normally with 4000–5000 axons per year [16, 17, 18, 19] and this is approximately 2.0 μm/decade or 0.2% per year at mean thickness 100 μm [20]. The ON consists of 700,000–1.4 million nerve fibers and the RNFL thickness in healthy people has a wide variety of a norm. The usage of absolute values restricts the process of distinguishing healthy from glaucoma patients [20]. Therefore some authors talk about “modulation of RNFL thickness”—it shows the relative loss of nerve fibers as difference between the biggest and smallest measured value of RNFL thickness in a retinal region of interest [21].
\n
\n
\n
1.2. RNFL and glaucoma
\n
When assessing glaucomatous damages it is appropriate to measure the RNFL thickness, because thinning of this layer correlated directly with ganglion cells loss, which is the basic pathophysiological event [22]. Evaluation of the RNFL thickness is important for early glaucoma diagnosis before appearing of the clinical manifestations of the disease. It is proven that 40–50% of the nerve fibers are should be dropped out before developing of the visual field defects [23]. Clinical evaluation of the RNFL with red-free photography shows that thinning of the layer can be seen in 60% of the pictures 6 years before appearing of clinical manifestation of the defects in visual field [24]. These facts show that structural changes occur before the functional ones. Typical visual field defects in glaucoma are nasal step, arcuate scotoma, paracentral scotoma, generalized depression, and progressive worsening of the indices of the standard automated perimetry (SAP) [25].
\n
Sometimes in glaucoma visual field defects can be seen without appearance of structural glaucomatous changes. It is possible also in equal RNFL loss to be obtained a different clinical finding according to initial RNFL thickness. This could be explained with the following: visual field defects appear after 40% loss of the nerve fibers. Each man is born with different quantity nerve fibers. If a person owns very thick for human population RNFL, the loss of 40% nerve fibers probably will not give any significant results in optical coherence tomography (OCT)—the line thickness will be in the middle of the green zone, the zone shows lack of disease. Then this individual is going to have functional defects with normal RNFL thickness. If another person is born with thin RNFL, the loss of 40% nerve fibers will give significant results—OCT line thickness will be close to the yellow zone or in the zone. Then this individual is going to have functional defects with pathological thin RNFL [14, 15].
\n
In the early glaucoma stage it is considered that the affected ganglion cells decrease their functional processes before they die and this leads to decreasing of the visual functions without an obvious structural changes. This is the reason why a patient has functional manifestations of glaucoma in combination with normal RNFL thickness [14].
\n
The most distant nerve fibers from ONH originate exactly from these farthest parts of the ganglion cells in the retina and they are located deeply in the RNFL. They pass closely to the scleral edge and most peripherally in the ON [26]. These nerve fibers that originate from the closest to the ONH parts of the retina are located superficially in the RNFL and pass centrally in the ON. It is thought that the nerve fibers, which are located superficially in the RNFL, are more vulnerable in glaucoma, and their damage is associated with an enlargement of the blind spot.
\n
It is also believed that chronically increased IOP leads to compression of the circulation of the Elschnig’s border tissue and its atrophy. Then lamina cribrosa starts posteriorization. It is considered therefore that it is a reason for stretching and rupturing of the nerve fibers which are closest to the scleral edge. Only nerve fibers in prelaminar region can drop out consequently, because they are separated and not in bundles. The affecting of the nerve fibers is from peripheral to central region [26, 27]. Unordered affecting of the nerve fibers can be seen in acute angle closure glaucoma.
\n
\n
\n
\n
2. Retinal nerve fiber layer and inner macular layers evaluation in primary open-angle glaucoma with spectral-domain optical coherence tomography
\n
\n
2.1. Purpose
\n
The aim of our research is to assess and compare the peripapillary RNFL (pRNFL) thickness diagnostic capability with those of three macular parameters—macular RNFL (mRNFL) thickness, GCL+ (ganglion cell layer with inner plexiform layer thickness), and GCL++ (mRNFL and GCL+) in primary open-angle glaucoma patients with spectral-domain OCT (SD-OCT).
\n
\n
\n
2.2. Material and methods
\n
\n
2.2.1. Material
\n
All participants (healthy volunteers and patients) included in current clinical study were examined in the university eye clinic of Alexandrovska Hospital, Sofia, Bulgaria for total period of time—a year and 3 months. This is a prospective observational study of 414 participants (483 eyes) aged 45–84 years (mean 66.7 ± 8.7), male—132, and female—282. All patients were distributed into six groups:
\n
Ist group (Controls)—150 eyes, 150 healthy volunteers, mean age 63.0 ± 9.
\n
IInd group (Ocular hypertension (OH))—50 eyes, 31 patients, mean age 60.1 ± 9.2.
\n
IIIrd group (Preperimetric glaucoma (PPG))—62 eyes, 49 patients, mean age 66.3 ± 7.5.
\n
IVth group (Early perimetric POAG)—96 eyes, 80 patients, mean age 69.7 ± 7.9.
\n
Vth group (Moderate perimetric POAG)—40 eyes, 34 patients, mean age 70.4 ± 8.5.
\n
VIth group (Advanced perimetric POAG)—85 eyes, 70 patients, mean age 69.5 ± 9.8.
\n
The following inclusion and exclusion criteria were defined for the groups:
\n
Inclusion criteria for the control group: healthy participants without congenital or acquired general or eye diseases exception of early age-related cataract; people without family history and other risk factors for glaucoma; best corrected visual acuity (BCVA) = 1.0; refraction error in ±4.00 dsph and ±1.00 dcyl; IOP under 21 mmHg measured with Goldmann tonometer according to central corneal thickness (CCT) values; open anterior chamber angle class III–IV Shaffer Angle Classification System; ocular fundus without glaucomatous damages—vital optic nerve head (ONH), ISNT rule in norm, C/D Ratio < 0.5 PD and interocular asymmetry in C/D Ratio ≤ 0.2 PD; normal SAP (Glaucoma Hemifield Test—within normal limits, p ˃ 0.05 for MD and PSD indices).
\n
Inclusion criteria for OH group: patients with OH and any other coexisting ocular and general pathology; BCVA = 1.0; refraction error in ±4.00 dsph and ±1.00 dcyl; permanent elevation of IOP more than 21 mmHg measured with Goldmann tonometer without treatment and corrected according to the CCT values and daytime pressure curves; open anterior chamber angle; lack of pathological changes in the fundus; normal SAP.
\n
Inclusion criteria for Preperimetric glaucoma group: BCVA = 1.0; refraction error in already shown limits; permanent elevation of IOP more than 21 mmHg; open anterior chamber angle; fundus glaucomatous changes: interocular asymmetry in C/D Ratio ≥ 0.2 PD, vertical elongated excavation, thinning of optic disc rim, local thinning of neuroretinal rim, violated ISNT rule, defects in RNFL thickness (diffuse or local), normal SAP.
\n
Inclusion criteria for perimetric glaucoma groups: BCVA = 1.0 for early stage glaucoma group and BCVA ≥ 0.2 for moderate and advanced stage of POAG; refraction error in already shown limits; permanent elevation of IOP more than 21 mmHg; open anterior chamber angle; fundus glaucomatous changes: interocular asymmetry in C/D Ratio ≥ 0.2 PD, vertical elongated excavation, thinning of optic disc rim, local thinning of neuroretinal rim, violated ISNT rule, defects in RNFL thickness (diffuse or local), ONH hemorrhages; typical for glaucoma visual field defects in SAP corresponding with changes in ONH; glaucoma perimetric stage was defined as changes in SAP based on Hodapp-Parrish-Anderson classification.
\n
Exclusion criteria: best corrected visual acuity ≤ 0.2; age < 45 years and > 85 years; refraction error beyond already shown limits; normotensive glaucoma, angle closure glaucoma; macular pathology, diabetic retinopathy, nonglaucomatous opticopathy; previous eye surgery (exception cataract refractive surgery with intraocular lens implantation); coexisting neurological pathology which can influence on the visual field results.
\n
\n
\n
2.2.2. Methods
\n
All patients underwent full ophthalmological examination including: a complete case history for eye and general diseases; family history; refraction and best corrected visual acuity; slit-lamp examination; indirect fundus biomicroscopy; contact ultrasound pachymetry (OcuScan RxP - Alcon, Forth Worth, Texas, USA); Goldmann tonometry; indirect gonioscopy (Goldmann three-mirror gonioprism/Shaffer classification, 1960); SAP - SITA Standard 24-2, HFA II (Carl Zeiss Meditec, Dublin, CA, USA) with near correction if necessary. Only reliable perimetry results with total error rate (loss of fixation and false-positive and false-negative results) lower than 25%. The stage of POAG changes was determined using Hodapp-Parrish-Anderson classification.
\n
Optical coherence tomography: All patients underwent SD-ОСТ of both eyes with dilated pupils by one examiner using Topcon 3D OCT 2000 (FA plus) (Topcon Corporation, Japan), software version - 8.11.
\n
The following programs were used:
Circle program evaluated peripapillary RNFL thickness. From Circle protocol we analyzed the following parameters: (1) Total pRNFL—showed the average thickness in 360°; (2) Sup pRNFL—showed the thickness in the superior 90°; (3) Inf pRNFL—showed the thickness in the inferior 90°; (4) Nas pRNFL—showed the thickness in the nasal 90°; (5) Temp pRNFL—showed the thickness in the temporal 90° (see Figure 2, right).
3D Macula (V) program is used for the internal macular layers thickness evaluation in area of 7 mm2. The following parameters were analyzed: (1) Sup mRNFL (Sup mRNFL)—mRNFL thickness in the upper half; (2) Inf mRNFL (Inf mRNFL)—in the lower half; (3) Total mRNFL (Total mRNFL)—in the whole macular area (see Figure 2, left).
\n
Figure 2.
Glaucoma analysis—Macula protocol (left) and Circle protocol (right).
\n
Only OCT protocols with scan quality over 50%, no artifacts from eye or body movements, blinking, and lack of macular pathology (edema, drusen, holes) were included in the analysis.
\n
Statistical methods: For statistically significant were considered the differences with P values <0.05. We used descriptive, dispersion and ROC-analysis to evaluate diagnostic accuracy, specificity, and sensitivity. A comparison was made between Ist group and IInd, Ist and IIIrd and so long. With comparison analysis we searched for statistical significant difference between some of the parameters’ values in specificity and sensitivity.
\n
\n
\n
\n
2.3. Results
\n
The descriptive statistics can be seen in Table 1, and mean values of the all RNFL parameters in Table 2. In Table 3 can be seen the ROC analysis and the diagnostic capabilities of the eight OCT parameters in each group. The RNFL parameter with highest diagnostic potential in the groups—PPG (AUROC = 0.879), early (AUROC = 0.929), moderate (AUROC = 0.989) and advanced glaucoma (AUROC = 1.000) is Total mRNFL followed by Inf mRNFL and Inf pRNFL. The RNFL parameters with lowest diagnostic potential in all glaucoma stages are Nas pRNFL, Temp RNFL. A single RNFL parameter (Total mRNFL) was measured with highest diagnostic accuracy for glaucoma in all of its stages - from PPG to advanced glaucoma. In the current investigation, it is shown for the first time the higher diagnostic ability of macular RNFL from those of peripapillary RNFL. In OH group, we found that Inf mRNFL has the highest diagnostic possibilities but without any clinical significance, because none of the RNFL parameters change significantly in patients with OH in comparison with control group.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Group
\n
Sex
\n
Number
\n
Age (years)
\n
\n
\n
Mean
\n
SD
\n
Min
\n
Max
\n
\n\n\n
\n
Controls
\n
Men—m
\n
30
\n
61.7
\n
9.7
\n
48.0
\n
81.0
\n
\n
\n
Women—f
\n
120
\n
63.4
\n
8.9
\n
45.0
\n
84.0
\n
\n
\n
All
\n
150
\n
63.0
\n
9.0
\n
45.0
\n
84.0
\n
\n
\n
OH
\n
m
\n
10
\n
59.4
\n
10.6
\n
45.0
\n
76.0
\n
\n
\n
f
\n
21
\n
60.4
\n
8.6
\n
45.0
\n
72.0
\n
\n
\n
All
\n
31
\n
60.1
\n
9.2
\n
45.0
\n
76.0
\n
\n
\n
Preperimetric glaucoma
\n
m
\n
20
\n
68.6
\n
6.9
\n
51.0
\n
81.0
\n
\n
\n
f
\n
29
\n
64.7
\n
7.6
\n
45.0
\n
74.0
\n
\n
\n
All
\n
49
\n
66.3
\n
7.5
\n
45.0
\n
81.0
\n
\n
\n
Early glaucoma
\n
m
\n
30
\n
71.7
\n
6.0
\n
58.0
\n
81.0
\n
\n
\n
f
\n
50
\n
68.5
\n
8.7
\n
45.0
\n
82.0
\n
\n
\n
All
\n
80
\n
69.7
\n
7.9
\n
45.0
\n
82.0
\n
\n
\n
Moderate glaucoma
\n
m
\n
11
\n
70.5
\n
10.2
\n
45.0
\n
82.0
\n
\n
\n
f
\n
23
\n
70.4
\n
7.9
\n
57.0
\n
81.0
\n
\n
\n
All
\n
34
\n
70.4
\n
8.5
\n
45.0
\n
82.0
\n
\n
\n
Advanced glaucoma
\n
m
\n
31
\n
67.7
\n
11.5
\n
45.0
\n
83.0
\n
\n
\n
f
\n
39
\n
70.7
\n
8.2
\n
45.0
\n
84.0
\n
\n
\n
All
\n
70
\n
69.5
\n
9.8
\n
45.0
\n
84.0
\n
\n\n
Table 1.
Descriptive statistics.
