\r\n\toxygen-free atmosphere. Biochar has been used for many years as a soil amendment and in general soil applications. Nonetheless, biochar is far more than a mere soil amendment. In this review, we report all the applications of biochar including environmental remediation, energy storage, composites, and catalyst production. In this book, we intend to collect contributions from worldwide experts in the field of biochar production and utilization providing a general overview of the recent uses of biochar in material science, thus presenting this cheap and waste-derived material as a high value-added carbonaceous source. Furthermore, we are aiming to give readers a handy and effective tool to easily understand how this field is interesting and diverse. It is a goal that this book could be easily used by any reader with a strong scientific background ranging from scientific company advisors to academic members. Nonetheless, students enrolled in scientific undergraduate and graduate programs could be consulted to this text for any further and deeper investigation. In the end, we intend to propose a very high scientific content book that could represent the reference text for any consideration and future study about biochar for the next years.
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1. Introduction
Recent advancements in artificial intelligence (AI) and machine learning (ML) have offered an opportunity for utilization of these advanced methodologies in the healthcare industry, while also at the same time improving upon the performance and accuracy benchmarks established by the classical statistical techniques [1]. A variety of ML techniques have been already applied to clinical data to examine a number of conditions and therapeutic areas, their onset, progression, and treatment options. In addition, deep learning algorithms such as convolutional neural network (CNN) have been employed in medical image data to predict disease onset and progression with even greater precision [2, 3, 4, 5].
ML algorithms applied to a large amount of structured and unstructured data and combined with available data processing technology have already improved researchers’ ability to mine the vast amount of data and assisted in making the patient healthcare decisions [6]. As a result of the high precision and robustness of ML algorithms compared to the classical statistical methods, the insights derived from the application of these methods became important in driving the strategies and processes related to healthcare access, patient care, as well as disease diagnostics, healthcare trend forecasting, drug discovery, etc., thereby, further impacting the ability to reducing medical costs, shortening the time to diagnoses and treatment, and enhancing patients’ quality of life and outcomes [7].
Endometriosis is one of the most commonly occurring disorders in women of menstruating age. Tissues, resembling the endometrium lining, grow on the outer part of the uterus and other organs of the pelvic area. The signs and symptoms differ across patients with some individuals experiencing mild symptoms, while others displaying moderate to severe signs. The most common symptoms of endometriosis include pain in the pelvic area, dysmenorrhea, and the inability to have children. Most commonly laparoscopy, surgery under general anesthesia, is performed to confirm the diagnosis of endometriosis [8]. Since it is an invasive procedure, it may not be suitable for all women. Laparoscopy is also quite expensive and women require a confirmation of a variety of indicatives of endometriosis before undergoing this procedure [9]. There are also a number of studies researching biomarkers of endometriosis via assessing endometrial tissue, uterine or menstrual fluids, immunological markers in blood or urine, gene expressions, etc. [10].
The availability of noninvasive methods to predict the likelihood of endometriosis could reduce the diagnostic delays and the number of women undergoing surgery unnecessarily, and thus avoiding unwanted complications and potential trauma [11]. In other research studies, researchers developed a new ensemble technique called GenomeForest that analyzed the gene expression data. The method systematically examined capabilities in classifying endometriosis and control samples, using both transcriptomics and methylomics data [12, 13].
Another research study developed symptom-based models that predicted the likelihood of endometriosis using logistic regression (LR). Symptomatic data including patient demographics, women’s past medical history, obstetrics, family history, etc. were collected through a 25-item self-administered questionnaire [14]. Researchers also systematically applied selected ultrasound techniques in the diagnosis of endometriosis and concluded that these methods should remain the first-line procedures in the evaluation of patients with endometriosis [15].
In recent years, researchers aimed at developing CNN-based CAD systems that could classify endometrial lesions images obtained from hysteroscopy and evaluate the diagnostic performance of the model [16]. Their system slightly outperformed gynecologists in classifying endometrial lesion images. With a large number of diagnostic procedures, there is, however, no guaranteed treatment for endometriosis at this time. With an early diagnosis and available medical and surgical options; however, healthcare providers might be able to reduce the risks of potential complications and improve the quality of life for their patients [17, 18].
In the above research studies, researchers used either relatively small samples, or a limited number of variables to develop models or systems to predict the likelihood of endometriosis. The source of data represented mostly clinics and care providers in a controlled environment. There have been a limited amount of research studies performed thus far leveraging US-based patient-level claims data in predicting endometriosis. Claims data consist of the entire patient medical journey, such as diagnosis, procedures, prescriptions, physician, and patient demographics [19, 20]. In this chapter, US patient-level claims datasets at a transactional level were leveraged to develop accurate ML algorithms to predict the likelihood of endometriosis onset. Predicting the probability of endometriosis occurrence via leveraging the diagnosed patients’ medical history might benefit both the diagnostics process as well as improved patients’ quality of life. The LR and eXtreme Gradient Boosting (XGB) algorithms were employed to identify the key drivers of endometriosis onset. An earlier version of this chapter is available on the Research Square website. The posting allowed for the dissemination of these important insights with the research community in advance, while at the same time, leveraging the received feedback to enhance the research design in this chapter.
2. Methodology overview
As mentioned earlier, the analysis design was described in the earlier version of the chapter available on the Research Square website. It leveraged the US healthcare claims patient-level database with the period from January 31, 2019 to December 31, 2019 [21]. Patients with a history of medical diagnosis ICD 10 codes for endometriosis were labeled as targets and the remaining patients were assigned as controls. As endometriosis is a women-only condition, female patients 18 and older were selected for the study target cohort. A control cohort, using a propensity matching algorithm, was built as a comparison group to the study targets. Thirty six (36) months of patients’ medical history before the first condition event in 2019 were extracted for both cohorts. The US healthcare claims data included diagnosis, medical, procedural, surgical, and hospital codes, as well as medical treatments and therapies prescribed to patients. The dataset was presented at the transactional level to ensure proper capture of medical events longitudinally [21]. Several analytical approaches were employed for the analysis from the rules-based patient qualification criteria to ML algorithms to derive the probability of endometriosis onset. The healthcare claims patient-level dataset considered in the analysis represented healthcare claims sourced for the United States regions only.
2.1 Healthcare claims patient-level database
The US healthcare claims patient-level database is an anonymous longitudinal patient dataset often applied by healthcare organizations to derive insights [22, 23], while at the same time informing the effective treatment outcome options, patient access strategies, and areas for improvement in the diagnostic process [19]. The US healthcare claims patient-level database employed for this chapter consisted of medical, procedural, surgical, hospital, and prescriptions claims across all types of insurance payments and all geographic areas in the United States [24, 25]. The healthcare claims database overall covered more than 317 million active patients with over more than 17 years of medical health history and involved more than 1.9 million healthcare providers [25]. Figure 1 presents the summary of information in the database.
Figure 1.
Healthcare claims patient level database summary.
2.2 Cohort selection
For this chapter, a sample of 314,101 confirmed endometriosis patients in 2019 in the US healthcare claims patient-level database was leveraged for the analysis. The patients were identified using predefined ICD 10 diagnosis codes (Table 1). Female patients of age 18 and older were identified for the target cohort. For the control cohort, a random sample of 3 million female patients with the same age specifications was selected from the database [21].
Diagnosis
Codes diagnosis long description
N80.0
Endometriosis of uterus
N80.1
Endometriosis of ovary
N80.2
Endometriosis of fallopian tube
N80.3
Endometriosis of pelvic peritoneum
N80.4
Endometriosis of rectovaginal septum and vagina
N80.5
Endometriosis of intestine
N80.6
Endometriosis in cutaneous scar
N80.8
Other endometriosis
N80.9
Endometriosis, unspecified
Table 1.
ICD 10 diagnosis codes of endometriosis.
To define a control cohort of an equal size to the study target group, a ‘propensity score matching’ methodology was employed [18]. The algorithm selected the controls based on several similar characteristics or covariates. Covariates included patient age and medical history [26, 27]. Table 2 presents the summary of the distribution comparison between the study target and control cohorts by age and Census geographies. The patient age variable was created via grouping age ranges, while states were grouped into the US regions [21].
Age group
Target (%)
Control (%)
18–24
6.45
6.55
25–34
25.01
25.24
35–44
37.57
37.08
45–54
23.13
23.18
55–64
6.22
6.31
65+
1.62
1.64
Region
Target (%)
Control (%)
South
39.90
39.90
Midwest
22.78
22.76
Northeast
18.82
18.84
West
17.02
17.02
Other
1.48
1.48
Table 2.
Comparison between target and control cohort by age and region respectively.
2.3 Data extraction
The next step in the analysis process was to pull the patients’ medical history from the available information in the US healthcare claims patient-level database [21]. The event date for the target cohort was established for each individual in the study to ensure the extraction of the healthcare information before the first condition event. For the control cohort, the first activity in 2019 was leveraged as the event date [21].
The approach for the data extraction and the study target and control setup was the same as presented in the earlier version of the chapter available on Research Square. Using the medical event dates, representing the first date of endometriosis diagnosis, as the index date, 36 months of medical history was extracted for each patient. Historical data presented all available medical events in the patients’ healthcare history before the condition diagnosis, including diagnoses for comorbid conditions, medical and surgical procedures, therapeutics, healthcare provider’s specialty, and treatments prescribed to patients. A transactional level dataset, representing the top 1000 diagnosis codes, top 800 medical and surgical procedures, and top 500 prescribed drugs, was utilized to enable additional insights since these top codes constituted more than 80% of the dataset [21].
A pivot table was built at the transaction level and aggregated at the patient-level. Each row of the dataset represented an individual patient and the values within the row represented the counts of transactions that were generated during the patient’s journey for the respective medical events. The columns of the table were the medical events, such as diagnosis and procedure codes, drugs prescribed, and physician specialties. The aggregated data table had more than 6 million rows and 2600 columns. The aggregated data table had missing values for selected patients and data elements, as not all records had complete medical information captured in the study period. Any medical events absent in the patient’s history were represented with the value of zero (0), which implied that no such event was observed in the individual’s medical history. The final aggregated dataset was leveraged as an analytical dataset for the remaining parts of the chapter [21].
The analytical dataset was further normalized and divided into two groups: a training and test set. A ratio of 70:30 was applied to the dataset [28]. The training dataset was employed to identify the key data elements driving endometriosis diagnoses, while the test group was used to confirm whether these elements would predict the condition occurrence accurately [29]. Splitting the data into training and test sets aided the assessment of the model performance and its ability to generalize the hidden data trends [30, 21].
2.4 Overview of machine learning algorithms
In this section of the chapter, a summary of the classical statistical modeling and ML approaches is presented to review the available methods for healthcare research, and also to summarize the selected methodology applied in this study. Statistical modeling has evolved in the last few decades and shaped the future of business analytics and data science, including the current use and applications of ML algorithms [31]. It represents a branch of applied mathematics, in which statistical methods are leveraged to analyze a dataset. Statistical models are the mathematical representation of real-world scenarios with certain assumptions undertaken. They play a fundamental role in making statistical inferences while studying the characteristics of a population, upon which hypotheses were framed [8]. These models are not only useful in finding relationships between variables and the significance of those relationships, but they are also useful in the prediction and forecasting of future events.
ML is a subfield of the AI area, which includes statistics, mathematics, computer algorithms, etc., focused on building applications that learn and improve their predictive capabilities automatically over time without being specifically programmed to do so. ML models are built upon a statistical framework since they involve a large amount of data elements often described using statistical distributions. In the last two decades, ML algorithms have received a significant amount of attention in the fields of computer vision, natural language processing, autonomous driving vehicles, healthcare and drug development, e-commerce, to list a few due to the increased amounts of data availability and significant advancements in the computing power. ML algorithms can be broadly categorized as supervised, unsupervised, and semi-supervised algorithms [5, 7, 32, 33].
