List of the VIs used in this chapter.
\r\n\t
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Kawsar Alam",coverURL:"https://cdn.intechopen.com/books/images_new/6805.jpg",editedByType:"Edited by",editors:[{id:"199691",title:"Dr.",name:"Md. Kawsar",surname:"Alam",slug:"md.-kawsar-alam",fullName:"Md. Kawsar Alam"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69269",title:"New Ways to Tackle Malaria",doi:"10.5772/intechopen.89467",slug:"new-ways-to-tackle-malaria",body:'\nMalaria is one of the oldest parasitic tropical diseases, and it takes a huge toll on human lives. It also causes great economic loss. Almost half of the population in the world is under the threat of malaria mostly in the tropical and sub-tropical countries. About 90% of the total malaria burden occurs in sub-Saharan African countries. Efforts to eradicate/or eliminate malaria began after the discovery of the role of mosquitoes in malaria transmission by Ronald Ross in 1897. In the beginning of the 20th century, most of the mosquito control operations were aimed at larval control using larvicidal oil, larvivorous fish and environmental management. These efforts made significant impacts on malaria control. Everything changed with the introduction of dichloro diphenyl trichloroethane (DDT) in the mid-1940s. Many European countries and the USA successfully eradicated malaria with the application of DDT and vector sanitation strategies, and improving general living standard [1].
\nMalaria eradication program in India haves had mixed success. After successful results from pilot studies on DDT, the National Malaria Eradication Program (NMEP) was launched in 1958 from the National Malaria Control Program (NMCP) in 1953. There was a huge success that resulted in almost complete malaria eradication in the mid-1960s with 0.1 million cases and no deaths. A kind of complacency led to a slow rise in malaria cases, and a total of 6.4 million cases were reported in 1976. This was due to the development of resistance to DDT by vector species, especially by
A Modified Plan of Operation (MPO) was launched aiming to treat each fever case suspected to be malarial infection with a presumptive dose of anti-malarial drug especially chloroquine. This provided some respite but the malaria cases remained at a static level with occasional regional outbreaks. In 1995, a revised guideline named Modified Action Plan (MAP) gave some lead which renamed as National Malaria Eradication Program (NMEP) in 1999. Subsequently this program was more disease centric and named as National Vector Borne Disease Control Program (NVBDCP) in 2002 [3].
\nIn 2017, 0.84 million malaria cases with 174 related deaths were reported from India, while WHO estimated 9.6 million cases with 16,723 malaria-related deaths. This may be due to different methods of case estimation. From this state of current situation in India, malaria elimination has been envisaged with an aim to achieve it by 2030 [1, 4].
\nThe global malaria elimination framework was launched in 2007, and a detailed Global Technical Strategy (GTS) was released in May 2015 aiming to eliminate malaria by 2030. The three recommendations to achieve this goal strongly emphasize strengthening of smart surveillance; prompt diagnosis and treatment; and enhance elimination process. The GTS thus focuses on 35 countries in which to eliminate malaria by 2030, and India is one of them [4].
\nIndia is one of the countries that have signed the National Framework for Malaria Elimination (NFME). The WHO estimated 219 million malaria cases with 435,000 related deaths in the world in 2017. This was higher than the previous years. The WHO Director-General has called an aggressive new approach `High Burden to High Impact’ [1]. Of the 11 high malaria burden countries 10 are from Africa, but India is also under this category. Nearly half of the global malaria occurred in Nigeria (25%), the Democratic Republic of the Congo (11%), Mozambique (5%) and 4% each by India and Uganda. This means India needs a special attention. The NFME has been designed to ease the burden in most high burden Indian states especially Odisha, Madhya Pradesh, Chhattisgarh and Jharkhand. The major attention should be on strengthening the surveillance which is still poor in many states [1, 3].
\nSurveillance is the main pillar in the malaria elimination process. In most situations, ongoing surveillance is not consistent with the national guidelines resulting in poor estimates of malaria burden. This needs to be converted into smart surveillance. In the digital era, all surveillance systems should follow the concept of the ‘test–treat–track’ strategy [5]. Android-based mobile apps can be applied for quick dispensation of surveillance data from the field to the local administrator for immediate action. This system at district-level management is implemented in many African countries. In this way, the time lag between diagnosis and treatment can be minimized [6]. Tracking the patient for completion and follow-up of the treatment has wider effects on the local cycle of malaria [1]. WHO has developed surveillance and data analysis dashboards using district health information software 2 (DHIS 2) [4]. Such digital-based data systems will make the surveillance system smart and efficient.
\nStrategy to change in surveillance is also an important step to accelerate the malaria elimination process. China adopted the ‘1-3-7’ strategy that promoted the elimination process with zero indigenous malaria cases in 2017. This strategy envisions the strategic action from diagnosis to treatment within 3 days and public health responses to vector management within day 7 of the case detection. This also makes an easy platform for establishment of personal communication in the community [7]. Indonesia also adopted the ‘1-2-5’ strategy for surveillance and response protocol in malaria elimination; on day 1 case management and notification; on day 2 case classification and foci investigation; and by day 5 foci response and elimination [8].
\nIn southwestern coastal Mangaluru city, Karnataka state, India, malaria has been endemic over two decades. The local authority has implemented indigenously developed digital handheld tablets (TABs) for smart surveillance. These TABs have been allotted to each health worker after proper training. Now no manual data collection is used in the city. The link of the software was also provided to the local hospitals and diagnostic labs. The data can be accessed to the local administrators for taking action on the feedback received. Here the ‘1-3-7-14’ strategy has been adopted where positive case is registered with start of treatment on day 1; completion of treatment by day 3; on day 7 vector control activities with follow-up smear check, and on day 14 follow-up smear check and completion of radical treatment for
A recent study in Bangladesh has found that the movement of people can be tracked from the mobile phone network which can help prediction of outbreaks of diseases such as malaria. This enables the health authorities to take preventive measures in time [10].