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Parameter (μm)
\n
Controls
\n
OH
\n
Preperimetric glaucoma
\n
Early glaucoma
\n
Moderate glaucoma
\n
Advanced glaucoma
\n
\n
\n
Mean ± SD
\n
Mean ± SD
\n
Mean ± SD
\n
Mean ± SD
\n
Mean ± SD
\n
Mean ± SD
\n
\n\n\n
\n
MD [dB]
\n
−0.24 ± 1.30
\n
−0.05 ± 1.15
\n
−0.60 ± 1.13
\n
−2.73 ± 1.85
\n
−8.65 ± 1.77
\n
−21.44 ± 5.81
\n
\n
\n
PSD [dB]
\n
1.72 ± 0.38
\n
1.59 ± 0.32
\n
1.82 ± 0.33
\n
3.72 ± 1.73
\n
7.84 ± 2.66
\n
9.26 ± 3.15
\n
\n
\n
Sup mRNFL
\n
36.09 ± 4.30
\n
36.46 ± 5.77
\n
30.58 ± 3.75
\n
28.90 ± 4.90
\n
24.60 ± 6.85
\n
16.78 ± 6.58
\n
\n
\n
Inf mRNFL
\n
39.22 ± 5.27
\n
38.16 ± 4.69
\n
31.34 ± 5.01
\n
29.44 ± 5.81
\n
25.05 ± 7.04
\n
14.20 ± 6.33
\n
\n
\n
Total mRNFL
\n
37.67 ± 4.23
\n
37.30 ± 4.67
\n
31.05 ± 4.06
\n
29.21 ± 4.37
\n
25.00 ± 4.75
\n
15.48 ± 5.58
\n
\n
\n
Sup pRNFL
\n
122.31 ± 12.09
\n
128.70 ± 14.86
\n
111.34 ± 17.17
\n
101.47 ± 14.18
\n
90.38 ± 20.29
\n
77.47 ± 16.26
\n
\n
\n
Inf pRNFL
\n
136.86 ± 14.46
\n
137.66 ± 14.65
\n
115.74 ± 18.14
\n
108.05 ± 22.62
\n
90.53 ± 23.59
\n
69.22 ± 15.03
\n
\n
\n
Nas pRNFL
\n
90.55 ± 14.73
\n
92.24 ± 18.47
\n
81.98 ± 19.99
\n
82.23 ± 18.00
\n
74.58 ± 19.36
\n
66.99 ± 16.63
\n
\n
\n
Temp pRNFL
\n
81.62 ± 11.76
\n
85.92 ± 16.12
\n
74.26 ± 14.88
\n
72.98 ± 15.62
\n
69.28 ± 17.88
\n
60.35 ± 15.76
\n
\n
\n
Total pRNFL
\n
107.84 ± 7.95
\n
111.14 ± 10.25
\n
95.92 ± 12.26
\n
90.81 ± 12.48
\n
81.15 ± 15.28
\n
68.46 ± 12.32
\n
\n\n
Table 2.
Mean values and standard deviation (SD) of RNFL in all groups.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Parameter
\n
OH
\n
Preperimetric glaucoma
\n
Early glaucoma
\n
Moderate glaucoma
\n
Advanced glaucoma
\n
\n
\n
AUROC
\n
AUROC
\n
AUROC
\n
AUROC
\n
AUROC
\n
\n\n\n
\n
Sup pRNFL
\n
0.364
\n
0.694
\n
0.866
\n
0.903
\n
0.983
\n
\n
\n
Inf pRNFL
\n
0.472
\n
0.820
\n
0.867
\n
0.957
\n
0.999
\n
\n
\n
Nas pRNFL
\n
0.486
\n
0.627
\n
0.643
\n
0.731
\n
0.874
\n
\n
\n
Temp pRNFL
\n
0.428
\n
0.678
\n
0.687
\n
0.719
\n
0.893
\n
\n
\n
Total pRNFL
\n
0.412
\n
0.791
\n
0.900
\n
0.947
\n
0.993
\n
\n
\n
Sup mRNFL
\n
0.514
\n
0.839
\n
0.886
\n
0.907
\n
0.996
\n
\n
\n
Inf mRNFL
\n
0.563
\n
0.864
\n
0.907
\n
0.951
\n
0.997
\n
\n
\n
Total mRNFL
\n
0.535
\n
0.879
\n
0.929
\n
0.989
\n
1.000
\n
\n\n
Table 3.
ROC-analysis.
\n
AUROC values allowed us to create ROC curves in all groups as we included the five RNFL parameters with the best results (Figures 3, 4, 5, 6).
\n
Figure 3.
PPG.
\n
Figure 4.
Early glaucoma.
\n
Figure 5.
Moderate glaucoma.
\n
Figure 6.
Advanced glaucoma.
\n
After that we used comparison analysis to demonstrate if statistical significant difference exists between diagnostic possibilities of RNFL parameters in all groups. In Table 4 can be seen only two significant differences in AUROC values between Sup mRNFL (0.907) and Total mRNFL, and between Total mRNFL (0.929) and Inf pRNFL (0.867). Although, we found the highest diagnostic potential in all glaucoma groups for Total mRNFL, our comparison analysis showed that these possibilities are not statistical significant with exception of the above-mentioned two examples. For instance, in the stage of PPG, we were not able to find any statistical significant differences between the best five diagnostic RNFL parameters (Table 4), so they have equal abilities to diagnose glaucoma patients in this particular stage. In the stage of early POAG, we found significant difference in diagnostic abilities between Inf pRNFL and Total mRNFL, so that it would be better if the clinician uses not the first five but the four best diagnostic parameters from Table 3.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
AUROC comparisons
\n
Controls vs.
\n
\n
\n
PPG
\n
Early glaucoma
\n
Moderate glaucoma
\n
Advanced glaucoma
\n
\n
\n
p
\n
p
\n
p
\n
p
\n
\n\n\n
\n
Sup mRNFL
\n
Inf mRNFL
\n
0.587
\n
0.486
\n
0.280
\n
0.806
\n
\n
\n
Sup mRNFL
\n
Total mRNFL
\n
0.339
\n
0.136
\n
0.019
\n
0.238
\n
\n
\n
Sup mRNFL
\n
Inf pRNFL
\n
0.680
\n
0.571
\n
0.178
\n
0.469
\n
\n
\n
Sup mRNFL
\n
Total pRNFL
\n
0.329
\n
0.673
\n
0.327
\n
0.603
\n
\n
\n
Inf mRNFL
\n
Total mRNFL
\n
0.702
\n
0.410
\n
0.115
\n
0.275
\n
\n
\n
Inf mRNFL
\n
Inf pRNFL
\n
0.315
\n
0.204
\n
0.832
\n
0.601
\n
\n
\n
Inf mRNFL
\n
Total pRNFL
\n
0.125
\n
0.796
\n
0.884
\n
0.451
\n
\n
\n
Total mRNFL
\n
Inf pRNFL
\n
0.172
\n
0.041
\n
0.050
\n
0.339
\n
\n
\n
Total mRNFL
\n
Total pRNFL
\n
0.062
\n
0.296
\n
0.068
\n
0.132
\n
\n
\n
Inf pRNFL
\n
Total pRNFL
\n
0.558
\n
0.326
\n
0.694
\n
0.245
\n
\n\n
Table 4.
Comparison analysis in AUROC values in all groups.
\n
We evaluated also sensitivity, specificity and cut-off values for the same RNFL parameters. In PPG group there are two parameters with highest and almost equal values of the sensitivity and specificity—Total mRNFL (sensitivity—0.83, specificity—0.77) and Inf mRNFL (sensitivity—0.82, specificity—0.79). These two parameters keep their high and close values of sensitivity also in the group of early perimetric glaucoma: Total mRNFL—0.93 и Inf mRNFL—0.90. The parameter with highest value of specificity in the same group is Total pRNFL—0.89, and after it are these parameters: Total mRNFL—0.81 and Inf mRNFL—0.79. In the PPG group with highest values is Total mRNFL (sensitivity—0.97 and specificity—0.95), and after it is Inf mRNFL (sensitivity—0.94 and specificity—0.85). It is observed very small differences in the values between investigated parameters, which decrease in advanced glaucoma group. With highest sensitivity (1.00) and specificity (1.00) in advanced glaucoma group is Total mRNFL, and after it is Inf pRNFL (1.00; 0.99) and Inf mRNFL (0.99, 0.99).
\n
In Table 5 can be seen the AUROC values of the macular parameters—Total mRNFL, Total GCL+ (ganglion cell layer/GCL + inner plexiform layer/IPL) and Total GCL++ (GCL + IPL + mRNFL) from protocol Glaucoma Analysis—Macula (see Figure 3, left). Lowest diagnostic accuracy for glaucoma in all investigated stages possesses the parameter—GCL+. The highest area under the curve has GCL++ (0.919, 0.932) in PPG group, Total mRNFL in the moderate glaucoma group (0.989), and the both parameters reach maximal possibilities for diagnosis in advanced glaucoma group (1.000). We also applied comparison analysis to find significance in diagnostic capabilities (AUROC values) between macular parameters. The results from this analysis could be seen in Table 6. Significance can be seen in the values between Total mRNFL and Total GCL+, and between Total GCL++ and Total GCL+. We did not find a difference between Total mRNFL and Total GCL++. This mean that the whole ganglion cell layer (consists of three sub-layers), which is presented by GCL++ parameter, has an equivalent diagnostic potential of those of Total mRNFL, which presents only one of the macular sub-layers (the most inner layer consists of the nerve fibers). The less accurate diagnostic potential from macular OCT parameters we found for GCL++. Therefore we exclude this parameter as an accurate in glaucoma diagnosis.
\n
\n
\n
\n
\n
\n
\n\n
\n
Parameter
\n
Controls vs.
\n
\n
\n
PPG
\n
Early glaucoma
\n
Moderate glaucoma
\n
Advanced glaucoma
\n
\n
\n
AUROC
\n
AUROC
\n
AUROC
\n
AUROC
\n
\n\n\n
\n
Total mRNFL
\n
0.879
\n
0.929
\n
0.989
\n
1.000
\n
\n
\n
Total GCL+
\n
0.839
\n
0.858
\n
0.939
\n
0.993
\n
\n
\n
Total GCL++
\n
0.919
\n
0.932
\n
0.987
\n
1.000
\n
\n\n
Table 5.
AUROC values of the GCL map parameters.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
AUROC comparisons
\n
Controls vs.
\n
\n
\n
PPG
\n
Early glaucoma
\n
Moderate glaucoma
\n
Advanced glaucoma
\n
\n
\n
p
\n
p
\n
p
\n
p
\n
\n\n\n
\n
Total mRNFL
\n
Total GCL+
\n
0.336
\n
0.022
\n
0.024
\n
0.034
\n
\n
\n
Total mRNFL
\n
Total GCL++
\n
0.268
\n
0.897
\n
0.819
\n
1.000
\n
\n
\n
Total GCL+
\n
Total GCL++
\n
0.036
\n
0.018
\n
0.028
\n
0.034
\n
\n\n
Table 6.
Comparison analysis between macular parameters’ AUROC values.
\n
\n
\n
2.4. Discussion
\n
In the current research we found that the Topcon OCT parameter—Total mRNFL has the highest diagnostic accuracy in the very early stage of glaucoma, in which only structural changes could be seen (PPG). It is important to know its diagnostic possibilities compared with those of other OCT parameters, because it allows the clinicians to precise the early diagnosis, appropriate treatment and the most important for the patients—prevention of the vision loss. The results showed that this parameter also has the highest diagnostic possibilities in all perimetric glaucoma stages. These conclusions we made only after comparative analysis in diagnostic accuracy between all OCT parameters (peripapillary and macular) had been applied.
\n
There are not many researches, which investigate macular RNFL as a separate parameter not as a part of whole ganglion cell complex. Not enough data was collected about characteristics, correlations and diagnostic possibilities of mRNFL.
\n
In 2005 for the first time was created software algorithm for automated segmentation of retinal layers in Stratus OCT (OCT III). It helped authors differentiate four macular layers—macular nerve fiber layer (mNFL); inner retinal complex (IRC) consisting of ganglion cells, inner plexiform layer and inner nuclear layer; outer plexiform layer (OPL); outer retinal complex (ORC), consisting of outer nuclear layer, inner and outer photoreceptor segments. When the authors investigated diagnostic accuracy they found the highest values in mNFL+IRC (0.97), and lowest in OPL (0.56). Diagnostic accuracy of OPL and ORC was significantly lower from mNFL, IRC, mNFL+IRC and circumpapillary nerve fiber layer (cpNFL) (p ≤ 0.01). They found that AUROC values of IRC, mNFL+IRC and cpNFL were significantly higher from whole retinal thickness (p ≤ 0.049). It was not found significant differences between parameters with best diagnostic possibilities—mNFL, IRC, mNFL+IRC and cpNFL (p ≥ 0.15). The two parameters—ORC and OPL were found also to have almost permanent thickness in patients with glaucoma in comparison with healthy volunteers [28].
\n
In the beginning of the era “OCT diagnostics in glaucoma” was found that the whole retinal thickness decreases. Later with the initiation of spectral domain OCT (SD-OCT) in the clinical practice inner macular layers (mRNFL, GCL, IPL) were called a complex (ganglion cell complex—GCC), which consists of the bodies, dendrites and axons of the ganglion cells [29]. A self-evident fact is that GCC has significantly higher possibilities for glaucoma diagnosis than the thickness of the whole retina.
\n
Mwanza et al. investigated diagnostic accuracy of GCIPL (ganglion cell + inner plexiform layer), RNFL ONH parameters [30]. They found that GCIPL diagnostic possibilities are between 0.918 and 0.956, and there values are comparable with the best diagnostic parameters—RNFL (between 0.933 and 0.939) and ONH parameters (0.910 and 0.962) without statistically significant difference between them.
\n
There are two conceptions of ganglion cell loss in glaucoma. In the first—the dendrites die before the bodies, and the most resistant part of the cell of glaucoma damage are their axons. Therefore, it is reasonable to investigate the thickness of GCL + IPL separately from mRNFL. On the other hand IPL consists of the dendrites not only the ganglion cells but also the bipolar cells, and it is believed as more correctly to measure the thickness of mRNFL+GCL together.
\n
\n
\n
2.5. Conclusion
\n
Peripapillary RNFL is a proved glaucoma diagnostic parameter and also ganglion cell complex. Predominantly of the glaucoma comparisons in diagnostic accuracy are between pRNFL and GCC in different OCT devices.