2.4.1 Supervised learning algorithms
Supervised learning is a set of algorithms that learn from the input space (X) to the output space (Y), i.e. Y = f(X) [34]. The major objective is to estimate the mapping function (f) to ensure that with an addition of a new data point (x), the outcome, (y), could be predicted [35]. Supervised learning algorithms are often applied to classification and prediction problems [32]. The following are the selected examples of supervised algorithms often employed in research studies: logistic regression, decision trees (DTs), random forest (RF), extreme gradient boosting, support vector machines (SVMs), Naïve Bayes, adaptive boosting (AdaBoost), artificial neural network (ANN), etc. [36]
2.4.2 Unsupervised learning algorithms
Different from the supervised learning algorithms, the unsupervised learning algorithms try to understand the hidden patterns within the input dataset (X) [37]. The algorithms learn and uncover the patterns without the researcher’s assistance [38]. These algorithms are often leveraged to find the naturally occurring clusters, reduce data dimensions, detect anomalies, etc. k-means clustering, principal component analysis (PCA), factor analysis (FA), singular value decomposition (SVD), apriori algorithm (association rule) represent a few examples of these types of algorithms [36]. In some cases, a semi-supervised approach is used to enhance the model performance with the help of a small amount of labeled data [36].
Depending on the study objectives and the availability and granularity of data, algorithms are reviewed for analytical relevance, tested for performance, data type fit, and selected as optimal algorithms accordingly. For this chapter, LR and XGB models were chosen to develop a predictive algorithm for the endometriosis onset. LR estimated the odds of the condition occurrence for a given medical event [39], while XGB provided more flexibility in fine-tuning the hyper-parameters when compared to other tree-based algorithms [40].
2.4.3 Logistic regression
An LR is a statistical model as well as the simplest version of ML algorithms that uses a logistic function to model a binary dependent variable with two possible outcomes: ‘0’ and ‘1’ [39, 41, 42]. A multinomial logistic regression is also often considered for research studies with multiple outcomes. LR is applied in a variety of fields, including healthcare research and social sciences [43].
In regression modeling, analysis often involves interpreting the independent variables’ coefficients. Regression coefficients describe the size and direction of the relationship between regressors (x) and the outcome variable (y). They explain the behavior of the dependent variable given a unit change in an independent variable while holding all other data elements constant. The magnitude and sign of the coefficients signify the resulting relationship with the dependent variable. Interpreting the LR’s coefficients also includetheir interpretation, as well as the odds and odds ratios [41].
Odds exemplify the ratio of probabilities of two mutually exclusive events [41], at the same time the odds ratio represents the ratio of two different odds. The simplest way to calculate the odds ratio in the LR is to exponentiate the coefficient of a predictor [39]. As a result, if the odds ratio for the age variable in years is 1.25, then for each additional year, the probability of event/success increases by 25%. For categorical features, the interpretation of the odds ratio can be more meaningful than the interpretation of odds [41].
2.4.4 xExtreme gradient boosting
A gradient boosting is another ML algorithm, which is an ensemble of simple, weak, and unreliable predictors, mainly decision trees [40]. When multiple trees are grouped, they create a robust and reliable algorithm [44]. XGB starts by creating a first simple tree [45] and builds upon the weaker learners. Each iteration revises the previous tree until an optimal point is reached [46].
Feature importance is the value generated by tree-based models, including decision trees, random forest, XGB, etc. [40]. The measure signifies the importance of features in the model as well as how good the feature is at reducing the node impurity. Feature importance is also known as ‘gini importance’ or ‘mean decrease impurity,’ and is defined as the total decrease in node impurity averaged over trees in the ensemble [44]. It is calculated as: weight, gain, and cover, where ‘weight’ represents the number of times a feature is observed in a tree, ‘gain’ denotes the average gain of splits, and ‘cover’ is defined as the average coverage of splits. Finally, coverage represents the number of samples impacted by the split [46].
2.4.5 Chi-Square test
The Chi-Square test is nonparametric [33], often employed to test the independence between the observed and expected frequencies of one or more data elements. It is known as the ‘goodness of fit test’ [47]. In this chapter, the Chi-Square test was utilized to select the top significant features [48].
2.4.6 p-value
The p-value is the probability of an observed result, assuming that the null hypothesis is correct. The p-value is used to test if the null hypothesis can be rejected in favor of the alternative hypothesis. A lower p-value implies a stronger indication in support of the alternative hypothesis [23]. In this analysis, the significance level was set at 5% to aid the feature importance evaluation and statistical results’ identification.
2.4.7 Classification metrics
The following classification metrics are often leveraged to validate the ML models’ performance. A confusion matrix is generated from the predicted probability values with 0.5 as the classification threshold. Patients with probability values greater than or equal to 0.5 are classified as 1 and below 0.5 are classified as 0. Below is the list of metrics used in evaluating models performance [32, 43, 46, 49]:
Confusion matrix:
True positive (TP)—Target patient correctly identified by the model as target patient
False positive (FP)—Control patient misclassified by the model as target patient
True negative (TN)—Control patient correctly classified by the model as a control patient
False negative (FN)—Target patient misclassified by the model as a control patient
Model performance metrics:
Accuracy: % of total patients correctly identified among total patients
Positive predictive value (PPV, Precision): % of true target patients among total predicted target patients
True positive rate (TPR, Sensitivity, Recall, Hit Rate): % of true target patients who were correctly identified among total target patients
False positive rate (FPR): % of true control patients incorrectly identified among total control patients
Specificity: % of those control who will have a negative target result
F1 score: is the harmonic mean of precision and recall
AUC: Area under the receiver operating characteristic (ROC) curve. To validate the trade-off between true positive rate and false-positive rate
In this chapter, the LR, being the simplest of all ML algorithms, was chosen as the base model. Both the LR and XGB models were trained on the analytical dataset defined in the earlier section of this chapter. The top 1000 features from each algorithm were selected to reduce the dataset dimension. As the next step, the Chi-Square test from the scikit-learn Python package was utilized to identify the top most significant features from the list of data elements employed in both models. Finally, algorithms were re-trained on the top significant features to identify the key data elements in predicting the endometriosis onset. All ML algorithms were trained on Python 3.5 using ‘scikit-learn’ and ‘xgboost’ libraries.
3. Results
3.1 Important features selection
Table 3 presents the ML model performance metrics of the initial run, where the objective was to select the top features and study whether the data captured was reasonably proven in disease prediction. Algorithms were trained on 70% of the analytical dataset and were tested on the remaining 30%. Metrics captured indicated that both the LR and XGB models performed relatively well in predicting the condition onset. The models’ accuracy ranged between 88% and 96%. Figure 2 presents the ROC curves on the test set for LR and XGB models respectively. The area under the ROC curve (AUC) values were 0.88 and 0.96, respectively for both models.
Algorithms
Statistic
Train set
Test set
LR
Accuracy
96%
96%
Sensitivity/TPR/recall
95%
95%
Specificity/TNR
98%
97%
Precision/PPV
98%
97%
f1-Score
0.96
0.96
AUC
0.96
0.96
XGB
Accuracy
90%
88%
Sensitivity/TPR/recall
86%
84%
Specificity/TNR
95%
93%
Precision/PPV
95%
92%
f1-Score
0.9
0.88
AUC
0.9
0.88
Table 3.
Classification metrics of train and test sets for LR and XGB model.
Figure 2.
XGB & LR ROC curves on test set.
From the outputs of the initial model run, the top 1000 features with absolute regressor coefficients in descending order greater than zero (0) were selected from the LR. Similarly, another set of top 1000 features with feature importance greater than zero (0) were identified from XGB. Both sets were combined to establish a unique list of top features. As the next step, the Chi-Square test for feature selection from Python scikit-learn package was applied to select the top 1000 most significant features for the final model run. The top features were selected at a standard significance level of 5% (α = 0.05). Most of the top significant features were associated with a series of medical and surgical procedures, as well as various diagnostic and comorbid conditions.
As noted above, Table 4 presents the list of most significant features identified by the Chi-Square test, which were associated with the endometriosis diagnosis. The table also presents the LR coefficients to provide relative direction between the endometriosis onset and the selected top regressors. As noted in the earlier version of the chapter available on Research Square, data elements including ‘non-inflammatory disorder of uterus,’ ‘pelvic and perineal pain’ presented examples of the diagnosis codes, indicated a positive relationship with symptoms of endometriosis [21, 50]. Procedure codes such as ‘anesthesia of lower abdomen for laparoscopy,’ ‘vaginal hysterectomy including biopsy’ were also identified as the procedures often correlated with the diagnosis as well treatment of endometriosis [50]. Furthermore, the Chi-Square test suggested that patients often consulted with a variety of healthcare specialists, including ‘emergency medicine (SPCLT_EM),’ ‘family medicine (SPCLT_FM),’ ‘obstetrics and gynecology (SPCLT_OBG)’ when experiencing gynecological symptoms and concerns; however, a larger number of office visits might negatively impact the likelihood for the condition diagnosis, as noted by the negative regressor coefficients.
Feature
Feature description
Chi–square
LR: feature coefficients
D N85_8
Other specified non-inflammatory disorder of uterus
0
3.48
D_N94_6
Dysmenorrhea, unspecifie
0
0.17
D_N94_9
Unspecified condition associated with female genital organs and menstrual cycle
0
6.9
D_R10_2
Pelvic and perineal pain
0
−0.04
D_Z01_419
Encounter for gynecological examination (general) (routine) without abnormal findings
0
−1.95
P_00840
Anesthesia intraperitoneal lower abd w/laps nos
0
1.54
P_00944
Anesthesia vaginal hysterectomy incl biopsy
0
1.55
P_52000
Cystourethroscopy
0
5.78
P_58571
Laps total hysterect 250 gm/<w/rmvl tube/ovary
0
3.25
P_58573
Laparoscopy tot hysterectomy >250 g w/tube/ovar
0
5.31
P_58662
Laps fulg/exc ovary viscera/ peritoneal surface
0
4.17
P_76830
Us transvaginal
0
1.93
P_J1950
Injection. Leuprolide acetate (for depot suspens)
0
3.74
R_Norethindrone_Acetate
Norethindrone acetate
0
0.26
SPCLT_EM
Emergency medicine
0
−9.47
SPCLT_FM
Family medicine
0
−3.63
SPCLT_HO
Hematology/oncology
0
−4.6
SPCLT_OBG
Obstetrics and gynecology
0
−2.43
Table 4.
Most significant features from LR, XGB, and Chi-Square test.
3.2 Feature selection for the cohort selection
The significant features from Section 3.1, which were specific to the target cohort, seemed promising in defining the drivers of the endometriosis condition onset, and hence, were selected to identify the patient base list for scorning. Therapeutics as well as medical and surgical procedure codes specific to endometriosis treatment such as Orilissa, Marilissa, and Lupron Depot, were excluded from the analysis to avoid introducing any biases into the next phase of the study. Around 9.5 million female patients age 18 and above qualified for the scoring process.
3.3 Machine learning model training and outcome validation
The LR and XGB models were re-trained, using the top significant features. A drop in the model performance at the beginning of the re-training process was observed. After several iterations and hyper-parameter tuning, the predictive power of the XGB model significantly improved compared to the previous iterations; however, no improvement in the LR model performance metrics was observed. Interestingly, both models were able to identify additional new features aligned with endometriosis.
Table 5 presents the top features identified by the XGB and LR models to be important in predicting the likelihood of endometriosis along with the statistical measures and metrics to assess the importance and significance of the features. The Chi-Square test (p-value) signified the importance of data elements in differentiating the target and control patients. The XGB feature importance weighed the value of features in the model in predicting the outcome. Similarly, the LR odds ratios helped to understand the odds of being diagnosed with endometriosis, given a particular medical event.