\nMalaria microscopy is still the best method and gold standard for malaria diagnosis. A microscopist normally examines 60 blood smears per day. This includes staining and data maintaining. Now expert microscopist can detect 20 to 50 parasites/μl blood that means a 0.001 to 0.005% level of parasitemia. This is not the cases with regular microscopists where the sensitivity is low. Routine in-house training on the line of continued medical education program can improve the efficiency of the microscopists [11].
\nRecent deployment of Rapid Detection Tests (RDTs) have changed the malaria diagnosis at large, but it has failed to detect when the level is <100 parasites/μl blood. This has become a nagging problem in detecting very low numbers of infected red blood cells and sub-microscopic parasites especially gametocytes in
In recent years, asymptomatic and sub-microscopic cases are reported from many endemic countries. In fact, these two aspects are non-synonymous. Sub-microscopic malaria cases present very low levels of parasitemia which generally missed in the routine microscopic examinations. Such cases may be symptomatic, and in most situations, these are asymptomatic cases. It has also been observed that in most high endemic areas asymptomatic cases with detectable levels of parasites do not show symptoms. This is because of a high immune status of the individual patients. It is suggested that such cases may be monitored under hospital supervision and clinical algorithms can be drawn to know more specific symptoms. Possibly such patients may show some kind of symptoms and may be on alternate days which are indicative of chronic malaria cases. Differential diagnosis of such cases becomes very difficult since they normally do not show any routine symptoms. However, experience clinicians can diagnose and successfully treat them with scheduled anti-malarials.
\nOn the other hand, asymptomatic cases do not show presentable routine symptoms. Once proper diagnosis is confirmed treatment becomes very easy. In our experience, patients having malaria-like symptoms who could not be diagnosed with routine tests even with RDTs, had been treated for other diseases, mostly with antibiotics, but also with anti-tubercular therapy (ATT) for a long time even months. It has been observed that most antibiotics with quinoline molecules and ATT with rifampicin have anti-malarial properties. But these therapies cannot completely eliminate malarial parasites rather reduces the cure rate [14]. Such cases show sub-microscopic level of parasites. Normally these parasites do not show normal morphological features under microscopy. Only expert microscopists can identify such drug-affected parasites. In such cases, it may deem necessary first to stop all medicines and wait for the fever or fever-like symptoms to appear, then treat them with effective anti-malarials after expert microscopy. All these exercises should be done under medical supervision. The post response and relief from agony of such patients are remarkable.
\nGenerally an important question is raised by most public health experts whether asymptomatic cases may cause potential risk of source of malaria transmission in endemic areas. In most endemic areas with high
Chloroquine the cheapest anti-malarial drug is no longer prescribed for the treatment of
Primaquine – an 8-aminoquinoline is used for radical cure. In case of
There are 465
Generally outdoor and residual transmissions are considered as the same phenomenon. But these are separate issues and would be dealt separately. Outdoor transmission occurs when local community engages on outdoor duties due to professional compulsion. This is most prevalent in forest fringe areas. For example,
The most disturbing fact is that in most village settings human and cattle have mixed dwellings. This encourages the zoophagic mosquitoes to move from the bovine host to the humans. Here, a community-level action is needed. Experts recommend that all cattle dwellings should be located on the periphery of the village so there would be a spatial barrier between the foraging mosquitoes and humans. In this way, a strong zooprophylaxis would be established and direct human biting can be avoided [26].
\nOutdoor transmission can be effectively contained when flowering shrubs around houses are removed. A study in Mali supports such concepts. The selected villages where flowering branches of invasive shrub
Like removing shrubs, ATSB is an alternate strategy to eliminate mosquitoes. Sugar bait of 10% sucrose mixed with 0.01% ivermectin soaked in sponge bait knocked down over 95% of
Studies should be carried out to define the bionomics of local vectors. This will provide valuable information for planning proper vector control strategies. This should be an ongoing program. In the malaria elimination program, routine monitoring of vectors will allow appropriate decisions for effective control. Residual transmission is a resultant of presence of sub-microscopic level of malarial parasites in the community. This happens when intensive control measures overlook the residual presence of parasites. Such a situation happens when a type of complacency prevails and the surveillance system becomes fatigued. Many local-level focal outbreaks happen, and the public health response activities for vector control fail to decimate such foci [30]. It is, thus important to have a strict surveillance system in place to avoid such residual transmission and outbreaks.
\nThe recent advances in genetic engineering technology of CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/Cas9), a system targets specific stretches of DNA and edit genomes at specific locations. This tool of gene editing/drive technology can revolutionize malaria elimination efforts by identifying and targeting the local vectors. The aim should be to create transgenic mosquitoes that will not be able to carry the malaria parasites. Some success to create transgenic species of the main malaria vector of African countries,
Entomopathogenic transgenic fungus
This is a holistic approach of vector control practiced in the beginning of the 20th century. The main aim of this approach is source reduction of larval breeding habitats. In other words, larval source management is the key strategy that mitigates challenges of larval control. Minor engineering, filling up of pools and puddles near human habitats, and biological control are some methods of this strategy. Since the mid-1980s the ICMR- National Institute of Malaria Research, New Delhi, India has made pioneering work on this front with great successes. In this strategy, health education and community engagement is an integral part [34].