\n
The current research investigate a new SD-OCT macular parameter—mRNFL and its diagnostic possibilities for different stages of POAG. It proves that mRNFL could be used in every day clinical practice of the ophthalmologist as independent parameter with very high diagnostic possibilities for early stages of glaucoma when only structural changes are visible.
\n
Now we are working on creating of staging system based on Total mRNFL values (cut-off values) in each glaucoma group. It could give possibilities for the ophthalmologists to use the values of this parameter in everyday clinical practice to make diagnosis and follow-up of the glaucoma patients. This grading system will be the only of the OCT structural systems created up to date. Total mRNFL has the potential to be one of the best OCT diagnostic parameters and we as researchers must find how to use it in the diagnosis of very early glaucoma changes.
\n
\n
\n
Acknowledgments
\n
We would like to acknowledge with much appreciation the crucial role of: Assoc. Prof. Todor Kundurjiev, PhD (Medical University of Sofia, Bulgaria, Department of Social Medicine and Health Management) who made the whole statistical analysis for the current research.
\n
Conflict interest
The authors declare that there is no conflict of interest.
\n
Notes/Thanks/Other declarations
\n
I would like to express my special appreciation and thanks to my teacher:
\n
Associate Professor Galina G. Dimitrova, MD, PhD.
\n
You have been a tremendous mentor for me. I would like to thank you for encouraging my work and for allowing me to grow as a research scientist. Your advice on both science and on my clinical practice has been invaluable. I appreciate this more than you know.
\n
\n',keywords:"primary open-angle glaucoma, retinal nerve fiber layer, inner macular layers, optical coherence tomography",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/62636.pdf",chapterXML:"https://mts.intechopen.com/source/xml/62636.xml",downloadPdfUrl:"/chapter/pdf-download/62636",previewPdfUrl:"/chapter/pdf-preview/62636",totalDownloads:1518,totalViews:229,totalCrossrefCites:1,totalDimensionsCites:1,totalAltmetricsMentions:0,introChapter:null,impactScore:0,impactScorePercentile:9,impactScoreQuartile:1,hasAltmetrics:0,dateSubmitted:"February 28th 2018",dateReviewed:"May 24th 2018",datePrePublished:"November 5th 2018",datePublished:"February 13th 2019",dateFinished:"July 16th 2018",readingETA:"0",abstract:"The aim of our research is to assess and compare the peripapillary retinal nerve fiber layer (pRNFL) thickness diagnostic capability with those of three macular parameters—macular RNFL (mRNFL) thickness, GCL+ (ganglion cell layer with inner plexiform layer thickness), and GCL++ (mRNFL and GCL+) in primary open-angle glaucoma patients with spectral-domain optical coherence tomography (SD-OCT).",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/62636",risUrl:"/chapter/ris/62636",book:{id:"6786",slug:"optic-nerve"},signatures:"Bilyana Mihaylova and Galina Dimitrova",authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Anatomical aspects of the RNFL",level:"2"},{id:"sec_2_2",title:"1.2. RNFL and glaucoma",level:"2"},{id:"sec_4",title:"2. Retinal nerve fiber layer and inner macular layers evaluation in primary open-angle glaucoma with spectral-domain optical coherence tomography",level:"1"},{id:"sec_4_2",title:"2.1. Purpose",level:"2"},{id:"sec_5_2",title:"2.2. Material and methods",level:"2"},{id:"sec_5_3",title:"2.2.1. Material",level:"3"},{id:"sec_6_3",title:"2.2.2. Methods",level:"3"},{id:"sec_8_2",title:"2.3. Results",level:"2"},{id:"sec_9_2",title:"2.4. Discussion",level:"2"},{id:"sec_10_2",title:"2.5. Conclusion",level:"2"},{id:"sec_12",title:"Acknowledgments",level:"1"},{id:"sec_15",title:"Conflict interest",level:"1"},{id:"sec_12",title:"Notes/Thanks/Other declarations",level:"1"}],chapterReferences:[{id:"B1",body:'American Academy of Ophthalmology. Base and Clinical Science Course, 2015/2016. Section 10 - Glaucoma. 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Transactions of the American Ophthalmological Society. 1987;84:920-966\n'},{id:"B12",body:'Samsonova B, Pr G-I. Analysis of factors, influencing the RNFL thickness and the optic disc rim width (Part II). Bulgarian Forum Glaucoma. 2014;4(5):201-207\n'},{id:"B13",body:'Samsonova B, Pr G-I. Analysis of factors, influencing the RNFL thickness and the optic disc rim width (Part I). Bulgarian Forum Glaucoma. 2014;4(4):161-168\n'},{id:"B14",body:'Samsonova B. Curious discrepancies between functional and structural findings in patients with glaucoma and suspicious for glaucoma (Part 1). Bulgarian Forum Glaucoma. 2013;3(1):10-14\n'},{id:"B15",body:'Samsonova B. Curious discrepancies between functional and structural findings in patients with glaucoma and suspicious for glaucoma (Part 2). Bulgarian Forum Glaucoma. 2013;3(2):60-72\n'},{id:"B16",body:'Balazsi AG, Rootman J, Drance SM, et al. The effect of age on the nerve fiber population of the human optic nerve. American Journal of Ophthalmology. 1984;97:760-766\n'},{id:"B17",body:'Mikelberg FS, Drance SM, Schuler M, et al. The normal human optic nerve. Ophthal-mology. 1989;96:1325-1328\n'},{id:"B18",body:'Jonas JB, Muller-Berg JA, Schlotzer-Schrehardt UM, et al. Histomorphometry of the human optic nerve. Investigative Ophthalmology & Visual Science. 1990;31:736-744\n'},{id:"B19",body:'Jonas JB, Schmidt AM, Muller-Berg JA, et al. Human optic nerve fibre count and optic disc size. Investigative Ophthalmology & Visual Science. 1992;33:2012-2018\n'},{id:"B20",body:'Budenz DL, Anderson DR, Varma R, et al. Determinants of normal retinal nerve fiber layer thickness measured by Stratus OCT. Ophthalmology. 2007;114:1046-1052\n'},{id:"B21",body:'Xu L, Chen PP, Chen YY, et al. Quantitative nerve fiber layer measurement using scanning laser polarimetry and modulation parameters in the detection of glaucoma. Journal of Glaucoma. 1998;7:270-277\n'},{id:"B22",body:'Blumenthal EZ, Weinreb RN. Assessment of the retinal nerve fiber layer in clinical trials of glaucoma neuroprotection. Survey of Ophthalmology. 2001;45(Suppl 3):S305-S312; S332-S334\n'},{id:"B23",body:'Wu H, De Boer J, Chen T. Diagnostic capability of spectral-domain optical coherence tomography for glaucoma. American Journal of Ophthalmology. 2012;153(5):815-826\n'},{id:"B24",body:'Sommer A, Katz J, Quigley HA, et al. Clinically detectable nerve fiber atrophy precedes the onset of glaucomatous filed loss. Archives of Ophthalmology. 1991;109(1):77-83\n'},{id:"B25",body:'Harwerth RS, Carter-Dawson L, Shen F, et al. Ganglion cell losses underlying visual field defects from experimental glaucoma. Investigative Ophthalmology & Visual Science. 1999;40:2242-2250\n'},{id:"B26",body:'Hasnain SS. The missing piece in glaucoma? Open Journal of Ophthalmology. 2016;6:56-62\n'},{id:"B27",body:'Hasnain SS. Pathogenesis of orderly loss of nerve fibers in glaucoma. Optometry: Open Access. 2016;1(2):110. DOI: 10.4172/2476-2075.1000110\n'},{id:"B28",body:'Ishikawa H, Stein DM, Wollstein G, et al. Macular segmentation with optical coherence tomography. Investigative Ophthalmology & Visual Science. 2005;46(6):2012-2017\n'},{id:"B29",body:'Tan O, Li G, Lu A, Varma R, Huang D. Advanced imaging for glaucoma study group. Mapping of macular substructures with optical coherence tomography for glaucoma diagnosis. Ophthalmology. 2008;115:949-956\n'},{id:"B30",body:'Mwanza JC, Durbin MK, Budenz DL, et al. Glaucoma diagnostic accuracy of ganglion cell–inner plexiform layer thickness: Comparison with nerve fiber layer and optic nerve head. Ophthalmology. 2012;119(6):1151-1158\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Bilyana Mihaylova",address:"b51@abv.bg",affiliation:'
Department of Ophthalmology, Medical University of Sofia, University Hospital ‘Alexandrovska’, Sofia, Bulgaria
Department of Ophthalmology, Medical University of Sofia, University Hospital ‘Alexandrovska’, Sofia, Bulgaria
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1. Introduction
There has been a global trend for populations to increasingly hold governments accountable to open government data (OGD) standards [1]. Because of this, governments have undertaken open data projects, such as providing public access to government data through publicly-accessible dashboards [2, 3]. However, government actors also may have an incentive to hide or obscure data, so there are barriers to accessing data for public dashboards [1]. This chapter focuses on the specific problem where governments attempt to demonstrate compliance with OGD standards through the presentation of a public dashboard, while at the same time, appearing to hide or obscure the data it is supposed to represent through poor dashboard design.
Our motivation to tackle this topic comes from our own disappointing experience trying to use a public dashboard implemented as part of OGD standards established where we live, in Massachusetts in the United States (US). Currently, in general, no standard guidance or recommendations are in place as a process to follow for the development of OGD public dashboards, and no framework or rubric has been proposed to evaluate them. These challenges are barriers to assessing how well public dashboards meet public need, and holding governments accountable for this. The significance of our contribution is that we propose a framework and rubric on which to base the evaluation of how well these public dashboards meet public need. The implication is that the application of this framework and rubric can be further researched in terms of utility in evaluating public dashboards. From this starting point, globally, we can begin to develop scientific consensus on what attributes in evaluate to a good-faith public dashboard implementation, and what the public should rightfully expect from the implementation of an OGD public dashboard.
2. Guidance for the design of public dashboards
The COVID-19 pandemic brought to attention a longstanding need for well-designed dashboards in public health and medicine [4]. It also brought to light that there are no uniform guiding principles behind developing publicly-facing dashboards intended to serve public interests. As a prime example, a recent review of United States (US) government public dashboards for COVID-19 found that “states engaged in dashboard practices that generally aligned with many of the goals set forth by the Centers for Disease Control and Prevention, Essential Public Health Services” (from abstract) [4]. However, the results of this review do not address whether the public was adequately served by any of these dashboards that were funded with the public’s money. Important questions not answered were: Did these dashboards meet the public’s information needs? Did they meet the information needs of public health practitioners? Or more importantly – whose information needs were these dashboards supposed to meet, and what were these needs?
2.1 Philosophies behind public dashboard design
At present, there is no overarching philosophy behind public dashboard design, for public health or other topics [2]. Although individual projects will publish use-cases where they discuss their design philosophy [2, 5, 6], there has not been an overall effort by the professional informatics societies or other academic groups to assemble principles behind the design for dashboards intended to serve the public. This may be because such an effort would be daunting, and would require a relatively narrow scope. The scope should be aimed at addressing high-level requirements focused on ensuring that the public’s needs are met by whatever dashboard solution is developed, regardless of the topic.
This chapter will attempt to summarize the literature into a framework that provides a general, generic rubric by which to evaluate how well a dashboard design for the public ensures that the public’s needs are met through measuring their adherence to high-level requirements. The framework will also put forth a method by which to compare alternative dashboard solutions aimed at meeting similar public needs as to how consistent the solution is with the public’s dashboard requirements.
The framework and rubric are intended to evaluate outcomes. Logically, a design process that adequately includes the public that the dashboard is intended to serve will inevitably produce a dashboard solution that meets these outcomes. Hence, there is no need to invest public funding in bloated efforts such as the Rapid Cycle Quality Improvement (RCQI) model, which is promoted by many health departments and organizations, and is extremely paperwork intensive [7, 8]. Part of what causes the RCQI model to be so effort-intensive is that it measures process outcomes. By contrast, the evaluation framework for public dashboards recommended in this chapter is streamlined, and focused on achieving a design solution, not a process solution.
Nevertheless, an optimal design solution will not be achieved without an adequate design process. Therefore, it is important to consider how the public should be involved in the process of designing public dashboards – especially those that are publicly-funded, and therefore have obligations to respond to the public’s needs.
2.2 Dashboard design process
As stated previously, there is currently no agreed-upon best-practices design process for dashboards in general, and public dashboards specifically [2]. Each time a dashboard is developed, a different design process is used. But a generic, logical process can be summarized in Figure 1.
Figure 1.
Generic logical dashboard design process. This design process produces an alpha prototype for initial testing, and a beta prototype for widespread field testing.
As shown in Figure 1, typically, before the dashboard is designed, some sort of design process is chosen, and this design process is followed to develop an “alpha prototype”. The alpha prototype represents a working mock-up that exists for the purposes of getting feedback and working out an initial design. Next, the alpha prototype undergoes a testing process to inform developers as to modifications that are necessary before widespread testing is done. Once those modifications are made, a beta prototype exists, and can be launched for field testing.
As described in Figure 1, depending upon the project, there can be different components included in the design process for the alpha prototype. First, there will be iterative design processes as part of designing the alpha prototype, as well as the development of design documentation and the actual creation of the prototype. The details behind each of these components will vary by project. Once the alpha prototype exists, the process to convert it to the beta prototype involves some sort of user testing, and some sort of evaluation for adherence to standards. Granted, an alpha prototype may be released into the field without having undergone the beta prototype process, but that means it has not been user-tested or evaluated for adhering to standards.
This logical process can apply to any dashboard development effort. As one example, researchers aimed to design a dashboard for clinicians [9]. They wrote requirements and developed an alpha prototype, then worked with clinicians to gain feedback to guide the development of a beta prototype (which would presumably be developed in the future and field-tested) [9]. This article focused mainly on the feedback process to improving the alpha prototype, but the focus of articles can be on any part of the dashboard design process. Another article focused on the development of a beta prototype aimed at both the public and leaders for real-time decision-making related to traffic flow [2]. While the beta prototype was developed and appeared ready for testing, the article did not report any results, so the current final stage of this project was not evident in the article [2].