Feature
Long description
Chi-square (p)
XGB_feature_importance
LR_beta_coeff
Odds_ratio
P_58662
Laps fulg/exc ovary viscera/peritoneal surface
0
0.0318
4.70
109.73
P_58571
Laps total hysterect 250 gm/< w/rmvl tube/ovary
0
0.0212
4.17
64.53
D_N85_8
Other specified noninflammatory disorders of uterus
0
0.0094
2.56
12.88
D_N83_291
Other ovarian cyst, right side
0
0.0092
2.84
17.06
P_58661
Laparoscopy w/rmvl adnexal structures
0
0.0089
2.43
11.32
D_N85_2
Hypertrophy of uterus
0
0.0088
2.67
14.42
P_00944
Anesthesia vaginal hysterectomy incl biopsy
0
0.0076
1.77
5.86
P_52000
Cystourethroscopy
0
0.0075
1.62
5.04
D_D25_2
Subserosal leiomyoma of uterus
0
0.0069
2.25
9.53
P_72197
mri pelvis w/o & w/contrast material
0
0.0067
2.72
15.17
R_ACETAMINOPHEN
Acetaminophen
0
0.0066
2.01
7.46
D_N81_4
Uterovaginal prolapse, unspecified
0
0.0063
1.86
6.40
D_N94_9
Unspecified condition associated with female genital organs and menstrual cycle
0
0.0063
2.57
13.10
D_N92_4
Excessive bleeding in the premenopausal period
0
0.0061
2.30
9.99
D_D25_0
Submucous leiomyoma of uterus
0
0.0059
2.46
11.76
D_R10_2
Pelvic and perineal pain
0
0.0056
0.60
1.83
D_N94_5
Secondary dysmenorrheal
0
0.0056
2.81
16.64
D_Z79_890
Hormone replacement therapy
0
0.0047
2.23
9.34
D_Z80_41
Family history of malignant neoplasm of ovary
0
0.0045
2.12
8.37
D_N94_3
Premenstrual tension syndrome
0
0.0042
2.43
11.37
R_LIDOCAINE_HCL
Lidocaine hcl
0
0.0041
2.12
8.30
R_MEGESTROL_ACETATE
Megestrol acetate
0
0.0039
2.19
8.94
D_F43_0
Acute stress reaction
0
0.0032
2.36
10.61
D_N94_12
Deep dyspareunia
0
0.0023
2.35
10.51
D_N97_0
Female infertility associated with anovulation
0
0.0022
2.19
8.89
SPCLT_AN
Anesthesiology
0
0.0012
(0.55)
0.58
SPCLT_DR
Diagnostic radiology
0
0.0009
(0.87)
0.42
SPCLT_OBG
Obstetrics and gynecology
0
0.0008
(0.64)
0.53
SPCLT_EM
Emergency medicine
0
0.0006
(1.92)
0.15
SPCLT_FM
Family medicine
0
0.0004
(1.05)
0.35
SPCLT_IM
Internal medicine
0
0.0004
(0.92)
0.40
SPCLT_HO
Hematology/oncology
0
0.0003
(0.79)
0.45
Table 5.
List of top features identified by the re-trained models.
Overall, results suggest that features including ‘other ovarian cyst, right side,’ ‘hypertrophy of uterus,’ ‘submucous leiomyoma of uterus,’ ‘excessive bleeding in the premenopausal period,’ ‘unspecified condition associated with female genital organs,’ and ‘menstrual cycle’ were important in predicting the likelihood of endometriosis. The models had also flagged ‘acetaminophen’ and ‘megestrol acetate’ drugs as strong predictors of the condition.
Table 6 shows that the XGB model performed better overall compared to the LR model. Figure 3 shows the receiver operating characteristic (ROC) curves on the test sets for both re-trained models. The area under the ROC curve (AUC) values of the LR and XGB models were 0.87 and 0.96, respectively. Furthermore, Figure 4 suggests that the XGB model was able to differentiate more accurately the targets from the controls than the LR model; hence, based on the final model results, the XGB model was utilized to score the qualified patients.
Algorithms
Statistic
Train set
Test set
LR
Accuracy
87%
87%
Sensitivity/TPR/recall
75%
75%
Specificity/TNR
98%
98%
Precision/PPV
98%
98%
f1-score
0.85
0.85
AUC
0.87
0.87
XGB
Accuracy
96%
94%
Sensitivity/TPR/recall
93%
90%
Specificity/TNR
99%
98%
Precision/PPV
99%
97%
f1-score
0.96
0.93
AUC
0.96
0.94
Table 6.
Classification metric of LR and XGB model on train and test set.
Figure 3.
ROC curves of LR and XG models on test set.
Figure 4.
Distribution of probability on test data set for both the LR and XGB models. Figure on right side is of XGB and most of scores are grouped at extreme values.
3.4 Scoring qualified patients
The last step of the model evaluation was to score the qualified patients to assess the model’s accuracy in predicting the endometriosis onset. A sample of 9.5 million patients was identified and complete medical history was extracted for 36 months. After dataset preparation, the probability of endometriosis was estimated, leveraging the re-trained XGB model.
Probability distribution of 9.5 million scored patients is shown in Figure 5. Most of the predicted probability values were concentrated either toward ‘0’ or ‘1’. When considering 0.5 as a threshold, the XGB model identified around 36% of the scored patients as being likely to receive an endometriosis diagnosis within the next 12 months. Assuming an ability to leverage the significant variables in diagnosing the condition onset, practitioners could provide focused and specialized medical care in time to their patients, thereby, reducing the risks of endometriosis and its related complications.
Figure 5.
Distribution of patients by predicted probability score.
There is also a different way to present the data elements driving the prediction of disease onset and the scoring of patients for the likelihood of the disease. A nomogram (otherwise known as nomograph) is defined as an alignment chart or a two-dimensional diagram applied to estimate the graphical computation of a mathematical function [51]. A nomogram comprises a set of scales, where each scale denotes a selected feature of the studied population.
The nomogram tool is often employed in clinical medicine to predict patients’ outcomes when considering their clinical features [52]. It is also used in clinical oncology to aid healthcare providers in their treatment decisions. It leverages regression models such as the LR and parametric survival model as the basis for its framework [53]. For this chapter, a nomogram was selected to present a selected group of top features important to predicting the likelihood of endometriosis, as shown in Figure 6. The following attributes were noted on the chart as important in driving the diagnosis: ‘laps total hysterect 250 gm/< w/rmvl tube/ovary,’ ‘other noninflammatory disorders of ovary, fallopian tube, and broad ligament,’ ‘other ovarian cyst, right side,’ ‘hypertrophy of uterus,’ ‘acetaminophen,’ and ‘pelvic and perineal pain.’
Figure 6.
Nomogram of top features to predict likelihood of endometriosis.
To predict the disease onset, the contribution of each feature was measured as a point score (topmost axis in the nomogram) based on the values that each feature could take with individual point scores being added to determine the likelihood of endometriosis onset. When the value of the feature was ‘0’, its contribution was ‘0’points. The dotted line depicted the point score for an individual value of each respective feature with the total point being 198, which implied a very high probability of the disease onset. Nomogram was found to be a helpful tool to graphically study the outcomes given a group of few features; however, it was also challenging to leverage it, knowing a large number of studied features [52, 53].
4. Discussion
As mentioned in Section 3, the LR and XGB ML models were able to identify the top features that could help to explain endometriosis onset in advance. Tables 4 and 5 present the important features to predict the condition onset. These features included diagnosis codes, medical and surgical procedure codes, as well as physician specialties that often support patients through their healthcare journey.
Furthermore, Table 5 also presents the LR odds ratio and XGB feature importance index to aid the understanding and interpretation of the results. As noted in the above section, odds ratios defined the odds of being diagnosed with endometriosis when the feature changes by a unit, holding other features constant. For example, the odds ratio of ‘uterovaginal prolapse, unspecified’ was 6.40, which implied that for every additional diagnosis of ‘uterovaginal prolapse, unspecified’, the odds of endometriosis went up by 540%. Similarly, if a patient had an additional appointment with an ‘obstetrics and gynecology’ specialist then the odds decreased by 47%.
As a reminder, the first part of the ML analysis was to identify the top features from an extensive list of data elements (Table 4). LR, XGB, and Chi-Square tests were employed to derive the final list of features to re-train the model. Table 5 presents the most promising features with their respective significance and importance values. A number of the variables from the model were also cited in other medical and scientific journal publications, including articles from Johns Hopkins Medicine [17] and Queensland Health [18] on endometriosis signs, symptoms, and diagnosis, which confirmed the model’s validity from the medical and clinical side.
In the next part of this section, the selected most important features by their respective groups were reviewed and evaluated for their relevance to the endometriosis diagnostic process. The preliminary insights for this research are available on the Research Square website. The advanced preview allowed for valuable feedback that helped to enhance the research design for this chapter.
Diagnoses codes: ‘other ovarian cyst, right side’, ‘unspecified condition associated with female genital organs and menstrual cycle,’ ‘other specified noninflammatory disorders of the uterus,’ ‘excessive bleeding in the premenopausal period,’ ‘female pelvic peritoneal adhesions (post-infective),’ ‘uterovaginal prolapse, unspecified’, etc. clearly showed association with the risks and symptoms of endometriosis [54]. Feature importance from XGB suggested that these features drove the model, whereas odds ratio from LR also indicated the direction of increase or decrease in odds of getting diagnosed with the condition. To further define the magnitude of importance, Table 5 presents that if a patient was diagnosed with ‘excessive bleeding in the premenopausal period’ then the odds of receiving endometriosis diagnosis in the near future increased by 899%. Similar to these findings, Mayo Clinic articles also stated that patients might experience occasional heavy bleeding before being diagnosed with the condition [55].
Medical and surgical procedures: ‘laps fulg/exc ovary viscera/peritoneal surface’, ‘laps total hysterect 250 gm/< w/rmvl tube/ovary’, ‘anesthesia vaginal hysterectomy incl biopsy’, ‘laparoscopy w/rmvl adnexal structures’, ‘MRI pelvis w/o & w/contrast material,’ ‘cystourethroscopy’, etc. were also associated with the diagnosis as well treatment of endometriosis. The finding showed that for every additional procedure on ‘mri of pelvis,’ the odds of endometriosis increased by 1471%. Recent research from Abdominal Radiology, published by Springer Nature, also supported this claim that MRI could be more precise in the diagnosis of endometriosis compared to other diagnostic techniques [56].
As presented in Table 5, the procedure ‘laps total hysterect 250 gm/< w/rmvl tube/ovary’ had the odds ratio of 64.53, which implied that if a patient had a ‘laparoscopy with hysterectomy’ then the odds of endometriosis onset increased significantly. Previous studies on endometriosis also cited ‘laparoscopy procedure as the gold standard’ in the diagnosis process [8]. However, while the nomogram graph (Figure 6) also suggested that a patient was likely to get diagnosed with endometriosis post this procedure, the data element was further analyzed to understand how it might have correlated to the actual diagnoses, knowing that many laparoscopic procedures were performed to treat other female gynecological conditions. Figure 6 shows that the feature ‘laparoscope days difference’ presented little importance in predicting the likelihood of the disease onset. The data element measured the significance of laparoscopic procedures in predicting the likelihood of endometriosis via calculating the days’ difference between the laparoscopic procedure and the event date for both target and control cohorts.
Furthermore, the additional analysis revealed that around 60% of the target patients compared to only about 5% of the control group were diagnosed with endometriosis after a laparoscopic procedure performed on the same day of diagnosis. This finding implies that laparoscopy might not actually be a significant driver of the endometriosis diagnosis as presented in the XGB model when accounting for the time component before the diagnosis, although there were statistical significant differences between the two groups.
From the patient medical journey and healthcare access side, the ML models suggested that patients often consult with multiple healthcare specialists, including ‘emergency medicine,’ ‘family medicine,’ ‘hematology/oncology,’ ‘internal medicine,’ ‘obstetrics and gynecology’ when experiencing endometriosis-related symptoms and gynecological issues. Since, endometriosis tends to be difficult to diagnose, patients often had a number of unrelated office visits with symptoms associated later with endometriosis. This finding presented that many female patients faced substantial challenges in receiving proper care and treatment. Consequently, patients visited multiple specialists in search of answers for their signs and symptoms [57]. In agreement with these statements, both LR and XGB models presented negative weights and low importance to these healthcare providers’ features, which suggested that if a patient visited these specialists more frequently, the longer it took to receive a confirmatory endometriosis diagnosis.
Furthermore, women with a history of endometriosis were found more likely to be diagnosed with either an ‘ovarian cancer’ or ‘endometriosis-associated adenocarcinoma’ in the future. [21, 58, 59, 60]. With this in mind, having the ML models identify ‘hematology/oncology (SPCLT_HO),’ as one of the top Board Certified specialties, further suggested that an office visit with an oncologist should be recommended for any patients presenting signs and symptoms as noted above to rule out any potential cancer risk [21, 61, 62].
LR and XGB models also identified additional data elements, which were important in predicting the likelihood of endometriosis onset. The models suggested, as noted in the earlier version of the chapter posted on the Research Square website that data elements like ‘deep dyspareunia,’ ‘female infertility associated with anovulation,’ ‘premenstrual tension syndrome,’ ‘hormone replacement therapy,’ ‘family history of malignant neoplasm of ovary’ were identified as highly significant to the prediction endometriosis. Past medical articles supported these claims of fibroids, ovarian cysts, infertility, menstrual period complications, family history of neoplasm of the ovary, hormone therapy, etc. having a strong association with the condition [21, 54]. Furthermore, the finding that women of reproductive age who experience chronic stress were also at a higher risk of developing endometriosis was noted in other medical articles, implying that healthcare providers should consider this symptom in their diagnostic process [21, 63].