\nParatransgenesis is a process by which the genetically modified symbionts from a target insect express molecules within the vector that show refraction to pathogens they transmit. This is a novel approach, now used for the control of malaria, trypanosomiasis and dengue. Recently, for the first time, we found
Jhum or jhoom cultivation or slash and burn cultivation is a common practice of cultivation among tribal populations of northeast India and also in some hilly districts of Bangladesh. This practice of cultivation is linked with malaria transmission [37]. Besides using LLINs, it is important to find the main breeding habitats of vector species mainly
Use of endectocide namely ivermectin in mass drug administration program is a potential intervention strategy to reduce residual transmission of malaria. This drug is used in elimination of human lymphatic filariasis and onchocerciasis programs. This old drug was developed from a natural substance by Satoshi Omura from Kitasato Institute, Japan and was further developed by William Campbell from Merck Lab originally for use in veterinary health program. But its use in onchocerciasis program was recognized for Nobel Prize for Physiology or Medicine in 2015. This is used as a potential tool in vector control program when anti-mosquito activities were recognized. Twenty-three projects under Malaria Elimination Science Alliance (MESA) are underway and their results will be available by 2020 that will be able to take a decision on the future use of endectocides in malaria control operations [38]. Besides the mentioned diseases, recent publication has given an overview on the use of ivermectin for various neglected tropical diseases (NTDs) that include ascariasis, trichuriasis, strongyloidiasis, loiasis (human
It is important to grow fit and healthy mosquitoes under laboratory conditions for anyone working on them. Various methods of culturing of several species of mosquitoes are available. Most of malaria research is linked with several species
Larval and adult rooms were maintained separately.
For larval room RH was maintained at 45–50%, while temperature at 28°C.
The adult room was maintained 12 hour dark and light periods; temperature at 28°C, and RH at 70–75%. Strict monitoring of temperature and humidity was maintained.
The eggs laid by adult females in containers were bleached with freshly prepared 1% sodium hypochlorite solution for 1 minute under controlled pressure in a vacuum pump. For all purposes reverse osmosis (RO) water was used in the laboratory and pH of the water maintained around 7.00. About 250 bleached eggs were placed in especially designed white polypropylene trays (Polylab®, India, 375 × 300 × 75 mm3) within a triangle made from disposable small straight straw pipes.
First instar larvae were emerged within 24 hours after bleaching. No food was given in the next 24 hours of hatching. Brewer’s yeast powder dissolved in RO water was added in the tray water (300 ml) for the next 2 days.
Subsequently a special larval food (Brewer’s yeast and dog food at 70:30 ratio) were given daily for a specific amount depending on the stage of the larvae. Special scoop measuring 5–10 gm larval food in each tray depending on the larval age was provided. Pedigree brand dog food (chicken and vegetable mixed) were powered in a small grinder and mixed with Brewer’s yeast. This formulation was different from earlier report. Pupae were visible from day 8 onwards.
All pupae were harvested that developed up to day 12, and rest larvae were discarded following proper procedure. The pupae were bleached with freshly prepared 1% sodium hypochlorite solution for 1 minute, and placed in mosquito cages for emergence in to adults.
The adult mosquitoes were provided with a mixture of 8% sucrose, 2% glucose mixed with 3% multivitamin kid syrup (Polybion® L, Merck Limited, India).
Strict operational procedures were followed for maintaining sterile conditions for all steps.
This modified protocol would be very useful for mosquito research.
The very decision to go for malaria elimination with the existing tools and intervention strategies was very challenging. Many initiatives have been undertaken. The President’s Malaria Initiative (PMI) in 2005 to Malaria Elimination Research Alliance—India (MERA—India) in 2019 are all to accelerate the process of malaria elimination in all the high burden areas especially in Africa, Mekong Delta region and India, respectively [1]. A special initiative by the WHO in 2016, 21 endemic countries was identified for malaria elimination by 2020 i.e. E-2020 initiative [1]. In this direction zero malaria cases were reported from China and El Salvador in 2017. In 2018, Paraguay was certified as malaria free by the WHO. In 2019, Algeria achieved this goal. Three countries—the Islamic Republic of Iran, Malaysia and Timor-Leste—achieved zero malaria cases in 2018. In 2016, Sri Lanka achieved zero malaria certification, but in 2018 local transmission was reported from a case imported from India. But the local authorities immediately took action. Such quick public health response is required to maintain no transmission threat [45].
\nVector control operations mainly rely on insecticide sprays. In most situations the spray operations are carried out by the local contract workers not properly trained; the spray equipments also not maintained properly; pressure not maintained while spraying; patchy and low coverage spraying; late supply of materials that force to defer the spray schedule; lack of supervision, low quality materials, improper storing warehouse, etc. All these confounding factors are responsible for continuation of transmission. Vector behavior also changes for prolonged insecticidal mode of operations [24].
\nAssessment of two important parameters—human blood index (HBI) and entomological inoculation rate (EIR) of important local vectors enable workers to develop an effective vector management. The global map of HBI of important malaria vectors revealed the highest index exists in African countries [26]. This indicates low ratio between human and animal populations forcing the vector mosquitoes feeding on human host. Emphasis on encouraging the local community to grow animals should be given priority which will change the transmission potential if the local vectors are primarily zoophagic [1].
\nBioenvironmental control of vector populations is a part of the integrated vector management (IVM) concept. In this process control of other vectors of related diseases can also be achieved.