Although, this logical design process should theoretically involve the intended users of the dashboard, and prototypes should undergo iterative testing, this is not always the case with public dashboards. Because public dashboards often involve government agencies and leaders at some level, whether as data sources or as intended audiences, these forces can have unintended impacts on the dashboard design and quality.
2.3 Governmental data suppression and misrepresentation
As a general trend, consumers are demanding more data transparency, and calls are being made for governments to make data available for public oversight [1]. Likewise, there is an increasing trend toward using dashboards for empowering the public [2, 3]. Not only do dashboards of public data provide a mechanism for public oversight of leaders, but they also reduce information asymmetry, which refers to the circumstance in which one party (the government) has more information than another party (the public), thus disempowering them [2, 10, 11].
However, governments are not always keen to share the data for various reasons. It has been argued that government agencies will be more likely to comply with open government data (OGD) practices if they see it as an opportunity to showcase their agency’s success [1]. However, if the agency believes the data will cast the agency in a negative light, the agency may be less likely to be inclined toward OGD practices. Ruijer and colleagues recommend that institutional incentives and pressure be created for OGD, because governments have a natural interest in suppressing data they think may be harmful to them in some way if analyzed [1].
However, data suppression is not the only method governments employ to prevent data use and interpretation. One limitation of legal requirements for OGD is that the agency may comply with the requirements in bad faith. During the COVID-19 outbreak in early 2020, a state epidemiologist in Florida said she was fired for refusing to manually falsify data behind a state dashboard [12]. Simply reviewing the limitations of big data can reveal ways to share big data in bad faith in a dashboard, such as visualizing too much data, visualizing incomprehensible or inappropriate data, and not visualizing needed data [13].
For this reason, in addition to holding governments to OGD standards, government efforts need to be evaluated as to whether or not they meet OGD standards in good faith. The framework presented here guides as to how to evaluate good vs. bad faith implementations of a public dashboard.
2.4 Dashboard requirements
The evaluation framework presented has six principles on which to judge the level of good or bad faith in a public dashboard: 1) ease of access to the underlying data, 2) the transparency of the underlying data, 3) approach to data classification, 4) utility of comparison functions, 5) utility of navigation functions, and 6) utility of metrics presented. These principles will be described below.
2.4.1 Access to underlying data
A dashboard is essentially a front-end, with data behind it being visualized [14]. Hence, once a dashboard is published, members of the public may want to access the underlying data for various reasons, including oversight of the dashboard. But governments resistant to data-sharing may use the dashboard in bad faith as a firewall between the public and the underlying data to prevent data access [1]. Hence, good faith OGD principles hold that public dashboards should not serve as barriers, but instead serve as facilitators to access the underlying data being visualized in the dashboard.
2.4.2 Transparency of underlying data
Although raw data are used for the dashboard, in the dashboarding process, they undergo many transformations to be properly visually displayed [9, 14]. The processing of the data can develop calculations that are then displayed in the dashboard. Therefore, to be transparent, the dashboard must not only facilitate access to the underlying raw data, but also to the transformations the data underwent in being displayed. A simple way to accomplish this kind of transparency is to use open source tools and publish the code, along with documentation [14]. This allows citizen data scientists an opportunity to review and evaluate the decisions made in the dashboard display.
2.4.3 Data classification
How data are classified in a dashboard can greatly impact the utility of the dashboard. As an example, developers of an emergency department (ED) dashboard that was in use for five years under beta testing found that after the ED experienced an outbreak of Middle East Respiratory Syndrome (MERS), major structural changes were needed to the dashboard [15]. Another paper about developing a visualization of patient histories for clinicians described in detail how each entity being displayed on the dashboard would be classified [16]. Hence, inappropriate classifications or ones deliberately made in bad faith can negatively impact data interpretation to the point that the dashboard could be incomprehensible to its users.
2.4.4 Comparison functions
Dashboards are used to inform decision-making, and therefore, being able to make needed comparisons is an important factor in a dashboard’s usability [14, 17]. As an example, the public traffic dashboard described earlier presented visualizations of the ten most congested areas of the city, as well as textual feedback on the two most suitable routes between downtown and outlying areas, to provide optimal comparators to allow the public to make the most-informed route decision [2]. While ultimately, optimal design choices could be debated, it is easy to conceive of how agencies looking to maintain opacity could obscure data interpretation in a dashboard in bad faith by deliberately limiting the ability to make useful comparisons.
2.4.5 Navigation functions
Dashboards are typically at least somewhat interactive, providing the user the ability to navigate through the data display, which responds to actions by the user [14, 18, 19]. When operating in good faith, developers often conduct extensive usability testing to ensure that the dashboard is intuitive to use in terms of navigating through the data display, and that any interactivity is useful [15]. But when implemented in bad faith, a dashboard could be designed to deliberately confuse the user as to how to navigate and interpret the data in the dashboard.
2.4.6 Metrics presented
One of the main purposes of dashboards is to present metrics that represent statistics or visualizations meant to summarize a particular state of the data [14, 15]. For example, in the traffic dashboard, the metrics presented are intended to communicate traffic congestion to the users, while the metrics presented in the clinical dashboard are intended for healthcare workers to use in clinical decision-making [2, 16]. In a good-faith effort, developers may conduct extensive user-testing to ensure that the metrics presented are communicative to dashboard users, as is often done with dashboards developed to serve workers in healthcare [9, 20]. However, in a bad faith effort, the metrics presented could be deliberately confusing to the user, and serve merely to hide ugly truths in the underlying data.
3. Use-case: dashboard for hospital-acquired infection rates at Massachusetts hospitals
In the US, the Commonwealth of Massachusetts (MA) Department of Public Health (DPH) posts annual healthcare-associated infection (HAI) reports about MA hospitals on their web page [21]. The purpose of the reports is public data transparency by law [21]. Briefly, HAI, such as catheter-acquired urinary tract infection (CAUTI), central line-associated bloodstream infections (CLABSI), and surgical site infections (SSI), are serious issues because they are the fault of the hospital, and can lead to sepsis, which is a systemic infection that can end in death [22, 23]. Since catheterization happens in the intensive care unit (ICU) setting, typically hospitals track CAUTI and CLABSI as part of healthcare quality activities centered around ICUs [14]. By contrast, SSIs are tracked in association with specific operative procedures (e.g., colon surgery) [24].
In the US compared to other countries, rates of HAI are relatively high, likely because they are not required to be tracked at the federal level [14, 25]. Hospitals can opt into the federal voluntary tracking system called the National Health Safety Network (NHSN), but the NHSN does not have a publicly-facing dashboard, and the data are inaccurate, especially in undercounting severe events [14, 24, 26]. As HAI is a serious public health issue, there has been a call for greater data transparency, so the reports posted on the MA DPH web site represent MA’s attempt to comply with state-level mandates for OGD.
Although summary reports are available for download on the MA DPH web site, it is not possible to access hospital-level reports directly from the web site. To download hospital-level reports, the user must access a dashboard presented on the web page in a link (Figure 2).
Figure 2.
MA HAI public dashboard landing page. Note: “A” labels a menu of tabs that can be used for navigation to view metrics on the various hospital-acquired infections (HAIs). In panel labeled “B”, tabs can be used to toggle between viewing state-level metrics and hospital-level metrics. Hospitals can be selected for display using a map labeled “C”.
As per Figure 2, once inside the dashboard, individual PDF-style reports can be found through navigation to the hospital in question, and the reports appear to present formatted output from a database. One way to navigate to the hospital record is to locate it on the map (“C” in Figure 2) and click on its icon, causing the panel “B” to display hospital-level metrics and a link to the hospital’s PDF report.
Each PDF report has a header displaying attribute data about the hospital (e.g., number of beds), followed by a series of ICU-, procedure-, and infection-level output. This mirrors the structure seen in the dashboard tabs and reports (Figure 2, “A” and “B”). For the ICUs at each hospital, the report displays a set of tables summarizing CAUTI and CLABSI rates, followed by time-series graphs. For a set of high-risk surgical procedures, SSI rates and graphs for the hospital are displayed. Medication-resistant staphylococcus aureus (MRSA) and C. Difficile infections are serious HAIs that can be acquired in any part of the hospital and are diagnosed using laboratory tests [27]. Rates and graphs of MRSA and C. Difficile infections are also displayed on the report.
The underlying data come from the NHSN. This is not stated on the dashboard. Instead, there is a summary report and presentation posted alongside the dashboard on the web site, and the analyses in these files are based on NHSN data [21]. It seems that the DPH is using this NHSN data using as a back-end to the dashboard, and the dashboard is an attempt to comply with OGD laws.
Because the authors are aware of the high rates of HAI in the US, and because we both live in MA and we both are women who are cognizant that sexism in US healthcare adds additional layers of risk to women [28], we identified that we were in a state of information asymmetry. Specifically, we had the information need to compare MA hospitals to choose the least risky or “lethal” one for elective surgery or childbearing (planned procedures), but we felt this need was not met by this OGD implementation.
In this section, we start by evaluating the existing MA DPH HAI dashboard against our good vs. bad faith framework. Next, we propose an alternative dashboard solution that improves the good vs. bad faith features of the implementation.
3.1 Considering existing dashboard: design process and requirements
Figure 3 provides a logical entity-relationship diagram (ERD) for the data behind the dashboard.
Figure 3.
Logical entity-relationship diagram for data behind dashboard. Note: The schema presented assumes four entities: The hospital entity (primary key [PK]: HospRowID), each intensive care unit (ICU) attached to a hospital which contains the frequency of infection and catheter days attributes to allow rate calculation (PK: ICURowID), each procedure type attached to a hospital (to support the analysis of surgical site infection [SSI], with PK: ProcRowID), and each other infection type at the hospital not tracked with ICUs (PK: LabID).
As described earlier, the landing page (Figure 2) provides a map by which users can select a hospital, causing the metrics for the hospital to appear in a panel. The user chooses which measurement to view (e.g., CAUTI) through navigation using the tabs. This suggests the dashboard is aimed at individuals with a working knowledge of MA geography who are intending on comparing and selecting hospitals least likely to cause HAI for an elective procedure (e.g., childbearing), or to establish as their top choice of the local hospital should they ever need to be admitted. This interface makes it difficult to compare HAI at different hospitals, because metrics from more than one hospital cannot be viewed at the same time. Further, metrics about different HAIs at the same hospital are on different panels, so within-hospital comparisons cannot be facilitated. There appears to be no overall metric to use by which to compare hospitals in terms of their HAI rates.
Figure 4 shows an example of the metrics reported by each hospital on the dashboard reporting panel (“B” in Figure 2). The figure also shows one of the two tables and one of the two figures displayed on the CAUTI tab for the selected hospital. In all, two tables and two figures are displayed in portrait style in panel “B” (Figure 2), and Figure 4 shows the top table and figure displayed. In the table displayed (labeled “1” in Figure 4), the metrics presented are the number of infections, predicted infections, standard infection ratios (SIRS), a confidence interval for the SIR, and an interpretation of the level. The figure (labeled “2” in Figure 4) displays a time-series graph of SIRs for the past five years. In the other table on the panel (not shown in Figure 4), ICU-level metrics are provided about catheter-days, predicted catheter-days, Standard Utilization Ratios (SURs) and their confidence interval, and an interpretation, and an analogous time-series graph of five years of SURs is presented (also not shown in Figure 4).
Figure 4.
Dashboard metric display for each hospital. Note: To view hospital-acquired infection (HAI) rates at hospitals, a hospital is selected (Figure 2, panel “C”), then the user selects the tab for the HAI of interest. In Figure 4, a hospital has been identified, and a tab for catheter-associated urinary tract infection (CAUTI) has been selected (see circle). Two tables and two figures are presented in portrait format on the reporting panel for each hospital (Figure 2, panel “B”). Figure 4 shows the first table and figure presented (“1” and “2”); the table reflects stratified metrics for CAUTI at each ICU at the hospital, and the graph reflects a time series of these metrics stratified by hospital vs. state levels, and intensive care unit (ICU) vs. ward (“ward” is not defined in the dashboard). The metrics provided in “1” are a number of infections, predicted infections, standard infection ratios (SIRs), a confidence interval for the SIR, and an interpretation of the level. In “B”, the SIR is graphed.
SIRs and SURs are not metrics used typically by the public to understand rates of HAI in hospitals. Risk communication about rates to the public is typically done in the format of n per 10,000 or 100,000, depending upon the magnitude of the rate [29]. Further, stratifying rates by ICU is confusing, as prospective patients may not know what ICU in which they will be placed. Because the hospital environment confers the strongest risk factors for HAI (e.g., worker burnout), HAI rates will be intra-correlated within each hospital [30]. Therefore, it is confusing to present all these rates and stratify them by ICU. Figure 4 only displays 50% of the information available about CAUTI at one hospital. With each tab displaying similar metrics about SSI and other infections, the experimental unit being used is so small, it obfuscates any summary statistics or comparisons. Also, it is unclear how the “predicted” metrics presented were calculated.
Ultimately, the design process and requirements behind this dashboard are not known. There is no documentation as to how this dashboard was designed, and what it is supposed to do. It appears to be an alpha prototype that was launched without a stated a priori design process, and without any user testing or formal evaluation.
3.2 Alternative design: design process and requirements
We chose to redesign the dashboard into a new alpha prototype that met requirements that we, as members of the public, delineated. Consistent with the good faith principles proposed, our requirements included the following: 1) the dataset we use should be easily downloadable by anyone using the dashboard, 2) the documentation of how the dashboard was developed should be easy to access, 3) hospitals should present summary metrics rather than stratified ones, 4) different HAI metrics for the same hospital should be presented together, and 5) there needs to be a way to easily compare hospitals and choose the least risky hospital. To do this, we first obtained the data underlying the original dashboard. Next, we analyzed it to determine better metrics to present. We also selected open-source software to use to redeploy an alpha prototype of a new dashboard. Finally, we conducted informal user testing on this alpha prototype.