As mentioned in the preliminary version of the chapter on the Research Square website, ‘acetaminophen,’ ‘megestrol acetate,’ ‘lidocaine hcl,’ etc. were found to be strong predictors of endometriosis occurrence, as these drugs were often prescribed as analgesics to help control pelvic pain. Data elements, including ‘submucous leiomyoma of the uterus’ and ‘hypertrophy of uterus,’ were identified as the significant predictors as well [55, 64]; however, more clinical research is required in support of this claim, as these diseases presented similar symptoms, which might impact the ability for healthcare providers to diagnose endometriosis [21, 65].
Overall, the analysis results presented the important data elements to be considered when diagnosing endometriosis in women of reproductive age, to time more accurately disease onset and aid the diagnostic process. As noted in Section 3, when leveraging these features in the diagnostic process, a high accuracy prediction of the disease occurrence was identified, with the model differentiating with high precision between patients with and without the condition. Furthermore, a nomogram graphical representation could be leveraged as one of the tools to graphically predict the outcome given a set of features. Top features were utilized to showcase the practicality of the tool; however, the tool has limitations on the number of data elements that could be applied in the analysis.
5. Conclusions
In this chapter, the crucial role of AI and ML algorithms in disease diagnosis prediction and forecasting was presented, studied, and validated. Patient medical history was leveraged for the ML analysis. LR and XGB models identified important medical attributes, which were then leveraged to predict the likelihood of endometriosis onset. Early diagnosis can offer an opportunity for women to receive required medical care much earlier in the patient journey.
Leveraging the findings of this study and other related studies can help inform the development of analytical tools and algorithms to be integrated into the Electronic Health Records (EHR) systems to simplify and enhance the diagnosing activities performed by healthcare providers. The enhancements could further inform the diagnostic processes to aid in a timely and precise diagnostic process, ultimately increasing the quality of patient care and life.
Future research should focus on enhancing the ML analysis and exploring advanced deep learning methodologies to improve the accuracy and precision of the current results. Furthermore, imputing the missing data elements with mean and mode values, or even predictive models, can further augment the model performance and increase the accuracy of the ML models in predicting the likelihood of the disease onset. Creating time-based variables (30, 60, 120 days before diagnosis) to account for the time to endometriosis diagnosis would add a significant improvement in the feature engineering step to help with establishing a timeline of events important in the endometriosis diagnostic process.
Acknowledgments
Authors would like to recognize Heather Valera, Suzanne Rosado, and Koichi Iwata for their review of document drafts, and their valuable feedback in improving the article content.
Funding
Authors work for Symphony Health, ICON plc Organization. The data used in the article is the property of Symphony Health, ICON plc Organization. Authors used the healthcare claims data for the sole purpose of publication of this article.
Competing interest
The authors declare that they have no competing interests.
Availability of data and materials
The dataset leveraged for this chapter is a property of Symphony Health, ICON, plc. Data sharing restrictions apply to the availability of these data, and therefore, the dataset is not available for public use.
\n',keywords:"endometriosis, infertility, likelihood, logistic regression, machine learning, eXtreme gradient boosting, nomogram, odds ratio",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/79689.pdf",chapterXML:"https://mts.intechopen.com/source/xml/79689.xml",downloadPdfUrl:"/chapter/pdf-download/79689",previewPdfUrl:"/chapter/pdf-preview/79689",totalDownloads:112,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"October 25th 2021",datePrePublished:"December 16th 2021",datePublished:null,dateFinished:"December 16th 2021",readingETA:"0",abstract:"Endometriosis is a commonly occurring progressive gynecological disorder, in which tissues similar to the lining of the uterus grow on other parts of the female body, including ovaries, fallopian tubes, and bowel. It is one of the primary causes of pelvic discomfort and fertility challenges in women. The actual cause of the endometriosis is still undetermined. As a result, the objective of the chapter is to identify the drivers of endometriosis’ diagnoses via leveraging selected advanced machine learning (ML) algorithms. The primary risks of infertility and other health complications can be minimized to a greater extent if a likelihood of endometriosis could be predicted well in advance. Logistic regression (LR) and eXtreme Gradient Boosting (XGB) algorithms leveraged 36 months of medical history data to demonstrate the feasibility. Several direct and indirect features were identified as important to an accurate prediction of the condition onset, including selected diagnosis and procedure codes. Creating analytical tools based on the model results that could be integrated into the Electronic Health Records (EHR) systems and easily accessed by healthcare providers might aid the objective of improving the diagnostic processes and result in a timely and precise diagnosis, ultimately increasing patient care and quality of life.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/79689",risUrl:"/chapter/ris/79689",signatures:"Ewa J. Kleczyk, Tarachand Yadav and Stalin Amirtharaj",book:{id:"11043",type:"book",title:"Endometriosis - Recent Advances, New Perspectives and Treatments",subtitle:null,fullTitle:"Endometriosis - Recent Advances, New Perspectives and Treatments",slug:null,publishedDate:null,bookSignature:"Associate Prof. Giovana Gonçalves",coverURL:"https://cdn.intechopen.com/books/images_new/11043.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-921-4",printIsbn:"978-1-83969-920-7",pdfIsbn:"978-1-83969-922-1",isAvailableForWebshopOrdering:!0,editors:[{id:"185930",title:"Associate Prof.",name:"Giovana",middleName:null,surname:"Gonçalves",slug:"giovana-goncalves",fullName:"Giovana Gonçalves"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methodology overview",level:"1"},{id:"sec_2_2",title:"2.1 Healthcare claims patient-level database",level:"2"},{id:"sec_3_2",title:"2.2 Cohort selection",level:"2"},{id:"sec_4_2",title:"2.3 Data extraction",level:"2"},{id:"sec_5_2",title:"2.4 Overview of machine learning algorithms",level:"2"},{id:"sec_5_3",title:"2.4.1 Supervised learning algorithms",level:"3"},{id:"sec_6_3",title:"2.4.2 Unsupervised learning algorithms",level:"3"},{id:"sec_7_3",title:"2.4.3 Logistic regression",level:"3"},{id:"sec_8_3",title:"2.4.4 xExtreme gradient boosting",level:"3"},{id:"sec_9_3",title:"2.4.5 Chi-Square test",level:"3"},{id:"sec_10_3",title:"2.4.6 p-value",level:"3"},{id:"sec_11_3",title:"2.4.7 Classification metrics",level:"3"},{id:"sec_14",title:"3. Results",level:"1"},{id:"sec_14_2",title:"3.1 Important features selection",level:"2"},{id:"sec_15_2",title:"3.2 Feature selection for the cohort selection",level:"2"},{id:"sec_16_2",title:"3.3 Machine learning model training and outcome validation",level:"2"},{id:"sec_17_2",title:"3.4 Scoring qualified patients",level:"2"},{id:"sec_19",title:"4. Discussion",level:"1"},{id:"sec_20",title:"5. Conclusions",level:"1"},{id:"sec_21",title:"Acknowledgments",level:"1"},{id:"sec_21",title:"Funding",level:"1"},{id:"sec_22",title:"Competing interest",level:"1"},{id:"sec_23",title:"Availability of data and materials",level:"1"}],chapterReferences:[{id:"B1",body:'Doupe P, Faghmous J, Basu S. Machine learning for health services researchers. Value Health. 2019;22(7):808-815. Available from: https://pubmed.ncbi.nlm.nih.gov/31277828/ [Accessed: October 1, 2020]'},{id:"B2",body:'Crown WH. Potential application of machine learning in health outcomes research and some statistical cautions. 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DOI: 10.1007/s43032-019-00053-0 [Accessed on October 5, 2020]'},{id:"B64",body:'Endometriosis vs. Adenomyosis: Similarities and Differences. Available from: https://www.healthline.com/health/womens-health/adenomyosis-vs-endometriosis [Accessed: October 5, 2020]'},{id:"B65",body:'Endometrial Hyperplasia. Available from: https://my.clevelandclinic.org/health/diseases/16569-atypical-endometrial-hyperplasia [Accessed: October 5, 2020]'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ewa J. Kleczyk",address:"ewa.kleczyk@symphonyhealth.com",affiliation:'
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Its application in the face and neck have been explored using lasers, temperature controlled monopolar and bipolar radiofrequency, and ultrasound. The purpose of this chapter is to explore the various applications for the face and neck using Renuvion™, a unique energy driven device based on plasma generated from the combination of helium gas and radiofrequency energy. The advantage of this technology is its ability to offer precise delivery of heat to tissue with minimal thermal spread, in part due to the rapid cooling aided by the helium gas. We will explore the options in which this technology can be incorporated to rejuvenate the face and neck, the patient selection considerations in choosing method of approach, surgical technique, anticipated outcomes, potential concerns and or complications associated with this and expected perioperative care. Applications in the face and neck include: (1) Subdermally in the neck as a stand alone procedure with or without liposuction. (2) Subdermally in a limited incision, non-excisional technique with a concomitant platysmaplasty either with an open approach or percutaneous use of suture suspension for the platysmal muscle. (3) Subdermally in conjunction with an open traditional rhytidectomy involving skin excision. (4) Ablative resurfacing—fractional or pulsed and full continuous modalities (non-FDA cleared at the time of this writing). It is the authors’ experience that with appropriate patient selection this can be a powerful tool that can deliver skin tightening and rhytid reduction not seen by other technologies available.",signatures:"Deirdre Leake and Janet Lee",authors:[{id:"346204",title:"Dr.",name:"Deirdre",surname:"Leake",fullName:"Deirdre Leake",slug:"deirdre-leake",email:"deirdreleake@hotmail.com"},{id:"436774",title:"Dr.",name:"Janet",surname:"Lee",fullName:"Janet Lee",slug:"janet-lee",email:"jlee@floridaentandllergy.com"}],book:{id:"10351",title:"Enhanced Liposuction",slug:"enhanced-liposuction-new-perspectives-and-techniques",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"73035",title:"Prof.",name:"Ercan",surname:"Karacaoglu",slug:"ercan-karacaoglu",fullName:"Ercan Karacaoglu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/73035/images/322_n.jpg",biography:"E R C A N K A R A C A O G L U\r\n\r\n\r\n\r\n\r\n\r\n\r\nAcademic Title: Associate Professor of Plastic Surgery\r\n\r\nSpeciality: Plastic, Aesthetic and Reconstructive Surgery\r\n\r\nMinor Speciality: Aesthetic surgery, Aestehetic Breast Surgery, Reconstructive Breast surgery\r\n\r\nPlace of Birth : Tekirdag, Turkey\r\nDate of Birth : December 18, 1967\r\n\r\nGraduated Faculty and Year: GATA School of Medicine, 1992 \r\n\r\nPlace and date of Speciality: GATA Haydarpasa Training Hospital\r\nIstanbul, Turkey, 2000\r\n\r\nPlace and date of Minor Speciality: 1.Brown University, School of Medicine, Department of Plastic surgery, Providence, Rhode Island, U.S.A., 2001\r\n\t\t\t\t 2. Brown University, School of Medicine, Department of Plastic surgery, Providence, Rhode Island, U.S.A., 2004\r\n\r\n\r\nForeign Languages: English\r\n\r\nVocational Awards: \r\n\r\nGrants: LifeCell Corporation\r\nAssessment of In-Vivo Integration of Allograft around implants\r\nPrinciple Researcher.