A new anti-larval product Aquatain AMF™ is available for anti-larval operation. It is a silicon-bases liquid (polydimethylsilicone—PDMS) formulation that forms a very thin film on standing water surface causing physical cover over its entire extension. The mosquito larvae are killed due to physical and mechanical action. There does not seem to develop insect resistance to this technique [47].
\nCommunity engagement through health education and empowering local policymakers help in taking appropriate decisions in vector control. Engaging some local school children as volunteers will laterally support such program [48]. In India, every year June is observed as anti-malaria month. Several activities highlighting the program on malaria are displayed. Local administration also actively takes part and makes some decisive actions.
\nTwo vaccine candidates RTS, S/AS01 (TRADE NAME Mosquirix) and PfSPZ are under trial even though their protection level is moderately low. The former is undergoing phase 3 trial in children in three African countries. Possibly this will help reducing child mortality which is a major concern in most of African countries [1].
\nIndia contributes most of
The recent identification of
There is a great movement and opportunity for malaria elimination globally. Many countries have already achieved this goal. The most success part of this movement is reporting zero malaria cases in China in 2017 and 2018, and preparing for malaria elimination certification in 2020. China made elaborate arrangements with full financial, administrative and operational commitment. This indicates that malaria elimination is possible with the existing tools and strategies. In the present situation dependence of insecticide should be minimized and promote other alternate strategies to avert the issue of insecticide resistance. India is making all efforts for a successful mission. Reduction of malaria cases and related deaths in 2017 is an indication. This was mainly the efforts made in eight most high burden districts in Odisha with the implementation of a program called
In the elimination phase, there is a need to strengthen the existing public health system. The local health system should be quick and responsive to any malaria-related fevers. Routine in-house training, workshops should be conducted to maintain the malaria elimination momentum. A recent 5 days WHO workshop for South-East Asia Region (SEAR) recommended the global vector control response (GVCR). Entomologists from 11 countries participated in this workshop. Detailed reviews were exercised to find out the ongoing program implementation in each country. Such state-level workshops would help in malaria elimination and also other vector borne diseases [53].
\nThe authors acknowledge the support from Indian Council of Medical Research, New Delhi; the Directors of ICMR-National Institute of Malaria Research, staff of ICMR- National Institute of Malaria Research, Bengaluru Field Unit. Also acknowledge the people in the community who helped and encouraged our studies from time to time.
\nThe authors declare no conflict of interest.
Bananas (
For treatment of the disease, and for crop planting adjustments, real-time monitoring and effective identification of banana Fusarium wilt play a significant role [5]. Traditionally, soil investigations have been the only effective means to monitor crop diseases, but such surveys take a lot of time and are often expensive. Recent years have witnessed the rapid development of the remote sensing technology, which has developed into a viable method for disease assessment and monitoring. The leaf pigment content, leaf area index (LAI) and water content of a plant which is infected with a disease will all undergo changes. And such biochemical and biophysical changes in the plant will also present in its spectral reflectance characteristics [6]. Remote sensing technology has been applied to monitor diseases, including Fusarium head blight [7, 8], rust infection [9, 10, 11], and powdery mildew [7, 8, 12, 13] in wheat, grey leaf spot in maize [14], bacterial leaf blight in rice [15, 16], and late blight disease and bacterial spot in tomatoes [17, 18] in some studies. However, the sensitivity of spectral bands and VIs varies with the category of diseases. For example, Bravo et al. [19] calculated the normalised difference vegetation index (NDVI) using wavelengths of 620–640 nm and 740–760 nm for extracting powdery mildew from wheat patches. Devadas et al. [20] distinguished yellow rusted wheat from healthy wheat using the anthocyanin reflection index (ARI). Huang et al. [10] suggested that the position of the red edge can be used as a disease indicator. With this in mind, it is of essence to identify which spectral bands and VIs are suitable for the identification of which specific diseases.
UAV remote sensing technology has been developed rapidly over recent years. It has become of interest due to its advantages of long flight time, real-time image transmission, effective detection of high-risk areas, low cost and easy manoeuvrability. It provides new means for the timely and non-destructive extraction of infected plants from the in-season crops [21]. Using UAV multispectral and hyperspectral images, a great number of studies have achieved significant progress in growth monitoring, crop classification, and the identification of diseases and insect pests [22, 23, 24]. Within banana production, a few studies have adopted UAV-based images to map the spatial patterns of photosynthetic activity in banana plantations [25]. Nonetheless, there are few studies that use UAV-based remote sensing to monitor banana Fusarium wilt [26, 27]. Furthermore, the spatial scale for remote sensing information and scaling remains one of the fundamental problems in geoscience [28]. Selecting an optimal spatial scale for remote sensing imagery plays a significant role in agricultural monitoring in particular.
Therefore, the goals of this chapter are to: (i) develop an identification method for Fusarium wilt based on UAV multispectral remote sensing, (ii) determine the optimal VI needed for the establishment of a quality identification model, and (iii) evaluate how different image resolutions affect the accuracy of Fusarium wilt identification in order to provide guidance for the application of satellite-based data in a massive scale.
The experiments were carried out at two experimental locations in Guangxi and Hainan, respectively.