3.2.1 Obtaining the data from the original dashboard
Scraping was done in open-source RStudio and predominantly used packages pdftools, and pdftables [31, 32]. All the PDF reports from each hospital were downloaded and placed in one directory. As a first step, a loop was used with the pdftools package which crawled through each report extracting the data into memory. This was done in conjunction with the pdftables package, which is essentially an online application that applies structure to the unstructured tabular data placed in memory from pdftools. To use this online application, an application programming interface (API) key is issued from the PDF Tables web site, and is used in the RStudio programming to pass the data to the online application [33]. The code resulted in the data being processed into a series of unstructured *.xlsx files and downloaded locally. Then, in a final data cleaning step, data were transformed into the tables in the format specified in Figure 3.
3.2.2 Determining metrics to present
We chose to focus our inquiry on the data from the hospital and ICU tables, as CAUTI and CLABSI are by far the most prevalent and deadly HAIs [23]. Therefore, we scoped our alpha prototype to only display data from the ICU and the hospital tables (although we make all the data we scraped available in the downloadable dataset). This limited us to basing the dashboard on hospital- and ICU-level metrics only.
Next, we intended to present CAUTI and CLABSI frequencies and rates, whereby the numerator would be the number of infections, and the denominator would be the “number of patients catheterized”. We felt that the dashboard’s use of catheter-days as the rate denominator was confusing to the public, and appeared to attenuate the prevalence of patients having experienced a CAUTI or CLABSI. Although, “number of patients catheterized” was not available in the data, “annual admissions” was. Since the proportion of patients admitted annually who are catheterized probably does not vary much from hospital to hospital, we chose to use the number of admissions as the denominator and a proxy measurement.
Third, we needed to develop a way of sorting hospitals as to their likelihood of causing an HAI to allow easy comparisons by public users, so we decided to develop an equation to predict the likelihood of an HAI at the hospital. We did this by developing a linear regression model with hospital-level attributes as independent variables (IVs), and CAUTI rate in 2019 as the dependent variable (DV). We chose CAUTI over CLABSI after observing the two rates were highly correlated, and CAUTI was more prevalent.
Table 1 describes the candidate IVs for the linear regression model. The table also includes the source of external data that were added to the hospital data. We studied our IVs, and found serious collinearity among several variables, so we used principal component analysis (PCA) to help us make informed choices about parsimony [37]. The data predominantly loaded on three factors (not shown). The first factor included all the size and utilization variables for the hospital; these were summed into a Factor 1 score. The second-factor loadings included the proportion of those aged 65 and older and the non-urban flag (Table 1), so those were summed as Factor 2. Proportion non-White was strongly inversely correlated with Factor 2, so it was kept for the model, and county population did not load, so it was removed from the analysis. Factor 3 loadings included teaching status, for-profit status, and Medicare Performance Score (MPS). Rather than create a score, we simply chose to include the variable from Factor 3 that led to the best model fit to represent the factor, which was MPS. Then we finalized our linear regression model, and developed a predicted CAUTI rate (ŷ) using our model that included the following IVs: MPS, Factor 1 score, Factor 2 score, and proportion of non-White residents in hospital county.
Variable
Source
Role
Source
Teaching hospital status
Original dashboard
Exposure
Scraped data from dashboard
Hospital profit status
Original dashboard
Confounder
Scraped data from dashboard
Measurements of hospital size (number of beds)
Original dashboard
Confounder
Scraped data from dashboard
Measurements of hospital utilization (number of admissions, number of patient days)
Calculated as the numerator for rates divided by admissions
Table 1.
Conceptual model specification.
Next, we used the regression equation to calculate ŷ as a “lethality score” for each hospital. Of the 71 hospitals in the dataset, 21 were missing MPS and 8 were missing other data in the model. Therefore, only the 42 with complete data (IVs and DVs) were used to develop the regression model. As a result, the lethality score was non-sensical for some hospitals; where the residual was large, the lethality score was replaced with the 2019 CAUTI rate. If CAUTI data were missing, it was assumed that the hospital had no CAUTI cases, and therefore was scored as 0.
Once the lethality score was calculated, we chose to sort the hospitals by score, and divide them into four categories: least probable (color-coded green), somewhat probable (color-coded yellow), more probable (color-coded red), and most probable (color-coded dark gray). Due to missing CAUTI information and many hospitals having zero CAUTI cases, our data were severely skewed left, so making quartiles of the lethality score to divide the hospitals into four categories was not meaningful. To compensate, we sorted the data by lethality score and placed the first 23 hospitals (32%), which included all the hospitals with zero cases, in the least probable category. We placed the next 16 (23%) in somewhat probable, the next 16 (23%) in more probable, and the final 16 (23%) are most probable. We chose to use this classification in data display on the dashboard to allow for easy comparison between hospitals of risk of a patient contracting HAI.
3.2.3 Choice of software and display
R is an open-source analytics software that allows for user-developed “packages” to be added a la carte to the main application [38]. RStudio was developed to be an integrated development environment (IDE) for R that allows for advanced visualization capabilities that support dashboard development [39]. In RStudio, web applications like dashboards can be placed on a host server and deployed on the internet such that as users interact with the web front-end, it can query and display data from the back-end hosted on the server.
The package Rshiny [40] was developed to support dashboarding in RStudio, and can work with other visualization packages depending upon the design goals of the dashboard. In our newly designed dashboard, the package leaflet was used for a base map on which we placed the hospital icons (like the original dashboard), and add-ons were made to display other items. These add-ons were adapted from other published codes [41]. JavaScript with wrapper DT (data table) was used to display stratified ICU rates, and CSS was used for formatting.
The dashboard we developed was deployed on a server (https://natasha-dukach.shinyapps.io/healthcare-associated-infections-reports/) and code for the dashboard was published (https://github.com/NatashaDukach/HAI-MA-2019). When accessing the link to the dashboard, the user initially sees a map with icons (in the form of dots) on it indicating hospitals. The icons are color-coded according to the lethality score described previously. Clicking on an icon will expand a bubble reporting information about the hospital (Figure 5).
Figure 5.
Alternative dashboard solution. Note: In our new version, two tabs are created (see “A”). The figure shows the first tab titled “ICU Rate Explorer”. The second tab, titled “data collection”, has information about the design of the dashboard and links to the original code. Each of the hospitals is indicated on the map by a color-coded icon that can be clicked on to display a bubble. The legend by “B” displays our color-coding scheme. When clicking on a hospital icon, hospital-level metrics are shown in a bubble, and there is a link that leads to the display of intensive care unit (ICU)-level metrics (see “C”).
As shown in Figure 5, like the original dashboard, this one has a map for navigation. Unlike the original, it only has two tabs: “ICU Rate Explorer” (the one shown in Figure 5), and “Data Collection”, which provides documentation and links to original data and code (see “A” in Figure 5). The hospital icons are placed on the map and coded according to our color scheme (see legend in Figure 5 by “B”). This allows for easy comparison between hospitals. When clicking on an icon for a hospital, a bubble appears that contains the following hospital metrics: Number of admissions, number of ICU beds, overall CAUTI rate, and overall CLABSI rate. There is also a link on the bubble where the user can click to open a new box that provides CAUTI and CLABSI rates stratified by ICU. Future development plans include adding other overall rates (e.g., for SSI), and adding in data from previous years to allow for the evaluation of trends.
3.2.4 User testing
Informally, members of two potential user bases were queried as to their reactions to the differences between the two dashboards: members of the academic public health space, and members of the MA public. When the dashboard redesign was pitched as a project to public health academics, it was dismissed as an unimportant escapade for various reasons. Some reasons cited were lack of agreement on terminology and patient safety priorities, the challenges with undercount of HAIs in NHSN data, and differential reporting accuracy in teaching vs. non-teaching hospitals. Academics also acknowledged that the system for tracking, addressing, and preventing HAIs is hopelessly broken in the US, and therefore it seems a waste of time to prop up such a system when it produces inaccurate data.
A few members of the MA public who are familiar with technology also provided informal feedback about the utility of the dashboard from a patient standpoint. They reported that the alternative solution was more intuitive than the original, and did a better job of representing the highly limited data from the NHSN.
These differences in reactions underscore the challenge of OGD and ensuring that public dashboards are developed and deployed in good faith. Those from the public health field expressed that since the system is broken and the data are inaccurate, they should be dismissed, while those in the public felt that since the data existed, it should be accessible, even if it was not completely accurate. It not only highlights the differing perspectives of those on either side of information asymmetry, it glaringly illustrates how those who are being held accountable by the usage of the data see dashboarding differently than those who are using the data to do oversight and accountability.
4. Application
We wanted to compare the original HAI dashboard with the one we developed based on the good faith principles described earlier. We started by creating the framework presented in Table 2, which guides as to the good faith and bad faith characteristics of public dashboards.
Dashboard function/characteristic
Good faith
Bad faith
Access to underlying data
Easy to download analytic dataset on which dashboard rides in *.csv format (e.g., through report export function)
Easy to access or download component datasets that went into the analytic dataset on which the dashboard rides
Lack of downloading functions, or downloading functions that provide only reports in non-tabular and non-data format
Lack of transparency on how the analytic dataset was developed
Lack of transparency about source datasets
Transparency of underlying data
For each native variable used in the dashboard, its source dataset is specified, and a link is given if available.
For each calculated variable used in the dashboard, clear documentation is available.
It is clear which data reflect real measurements, and which reflect simulations, imputations, or predictions
Source datasets may be specified, but little information about the use of their variables in the dashboard is provided
It is not made clear which dashboard variables are calculated, and how they are calculated is also not made clear
It is not clear which data reflect real measurements, and which data have been simulated, imputed, or otherwise fabricated
Data classification
Data are classified in ways that are intuitive to the consumer, and results are presented according to those classifications
Data are classified in ways that either make the development work easier for the analyst, or serve to mask negative indicators
Data are not grouped into classifications consumers use, making it impossible to obtain summary statistics for these classification levels
Comparison functions
Dashboard allows for comparisons that provide useful consumer decision-support
Dashboard prevents comparisons that would provide consumer decision-support, or makes them very difficult to make using the dashboard functions
Navigation functions
Navigation functions reflect how users conceive of accessing the entities in the dashboard
Specifically, map navigation reflects how users conceive of their geographic locale when searching for information
This allows consumers to intuitively ingest and assimilate information as they interact with the dashboard
Navigation functions reflect how public officials want consumers to navigate the entities in the dashboard
This forces consumers to think differently about the topic, and disrupts their ability to ingest and assimilate information
Map functions force the consumer to conceive of their geographic locale in an unintuitive way, making map navigation confusing
Metrics presented
Metrics are intuitive to consumers
Metrics are presented in such a way that they are intuitive to ingest and assimilate
Metrics that are presented to make comparisons between entities intuitive to support decision-making
Metrics reflect jargon, and are unintuitive to consumers
Metrics require consumers to read documentation to understand
Metrics are presented in such a way as to be confusing, making them impossible to be used for decision-support
Table 2.
Proposed framework for evaluation of good faith and bad faith public dashboards.
Using this framework, we applied a rating system. We chose zero to represent “neither bad faith nor good faith”, −5 to represent “mostly bad faith” and + 5 to represent “mostly good faith”. Then, based on our experience and available information, we rated the original MA HAI dashboard and our alternative dashboard solution to compare the ratings. To experiment with applying our framework to another public dashboard, we used the information published in the article described earlier to rate the traffic dashboard which was in Rio de Janeiro [2]. Our ratings appear in Table 3.
Dashboard function/characteristic
MA Hosp
Alt. MA Hosp
Comment: MA Hosp vs. Alt. MA Hosp
Rio
Comment: MA Hosp vs. Rio
ACCESS TO UNDERLYING DATA
−5
5
The original solution had no access to underlying data (except by way of PDF-style reports). Alternative solution posts data publicly for download.
5
Rio dashboard uses open data from City Hall with user-generated content collected through Waze
Transparency of Underlying Data
−5
3
It was difficult to identify the source of the data in the original solution. The alternative solution uses the same data, which is from NHSN. Because NHSN itself is somewhat opaque, the final solution lacks transparency.
0
Unclear from the article, but it appears that it is possible to audit Rio dashboard design if a member of a certain role (e.g., public data scientist). Not all tools used were open source.
Data Classification
0
3
In informal user testing, public users found the data classifications much more intuitive in the alternative compared to the original solution. However, formal user testing was not conducted.
5
Much effort was made to classify data in Rio dashboard to make it useful for the public to make route decisions.
Comparison Functions
−5
5
In informal user testing, users found the comparison function in the alternative solution useful for decision-making, and could not find a comparison function in the original solution.
5
Rio dashboard was designed to allow the public to make comparisons about potential traffic routes.
Navigation Functions
0
5
In informal user testing, users reported being able to easily navigate the data and dashboard in the alternative solution, but having extreme difficulty in navigating the original solution.
5
Rio dashboard for the public had a very simple, intuitive interface with images and only a few metrics critical to decision-making. This made it possible to easily navigate the dashboard display.
Metrics Presented
−5
5
In informal testing, users indicated that they did not understand the metrics presented on the original solution but found the color-coding of the alternative solution intuitive for decision-making.
3
The few metrics presented on the Rio dashboard were geared specifically to helping the public make route decisions based on traffic metrics. However, no formal user testing is presented.
Total
−20
26
23
Table 3.
Application of rating system.
Note: MA Hosp = original hospital-acquired infection (HAI) dashboard from the Commonwealth of Massachusetts (MA), MA Hosp Alt. = alternative solution, Rio = Rio traffic dashboard [2], and NHSN = National Healthcare Safety Network.
As shown in Table 3, using Table 2 as a rubric and our rating scale, we were able to rate each dashboard and assign a score. We were also able to define in the comments in the table the evidence on which we based our score. Table 3 demonstrates that this framework can be used to compare two different alternatives of a public dashboard displaying the same data, as well as two completely different public dashboards. The total scores show that while our redesigned prototype of the HAI dashboard had a similar level of good faith implementation compared to the Rio traffic dashboard (scores 26 vs. 23, respectively), the original HAI dashboard had a very low level of good faith implementation compared to the other two (score − 20).