\r\nAward: $30,000 (2004)\r\nHonors and Awards: The second best scientific presentation award in 22nd National Congress of Turkish Plastic and Reconstructive Surgery Society (Sept 2000).\r\n\r\n-The best grade of graduation in 1990-1991 Training Year, Gulhane Military Medical \r\n Academy, School of Medicine (1991)\r\n\r\n\r\nVocational Publications: \r\n\r\n\tNumber of Foreign Publications: 26\r\n\r\n\tNumber of Turkish Publications: 14\r\n\r\n\tNumber of National Congress Presentations: 35\r\n\r\n\tNumber of International Congress Presentations: 32\r\n\t\r\n\tNumber of Book/ Chapters: 3\r\n\r\nSummary of the Career: \r\n\r\n-Since June 2009: Associate Professor of Plastic Surgery in Yeditepe University, School of Medicine, Dept of Plastic Surgery\t Istanbul, Turkey.\r\n-March 2005- June 2009: Assistant Professor \r\n-Oct 2003- Dec 2004: Fellowship in Breast Surgery (Teaching and Clinical Fellow) Brown University, School of Medicine\r\nRI Hospital, Div of Plastic Surgery Providence, RI U.S.A. \r\n-Jun 2003- Sept 2003 : Private Practicing, Istanbul, Turkey. \r\n-Sept 2001- Jun 2003 : Chief (Captain, M.D), Plastic Surgery Service Golcuk 600-Bed Naval Hospital, Golcuk, Kocaeli Turkey. \r\n-Dec 2000- Aug 2001 : Fellowship in Aesthetic and Reconstructive Breast Surgery Brown University, School of Medicine RI Hospital, Div of Plastic Surgery Providence, RI, U.S.A. \r\n-Nov 2000 : Chief (Captain, M.D), Plastic Surgery Service Golcuk 600-Bed Naval Hospital, Golcuk, Kocaeli Turkey. \r\n-Sept '95-Oct 2000 : Residency in Plastic and Reconstructive Surgery. GATA Military Medical Academy, Haydarpasa Training Hospital, Kadikoy, Istanbul Turkey. \r\n-May.’94-Sept ’95 : Chief of Emergency Medical Care Service Turkish Navy, Heybeliada Naval Hospital, Heybeliada, Istanbul Turkey. \r\n-June’93-May.’94 : Emergency Care Staff and Destroyer Doctor, Turkish Navy, Golcuk 600-Bed Naval Hospital, 41657 Golcuk, Kocaeli Turkey. \r\n-Sept ’92- June ’93 : Postgraduate Internship \r\nGATA 1200-Bed Military Medical Academy Hospital Etlik, Anakara Turkey\r\n\r\n\r\nMemberships in Associations/Clubs: \r\n\r\nSince 2006: Fellow, EBOPRAS Board Certificate (EBOPRAS, European Board of Plastic, Reconstructive and Aesthetic Surgery)\r\n\r\nSince 2006: ASPS (American Society of Plastic Surgeons)\r\n\r\nSince 2004: ISAPS ( International Society of Aesthetic Plastic Surgery)\r\n\r\nSince 2004: TASSSA (Turkish American Scientists Scholars Association)\r\n\r\nSince 2007: Society of Turkish Aesthetic Surgeons\r\n\r\nSince 1995 : Society of Turkish Plastic and Reconstructive Surgeons \r\n\r\nSince 1992 : Association of Turkish Medical Doctors \r\n\r\n\r\n\r\nE-Mail: drercanka@yahoo.com",institutionString:null,institution:{name:"Yeditepe University",institutionURL:null,country:{name:"Turkey"}}},{id:"342452",title:"Dr.",name:"Roger E.",surname:"Amar",slug:"roger-e.-amar",fullName:"Roger E. Amar",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"343312",title:"Dr.",name:"Engin",surname:"Selamioglu",slug:"engin-selamioglu",fullName:"Engin Selamioglu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bahçeşehir University",institutionURL:null,country:{name:"Turkey"}}},{id:"343659",title:"Dr.",name:"Stephen",surname:"Mulholland",slug:"stephen-mulholland",fullName:"Stephen Mulholland",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"343782",title:"M.D.",name:"Igumnov Vitaliy",surname:"Aleksandrovich",slug:"igumnov-vitaliy-aleksandrovich",fullName:"Igumnov Vitaliy Aleksandrovich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"343918",title:"Dr.",name:"Jamil",surname:"Hayek",slug:"jamil-hayek",fullName:"Jamil Hayek",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"344364",title:"Mr.",name:"Simon",surname:"Davies",slug:"simon-davies",fullName:"Simon Davies",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"344573",title:"Dr.",name:"Vaishali B.",surname:"Doolabh",slug:"vaishali-b.-doolabh",fullName:"Vaishali B. Doolabh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/344573/images/15430_n.jpg",biography:null,institutionString:null,institution:null},{id:"347622",title:"Ph.D.",name:"Michael",surname:"Kreindel",slug:"michael-kreindel",fullName:"Michael Kreindel",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"351015",title:"Dr.",name:"Ali",surname:"Juma",slug:"ali-juma",fullName:"Ali Juma",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"our-story",title:"Our story",intro:"
The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.
",metaTitle:"Our story",metaDescription:"The company was founded in Vienna in 2004 by Alex Lazinica and Vedran Kordic, two PhD students researching robotics. While completing our PhDs, we found it difficult to access the research we needed. So, we decided to create a new Open Access publisher. A better one, where researchers like us could find the information they needed easily. The result is IntechOpen, an Open Access publisher that puts the academic needs of the researchers before the business interests of publishers.",metaKeywords:null,canonicalURL:"/page/our-story",contentRaw:'[{"type":"htmlEditorComponent","content":"
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\\n\\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\\n\\n
The IntechOpen timeline
\\n\\n
2004
\\n\\n
\\n\\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\\n\\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\\n
\\n\\n
2005
\\n\\n
\\n\\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\\n
\\n\\n
2006
\\n\\n
\\n\\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\\n
\\n\\n
2008
\\n\\n
\\n\\t
Downloads milestone: 200,000 downloads reached
\\n
\\n\\n
2009
\\n\\n
\\n\\t
Publishing milestone: the first 100 Open Access STM books are published
\\n
\\n\\n
2010
\\n\\n
\\n\\t
Downloads milestone: one million downloads reached
\\n\\t
IntechOpen expands its book publishing into a new field: medicine.
\\n
\\n\\n
2011
\\n\\n
\\n\\t
Publishing milestone: More than five million downloads reached
\\n\\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\\n\\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\\n\\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\\n
\\n\\n
2012
\\n\\n
\\n\\t
Publishing milestone: 10 million downloads reached
\\n\\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\\n
\\n\\n
2013
\\n\\n
\\n\\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\\n
\\n\\n
2014
\\n\\n
\\n\\t
IntechOpen turns 10, with more than 30 million downloads to date.
\\n\\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\\n
\\n\\n
2015
\\n\\n
\\n\\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\\n\\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\\n\\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\\n\\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\\n
\\n\\n
2016
\\n\\n
\\n\\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\\n
\\n\\n
2017
\\n\\n
\\n\\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\\n\\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
We started by publishing journals and books from the fields of science we were most familiar with - AI, robotics, manufacturing and operations research. Through our growing network of institutions and authors, we soon expanded into related fields like environmental engineering, nanotechnology, computer science, renewable energy and electrical engineering, Today, we are the world’s largest Open Access publisher of scientific research, with over 4,200 books and 54,000 scientific works including peer-reviewed content from more than 116,000 scientists spanning 161 countries. Our authors range from globally-renowned Nobel Prize winners to up-and-coming researchers at the cutting edge of scientific discovery.
\n\n
In the same year that IntechOpen was founded, we launched what was at the time the first ever Open Access, peer-reviewed journal in its field: the International Journal of Advanced Robotic Systems (IJARS).
\n\n
The IntechOpen timeline
\n\n
2004
\n\n
\n\t
Intech Open is founded in Vienna, Austria, by Alex Lazinica and Vedran Kordic, two PhD students, and their first Open Access journals and books are published.
\n\t
Alex and Vedran launch the first Open Access, peer-reviewed robotics journal and IntechOpen’s flagship publication, the International Journal of Advanced Robotic Systems (IJARS).
\n
\n\n
2005
\n\n
\n\t
IntechOpen publishes its first Open Access book: Cutting Edge Robotics.
\n
\n\n
2006
\n\n
\n\t
IntechOpen publishes a special issue of IJARS, featuring contributions from NASA scientists regarding the Mars Exploration Rover missions.
\n
\n\n
2008
\n\n
\n\t
Downloads milestone: 200,000 downloads reached
\n
\n\n
2009
\n\n
\n\t
Publishing milestone: the first 100 Open Access STM books are published
\n
\n\n
2010
\n\n
\n\t
Downloads milestone: one million downloads reached
\n\t
IntechOpen expands its book publishing into a new field: medicine.
\n
\n\n
2011
\n\n
\n\t
Publishing milestone: More than five million downloads reached
\n\t
IntechOpen publishes 1996 Nobel Prize in Chemistry winner Harold W. Kroto’s “Strategies to Successfully Cross-Link Carbon Nanotubes”. Find it here.
\n\t
IntechOpen and TBI collaborate on a project to explore the changing needs of researchers and the evolving ways that they discover, publish and exchange information. The result is the survey “Author Attitudes Towards Open Access Publishing: A Market Research Program”.
\n\t
IntechOpen hosts SHOW - Share Open Access Worldwide; a series of lectures, debates, round-tables and events to bring people together in discussion of open source principles, intellectual property, content licensing innovations, remixed and shared culture and free knowledge.
\n
\n\n
2012
\n\n
\n\t
Publishing milestone: 10 million downloads reached
\n\t
IntechOpen holds Interact2012, a free series of workshops held by figureheads of the scientific community including Professor Hiroshi Ishiguro, director of the Intelligent Robotics Laboratory, who took the audience through some of the most impressive human-robot interactions observed in his lab.
\n
\n\n
2013
\n\n
\n\t
IntechOpen joins the Committee on Publication Ethics (COPE) as part of a commitment to guaranteeing the highest standards of publishing.
\n
\n\n
2014
\n\n
\n\t
IntechOpen turns 10, with more than 30 million downloads to date.
\n\t
IntechOpen appoints its first Regional Representatives - members of the team situated around the world dedicated to increasing the visibility of our authors’ published work within their local scientific communities.
\n
\n\n
2015
\n\n
\n\t
Downloads milestone: More than 70 million downloads reached, more than doubling since the previous year.
\n\t
Publishing milestone: IntechOpen publishes its 2,500th book and 40,000th Open Access chapter, reaching 20,000 citations in Thomson Reuters ISI Web of Science.
\n\t
40 IntechOpen authors are included in the top one per cent of the world’s most-cited researchers.
\n\t
Thomson Reuters’ ISI Web of Science Book Citation Index begins indexing IntechOpen’s books in its database.
\n
\n\n
2016
\n\n
\n\t
IntechOpen is identified as a world leader in Simba Information’s Open Access Book Publishing 2016-2020 report and forecast. IntechOpen came in as the world’s largest Open Access book publisher by title count.