The Guangxi experiment site is located in Guangxi Province of China (23°7′53″ to 23°8′4″ N, 107°43′45 to 107°44′7″ E) (Figure 1). It has a subtropical monsoon climate characterised by year-round sunshine and rainfall, with a mean annual temperature between 20.8 to 22.4°C, and an average annual rainfall of 1200 mm. The soil type according to the FAO soil classification system is Ferralsol [29]. The banana variety in the study area was “Williams B6”. The leaf number of this variety is 34–36, the plant height is about 2.4–3 m, and the growth period is 10–12 months. The banana plantation was established in September 2015, with the planting distance of 2.0 m by 2.6 m. The first harvest was carried out in November 2016. As of August 2018 (the time of the field investigation discussed in this chapter), the third generation of bananas was in the fields and more than 40% of the banana plants were infected with Fusarium wilt.
Location of the experimental sites with the survey sites.
The Hainan experiment site is located in Hainan Province, China (19°49′4″ to 19°49′16″ N, 109°54′40″ to 109°54′53″ E) (Figure 1). It has a tropical monsoon climate characterised by year-round sunshine and rainfall, with a mean annual temperature between 23.1 to 24.5°C and an average annual rainfall of 1750 mm. The soil type according to the FAO soil classification system is Humic Acrisol [29]. This experimental field was divided into two sub-fields (left area and right area) with the middle road as the boundary (Figure 1). The left area was developed in June 2017, with the planting distance of 2.0 m by 2.3 m. The first harvest was carried out in July 2018. The banana variety was “Baxijiao”. the plant height of this variety is about 2.6–3.2 m and the growth period is 9–12 months. In this field, the rate of banana Fusarium wilt infection was about 10%.
The right area was developed in August 2018. The planting distance was the same as that in the left field. The banana variety was “Nantianhuang”. The plant height of this variety is about 2.5–3.0 m and the growth period is 10–13 months. At the time of the field investigation in December 2018, no banana plants were found to be infected with Fusarium wilt.
In this chapter, the experimental data obtained from the Guangxi site was used for calibration and validation of the Fusarium wilt identification model, and from the Hainan site used for model validation.
The experiment at Guangxi site was carried out on August 7, 2018. A total of 120 sample plots were investigated to assess the occurrence or non-occurrence of Fusarium wilt (Figure 1). Among them, there were 57 healthy samples and 63 diseased samples. The size of each sample plot encompassed one banana plant. Eventually, 75% samples were randomly extracted and employed for the construction of Fusarium wilt identification model denoted by modelling dataset (MD); and the remaining 25% for model validation, denoted by validation dataset 1 (VD1). The experiment at Hainan site was performed on December 11, 2018. The survey strategy was in line with that of the experiment at Guangxi site. A total of 35 sample plots were finally investigated, of which 16 were healthy and 19 were diseased. All the sample plots from Hainan sties were served for model validation, denoted by validation dataset 2 (VD2).
The surveys were carried out by a DJI Phantom 4 Pro quadcopter (DJI Innovations, Shenzhen, China) equipped a MicaSense RedEdge-M multispectral camera (MicaSense, Inc., Seattle, WA, USA). The camera is configured with five bands: Blue (475 nm center, 20 nm bandwidth), Green (560 nm center, 20 nm bandwidth), Red (668 nm center, 10 nm bandwidth), Red edge (717 nm center, 10 nm bandwidth), Near-IR (840 nm center, 40 nm bandwidth). The flight experiment at the Guangxi site was performed between 12:30 p.m.–13:30 p.m. on 7 August 2018, covering an area of 21 ha. While the flight experiment at the Hainan site was implemented between 11:00 a.m.–12:00 p.m. on December11, 2018, covering an area of 11 hectares. The flight altitude above ground level was 120 m with an 8 cm ground sample distance (GSD). Then, the original UAV imagery was resampled to generate images with five resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m) by using nearest neighbour resampling algorithm.
In this section, the VIs method was applied to assess the infection status of Fusarium wilt in banana plantations. Eight VIs that related to plant growth and pigment absorption were selected to characterise the biophysical and biochemical variations due to individual infections. These VIs included the NDVI, normalised difference red edge index (NDRE), structural independent pigment index (SIPI), red-edge structural independent pigment index (SIPIRE), green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), anthocyanin reflectance index (ARI), and carotenoid index (CARI). Table 1 lists the formulations of the VIs.
VI | Formulation | Sensitive Parameter | Reference |
---|---|---|---|
NDVI | ( | Green biomass, LAI | [30] |
NDRE | ( | Green biomass, LAI | [31] |
SIPI | ( | Leaf pigment content | [32] |
SIPIRE | ( | Leaf pigment content | [33] |
CIgreen | Leaf chlorophyll content | [34] | |
CIRE | Leaf chlorophyll content | [35] | |
ARI | 1/ | Leaf anthocyanin content | [36] |
CARI | Leaf carotenoid content | [37] |
List of the VIs used in this chapter.
The binary logistic regression (BLR) was used to established the relationships between the VIs and the plants infected or uninfected with Fusarium wilt. As one of the most common multivariate analysis methods, BLR has a dependent variable as a binary variable that represents the presence or absence of an event. The BLR dependent variable is a probability function, which can be expressed as [38]:
where
where
Following the model fitting, the validation datasets were used to verify the accuracy of Fusarium wilt identification models, with indicators of the Kappa coefficient and overall accuracy (OA) [39, 40]. The Kappa coefficient ranges between −1 and 1, kappa ≥0.75 represents excellent agreement, 0.75 > kappa ≥0.4 represents fair to good agreement, kappa <0.4 represents poor represents [41]. The OA is the sum of the correctly identified plots divided by the total number of plots.