5. Discussion
As is consistent with the global trend, the state of MA implemented an OGD requirement to share HAI data with the public through posting a public dashboard on a web page. However, as residents of MA, the authors found that this dashboard did not serve our information needs, and essentially obscured the data it was supposed to present. To address this challenge, we not only redesigned the dashboard into a new prototype, but we also tested our proposed framework for evaluating the level of good faith in public dashboards by applying it. Using our proposed framework and rubric, we evaluated the original HAI dashboard, our redesigned prototype, and a public dashboard on another topic presented in the scientific literature on the level of good faith implementation. Through this exercise, we demonstrated that the proposed framework is reasonable to use when evaluating the level of good faith in a public dashboard.
The next step in the pursuit of holding governments accountable for meeting OGD standards in public dashboards is to improve upon this framework and rubric through rigorous research. As part of this research, entire groups of individuals could be asked to score dashboards on each of these characteristics, and the results could easily be summarized to allow an evidence-based comparison between dashboards. Results can be easily visualized in a dumbbell plot (using packages ggplot2, ggalt, and tidyverse [42, 43, 44]), which we have done with our individual scores, but could be done with summary scores (Figure 6).
Figure 6.
Example of visualization of framework score comparison. Note: MA Hosp = original hospital-acquired infection (HAI) dashboard from the Commonwealth of Massachusetts (MA), MA Hosp Alt. = alternative solution, and Rio = Rio traffic dashboard [2].
As visualized in Figure 5 and summed in Table 3, our scoring system suggested that the alternative HAI dashboard we developed was done in a level of good faith (score = 26) similar to that of the Rio traffic dashboard (score = 23), and that the original HAI dashboard appears to not have been done in good faith (score = −20), and may serve as a governmental attempt to hide or obscure uncomfortable data. This exercise shows that the framework and rubric developed can be used to compare the level of good faith in public dashboards, and to provide evidence-based recommendations on how governments can improve them so they meet both the spirit and the letter of OGD requirements.
6. Conclusion
In conclusion, in this chapter, we describe the challenge of holding governments accountable for developing public dashboards to meet OGD requirements in a way that also serves the public’s information needs. To address this challenge, we propose a framework of six principles of good faith OGD by which public dashboards could be evaluated to ensure data shared by the government under OGD policies and laws are done so in good faith. We also demonstrate applying this framework to the use-case of a public dashboard intended for residents of MA in the US to use to compare and select hospitals based on their HAI rates. As a demonstration, we present our redesign of the dashboard, then use a rubric based on the framework to score and compare the original dashboard and our alternative in terms of levels of good faith OGD. We also demonstrate using the rubric on a published use-case in the literature. As our framework and rubric provide a reasonable starting point as a method for evaluating and comparing the level of good faith in public dashboards, we strongly recommend that future research into this topic consider our framework and rubric, and build upon it through gathering evidence in the field.
\n',keywords:"public reporting of healthcare data, quality of healthcare, cross infection, public health informatics, data visualization",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80394.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80394.xml",downloadPdfUrl:"/chapter/pdf-download/80394",previewPdfUrl:"/chapter/pdf-preview/80394",totalDownloads:61,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 23rd 2021",dateReviewed:"December 9th 2021",datePrePublished:"February 8th 2022",datePublished:"April 13th 2022",dateFinished:"February 8th 2022",readingETA:"0",abstract:"To hold governments accountable to open government data (GD) standards, public dashboards need to be evaluated in terms of how well they meet public needs. To assist with that effort, this chapter presents a framework and rubric by which public dashboards can be evaluated for their level of good faith implementation. It starts by reviewing challenges to governments sharing data in good faith despite increasing open government data (OGD) policies and laws being put in place globally. Next, it presents a use-case in which the authors explain how they examined a public dashboard in their local context that appeared to be following OGD, but not in good faith, and developed an alternative implementation that appeared to increase the level of good faith. The framework and rubric proposed were used to successfully compare and contrast the level of good faith of both implementations, as well as another public dashboard described in the scientific literature, and to generate recommendations to increase the level of good faith. In conclusion, the utility of this framework and rubric for evaluating and comparing good faith in public implementations of dashboards was demonstrated, and researchers are encouraged to build upon this research to quantify the level of good faith in public dashboards as a way of increasing oversight of OGD compliance.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80394",risUrl:"/chapter/ris/80394",signatures:"Monika M. Wahi and Natasha Dukach",book:{id:"10693",type:"book",title:"Open Data",subtitle:null,fullTitle:"Open Data",slug:"open-data",publishedDate:"April 13th 2022",bookSignature:"Vijayalakshmi Kakulapati",coverURL:"https://cdn.intechopen.com/books/images_new/10693.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83968-316-9",printIsbn:"978-1-83968-315-2",pdfIsbn:"978-1-83968-317-6",isAvailableForWebshopOrdering:!0,editors:[{id:"355072",title:"Prof.",name:"Vijayalakshmi",middleName:null,surname:"Kakulapati",slug:"vijayalakshmi-kakulapati",fullName:"Vijayalakshmi Kakulapati"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"190958",title:"Ms.",name:"Monika M.",middleName:null,surname:"Wahi",fullName:"Monika M. 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Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly. 2020;37:101284'},{id:"B3",body:'Janssen M, van den Hoven J. Big and open linked data (BOLD) in government: A challenge to transparency and privacy? Government Information Quarterly. 2015;32:363-368'},{id:"B4",body:'Fareed N, Swoboda CM, Chen S, et al. U.S. COVID-19 state government public dashboards: An expert review. Applied Clinical Informatics. 2021;12:208-221'},{id:"B5",body:'Vila RA, Estevez E, Fillottrani PR. The design and use of dashboards for driving decision-making in the public sector. In: Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance. New York: Association for Computing Machinery. pp. 382-388'},{id:"B6",body:'Joshi A, Amadi C, Katz B, et al. A human-centered platform for hiv infection reduction in New York: Development and usage analysis of the ending the epidemic (ETE) dashboard. JMIR Public Health Surveillance. 2017;3. DOI: 10.2196/publichealth.8312'},{id:"B7",body:'Guerrero LR, Richter Lagha R, Shim A, et al. Geriatric workforce development for the underserved: Using RCQI methodology to evaluate the training of IHSS caregivers. Journal of Applied Gerontology. 2020;39:770-777'},{id:"B8",body:'Mullins CM, Hall KC, Diffenderfer SK, et al. Development and implementation of APRN competency validation tools in four nurse-led clinics in rural east Tennessee. Journal of Doctoral Nursing Practice. 2019;12:189-195'},{id:"B9",body:'Giordanengo A, Årsand E, Woldaregay AZ, et al. Design and prestudy assessment of a dashboard for presenting self-collected health data of patients with diabetes to clinicians: iterative approach and qualitative case study. JMIR Diabetes. 2019;4. DOI: 10.2196/14002'},{id:"B10",body:'Jensen MC, Meckling WH. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics. 1976;3:305-360'},{id:"B11",body:'Bugaric B. Openness and transparency in public administration: Challenges for public law. Wisconsin International Law Journal. 2004;22:483'},{id:"B12",body:'Martin R. Florida Scientist Says She Was Fired For Not Manipulating COVID-19 Data. 2020. Available from: https://www.npr.org/2020/06/29/884551391/florida-scientist-says-she-was-fired-for-not-manipulating-covid-19-data [Accessed: September 13, 2021]'},{id:"B13",body:'Lee CH, Yoon H-J. Medical big data: Promise and challenges. Kidney Research and Clinical Practice. 2017;36:3-11'},{id:"B14",body:'Wahi MM, Dukach N. Visualizing infection surveillance data for policymaking using open source dashboarding. Applied Clinical Informatics. 2019;10:534-542'},{id:"B15",body:'Yoo J, Jung KY, Kim T, et al. A real-time autonomous dashboard for the emergency department: 5-year case study. JMIR mHealth and uHealth. 2018;6:e10666'},{id:"B16",body:'Bernard J, Sessler D, Kohlhammer J, et al. Using dashboard networks to visualize multiple patient histories: A design study on post-operative prostate cancer. IEEE Transactions on Visualization and Computer Graphics. 2019;25:1615-1628'},{id:"B17",body:'Sedrakyan G, Mannens E, Verbert K. Guiding the choice of learning dashboard visualizations: Linking dashboard design and data visualization concepts. Journal of Computer Languages. 2019;50:19-38'},{id:"B18",body:'Thoma B, Bandi V, Carey R, et al. Developing a dashboard to meet competence committee needs: A design-based research project. Canadian Medical Education Journal. 2020;11:e16-e34'},{id:"B19",body:'Ahn J, Campos F, Hays M, et al. Designing in context: Reaching beyond usability in learning analytics dashboard design. Journal of Learning Analytics. 2019;6:70-85'},{id:"B20",body:'Mlaver E, Schnipper JL, Boxer RB, et al. User-centered collaborative design and development of an inpatient safety dashboard. Joint Commission Journal on Quality and Patient Safety. 2017;43:676-685'},{id:"B21",body:'Mass.gov. Healthcare Associated Infections Reports. Available from: https://www.mass.gov/lists/healthcare-associated-infections-reports [Accessed: March 18, 2021]'},{id:"B22",body:'Hassan KA, Fatima BK, Riffat M. Nosocomial infections: Epidemiology, prevention, control and surveillance. Asian Pacific Journal of Tropical Biomedicine. 2017;7:478-482'},{id:"B23",body:'Rhee C, Jones TM, Hamad Y, et al. Prevalence, underlying causes, and preventability of sepsis-associated mortality in US acute care hospitals. JAMA Network Open. 2019;2:e187571'},{id:"B24",body:'Bordeianou L, Cauley CE, Antonelli D, et al. Truth in reporting: how data capture methods obfuscate actual surgical site infection rates within a healthcare network system. Diseases of the Colon and Rectum. 2017;60:96-106'},{id:"B25",body:'Isikgoz Tasbakan M, Durusoy R, Pullukcu H, et al. Hospital-acquired urinary tract infection point prevalence in Turkey: Differences in risk factors among patient groups. Annals of Clinical Microbiology and Antimicrobials. 2013;12:31'},{id:"B26",body:'Neelakanta A, Sharma S, Kesani VP, et al. Impact of changes in the NHSN catheter-associated urinary tract infection (CAUTI) surveillance criteria on the frequency and epidemiology of CAUTI in intensive care units (ICUs). Infection Control and Hospital Epidemiology. 2015;36:346-349'},{id:"B27",body:'Conlon-Bingham GM, Aldeyab M, Scott M, et al. Effects of antibiotic cycling policy on incidence of healthcare-associated MRSA and clostridioides difficile infection in secondary healthcare settings. Emerging Infectious Diseases. 2019;25:52-62'},{id:"B28",body:'Homan P. Structural sexism and health in the United States: A new perspective on health inequality and the gender system. American Sociological Review. 2019;84:486-516'},{id:"B29",body:'Oehmke JF, Oehmke TB, Singh LN, et al. Dynamic panel estimate–based health surveillance of SARS-CoV-2 infection rates to inform public health policy: Model development and validation. Journal of Medical Internet Research. 2020;22:e20924'},{id:"B30",body:'de Garcia CL, de Abreu LC, JLS R, et al. Influence of burnout on patient safety: Systematic review and meta-analysis. Medicina. 2019;55:553'},{id:"B31",body:'Persson E. pdftables: Programmatic Conversion of PDF Tables. 2016. Available from: https://CRAN.R-project.org/package=pdftables [Accessed: March 20, 2021]'},{id:"B32",body:'Ooms J. pdftools: Text Extraction, Rendering and Converting of PDF Documents. 2020. Available from: https://cran.r-project.org/web/packages/pdftools/index.html [Accessed: March 20, 2021]'},{id:"B33",body:'PDFTables. Available from: https://pdftables.com/ [Accessed: March 20, 2021]'},{id:"B34",body:'Medicare.gov. Compare Care Near You. Available from: https://www.medicare.gov/care-compare/?providerType=Hospital&redirect=true [Accessed: March 18, 2021]'},{id:"B35",body:'Rural Information Hub. Rural health for Massachusetts. 2020. Available from: https://www.ruralhealthinfo.org/states/massachusetts [Accessed: March 20, 2021]'},{id:"B36",body:'US Census Bureau. QuickFacts. The United States Census Bureau. Available from: https://www.census.gov/programs-surveys/sis/resources/data-tools/quickfacts.html [Accessed: March 20, 2021]'},{id:"B37",body:'Frost J. Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models. Statistics By Jim Publishing; 2020'},{id:"B38",body:'Wahi M, Seebach P. Analyzing Health Data in R for SAS Users. London: CRC Press, Statistics by Jim Publishing; 2017. Available from: https://www.amazon.com/Regression-Analysis-Intuitive-Interpreting-Linear/dp/1735431184'},{id:"B39",body:'RStudio. Available from: https://www.rstudio.com/ [Accessed: March 22, 2019]'},{id:"B40",body:'Chang W, Cheng J, Allaire JJ, et al. shiny: Web Application Framework for R. 2018. Available from: https://CRAN.R-project.org/package=shiny [Accessed: March 12, 2019]'},{id:"B41",body:'Cheng J, Karambelkar B, Xie Y, et al. leaflet: Create Interactive Web Maps with the JavaScript ‘Leaflet’ Library. 2021. Available from: https://CRAN.R-project.org/package=leaflet [Accessed: September 15, 2021]'},{id:"B42",body:'Wickham H, Chang W, Henry L, et al. ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. 2018. Available from: https://CRAN.R-project.org/package=ggplot2 [Accessed: April 3, 2019]'},{id:"B43",body:'Rudis B, Bolker B, Marwick B, et al. ggalt: Extra Coordinate Systems, ‘Geoms’, Statistical Transformations, Scales and Fonts for ‘ggplot2’. 2017. Available from: https://CRAN.R-project.org/package=ggalt [Accessed: September 15, 2021]'},{id:"B44",body:'Wickham H. tidyverse: Easily Install and Load the ‘Tidyverse’. 2021. Available from: https://CRAN.R-project.org/package=tidyverse [Accessed: September 15, 2021]'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Monika M. Wahi",address:"dethwench@gmail.com",affiliation:'
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It is assumed that during the extraction of indium in aqueous solutions of hydrochloric acid at various concentrations and constant ionic strength maintained by the addition of perchloric acid, indium is present in the organic phase in the form of ionic aggregates HClO4 · HInCl4. To study the hydration of Ions, the method of electrical conductivity was carried out in the “In(ClO4)3 - HClO4 - H2O” system. The structure of aqueous solutions of India perchlorates was determined by IR spectroscopy. 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If your research is financed through any of the below-mentioned funders, please consult their Open Access policies or grant ‘terms and conditions’ to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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IMPORTANT: You must be a member or grantee of the listed funders in order to apply for their Open Access publication funds. Do not attempt to contact the funders if this is not the case.