\n
\n\n
2017
\n\n
\n\t
Downloads milestone: IntechOpen reaches more than 100 million downloads
\n\t
Publishing milestone: IntechOpen publishes its 3,000th Open Access book, making it the largest Open Access book collection in the world
\n
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Shohel"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"350",title:"Agrology",slug:"agrology",parent:{id:"41",title:"Plant Biology",slug:"agricultural-and-biological-sciences-plant-biology"},numberOfBooks:3,numberOfSeries:0,numberOfAuthorsAndEditors:121,numberOfWosCitations:102,numberOfCrossrefCitations:109,numberOfDimensionsCitations:204,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicId:"350",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"7000",title:"Legume Crops",subtitle:"Characterization and Breeding for Improved Food Security",isOpenForSubmission:!1,hash:"4d0f73bf883bbb984cc2feef1259a9a7",slug:"legume-crops-characterization-and-breeding-for-improved-food-security",bookSignature:"Mohamed Ahmed El-Esawi",coverURL:"https://cdn.intechopen.com/books/images_new/7000.jpg",editedByType:"Edited by",editors:[{id:"191770",title:"Dr.",name:"Mohamed A.",middleName:null,surname:"El-Esawi",slug:"mohamed-a.-el-esawi",fullName:"Mohamed A. El-Esawi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5482",title:"Soybean",subtitle:"The Basis of Yield, Biomass and Productivity",isOpenForSubmission:!1,hash:"2b6f5b827869f467dda14e78f1c45570",slug:"soybean-the-basis-of-yield-biomass-and-productivity",bookSignature:"Minobu Kasai",coverURL:"https://cdn.intechopen.com/books/images_new/5482.jpg",editedByType:"Edited by",editors:[{id:"29226",title:"Dr.",name:"Minobu",middleName:null,surname:"Kasai",slug:"minobu-kasai",fullName:"Minobu Kasai"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5463",title:"Advances in International Rice Research",subtitle:null,isOpenForSubmission:!1,hash:"92ccc84a75f33d3dac5e3cd4b6a00474",slug:"advances-in-international-rice-research",bookSignature:"Jinquan Li",coverURL:"https://cdn.intechopen.com/books/images_new/5463.jpg",editedByType:"Edited by",editors:[{id:"96434",title:"Dr.",name:"Jin Quan",middleName:null,surname:"Li",slug:"jin-quan-li",fullName:"Jin Quan Li"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:3,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"53518",doi:"10.5772/66744",title:"Application and Conversion of Soybean Hulls",slug:"application-and-conversion-of-soybean-hulls",totalDownloads:2230,totalCrossrefCites:10,totalDimensionsCites:24,abstract:"Soybean is one of the most cultivated crops in the world, with a global production of approximately 240 million tons, generating about 18–20 million tons of hulls, the major by-product of soy industry. The chemical composition of soybean hulls depends on the efficiency of the dehulling process, and so, the soybean hulls may contain variable amounts of cellulose (29–51%), hemicelluloses (10–25%), lignin (1–4%), pectins (4–8%), proteins (11–15%), and minor extractives. This chapter provides a review on the composition and structure of soybean hulls, especially in regard to the application and conversion of the compositions. Current applications of soybean hulls are utilizations to animal feed, treatment of wastewater, dietary fiber, and herbal medicine. The conversion of soybean hulls is concerned with ethanol production, bio-oil, polysaccharides, microfibrils, peroxidase, and oligopeptides. On the basis of the relevant findings, we recommend the use of soybean hulls as important source on environment, energy, animal breeding, materials, chemicals, medicine, and food.",book:{id:"5482",slug:"soybean-the-basis-of-yield-biomass-and-productivity",title:"Soybean",fullTitle:"Soybean - The Basis of Yield, Biomass and Productivity"},signatures:"Hua-Min Liu and Hao-Yang Li",authors:[{id:"190617",title:"Dr.",name:"Hua-Min",middleName:null,surname:"Liu",slug:"hua-min-liu",fullName:"Hua-Min Liu"}]},{id:"54259",doi:"10.5772/67361",title:"Genetics and Genomics of Bacterial Blight Resistance in Rice",slug:"genetics-and-genomics-of-bacterial-blight-resistance-in-rice",totalDownloads:2476,totalCrossrefCites:13,totalDimensionsCites:23,abstract:"Rice is an important food crop for half the world’s population and has been in cultivation for over 10,000 years. During the last few decades, rice has evolved intricate relationships with associated pathogens and pests, bacterial blight (BB) being one of the most important among them. Utilization of resistant varieties with agricultural management practices is a more effective way to control BB. Of the 42 different resistance (R) genes identified to confer BB resistance, 9 have been isolated and cloned, whereas a few of the avirulence genes and a large number of candidate pathogenicity genes have been isolated from Xanthomonas oryzae pv. oryzae. The complete genome sequences of two different rice subspecies japonica and indica and three different races of BB pathogen are available. Therefore, the interaction between rice-Xoo could be deciphered and pave a way to study the molecular aspects of bacterial pathogenesis and host counter measures like innate immunity and R gene–mediated immunity. Although several of the type III effectors of Xoo have been characterized and the host targets of a few of them identified, a relatively large number of candidate effectors remain to be studied and their functional analysis may provide key for developing broad spectrum and durable resistance to BB.",book:{id:"5463",slug:"advances-in-international-rice-research",title:"Advances in International Rice Research",fullTitle:"Advances in International Rice Research"},signatures:"Yogesh Vikal and Dharminder Bhatia",authors:[{id:"189992",title:"Dr.",name:"Yogesh",middleName:null,surname:"Vikal",slug:"yogesh-vikal",fullName:"Yogesh Vikal"},{id:"195667",title:"Dr.",name:"Dharminder",middleName:null,surname:"Bhatia",slug:"dharminder-bhatia",fullName:"Dharminder Bhatia"}]},{id:"53538",doi:"10.5772/66743",title:"Role of Nitrogen on Growth and Seed Yield of Soybean and a New Fertilization Technique to Promote Nitrogen Fixation and Seed Yield",slug:"role-of-nitrogen-on-growth-and-seed-yield-of-soybean-and-a-new-fertilization-technique-to-promote-ni",totalDownloads:3305,totalCrossrefCites:11,totalDimensionsCites:20,abstract:"Soybean is an important crop for human food and feed for livestock. World soybean production is increasing especially in North and South America. Soybean seeds contain a high percentage of protein about 35–40%, and they require a large amount of nitrogen compared with other crops. Soybean plants make root nodules with rhizobia, and rhizobia can fix atmospheric N2 and give the fixed N to the host soybean plants. Also, soybean can absorb nitrogen usually nitrate from soil or fertilizers. The amount of total assimilated nitrogen in shoot is proportional to the soybean seed yield either from nitrogen fixation or from nitrogen absorption, and the nitrogen availability is very important for soybean cultivation. Maintenance of a high and long-term nitrogen fixation activity is very important for a high production of soybean. However, application of chemical nitrogen fertilizers usually depresses nodule formation and nitrogen fixation. Nitrate in direct contact with a nodulated part of roots causes severe inhibition of nodule growth and nitrogen fixation, although a distant part of nodules from nitrate application gives no or little effect. Deep placement of slow-release nitrogen fertilizers, coated urea, or lime nitrogen promoted the growth and seed yield and quality of soybean without depressing nitrogen fixation.",book:{id:"5482",slug:"soybean-the-basis-of-yield-biomass-and-productivity",title:"Soybean",fullTitle:"Soybean - The Basis of Yield, Biomass and Productivity"},signatures:"Takuji Ohyama, Kaushal Tewari, Shinji Ishikawa, Kazuya Tanaka,\nSatoshi Kamiyama, Yuki Ono, Soshi Hatano, Norikuni Ohtake, Kuni\nSueyoshi, Hideo Hasegawa, Takashi Sato, Sayuri Tanabata,\nYoshifumi Nagumo, Yoichi Fujita and Yoshihiko Takahashi",authors:[{id:"30061",title:"Prof.",name:"Takuji",middleName:null,surname:"Ohyama",slug:"takuji-ohyama",fullName:"Takuji Ohyama"},{id:"41349",title:"Dr.",name:"Norikuni",middleName:null,surname:"Ohtake",slug:"norikuni-ohtake",fullName:"Norikuni Ohtake"},{id:"41350",title:"Dr.",name:"Kuni",middleName:null,surname:"Sueyoshi",slug:"kuni-sueyoshi",fullName:"Kuni Sueyoshi"},{id:"41351",title:"Dr.",name:"Yoshihiko",middleName:null,surname:"Takahashi",slug:"yoshihiko-takahashi",fullName:"Yoshihiko Takahashi"},{id:"169171",title:"Dr.",name:"Sayuri",middleName:null,surname:"Tanabata",slug:"sayuri-tanabata",fullName:"Sayuri Tanabata"},{id:"195270",title:"Dr.",name:"Kaushal",middleName:null,surname:"Tewari",slug:"kaushal-tewari",fullName:"Kaushal Tewari"},{id:"195271",title:"Dr.",name:"Shinji",middleName:null,surname:"Ishikawa",slug:"shinji-ishikawa",fullName:"Shinji Ishikawa"},{id:"195272",title:"MSc.",name:"Kazuya",middleName:null,surname:"Tanaka",slug:"kazuya-tanaka",fullName:"Kazuya Tanaka"},{id:"195274",title:"MSc.",name:"Satoshi",middleName:null,surname:"Kamiyama",slug:"satoshi-kamiyama",fullName:"Satoshi Kamiyama"},{id:"195275",title:"BSc.",name:"Yuki",middleName:null,surname:"Ono",slug:"yuki-ono",fullName:"Yuki Ono"},{id:"195276",title:"M.Sc.",name:"Soshi",middleName:null,surname:"Hatano",slug:"soshi-hatano",fullName:"Soshi Hatano"},{id:"195277",title:"Prof.",name:"Hideo",middleName:null,surname:"Hasegawa",slug:"hideo-hasegawa",fullName:"Hideo Hasegawa"},{id:"195278",title:"Prof.",name:"Takashi",middleName:null,surname:"Sato",slug:"takashi-sato",fullName:"Takashi Sato"},{id:"195279",title:"Dr.",name:"Yoshifumi",middleName:null,surname:"Nagumo",slug:"yoshifumi-nagumo",fullName:"Yoshifumi Nagumo"},{id:"195280",title:"MSc.",name:"Yoichi",middleName:null,surname:"Fujita",slug:"yoichi-fujita",fullName:"Yoichi Fujita"}]},{id:"53774",doi:"10.5772/67098",title:"Salt Stress Tolerance in Rice: Emerging Role of Exogenous Phytoprotectants",slug:"salt-stress-tolerance-in-rice-emerging-role-of-exogenous-phytoprotectants",totalDownloads:3293,totalCrossrefCites:8,totalDimensionsCites:19,abstract:"Excess salinity in soil is one of the major environmental factors that limit plant growth and yield of a wide variety of crops including rice. On the basis of tolerance ability toward salinity, rice is considered as salt-sensitive crop, and growth and yield of rice are greatly affected by salinity. In general, rice can tolerate a small amount of saltwater without compromising the growth and yield. However, it greatly depends on the types and species of rice and their growth stage. Salinity-induced ionic and osmotic stresses reduce rate of photosynthesis and consequently cause oxidative stress, which is also responsible for growth reduction. The negative effects of salt stress that mentioned ultimately reduced yield of most crops including rice, except some halophytes. In recent decades, researchers have developed various approaches toward making salt-tolerant rice varieties. Using phytoprotectants is found to be effective in conferring salt tolerance to rice plants. In this chapter, we reviewed the recent reports on different aspects on salt stress tolerance strategies in light of using phytoprotectants.",book:{id:"5463",slug:"advances-in-international-rice-research",title:"Advances in International Rice Research",fullTitle:"Advances in International Rice Research"},signatures:"Anisur Rahman, Kamrun Nahar, Jubayer Al Mahmud, Mirza\nHasanuzzaman, Md. Shahadat Hossain and Masayuki Fujita",authors:[{id:"47687",title:"Prof.",name:"Masayuki",middleName:null,surname:"Fujita",slug:"masayuki-fujita",fullName:"Masayuki Fujita"},{id:"76477",title:"Prof.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman"},{id:"166818",title:"MSc.",name:"Kamrun",middleName:null,surname:"Nahar",slug:"kamrun-nahar",fullName:"Kamrun Nahar"},{id:"176201",title:"MSc.",name:"Jubayer-Al-",middleName:null,surname:"Mahmud",slug:"jubayer-al-mahmud",fullName:"Jubayer-Al- Mahmud"},{id:"189983",title:"Dr.",name:"Anisur",middleName:null,surname:"Rahman",slug:"anisur-rahman",fullName:"Anisur Rahman"},{id:"189984",title:"Mr.",name:"Md. Shahadat",middleName:null,surname:"Hossain",slug:"md.