Table 2 shows the VI values of the diseased and healthy sample plots. Significant differences (independent
Experiment Site | VI | Sample plot | Mean | Std. Deviation | |
---|---|---|---|---|---|
Guangxi site | NDVI | Healthy | 0.54 | 0.11 | 0.00 |
Diseased | 0.34 | 0.14 | |||
NDRE | Healthy | 0.20 | 0.08 | 0.00 | |
Diseased | 0.02 | 0.09 | |||
SIPI | Healthy | 0.88 | 0.36 | 0.24 | |
Diseased | 1.68 | 5.26 | |||
SIPIRE | Healthy | 0.58 | 0.71 | 0.25 | |
Diseased | 2.07 | 9.77 | |||
CIgreen | Healthy | 1.08 | 0.32 | 0.00 | |
Diseased | 0.43 | 0.33 | |||
CIRE | Healthy | 0.56 | 0.22 | 0.00 | |
Diseased | 0.09 | 0.22 | |||
ARI | Healthy | 0.85 | 0.15 | 0.00 | |
Diseased | 0.62 | 0.16 | |||
CARI | Healthy | 0.34 | 0.04 | 0.00 | |
Diseased | 0.30 | 0.06 | |||
Hainan site | NDVI | Healthy | 0.44 | 0.05 | 0.00 |
Diseased | 0.36 | 0.06 | |||
NDRE | Healthy | 0.35 | 0.10 | 0.00 | |
Diseased | 0.12 | 0.09 | |||
SIPI | Healthy | 1.07 | 0.07 | 0.06 | |
Diseased | 1.18 | 0.12 | |||
SIPIRE | Healthy | 1.11 | 0.11 | 0.04 | |
Diseased | 1.23 | 0.16 | |||
CIgreen | Healthy | 0.92 | 0.26 | 0.00 | |
Diseased | 0.49 | 0.26 | |||
CIRE | Healthy | 0.35 | 0.10 | 0.00 | |
Diseased | 0.12 | 0.09 | |||
ARI | Healthy | 0.87 | 0.30 | 0.03 | |
Diseased | 0.61 | 0.35 | |||
CARI | Healthy | 0.43 | 0.16 | 0.01 | |
Diseased | 0.33 | 0.19 |
Statistical characteristics of the VI values of the diseased and healthy sample plots.
In this section, the relationships between the VIs and the plants infected or uninfected with Fusarium wilt were described by using the BLR method with dataset MD. The classification accuracy of the relational models was verified via both dataset VD1 and VD2. It was found that the use of the NDVI, NDRE, CIgreen, and CIRE led to relatively good fitting recognition models with the OA values greater than 80% (Table 3). Of all the VIs, CIRE obtained the highest verified OA and Kappa coefficient for both VD1 (91.7% for OA and 0.83 for Kappa) and VD2 (80.0% for OA and 0.59 for Kappa), thereby indicating that CIRE performed best in the identification of Fusarium wilt. It could be seen that those VIs containing red-edge band (e.g., NDRE vs. NDVI and CIRE vs. CIgreen) obtained higher verified OA and Kappa coefficients. Nonetheless, CARI and ARI achieved relatively low verified OA and Kappa coefficients.
VI | Recognition model | Dataset VD1 | Dataset VD2 | ||
---|---|---|---|---|---|
OA (%) | Kappa | OA (%) | Kappa | ||
NDVI | 83.3 | 0.66 | 62.9 | 0.22 | |
NDRE | 87.5 | 0.75 | 65.7 | 0.39 | |
CIgreen | 87.5 | 0.74 | 74.3 | 0.47 | |
CIRE | 91.7 | 0.83 | 80.0 | 0.59 | |
ARI | 83.3 | 0.66 | 68.6 | 0.37 | |
CARI | 66.7 | 0.35 | 60.0 | 0.21 |
Recognition models of banana fusarium wilt for different VIs.
Evaluating the impact of image resolutions on the accuracy of Fusarium wilt recognition can provide guidance for the large-scale application of satellite-based data. In this chapter, the original UAV images were first resampled to five different spatial resolutions (0.5-m, 1-m, 2-m, 5-m, and 10-m), which were then used for Fusarium wilt monitoring. We calculated both the optimal VI without a red-edge band (CIgreen) and optimal VI with a red-edge band (CIRE) at different resolutions. Table 4 lists the results of Fusarium wilt recognition model for the CIgreen and CIRE VIs at different resolutions. As indicated by the verified results, the CIRE at resolution 0.5-m, 1-m, and 2-m were all obtained the acceptable verified OA (over 70%) and Kappa coefficients (over 0.40). When using the dataset VD1, the verified OA at resolution 0.5-m, 1-m, and 2-m were 91.7%, 79.2%, and 75.0%, respectively, and the Kappa coefficients were 0.83, 0.60, and 0.53, respectively. When using dataset VD2, the verified OA at resolution 0.5-m, 1-m, and 2-m were 85.7%, 74.3%, and 71.4%, respectively, and the Kappa coefficients were 0.71, 0.48, and 0.41, respectively. Despite that, the OA and Kappa coefficients at resolution 5-m and 10-m resolution were relatively low, and their values dropped as the resolution decreased. Moreover, at the same resolution, the accuracy of the CIgreen-based model for Fusarium wilt recognition was lower than that of CIRE-based model. In fact, the only acceptable result for the CIgreen was at 0.5-m resolution.