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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The recent developments and the future scope for biosorption as a wastewater treatment option are discussed.",book:{id:"6137",slug:"biosorption",title:"Biosorption",fullTitle:"Biosorption"},signatures:"Sri Lakshmi Ramya Krishna Kanamarlapudi, Vinay Kumar\nChintalpudi and Sudhamani Muddada",authors:[{id:"238433",title:"Associate Prof.",name:"Sudhamani",middleName:null,surname:"Muddada",slug:"sudhamani-muddada",fullName:"Sudhamani Muddada"},{id:"244937",title:"Mrs.",name:"S L Ramyakrishna",middleName:null,surname:"Kanamarlapudi",slug:"s-l-ramyakrishna-kanamarlapudi",fullName:"S L Ramyakrishna Kanamarlapudi"},{id:"244938",title:"Mr.",name:"Vinay Kumar",middleName:null,surname:"Chintalpudi",slug:"vinay-kumar-chintalpudi",fullName:"Vinay Kumar Chintalpudi"}]},{id:"53211",doi:"10.5772/66416",title:"Biofloc Technology (BFT): A Tool for Water Quality Management in Aquaculture",slug:"biofloc-technology-bft-a-tool-for-water-quality-management-in-aquaculture",totalDownloads:16966,totalCrossrefCites:65,totalDimensionsCites:148,abstract:"Biofloc technology (BFT) is considered the new “blue revolution” in aquaculture. 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The present chapter will discuss some insights of the role of microorganisms in BFT, main water quality parameters, the importance of the correct carbon-to-nitrogen ratio in the culture media, its calculations, and different types, as well as metagenomics of microorganisms and future perspectives.",book:{id:"5355",slug:"water-quality",title:"Water Quality",fullTitle:"Water Quality"},signatures:"Maurício Gustavo Coelho Emerenciano, Luis Rafael Martínez-\nCórdova, Marcel Martínez-Porchas and Anselmo Miranda-Baeza",authors:[{id:"146126",title:"Dr.",name:"Maurício Gustavo Coelho",middleName:null,surname:"Emerenciano",slug:"mauricio-gustavo-coelho-emerenciano",fullName:"Maurício Gustavo Coelho Emerenciano"},{id:"186970",title:"Prof.",name:"Marcel",middleName:null,surname:"Martínez-Porchas",slug:"marcel-martinez-porchas",fullName:"Marcel Martínez-Porchas"},{id:"186971",title:"Prof.",name:"Anselmo",middleName:null,surname:"Miranda-Baeza",slug:"anselmo-miranda-baeza",fullName:"Anselmo Miranda-Baeza"},{id:"195101",title:"Dr.",name:"Luis Rafael",middleName:null,surname:"Martínez-Córdoba",slug:"luis-rafael-martinez-cordoba",fullName:"Luis Rafael Martínez-Córdoba"}]}],mostDownloadedChaptersLast30Days:[{id:"69568",title:"Water Quality Parameters",slug:"water-quality-parameters",totalDownloads:10201,totalCrossrefCites:14,totalDimensionsCites:37,abstract:"Since the industrial revolution in the late eighteenth century, the world has discovered new sources of pollution nearly every day. So, air and water can potentially become polluted everywhere. Little is known about changes in pollution rates. The increase in water-related diseases provides a real assessment of the degree of pollution in the environment. This chapter summarizes water quality parameters from an ecological perspective not only for humans but also for other living things. According to its quality, water can be classified into four types. Those four water quality types are discussed through an extensive review of their important common attributes including physical, chemical, and biological parameters. These water quality parameters are reviewed in terms of definition, sources, impacts, effects, and measuring methods.",book:{id:"7718",slug:"water-quality-science-assessments-and-policy",title:"Water Quality",fullTitle:"Water Quality - Science, Assessments and Policy"},signatures:"Nayla Hassan Omer",authors:null},{id:"58138",title:"Water Pollution: Effects, Prevention, and Climatic Impact",slug:"water-pollution-effects-prevention-and-climatic-impact",totalDownloads:21566,totalCrossrefCites:18,totalDimensionsCites:38,abstract:"The stress on our water environment as a result of increased industrialization, which aids urbanization, is becoming very high thus reducing the availability of clean water. Polluted water is of great concern to the aquatic organism, plants, humans, and climate and indeed alters the ecosystem. The preservation of our water environment, which is embedded in sustainable development, must be well driven by all sectors. While effective wastewater treatment has the tendency of salvaging the water environment, integration of environmental policies into the actor firms core objectives coupled with continuous periodical enlightenment on the present and future consequences of environmental/water pollution will greatly assist in conserving the water environment.",book:{id:"6157",slug:"water-challenges-of-an-urbanizing-world",title:"Water Challenges of an Urbanizing World",fullTitle:"Water Challenges of an Urbanizing World"},signatures:"Inyinbor Adejumoke A., Adebesin Babatunde O., Oluyori Abimbola\nP., Adelani-Akande Tabitha A., Dada Adewumi O. and Oreofe Toyin\nA.",authors:[{id:"101570",title:"MSc.",name:"Babatunde Olufemi",middleName:null,surname:"Adebesin",slug:"babatunde-olufemi-adebesin",fullName:"Babatunde Olufemi Adebesin"},{id:"187738",title:"Dr.",name:"Adejumoke",middleName:"Abosede",surname:"Inyinbor",slug:"adejumoke-inyinbor",fullName:"Adejumoke Inyinbor"},{id:"188818",title:"Dr.",name:"Abimbola",middleName:null,surname:"Oluyori",slug:"abimbola-oluyori",fullName:"Abimbola Oluyori"},{id:"188819",title:"Mrs.",name:"Tabitha",middleName:null,surname:"Adelani-Akande",slug:"tabitha-adelani-akande",fullName:"Tabitha Adelani-Akande"},{id:"208501",title:"Dr.",name:"Adewumi",middleName:null,surname:"Dada",slug:"adewumi-dada",fullName:"Adewumi Dada"},{id:"208502",title:"Ms.",name:"Toyin",middleName:null,surname:"Oreofe",slug:"toyin-oreofe",fullName:"Toyin Oreofe"}]},{id:"45422",title:"Urban Waterfront Regenerations",slug:"urban-waterfront-regenerations",totalDownloads:14246,totalCrossrefCites:4,totalDimensionsCites:12,abstract:null,book:{id:"3560",slug:"advances-in-landscape-architecture",title:"Advances in Landscape Architecture",fullTitle:"Advances in Landscape Architecture"},signatures:"Umut Pekin Timur",authors:[{id:"165480",title:"Dr.",name:"Umut",middleName:null,surname:"Pekin Timur",slug:"umut-pekin-timur",fullName:"Umut Pekin Timur"}]},{id:"24941",title:"Tsunami in Makran Region and Its Effect on the Persian Gulf",slug:"tsunami-in-makran-region-and-its-effect-on-the-persian-gulf",totalDownloads:7604,totalCrossrefCites:4,totalDimensionsCites:7,abstract:null,book:{id:"406",slug:"tsunami-a-growing-disaster",title:"Tsunami",fullTitle:"Tsunami - A Growing Disaster"},signatures:"Mohammad Mokhtari",authors:[{id:"52451",title:"Dr.",name:"Mohammad",middleName:null,surname:"Mokhtari",slug:"mohammad-mokhtari",fullName:"Mohammad Mokhtari"}]},{id:"66307",title:"Bio-hydrogen and Methane Production from Lignocellulosic Materials",slug:"bio-hydrogen-and-methane-production-from-lignocellulosic-materials",totalDownloads:2957,totalCrossrefCites:6,totalDimensionsCites:8,abstract:"This chapter covers the information on bio-hydrogen and methane production from lignocellulosic materials. Pretreatment methods of lignocellulosic materials and the factors affecting bio-hydrogen production, both dark- and photo-fermentation, and methane production are addressed. Last but not least, the processes for bio-hydrogen and methane production from lignocellulosic materials are discussed.",book:{id:"7608",slug:"biomass-for-bioenergy-recent-trends-and-future-challenges",title:"Biomass for Bioenergy",fullTitle:"Biomass for Bioenergy - Recent Trends and Future Challenges"},signatures:"Apilak Salakkam, Pensri Plangklang, Sureewan Sittijunda, Mallika Boonmee Kongkeitkajorn, Siriporn Lunprom and Alissara Reungsang",authors:null}],onlineFirstChaptersFilter:{topicId:"12",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82624",title:"Protective Forests for Ecosystem-based Disaster Risk Reduction (Eco-DRR) in the Alpine Space",slug:"protective-forests-for-ecosystem-based-disaster-risk-reduction-eco-drr-in-the-alpine-space",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.99505",abstract:"Mountain forests are an efficient Forest-based Solution (FbS) for Ecosystem-based Disaster Risk Reduction (Eco-DRR) by lowering the frequency, magnitude, and/or intensity of natural hazards. Technical protection measures are often poor solutions as stand-alone measures to reduce disaster risk limited by material wear and fatigue or financial resources and aesthetical values. Protective forests should therefore be considered as key elements in integrated risk management strategies. However, the definition of protective forests and the understanding and assessment of their protective functions and effects differ greatly among Alpine Space countries. In this chapter, we present a short introduction to the concept of Eco-DRR and companion terms and propose a definition of FbS as a specific case of Nature-based Solutions for an ecosystem-based and integrated risk management of natural hazards. That is, we guide the reader through the maze of existing definitions and concepts and try to disentangle their meanings. Furthermore, we present an introduction to forest regulations in the Alpine Space and European protective forest management guidelines. Our considerations and recommendations can help strengthen the role of protective forests as FbS in Eco-DRR and the acknowledgment of the key protective function they have and the crucial protective effects they provide in mountain areas.",book:{id:"10812",title:"Protective forests as Ecosystem-based solution for Disaster Risk Reduction (ECO-DRR)",coverURL:"//cdnintech.com/web/frontend/www/assets/cover.jpg"},signatures:"Michaela Teich, Cristian Accastello, Frank Perzl and Frédéric Berger"},{id:"82465",title:"Agroforestry: An Approach for Sustainability and Climate Mitigation",slug:"agroforestry-an-approach-for-sustainability-and-climate-mitigation",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.105406",abstract:"Agroforestry Systems (AFS), or the association of trees with crops (or animals), is a strategy for land management and use that allows production within the sustainable development: (a) environmentally (production environmentally harmonic); (b) technically (integrating existing resources on the farm); (c) economically (increase in production), and (d) socially (equality of duties and opportunities, quality of life of the family group). As an intentional integration of trees or shrubs with crop and animal production, this practice makes environmental, economic, and social benefits to farmers. Given that there is a set of definitions, rather than a single definition of Agroforestry (AF) and AFS, it is justified to explore the historical evolution and the minimum coincidences of criteria to define them and apply them in the recovery of degraded areas. Knowing how to classify AFS allows us to indicate which type or group of AFS is suitable for a particular area with its characteristics. The greatest benefit that AFS can bring to degraded or sloping areas lies in their ability to combine soil conservation with productive functions. In other words, AF is arborizing agriculture and animal production to obtain more benefits including climate change adaptation and mitigation by ecosystem services.",book:{id:"11663",title:"Vegetation Dynamics, Changing Ecosystems and Human Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11663.jpg"},signatures:"Ricardo O. Russo"},{id:"82754",title:"Impact of Revegetation on Ecological Restoration of a Constructed Soil in a Coal Mining in Southern Brazil",slug:"impact-of-revegetation-on-ecological-restoration-of-a-constructed-soil-in-a-coal-mining-in-southern-",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.105895",abstract:"The main problems in the constructed soils are the generation of acid mine drainage promoted by the presence of coal debris in the overburden layer and the compaction of the topsoil promoted by the machine traffic when the material used in the overburden cover is more clayey. This book chapter aimed to show an overview of the impact of more than a decade of revegetation with different perennial grasses on the chemical, physical, and biological quality of constructed soil after coal mining. The study was carried out in a coal mining area, located in southern Brazil. The soil was constructed in early 2003 and the perennial grasses, Hemarthria altissima; Paspalum notatum cv. Pensacola; Cynodon dactylon cv Tifton; and Urochloa brizantha; were implanted in November/December 2003. In 11.5, 17.6 and 18 years of revegetation soil samples were collected and the chemical, physical, and biological attributes were determined. Our results show that liming is an important practice in the restoration of these strongly anthropized soils because this positively impacts the plants’ development, facilitating the roots system expansion. Biological attributes such as soil fauna and the microorganism’s population are the attributes that possibly takes longer to establish itself in these areas.",book:{id:"11663",title:"Vegetation Dynamics, Changing Ecosystems and Human Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11663.jpg"},signatures:"Lizete Stumpf, Maria Bertaso De Garcia Fernandez, Pablo Miguel, Luiz Fernando Spinelli Pinto, Ryan Noremberg Schubert, Luís Carlos Iuñes de Oliveira Filho, Tania Hipolito Montiel, Lucas Da Silva Barbosa, Jeferson Diego Leidemer and Thábata Barbosa Duarte"},{id:"82936",title:"Soil Degradation Processes Linked to Long-Term Forest-Type Damage",slug:"soil-degradation-processes-linked-to-long-term-forest-type-damage",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.106390",abstract:"Forest degradation impairs ability of the whole landscape adaptation to environmental change. The impacts of forest degradation on landscape are caused by a self-organization decline. At the present time, the self-organization decline was largely due to nitrogen deposition and deforestation which exacerbated impacts of climate change. Nevertheless, forest degradation processes are either reversible or irreversible. Irreversible forest degradation begins with soil damage. In this paper, we present processes of forest soil degradation in relation to vulnerability of regulation adaptability on global environmental change. The regulatory forest capabilities were indicated through soil organic matter sequestration dynamics. We devided the degradation processes into quantitative and qualitative damages of physical or chemical soil properties. Quantitative soil degradation includes irreversible loss of an earth’s body after claim, erosion or desertification, while qualitative degradation consists of predominantly reversible consequences after soil disintegration, leaching, acidification, salinization and intoxication. As a result of deforestation, the forest soil vulnerability is spreading through quantitative degradation replacing hitherto predominantly qualitative changes under continuous vegetation cover. Increasing needs to natural resources using and accompanying waste pollution destroy soil self-organization through biodiversity loss, simplification in functional links among living forms and substance losses from ecosystem. We concluded that subsequent irreversible changes in ecosystem self-organization cause a change of biome potential natural vegetation and the land usability