-shahadat-hossain",fullName:"Md. Shahadat Hossain"}]},{id:"53124",doi:"10.5772/66450",title:"The Use of Rice in Brewing",slug:"the-use-of-rice-in-brewing",totalDownloads:4536,totalCrossrefCites:11,totalDimensionsCites:17,abstract:"Rice could be a useful raw material for the production of a gluten-free beer-like beverage. In today’s beer brewing industry, rice is primarily used as an adjunct in combination with barley malt. But, recently, there is some information about rice malt for brewing an all-rice malt beer. The use of rice as an adjunct in brewing is described highlighting the quality attributes of the final beer. The rice grain quality attributes of different samples are reported in order to evaluate their attitude to malting and brewing and also considering their enzymatic activity. Then, the different brewing processes to produce all-rice malt beers will be described and the final gluten-free rice beers is evaluated and compared to a barley malt beer. Finally, the levels of major aroma-active components of an all-rice malt beer and the results of the sensory analysis assessing the beer-like character of the rice beverage are reported. The obtained beer samples show a content of volatile compounds comparable with a barley malt beer. The sensory profile of the rice malt beer is similar to a barley malt beer in aroma, taste and mouthfeel.",book:{id:"5463",slug:"advances-in-international-rice-research",title:"Advances in International Rice Research",fullTitle:"Advances in International Rice Research"},signatures:"Ombretta Marconi, Valeria Sileoni, Dayana Ceccaroni and Giuseppe\nPerretti",authors:[{id:"189703",title:"Ph.D.",name:"Ombretta",middleName:null,surname:"Marconi",slug:"ombretta-marconi",fullName:"Ombretta Marconi"},{id:"189706",title:"Dr.",name:"Valeria",middleName:null,surname:"Sileoni",slug:"valeria-sileoni",fullName:"Valeria Sileoni"},{id:"189707",title:"Prof.",name:"Giuseppe",middleName:null,surname:"Perretti",slug:"giuseppe-perretti",fullName:"Giuseppe Perretti"},{id:"190973",title:"Dr.",name:"Dayana",middleName:null,surname:"Ceccaroni",slug:"dayana-ceccaroni",fullName:"Dayana Ceccaroni"}]}],mostDownloadedChaptersLast30Days:[{id:"66478",title:"Mungbean (Vigna radiata L. Wilczek): Retrospect and Prospects",slug:"mungbean-em-vigna-radiata-em-l-wilczek-retrospect-and-prospects",totalDownloads:1195,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Mungbean (Vigna radiata L. Wilczek) is economically most important crop of Vigna group. It is also known as green gram, golden gram, moong, Chickasaw, Oregon pea, and chop suey bean and this legumes have a strategic position in Southeast Asian countries for nutritional security and sustainable crop production. Being rich in quality protein, minerals and vitamins, they are inseparable ingredients in the diets of a vast majority of Indian population. When supplemented with cereals, they provide a perfect mix of essential amino acids with high biological value. These crops have the ability to fix atmospheric nitrogen (58–109 kg per ha in kg per ha mungbean) in symbiotic association with Rhizobium bacteria, which enables them to meet their own nitrogen requirement and also benefit the succeeding crops. This crop has also been reported to smother weed flora appreciably (20–45%) when intercropped with tall cereals or pigeonpea and consequently, minimize the cost incurred on weed control. On account of short duration and photo-thermo insensitivity, they are considered excellent crops for crop intensification and diversification. A seed of mungbean is highly nutritious containing 24–28% protein, 1.0–1.5% fat, 3.5–4.5% fibre, 4.5–5.5% ash and 59–65% carbohydrates on dry weight basis and provide 334–344 kcal energy. Mungbean protein is considered to be easily digestible. Mungbean are tropical grain legumes widely grown in the sub-tropical countries of South and Southeast Asia. Nevertheless, these crops are cultivated over a wide range of latitudes in the regions where average diurnal temperatures during the growing season are warmer than about 20°C.",book:{id:"7000",slug:"legume-crops-characterization-and-breeding-for-improved-food-security",title:"Legume Crops",fullTitle:"Legume Crops - Characterization and Breeding for Improved Food Security"},signatures:"Suhel Mehandi, Syed Mohd. Quatadah, Sudhakar Prasad Mishra, Indra Prakash Singh, Nagmi Praveen and Namrata Dwivedi",authors:[{id:"275243",title:"Dr.",name:"Suhel",middleName:null,surname:"Mehandi",slug:"suhel-mehandi",fullName:"Suhel Mehandi"},{id:"275245",title:"Dr.",name:"Indra Prakash",middleName:null,surname:"Singh",slug:"indra-prakash-singh",fullName:"Indra Prakash Singh"},{id:"275246",title:"Prof.",name:"Sudhakar",middleName:null,surname:"Prasad Mishra",slug:"sudhakar-prasad-mishra",fullName:"Sudhakar Prasad Mishra"},{id:"290295",title:"Dr.",name:"Syed",middleName:null,surname:"Mohd. Quatadah",slug:"syed-mohd.-quatadah",fullName:"Syed Mohd. Quatadah"},{id:"290728",title:"MSc.",name:"Nagmi",middleName:null,surname:"Praveen",slug:"nagmi-praveen",fullName:"Nagmi Praveen"},{id:"290731",title:"Dr.",name:"Namrata",middleName:null,surname:"Dwivedi",slug:"namrata-dwivedi",fullName:"Namrata Dwivedi"}]},{id:"53518",title:"Application and Conversion of Soybean Hulls",slug:"application-and-conversion-of-soybean-hulls",totalDownloads:2227,totalCrossrefCites:10,totalDimensionsCites:24,abstract:"Soybean is one of the most cultivated crops in the world, with a global production of approximately 240 million tons, generating about 18–20 million tons of hulls, the major by-product of soy industry. The chemical composition of soybean hulls depends on the efficiency of the dehulling process, and so, the soybean hulls may contain variable amounts of cellulose (29–51%), hemicelluloses (10–25%), lignin (1–4%), pectins (4–8%), proteins (11–15%), and minor extractives. This chapter provides a review on the composition and structure of soybean hulls, especially in regard to the application and conversion of the compositions. Current applications of soybean hulls are utilizations to animal feed, treatment of wastewater, dietary fiber, and herbal medicine. The conversion of soybean hulls is concerned with ethanol production, bio-oil, polysaccharides, microfibrils, peroxidase, and oligopeptides. On the basis of the relevant findings, we recommend the use of soybean hulls as important source on environment, energy, animal breeding, materials, chemicals, medicine, and food.",book:{id:"5482",slug:"soybean-the-basis-of-yield-biomass-and-productivity",title:"Soybean",fullTitle:"Soybean - The Basis of Yield, Biomass and Productivity"},signatures:"Hua-Min Liu and Hao-Yang Li",authors:[{id:"190617",title:"Dr.",name:"Hua-Min",middleName:null,surname:"Liu",slug:"hua-min-liu",fullName:"Hua-Min Liu"}]},{id:"54205",title:"The Application of Genomic Approaches in Studying a Bacterial Blight-Resistant Mutant in Rice",slug:"the-application-of-genomic-approaches-in-studying-a-bacterial-blight-resistant-mutant-in-rice",totalDownloads:4065,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Rice bacterial blight disease (BBD), caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the serious diseases in most rice production regions. In this report, we screened for resistance mutants from the mutation pool of TNG67 variety derived by sodium azide (SA) mutagenesis with phenotype investigation and assisted with fluorescent detection. SA0423 is a mutant of broad range resistance against Xoo for many years; the resistance was studied following the concept of central dogma. The inheritance of resistance was characterized, and three QTLs were mapped onto the genome of SA0423 using simple sequence repeat (SSR) markers and R/qtl by genomic approach. In transcriptomic approach, only one differential expression QTLs (eQTLs) were identified; two differentially expressed proteins (pQTLs) were identified and genetically characterized by proteomics after Xoo challenged in SA0423 mutant. To improve the bacterial blight resistance, makers are developed from QTLs, eQTLs and pQTLs to pyramid the resistance genes through marker-assisted breeding in our rice breeding programs.",book:{id:"5463",slug:"advances-in-international-rice-research",title:"Advances in International Rice Research",fullTitle:"Advances in International Rice Research"},signatures:"Chang-Sheng Wang and Da-Gin Lin",authors:[{id:"189870",title:"Prof.",name:"Chang-Sheng",middleName:null,surname:"Wang",slug:"chang-sheng-wang",fullName:"Chang-Sheng Wang"},{id:"194983",title:"Dr.",name:"Da-Gin",middleName:null,surname:"Lin",slug:"da-gin-lin",fullName:"Da-Gin Lin"}]},{id:"53124",title:"The Use of Rice in Brewing",slug:"the-use-of-rice-in-brewing",totalDownloads:4533,totalCrossrefCites:11,totalDimensionsCites:17,abstract:"Rice could be a useful raw material for the production of a gluten-free beer-like beverage. In today’s beer brewing industry, rice is primarily used as an adjunct in combination with barley malt. But, recently, there is some information about rice malt for brewing an all-rice malt beer. The use of rice as an adjunct in brewing is described highlighting the quality attributes of the final beer. The rice grain quality attributes of different samples are reported in order to evaluate their attitude to malting and brewing and also considering their enzymatic activity. Then, the different brewing processes to produce all-rice malt beers will be described and the final gluten-free rice beers is evaluated and compared to a barley malt beer. Finally, the levels of major aroma-active components of an all-rice malt beer and the results of the sensory analysis assessing the beer-like character of the rice beverage are reported. The obtained beer samples show a content of volatile compounds comparable with a barley malt beer. The sensory profile of the rice malt beer is similar to a barley malt beer in aroma, taste and mouthfeel.",book:{id:"5463",slug:"advances-in-international-rice-research",title:"Advances in International Rice Research",fullTitle:"Advances in International Rice Research"},signatures:"Ombretta Marconi, Valeria Sileoni, Dayana Ceccaroni and Giuseppe\nPerretti",authors:[{id:"189703",title:"Ph.D.",name:"Ombretta",middleName:null,surname:"Marconi",slug:"ombretta-marconi",fullName:"Ombretta Marconi"},{id:"189706",title:"Dr.",name:"Valeria",middleName:null,surname:"Sileoni",slug:"valeria-sileoni",fullName:"Valeria Sileoni"},{id:"189707",title:"Prof.",name:"Giuseppe",middleName:null,surname:"Perretti",slug:"giuseppe-perretti",fullName:"Giuseppe Perretti"},{id:"190973",title:"Dr.",name:"Dayana",middleName:null,surname:"Ceccaroni",slug:"dayana-ceccaroni",fullName:"Dayana Ceccaroni"}]},{id:"53218",title:"Evaluation of Palatability of Cooked Rice",slug:"evaluation-of-palatability-of-cooked-rice",totalDownloads:2265,totalCrossrefCites:4,totalDimensionsCites:7,abstract:"Quality evaluations of rice in Japan are performed by sensory testing and physicochemical measurements. The former is a basic method that requires large amounts of samples and several panelists. The latter is an indirect method that estimates the eating quality based on the chemical composition, cooking quality, gelatinization properties, and physical properties of cooked rice. Satake Co Ltd. developed a taste analyzer in the 1980s that is equipped with a palatability estimation formula that was based on the combination of near‐infrared spectroscopy (NIR) and physicochemical measurements related with sensory test. A novel method to evaluate the quality of the cooked rice is necessary to breed high‐quality rice cultivars and to select the suitable rice for each consumer and each purpose. We try to develop the novel method to evaluate the rice quality using various kinds of apparatus, such as Tensipresser, RVA, NIR, and spectrophotometer. Simple, rapid, and accurate method to evaluate the quality of rice grains is very valuable. We evaluated 16 Japanese and Chinese rice cultivars in terms of their physicochemical properties. Based on these quality evaluations, we concluded that Chinese rice cultivars are characterized by a high protein and that the grain texture after cooking has higher hardness and lower stickiness than Japanese ones reflecting the difference in consumers’ preference. The relationship between the palatability of rice and agronomical condition to preserve the bio‐diversity for Crested Ibis was investigated. Furthermore, the quality of rice grown in Sado Island, Japan, was assayed using rice grains grown in mountainous areas and in the field areas as samples.",book:{id:"5463",slug:"advances-in-international-rice-research",title:"Advances in International Rice Research",fullTitle:"Advances in International Rice Research"},signatures:"Ken'ichi Ohtsubo and Sumiko Nakamura",authors:[{id:"190638",title:"Prof.",name:"Ken\\'Ichi",middleName:null,surname:"Ohtsubo",slug:"ken'ichi-ohtsubo",fullName:"Ken\\'Ichi Ohtsubo"}]}],onlineFirstChaptersFilter:{topicId:"350",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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"}}}}]},series:{item:{id:"25",title:"Environmental Sciences",doi:"10.5772/intechopen.100362",issn:"2754-6713",scope:"
\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems. \r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.