Resolution | Recognition model | Dataset VD1 | Dataset VD2 | ||
---|---|---|---|---|---|
OA (%) | Kappa | OA (%) | Kappa | ||
CIRE | |||||
0.5-m | y = 1.987–5.826 × CIRE | 91.7 | 0.83 | 85.7 | 0.71 |
1-m | y = 1.645–4.896 × CIRE | 79.2 | 0.60 | 74.3 | 0.48 |
2-m | y = 1.475–4.178 × CIRE | 75.0 | 0.53 | 71.4 | 0.41 |
5-m | y = 1.027–2.854 × CIRE | 70.8 | 0.42 | 65.7 | 0.30 |
10-m | y = 0.761–1.817 × CIRE | 62.5 | 0.25 | 62.9 | 0.24 |
CIgreen | |||||
0.5-m | y = 3.166–3.946 × CIgreen | 87.5 | 0.75 | 74.3 | 0.48 |
1-m | y = 2.633–3.266 × CIgreen | 75.0 | 0.51 | 65.7 | 0.32 |
2-m | y = 2.421–2.936 × CIgreen | 75.0 | 0.51 | 62.9 | 0.26 |
5-m | y = 1.552–1.862 × CIgreen | 66.7 | 0.35 | 48.6 | 0.01 |
10-m | y = 1.044–1.158 × CIgreen | 58.3 | 0.18 | 45.7 | −0.01 |
Recognition models of banana fusarium wilt for the CIRE and CIgreen at different resolutions.
With the aim to further explore the visual effects of image resolutions, the distribution of Fusarium wilt infected and uninfected areas at the Guangxi site were mapped using different resolution images. CIRE-based and CIgreen-based Fusarium wilt identification models were respectively used to create the Fusarium wilt distribution maps. As can be seen in Figures 2 and 3, the maps with 0.08-m, 0.5-m, 1-m and 2-m resolution show quite similar distributions of the occurrence of Fusarium wilt; however, the maps with 5-m and 10-m resolutions exhibited very little detail. Table 5 lists the area and percentage of the areas infected with Fusarium wilt at different resolutions. For the maps based on CIRE models, the total areas of Fusarium wilt were between 5.69 ha and 6.59 ha, accounting for 38.2% and 44.3% of the banana plantation area. Taking a map with a resolution of 2 m as an example, the incidence of Fusarium wilt is between 40.8% and 43.6%. For the maps based on CIgreen models, the total areas of Fusarium wilt were between 5.09 ha and 6.63 ha, accounting for 34.2% and 44.6% of the banana plantation area. Among them, the percentages of Fusarium wilt of the 0.08-m and 0.5-m resolution maps were 40.1% and 44.6%, respectively.
Maps of the distribution of fusarium wilt based on the CIRE with different resolution images at the Guangxi site.
Maps of the distribution of fusarium wilt based on the CIgreen with different resolution images at the Guangxi site.
Resolution | Diseased area (ha) | Proportion of diseased area (%) |
---|---|---|
CIRE | ||
0.08-m | 6.04 | 40.8 |
0.5-m | 6.59 | 44.3 |
1-m | 6.28 | 42.2 |
2-m | 6.47 | 43.6 |
5-m | 5.70 | 38.5 |
10-m | 5.69 | 38.2 |
CIgreen | ||
0.08-m | 5.95 | 40.1 |
0.5-m | 6.63 | 44.6 |
1-m | 6.44 | 43.3 |
2-m | 6.63 | 44.6 |
5-m | 5.69 | 38.4 |
10-m | 5.09 | 34.2 |
Areas of fusarium wilt based on the CIRE and CIgreen with different resolution images at the Guangxi site.
It was found that among all the VIs used in this chapter, CIRE was the best red-edge VI and CIgreen was the best non-red-edge VI for Fusarium wilt identification. This is because these two VIs are sensitive to the changes of chlorophyll content of a plant, and Fusarium wilt infection in banana will cause a decrease in leaf chlorophyll content [34, 35, 42]. Furthermore, compared with VIs without the red-edge band, VIs with the red-edge band had higher OA and Kappa coefficients (e.g., NDRE vs. NDVI, and CIRE vs. CIgreen). It has been widely proved that the red-edge position is very sensitive to the changes of the plant chlorophyll content [43, 44]. Nevertheless, the UAV-based multispectral imagery used in this chapter only possessed 5 bands, which still cannot fully characterise the differences of the spectral characteristics between the diseased and healthy plants. It is therefore of great significance to use hyperspectral data to further study the sensitivity of certain wavebands to banana Fusarium wilt.
The results also showed the potential of combining BLR and VIs to accurately identify Fusarium wilt of banana. Based on this method, an ideal framework for the use of spectral features can be obtained, so as to clarify the pathological mechanisms. In this chapter, the dependent variable was the occurrence of banana Fusarium wilt. Under the circumstance that the predicted variable has a binary nature, BLR can be regarded as a suitable approach [38]. In addition, BLR can deliver better performance than discriminant analysis in the case that the predictor variables are continuous, categorical, or a combination of the two [45]. BLR is highly interpretable, very efficient, and does not require large computational resources, so it is widely used to describe the relationship between a dependent variable and multiple independent variables [38]. Moreover, due to its linear decision surface, non-linear problems cannot be solved by the logistic regression. With the development of artificial intelligence, pattern recognition and machine learning methods will become more common in the use of remote sensing to monitor and predict plant diseases [46].