decrease.",book:{id:"11457",title:"Forest Degradation Under Global Change",coverURL:"https://cdn.intechopen.com/books/images_new/11457.jpg"},signatures:"Pavel Samec, Aleš Kučera and Gabriela Tomášová"},{id:"82828",title:"Vegetation and Avifauna Distribution in the Serengeti National Park",slug:"vegetation-and-avifauna-distribution-in-the-serengeti-national-park",totalDownloads:6,totalDimensionsCites:0,doi:"10.5772/intechopen.106165",abstract:"In order to examine the bird species changes within different vegetation structures, the variations were compared between Commiphora-dominated vegetations with those of Vachellia tortilis and Vachellia robusta-dominated vegetations, and also compared the birds of grassland with those of Vachellia drepanolobium and Vachellia seyal-dominated vegetations. This study was conducted between February 2010 and April 2012. A total of 40 plots of 100 m × 100 m were established. Nonparametric Mann-Whitney U-test was used to examine differences in bird species between vegetations. Species richness estimates were obtained using the Species Diversity and Richness. A total of 171 bird species representing 103 genera, 12 orders, and 54 families were recorded. We found differences in bird species distribution whereby V. tortilis has higher bird species richness (102 species), abundance, and diversity when compared with Commiphora with 66 species and V. robusta with 59 species. These results suggest that variations in bird species abundance, diversity, and distribution could be attributed to differences in the structural diversity of vegetation. Therefore it is important to maintain different types of vegetation by keeping the frequency of fire to a minimum and prescribed fire should be employed and encouraged to control wildfire and so maintain a diversity of vegetation and birds community.",book:{id:"11663",title:"Vegetation Dynamics, Changing Ecosystems and Human Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11663.jpg"},signatures:"Ally K. Nkwabi and Pius Y. Kavana"},{id:"82808",title:"Climate Change and Anthropogenic Impacts on the Ecosystem of the Transgressive Mud Coastal Region of Bight of Benin, Nigeria",slug:"climate-change-and-anthropogenic-impacts-on-the-ecosystem-of-the-transgressive-mud-coastal-region-of",totalDownloads:8,totalDimensionsCites:0,doi:"10.5772/intechopen.105760",abstract:"The transgressive mud coastal area of Bight of Benin is a muddy coastal complex that lies east of the Barrier/lagoon coast and stretches to the Benin River in the northwestern flank of the Niger Delta Nigeria. It constitutes a fragile buffer zone between the tranquil waters of the swamps and the menacing waves of the Atlantic Ocean. Extensive breaching of this narrow coastal plain results in massive incursion of the sea into the inland swamps with serious implications for national security and the economy. Climate change impacts from the results of meteorological information of the regions shows a gradual degradation in the past 30 years. Temperature, rainfall and humidity increase annually depict climate change, resulting from uncontrolled exploitation of natural resources is rapidly pushing the region towards ecological disasters. The ecosystem is very unique being the only transgressive mud coastal area of the Gulf of Guinea. The chapter describes the geomorphology, tidal hydrology, relief/drainage, topography, climate/meteorology, vegetation, economic characteristics, anthropogenic activities and their impacts on the ecosystem.",book:{id:"11663",title:"Vegetation Dynamics, Changing Ecosystems and Human Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11663.jpg"},signatures:"Patrick O. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 17th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"38",title:"Pollution",coverUrl:"https://cdn.intechopen.com/series_topics/covers/38.jpg",isOpenForSubmission:!0,editor:{id:"110740",title:"Dr.",name:"Ismail M.M.",middleName:null,surname:"Rahman",slug:"ismail-m.m.-rahman",fullName:"Ismail M.M. Rahman",profilePictureURL:"https://mts.intechopen.com/storage/users/110740/images/2319_n.jpg",biography:"Ismail Md. Mofizur Rahman (Ismail M. M. Rahman) assumed his current responsibilities as an Associate Professor at the Institute of Environmental Radioactivity, Fukushima University, Japan, in Oct 2015. He also has an honorary appointment to serve as a Collaborative Professor at Kanazawa University, Japan, from Mar 2015 to the present. \nFormerly, Dr. Rahman was a faculty member of the University of Chittagong, Bangladesh, affiliated with the Department of Chemistry (Oct 2002 to Mar 2012) and the Department of Applied Chemistry and Chemical Engineering (Mar 2012 to Sep 2015). Dr. Rahman was also adjunctly attached with Kanazawa University, Japan (Visiting Research Professor, Dec 2014 to Mar 2015; JSPS Postdoctoral Research Fellow, Apr 2012 to Mar 2014), and Tokyo Institute of Technology, Japan (TokyoTech-UNESCO Research Fellow, Oct 2004–Sep 2005). \nHe received his Ph.D. degree in Environmental Analytical Chemistry from Kanazawa University, Japan (2011). He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. Begum received her Ph.D. in Environmental Analytical Chemistry from Kanazawa University in 2012. She achieved her Master of Science (M.Sc.) degree with a major in Applied Chemistry and a Bachelor of Science (B.Sc.) in Chemistry, all from the University of Chittagong, Bangladesh. Her work affiliations include Fukushima University, Japan (Visiting Research Fellow, Institute of Environmental Radioactivity: Mar 2016 to present), Southern University Bangladesh (Assistant Professor, Department of Civil Engineering: Jan 2015 to present), and Kanazawa University, Japan (Postdoctoral Fellow, Institute of Science and Engineering: Oct 2012 to Mar 2014; Research fellow, Venture Business Laboratory, Advanced Science and Social Co-Creation Promotion Organization: Apr 2018 to Mar 2021). The research focus of Dr. Zinnat includes the effect of the relative stability of metal-chelator complexes in the environmental remediation process designs and the development of eco-friendly soil washing techniques using biodegradable chelators.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorThree:null},{id:"39",title:"Environmental Resilience and Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/39.jpg",isOpenForSubmission:!0,editor:{id:"137040",title:"Prof.",name:"Jose",middleName:null,surname:"Navarro-Pedreño",slug:"jose-navarro-pedreno",fullName:"Jose Navarro-Pedreño",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRAXrQAO/Profile_Picture_2022-03-09T15:50:19.jpg",biography:"Full professor at University Miguel Hernández of Elche, Spain, previously working at the University of Alicante, Autonomous University of Madrid and Polytechnic University of Valencia. 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is a Professor of Statistics and Dean of the School of Mathematics and Statistics, Yunnan University, China. He was elected a Yangtze River Scholars Distinguished Professor in 2013, a member of the International Statistical Institute (ISI) in 2016, a member of the board of the International Chinese Statistical Association (ICSA) in 2018, and a fellow of the Institute of Mathematical Statistics (IMS) in 2021. He received the ICSA Outstanding Service Award in 2018 and the National Science Foundation for Distinguished Young Scholars of China in 2012. He serves as a member of the editorial board of Statistics and Its Interface and Journal of Systems Science and Complexity. He is also a field editor for Communications in Mathematics and Statistics. His research interests include biostatistics, empirical likelihood, missing data analysis, variable selection, high-dimensional data analysis, Bayesian statistics, and data science. He has published more than 190 research papers and authored five books.",institutionString:"Yunnan University",institution:{name:"Yunnan University",country:{name:"China"}}},{id:"1177",title:"Prof.",name:"António",middleName:"J. R.",surname:"José Ribeiro Neves",slug:"antonio-jose-ribeiro-neves",fullName:"António José Ribeiro Neves",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1177/images/system/1177.jpg",biography:"Prof. António J. R. Neves received a Ph.D. in Electrical Engineering from the University of Aveiro, Portugal, in 2007. Since 2002, he has been a researcher at the Institute of Electronics and Informatics Engineering of Aveiro. Since 2007, he has been an assistant professor in the Department of Electronics, Telecommunications, and Informatics, University of Aveiro. He is the director of the undergraduate course on Electrical and Computers Engineering and the vice-director of the master’s degree in Electronics and Telecommunications Engineering. He is an IEEE Senior Member and a member of several other research organizations worldwide. His main research interests are computer vision, intelligent systems, robotics, and image and video processing. He has participated in or coordinated several research projects and received more than thirty-five awards. He has 161 publications to his credit, including books, book chapters, journal articles, and conference papers. He has vast experience as a reviewer of several journals and conferences. As a professor, Dr. Neves has supervised several Ph.D. and master’s students and was involved in more than twenty-five different courses.",institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"11317",title:"Dr.",name:"Francisco",middleName:null,surname:"Javier Gallegos-Funes",slug:"francisco-javier-gallegos-funes",fullName:"Francisco Javier Gallegos-Funes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/11317/images/system/11317.png",biography:"Francisco J. Gallegos-Funes received his Ph.D. in Communications and Electronics from the Instituto Politécnico Nacional de México (National Polytechnic Institute of Mexico) in 2003. He is currently an associate professor in the Escuela Superior de Ingeniería Mecánica y Eléctrica (Mechanical and Electrical Engineering Higher School) at the same institute. His areas of scientific interest are signal and image processing, filtering, steganography, segmentation, pattern recognition, biomedical signal processing, sensors, and real-time applications.",institutionString:"Instituto Politécnico Nacional",institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"428449",title:"Dr.",name:"Ronaldo",middleName:null,surname:"Ferreira",slug:"ronaldo-ferreira",fullName:"Ronaldo Ferreira",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428449/images/21449_n.png",biography:null,institutionString:null,institution:{name:"University of Aveiro",country:{name:"Portugal"}}},{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. 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In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},subseries:[{id:"3",title:"Bacterial Infectious Diseases",keywords:"Antibiotics, Biofilm, Antibiotic Resistance, Host-microbiota Relationship, Treatment, Diagnostic Tools",scope:"
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\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
",annualVolume:11399,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null,editorialBoard:[{id:"190041",title:"Dr.",name:"Jose",middleName:null,surname:"Gutierrez Fernandez",fullName:"Jose Gutierrez Fernandez",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"156556",title:"Prof.",name:"Maria Teresa",middleName:null,surname:"Mascellino",fullName:"Maria Teresa Mascellino",profilePictureURL:"https://mts.intechopen.com/storage/users/156556/images/system/156556.jpg",institutionString:"Sapienza University",institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"164933",title:"Prof.",name:"Mónica Alexandra",middleName:null,surname:"Sousa Oleastro",fullName:"Mónica Alexandra Sousa Oleastro",profilePictureURL:"https://mts.intechopen.com/storage/users/164933/images/system/164933.jpeg",institutionString:"National Institute of Health Dr Ricardo Jorge",institution:{name:"National Institute of Health Dr. Ricardo Jorge",institutionURL:null,country:{name:"Portugal"}}}]},{id:"4",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",annualVolume:11400,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"302145",title:"Dr.",name:"Felix",middleName:null,surname:"Bongomin",fullName:"Felix Bongomin",profilePictureURL:"https://mts.intechopen.com/storage/users/302145/images/system/302145.jpg",institutionString:null,institution:{name:"Gulu University",institutionURL:null,country:{name:"Uganda"}}},{id:"45803",title:"Ph.D.",name:"Payam",middleName:null,surname:"Behzadi",fullName:"Payam Behzadi",profilePictureURL:"https://mts.intechopen.com/storage/users/45803/images/system/45803.jpg",institutionString:"Islamic Azad University, Tehran",institution:{name:"Islamic Azad University, Tehran",institutionURL:null,country:{name:"Iran"}}}]},{id:"5",title:"Parasitic Infectious Diseases",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",annualVolume:11401,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188881",title:"Dr.",name:"Fernando José",middleName:null,surname:"Andrade-Narváez",fullName:"Fernando José Andrade-Narváez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRIV7QAO/Profile_Picture_1628834308121",institutionString:null,institution:{name:"Autonomous University of Yucatán",institutionURL:null,country:{name:"Mexico"}}},{id:"269120",title:"Dr.",name:"Rajeev",middleName:"K.",surname:"Tyagi",fullName:"Rajeev Tyagi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRaBqQAK/Profile_Picture_1644331884726",institutionString:"CSIR - Institute of Microbial Technology, India",institution:null},{id:"336849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Izurieta",fullName:"Ricardo Izurieta",profilePictureURL:"https://mts.intechopen.com/storage/users/293169/images/system/293169.png",institutionString:null,institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}}]},{id:"6",title:"Viral Infectious Diseases",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",annualVolume:11402,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",fullName:"Emmanuel Drouet",profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",institutionString:null,institution:{name:"Grenoble Alpes University",institutionURL:null,country:{name:"France"}}},{id:"188219",title:"Prof.",name:"Imran",middleName:null,surname:"Shahid",fullName:"Imran Shahid",profilePictureURL:"https://mts.intechopen.com/storage/users/188219/images/system/188219.jpeg",institutionString:null,institution:{name:"Umm al-Qura University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"214235",title:"Dr.",name:"Lynn",middleName:"S.",surname:"Zijenah",fullName:"Lynn Zijenah",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSEJGQA4/Profile_Picture_1636699126852",institutionString:null,institution:{name:"University of Zimbabwe",institutionURL:null,country:{name:"Zimbabwe"}}},{id:"178641",title:"Dr.",name:"Samuel Ikwaras",middleName:null,surname:"Okware",fullName:"Samuel Ikwaras Okware",profilePictureURL:"https://mts.intechopen.com/storage/users/178641/images/system/178641.jpg",institutionString:null,institution:{name:"Uganda Christian University",institutionURL:null,country:{name:"Uganda"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/332986",hash:"",query:{},params:{id:"332986"},fullPath:"/profiles/332986",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()