",coverUrl:"https://cdn.intechopen.com/series/covers/25.jpg",latestPublicationDate:"April 13th, 2022",hasOnlineFirst:!1,numberOfPublishedBooks:1,editor:{id:"197485",title:"Dr.",name:"J. Kevin",middleName:null,surname:"Summers",slug:"j.-kevin-summers",fullName:"J. Kevin Summers",profilePictureURL:"https://mts.intechopen.com/storage/users/197485/images/system/197485.jpg",biography:"J. Kevin Summers is a Senior Research Ecologist at the Environmental Protection Agency’s (EPA) Gulf Ecosystem Measurement and Modeling Division. He is currently working with colleagues in the Sustainable and Healthy Communities Program to develop an index of community resilience to natural hazards, an index of human well-being that can be linked to changes in the ecosystem, social and economic services, and a community sustainability tool for communities with populations under 40,000. He leads research efforts for indicator and indices development. Dr. Summers is a systems ecologist and began his career at the EPA in 1989 and has worked in various programs and capacities. This includes leading the National Coastal Assessment in collaboration with the Office of Water which culminated in the award-winning National Coastal Condition Report series (four volumes between 2001 and 2012), and which integrates water quality, sediment quality, habitat, and biological data to assess the ecosystem condition of the United States estuaries. He was acting National Program Director for Ecology for the EPA between 2004 and 2006. He has authored approximately 150 peer-reviewed journal articles, book chapters, and reports and has received many awards for technical accomplishments from the EPA and from outside of the agency. Dr. Summers holds a BA in Zoology and Psychology, an MA in Ecology, and Ph.D. in Systems Ecology/Biology.",institutionString:null,institution:{name:"Environmental Protection Agency",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. Graduate in Sciences (Chemist), graduate in Geography and History (Geography), master in Water Management, Treatment, master in Fertilizers and Environment and master in Environmental Management; Ph.D. in Environmental Sciences. His research is focused on soil-water and waste-environment relations, mainly on soil-water and soil-waste interactions under different management and waste reuse. His work is reflected in more than 230 communications presented in national and international conferences and congresses, 29 invited lectures from universities, associations and government agencies. Prof. Navarro-Pedreño is also a director of the Ph.D. Program Environment and Sustainability (2012-present) and a member of several societies among which are the Spanish Society of Soil Science, International Union of Soil Sciences, European Society for Soil Conservation, DessertNet and the Spanish Royal Society of Chemistry.",institutionString:"Miguel Hernández University of Elche, Spain",institution:null},editorTwo:null,editorThree:null},{id:"40",title:"Ecosystems and Biodiversity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/40.jpg",isOpenForSubmission:!0,editor:{id:"209149",title:"Prof.",name:"Salustiano",middleName:null,surname:"Mato",slug:"salustiano-mato",fullName:"Salustiano Mato",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLREQA4/Profile_Picture_2022-03-31T10:23:50.png",biography:"Salustiano Mato de la Iglesia (Santiago de Compostela, 1960) is a doctor in biology from the University of Santiago and a Professor of zoology at the Department of Ecology and Animal Biology at the University of Vigo. He has developed his research activity in the fields of fauna and soil ecology, and in the treatment of organic waste, having been the founder and principal investigator of the Environmental Biotechnology Group of the University of Vigo.\r\nHis research activity in the field of Environmental Biotechnology has been focused on the development of novel organic waste treatment systems through composting. The result of this line of work are three invention patents and various scientific and technical publications in prestigious international journals.",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorTwo:{id:"60498",title:"Prof.",name:"Josefina",middleName:null,surname:"Garrido",slug:"josefina-garrido",fullName:"Josefina Garrido",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRj1VQAS/Profile_Picture_2022-03-31T10:06:51.jpg",biography:"Josefina Garrido González (Paradela de Abeleda, Ourense 1959), is a doctor in biology from the University of León and a Professor of Zoology at the Department of Ecology and Animal Biology at the University of Vigo. She has focused her research activity on the taxonomy, fauna and ecology of aquatic beetles, in addition to other lines of research such as the conservation of biodiversity in freshwater ecosystems; conservation of protected areas (Red Natura 2000) and assessment of the effectiveness of wetlands as priority areas for the conservation of aquatic invertebrates; studies of water quality in freshwater ecosystems through biological indicators and physicochemical parameters; surveillance and research of vector arthropods and invasive alien species.",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorThree:{id:"464288",title:"Dr.",name:"Francisco",middleName:null,surname:"Ramil",slug:"francisco-ramil",fullName:"Francisco Ramil",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003RI7lHQAT/Profile_Picture_2022-03-31T10:15:35.png",biography:"Fran Ramil Blanco (Porto de Espasante, A Coruña, 1960), is a doctor in biology from the University of Santiago de Compostela and a Professor of Zoology at the Department of Ecology and Animal Biology at the University of Vigo. His research activity is linked to the taxonomy, fauna and ecology of marine benthic invertebrates and especially the Cnidarian group. Since 2004, he has been part of the EcoAfrik project, aimed at the study, protection and conservation of biodiversity and benthic habitats in West Africa. He also participated in the study of vulnerable marine ecosystems associated with seamounts in the South Atlantic and is involved in training young African researchers in the field of marine research.",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}}},{id:"41",title:"Water Science",coverUrl:"https://cdn.intechopen.com/series_topics/covers/41.jpg",isOpenForSubmission:!0,editor:{id:"349630",title:"Dr.",name:"Yizi",middleName:null,surname:"Shang",slug:"yizi-shang",fullName:"Yizi Shang",profilePictureURL:"https://mts.intechopen.com/storage/users/349630/images/system/349630.jpg",biography:"Prof. Dr. Yizi Shang is a pioneering researcher in hydrology and water resources who has devoted his research career to promoting the conservation and protection of water resources for sustainable development. He is presently associate editor of Water International (official journal of the International Water Resources Association). He was also invited to serve as an associate editor for special issues of the Journal of the American Water Resources Association. He has served as an editorial member for international journals such as Hydrology, Journal of Ecology & Natural Resources, and Hydro Science & Marine Engineering, among others. He has chaired or acted as a technical committee member for twenty-five international forums (conferences). Dr. Shang graduated from Tsinghua University, China, in 2010 with a Ph.D. in Engineering. Prior to that, he worked as a research fellow at Harvard University from 2008 to 2009. Dr. Shang serves as a senior research engineer at the China Institute of Water Resources and Hydropower Research (IWHR) and was awarded as a distinguished researcher at National Taiwan University in 2017.",institutionString:"China Institute of Water Resources and Hydropower Research",institution:{name:"China Institute of Water Resources and Hydropower Research",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:0,paginationItems:[]},overviewPagePublishedBooks:{paginationCount:1,paginationItems:[{type:"book",id:"10843",title:"Persistent Organic Pollutants (POPs)",subtitle:"Monitoring, Impact and Treatment",coverURL:"https://cdn.intechopen.com/books/images_new/10843.jpg",slug:"persistent-organic-pollutants-pops-monitoring-impact-and-treatment",publishedDate:"April 13th 2022",editedByType:"Edited by",bookSignature:"Mohamed Nageeb Rashed",hash:"f5b1589f0a990b6114fef2dadc735dd9",volumeInSeries:1,fullTitle:"Persistent Organic Pollutants (POPs) - Monitoring, Impact and Treatment",editors:[{id:"63465",title:"Prof.",name:"Mohamed Nageeb",middleName:null,surname:"Rashed",slug:"mohamed-nageeb-rashed",fullName:"Mohamed Nageeb Rashed",profilePictureURL:"https://mts.intechopen.com/storage/users/63465/images/system/63465.gif",biography:"Prof. Mohamed Nageeb Rashed is Professor of Analytical and Environmental Chemistry and former vice-dean for environmental affairs, Faculty of Science, Aswan University, Egypt. He received his Ph.D. in Environmental Analytical Chemistry from Assiut University, Egypt, in 1989. His research interest is in analytical and environmental chemistry with special emphasis on: (1) monitoring and assessing biological trace elements and toxic metals in human blood, urine, water, crops, vegetables, and medicinal plants; (2) relationships between environmental heavy metals and human diseases; (3) uses of biological indicators for monitoring water pollution; (4) environmental chemistry of lakes, rivers, and well water; (5) water and wastewater treatment by adsorption and photocatalysis techniques; (6) soil and water pollution monitoring, control, and treatment; and (7) advanced oxidation treatment. Prof. Rashed has supervised several MSc and Ph.D. theses in the field of analytical and environmental chemistry. He served as an examiner for several Ph.D. theses in analytical chemistry in India, Kazakhstan, and Botswana. He has published about ninety scientific papers in peer-reviewed international journals and several papers in national and international conferences. He participated as an invited speaker at thirty international conferences. Prof. Rashed is the editor-in-chief and an editorial board member for several international journals in the fields of chemistry and environment. He is a member of several national and international societies. He received the Egyptian State Award for Environmental Research in 2001 and the Aswan University Merit Award for Basic Science in 2020. Prof. Rashed was recognized in Stanford University’s list of the World’s Top 2% Scientists in 2020 and 2021.",institutionString:null,institution:{name:"Aswan University",institutionURL:null,country:{name:"Egypt"}}}]}]},openForSubmissionBooks:{paginationCount:1,paginationItems:[{id:"11601",title:"Econometrics - Recent Advances and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/11601.jpg",hash:"bc8ab49e2cf436c217a49ca8c12a22eb",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 13th 2022",isOpenForSubmission:!0,editors:[{id:"452331",title:"Dr.",name:"Brian",surname:"Sloboda",slug:"brian-sloboda",fullName:"Brian Sloboda"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:19,paginationItems:[{id:"81793",title:"Canine parvovirus-2: An Emerging Threat to Young Pets",doi:"10.5772/intechopen.104846",signatures:"Mithilesh Singh, Rajendran Manikandan, Ujjwal Kumar De, Vishal Chander, Babul Rudra Paul, Saravanan Ramakrishnan and Darshini Maramreddy",slug:"canine-parvovirus-2-an-emerging-threat-to-young-pets",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"81271",title:"The Diversity of Parvovirus Telomeres",doi:"10.5772/intechopen.102684",signatures:"Marianne Laugel, Emilie Lecomte, Eduard Ayuso, Oumeya Adjali, Mathieu Mével and Magalie Penaud-Budloo",slug:"the-diversity-of-parvovirus-telomeres",totalDownloads:23,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Recent Advances in Canine Medicine",coverURL:"https://cdn.intechopen.com/books/images_new/11580.jpg",subseries:{id:"19",title:"Animal Science"}}},{id:"79909",title:"Cryopreservation Methods and Frontiers in the Art of Freezing Life in Animal Models",doi:"10.5772/intechopen.101750",signatures:"Feda S. Aljaser",slug:"cryopreservation-methods-and-frontiers-in-the-art-of-freezing-life-in-animal-models",totalDownloads:170,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Animal Reproduction",coverURL:"https://cdn.intechopen.com/books/images_new/10664.jpg",subseries:{id:"28",title:"Animal Reproductive Biology and Technology"}}},{id:"79782",title:"Avian Reproduction",doi:"10.5772/intechopen.101185",signatures:"Kingsley Omogiade Idahor",slug:"avian-reproduction",totalDownloads:151,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Kingsley O.",surname:"Idahor"}],book:{title:"Animal Reproduction",coverURL:"https://cdn.intechopen.com/books/images_new/10664.jpg",subseries:{id:"28",title:"Animal Reproductive Biology and Technology"}}},{id:"78802",title:"Intraovarian Gestation in Viviparous Teleosts: Unique Type of Gestation among Vertebrates",doi:"10.5772/intechopen.100267",signatures:"Mari-Carmen Uribe, Gabino De la Rosa-Cruz, Adriana García-Alarcón and Juan Carlos Campuzano-Caballero",slug:"intraovarian-gestation-in-viviparous-teleosts-unique-type-of-gestation-among-vertebrates",totalDownloads:184,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Animal Reproduction",coverURL:"https://cdn.intechopen.com/books/images_new/10664.jpg",subseries:{id:"28",title:"Animal Reproductive Biology and Technology"}}},{id:"79209",title:"Virtual Physiology: A Tool for the 21st Century",doi:"10.5772/intechopen.99671",signatures:"Carmen Nóbrega, Maria Aires Pereira, Catarina Coelho, Isabel Brás, Ana Cristina Mega, Carla Santos, Fernando Esteves, Rita Cruz, Ana I. 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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:null},{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:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{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:null},{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:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}}]}},subseries:{item:{id:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular economy, Contingency planning and response to disasters, Ecosystem services, Integrated urban water management, Nature-based solutions, Sustainable urban development, Urban green spaces",scope:"
\r\n\tIf we aim to prosper as a society and as a species, there is no alternative to sustainability-oriented development and growth. Sustainable development is no longer a choice but a necessity for us all. Ecosystems and preserving ecosystem services and inclusive urban development present promising solutions to environmental problems. Contextually, the emphasis on studying these fields will enable us to identify and define the critical factors for territorial success in the upcoming decades to be considered by the main-actors, decision and policy makers, technicians, and public in general.
\r\n
\r\n\tHolistic urban planning and environmental management are therefore crucial spheres that will define sustainable trajectories for our urbanizing planet. This urban and environmental planning topic aims to attract contributions that address sustainable urban development challenges and solutions, including integrated urban water management, planning for the urban circular economy, monitoring of risks, contingency planning and response to disasters, among several other challenges and solutions.
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Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:null},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null,series:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null},editorialBoard:[{id:"181486",title:"Dr.",name:"Claudia",middleName:null,surname:"Trillo",slug:"claudia-trillo",fullName:"Claudia 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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.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",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.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",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.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/436774",hash:"",query:{},params:{id:"436774"},fullPath:"/profiles/436774",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)}()