The Fusarium wilt detection models were verified both using the dataset VD1 VD2. It can be seen from the verification results that both CIRE and CIgreen performed well in the identification of Fusarium wilt (OA > 70%, and Kappa values >0.4). This indicates that the detection models of Fusarium wilt have a good transferability in other fields. Tables 3 and 4 show that the Kappa coefficients of the dataset VD2 were lower than those of the dataset VD1, thus indicating that applying the detection methodology of Fusarium wilt in other fields would cause some precision loss. This situation may be due to the following factors. First of all, one of the most important factors affecting the verification results could be the fact that there were two different banana varieties at the experimental sites (“Williams B6” in VD1 and “Baxijiao” in VD2). These showed that there were differences in their biophysical and biochemical characteristics, which may cause differences in spectral characteristic information. Secondly, due to the differences in the planting time and climatic conditions of the two experimental sites, their growth stages differed greatly. In fact, the banana plants of two experimental areas were at different growth stages during the investigation. Moreover, soil types, planting density, and environmental conditions for crop growth are also important factors that affect the applicability of the Fusarium wilt identification model. Therefore, it is recommended to appropriately optimise the BLR parameters when applying this method in other regions.
In this chapter, the original UAV images were resampled to generate five resolution images (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m) to evaluate the impact of different resolutions on the accuracy of Fusarium wilt monitoring. It was found that imagery with a resolution smaller than 2 meters had a good accuracy for Fusarium wilt monitoring, which may be related to the planting spacing and the canopy size of banana. With the reduction of the resolution, the mixed pixel problem influences the precision of object recognition and classification. However, image resolution is not the only difference seen between UAV-based and satellite-based sensors. The wavelength information captured by the satellite-based sensor is different from that of UAV-based sensors. Thus, the simulation results at different resolutions should be further verified with actual satellite-based data. In this chapter, single-period multispectral images were used, which limits the spectral response mechanism to determine the changes in the biophysical and chemical parameters caused by Fusarium wilt. In order to overcome this problem, it is necessary to use multi-temporal and hyperspectral images for dynamic monitoring of the occurrence of Fusarium wilt. Additionally, it is also of great value to explore the differences in the spectral response characteristics of Fusarium wilt and other yellowing stresses (i.e., nutrition deficiency and drought stress).
This research used UAV multispectral images to develop a method for identifying Fusarium wilt of banana. The results revealed that the VIs method with BLR analysis can well identify Fusarium wilt. of all the VIs investigated, the CIRE exhibited the optimal performance, with the OA and Kappa coefficients of 91.7% and 0.83 for dataset VD1 and 80.0% and 0.59 for dataset VD2. VIs that included a red edge band obtained better results than those that did not have one. According to the analysis of different resolutions, a resolution smaller than 2 m produced a good identification accuracy of Fusarium wilt. As the resolution decreased however, the identification accuracy decreased. The results indicate that UAV-based multispectral imagery can be applied to identify Fusarium wilt of banana, thus providing reference for disease treatment and crop planting adjustments.
This research was funded by Hainan Provincial Major Science and Technology Program of China (ZDKJ2019006); Youth Innovation Promotion Association CAS (2021119); Future Star Talent Program of Aerospace Information Research Institute, Chinese Academy of Sciences (2020KTYWLZX08); National special support program for high-level personnel recruitment (Wenjiang Huang).
The authors declare no conflict of interest.
We gratefully acknowledge the National Meteorological Information Center of China, Guangxi Jiejiarun Technology Co., Ltd. and Guangxi Jinsui Agriculture Group Co., Ltd. for the experiments.
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A background and a review on prior work are presented along with used materials, developed applications and potential of IPT technology. The main features of the different printing technologies, advantages and main challenges are also compared.",book:{id:"6765",slug:"flexible-electronics",title:"Flexible Electronics",fullTitle:"Flexible Electronics"},signatures:"Sílvia Manuela Ferreira Cruz, Luís A. Rocha and Júlio C. 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This chapter presents the basic and key technologies of switched-reluctance machine in motor and generator operations. The contents in this chapter include: (1) structures and governing equations of SRM; (2) some commonly used SRM converters; (3) estimation of key parameters and performance evaluation of SRM drive; (4) commutation scheme, current control scheme, and speed control scheme of SRM drive; (5) some commonly used front-end converters and their operation controls for SRM drive; (6) reversible and regenerative braking operation controls for SRM drive; (7) some tuning issues for SRM drive; (8) operation control and some tuning issues of switched-reluctance generators; and (9) experimental application exploration for SRM systems—(a) wind generator and microgrid and (b) EV SRM drive.",book:{id:"8899",slug:"modelling-and-control-of-switched-reluctance-machines",title:"Modelling and Control of Switched Reluctance Machines",fullTitle:"Modelling and Control of Switched Reluctance Machines"},signatures:"Chang-Ming Liaw, Min-Ze Lu, Ping-Hong Jhou and Kuan-Yu Chou",authors:[{id:"37616",title:"Prof.",name:"Chang-Ming",middleName:null,surname:"Liaw",slug:"chang-ming-liaw",fullName:"Chang-Ming Liaw"},{id:"306461",title:"Mr.",name:"Min-Ze",middleName:null,surname:"Lu",slug:"min-ze-lu",fullName:"Min-Ze Lu"},{id:"306463",title:"Mr.",name:"Ping-Hong",middleName:null,surname:"Jhou",slug:"ping-hong-jhou",fullName:"Ping-Hong Jhou"},{id:"306464",title:"Mr.",name:"Kuan-Yu",middleName:null,surname:"Chou",slug:"kuan-yu-chou",fullName:"Kuan-Yu Chou"}]},{id:"52822",title:"Non-Orthogonal Multiple Access (NOMA) for 5G Networks",slug:"non-orthogonal-multiple-access-noma-for-5g-networks",totalDownloads:14704,totalCrossrefCites:25,totalDimensionsCites:35,abstract:"In this chapter, we explore the concept of non-orthogonal multiple access (NOMA) scheme for the future radio access for 5G. 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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. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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