A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease.

Huntington disease (HD) is a degenerative brain disease caused by the expansion of CAG (cytosine-adenine-guanine) repeats, which is inherited as a dominant trait and progressively worsens over time possessing threat. Although HD is monogenetic, the specific pathophysiology and biomarkers are yet unknown specifically, also, complex to diagnose at an early stage, and identification is restricted in accuracy and precision. This study combined bioinformatics analysis and network-based system biology approaches to discover the biomarker, pathways, and drug targets related to molecular mechanism of HD etiology. The gene expression profile data sets GSE64810 and GSE95343 were analyzed to predict the molecular markers in HD where 162 mutual differentially expressed genes (DEGs) were detected. Ten hub genes among them (DUSP1, NKX2-5, GLI1, KLF4, SCNN1B, NPHS1, SGK2, PITX2, S100A4, and MSX1) were identified from protein-protein interaction (PPI) network which were mostly expressed as down-regulated. Following that, transcription factors (TFs)-DEGs interactions (FOXC1, GATA2, etc), TF-microRNA (miRNA) interactions (hsa-miR-340, hsa-miR-34a, etc), protein-drug interactions, and disorders associated with DEGs were predicted. Furthermore, we used gene set enrichment analysis (GSEA) to emphasize relevant gene ontology terms (eg, TF activity, sequence-specific DNA binding) linked to DEGs in HD. Disease interactions revealed the diseases that are linked to HD, and the prospective small drug molecules like cytarabine and arsenite was predicted against HD. This study reveals molecular biomarkers at the RNA and protein levels that may be beneficial to improve the understanding of molecular mechanisms, early diagnosis, as well as prospective pharmacologic targets for designing beneficial HD treatment.

Biometrical and histomorphometrical changes of testis in the dynamics of postnatal ontogenesis from birth to puberty of Black Bengal goat.

Objectives: The study aimed to account for baseline biometrical and histomorphometric testicular changes in Black Bengal goats during postnatal development. Materials and Methods: Black Bengal goats, divided into group I of VII; day 0; 1, 2 weeks; 1, 2, 4, and 6 months of age, respectively, were used in this study. Results: The biometrical and histomorphometric values of the testis varied significantly (p < 0.05) from postnatal 1-2 months. From day 0 to 2 months, seminiferous tubules, called sex cords, contained simply peripherally placed Sertoli cells and centrally placed gonocytes. Gonocytes, positioned in the center, moved centrifugally in the direction of the basement membrane of sex cords with the advancement of age, transformed into prespermatogonia, and were distributed among the Sertoli cells at the edge of sex cords that make up the basal cell layer in nearly all of the seminiferous tubules by 2 months after birth. Initiation of spermatogenesis, i.e., stratification and lumination of seminiferous epithelium, took place in the 4th months. At 6 months, all types of spermatogenic cells had been identified. The onset of puberty, i.e., the establishment of spermatogenesis, was noticed to have been established at 6 months of postnatal age in Black Bengal goats, as shown by the spermatozoa that were adhered to the ad luminal border of the Sertoli cells and also in the tubular lumen. Conclusion: This research is the first to document the varying biometrical and histomorphometric measurements of the testis in Black Bengal goats from birth to puberty.

Prevalence and factors associated with chronic school absenteeism among 207,107 in-school adolescents: Findings from cross-sectional studies in 71 low-middle and high-income countries.

BACKGROUND: Despite the negative impact of chronic school absenteeism on the psychological and physical health of adolescents, data on the burden of adolescent chronic school absenteeism (ACSA) and interventions and programs to address it are lacking. We estimated the global, regional and national level prevalence of ACSA and its correlation with violence and unintentional injury, psychosocial, protective, lifestyle, and food security-related factors among in-school adolescents across low and middle-income, and high-income countries (LMICs-HICs). OBJECTIVES: This study aimed to estimate the prevalence of chronic school absenteeism (CSA) as well as to determine its associated factors among in-school adolescents across 71 low-middle and high-income countries. METHODS: We used data from the most recent Global School-based Student Health Survey of 207,107 in-school adolescents aged 11-17 years in 71 LMICs-HICs countries across six WHO regions. We estimated the weighted prevalence of ACSA from national, regional and global perspectives. Multiple binary logistic regression analyses were used to estimate the adjusted effect of independent factors on ACSA. RESULTS: The overall population-weighted prevalence of CSA was 11·43% (95% confidence interval, CI: 11·29-11·57). Higher likelihood of CSA was associated with severe food insecurity, peer victimisation, loneliness, high level of anxiety, physically attack, physical fighting, serious injury, poor peer support, not having close friends, lack of parental support, being obese, and high levels of sedentary behaviours. Lower likelihood of CSA was associated with being female (odds ratio, OR = 0·76, 95% CI: 0·74-0·78). CONCLUSION: Our findings indicate that a combination of different socio-economic factors, peer conflict and injury factors, factors exacerbate CSA among adolescents. Interventions should be designed to focus on these risk factors and should consider the diverse cultural and socioeconomic contexts.

Bioinformatics and System Biology Techniques to Determine Biomolecular Signatures and Pathways of Prion Disorder.

Prion disorder (PD) is caused by misfolding and the formation of clumps of proteins in the brain, notably Prion proteins resulting in a steady decrease in brain function. Early detection of PD is difficult due to its unpredictable nature, and diagnosis is limited regarding specificity and sensitivity. Considering the uncertainties, the current study used network-based integrative system biology approaches to reveal promising molecular biomarkers and therapeutic targets for PD. In this study, brain transcriptomics gene expression microarray datasets (GSE160208 and GSE124571) of human PD were evaluated and 35 differentially expressed genes (DEGs) were identified. By employing network-based protein-protein interaction (PPI) analysis on these DEGs, 10 central hub proteins, including SPP1, FKBP5, HPRT1, CDKN1A, BAG3, HSPB1, SYK, TNFRSF1A, PTPN6, and CD44, were identified. Employing bioinformatics approaches, a variety of transcription factors (EGR1, SSRP1, POLR2A, TARDP, and NR2F1) and miRNAs (hsa-mir-8485, hsa-mir-148b-3p, hsa-mir-4295, hsa-mir-26b-5p, and hsa-mir-16-5p) were predicted. EGR1 was found as the most imperative transcription factor (TF), and hsa-mir-16-5p and hsa-mir-148b-3p were found as the most crucial miRNAs targeted in PD. Finally, resveratrol and hypochlorous acid were predicted as possible therapeutic drugs for PD. This study could be helpful in better understanding of molecular systems and prospective pharmacological targets for developing effective PD treatments.

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.

Structural-Guided Identification of Small Molecule Inhibitor of UHRF1 Methyltransferase Activity.

Ubiquitin-like containing plant homeodomain Ring Finger 1 (UHRF1) protein is recognized as a cell-cycle-regulated multidomain protein. UHRF1 importantly manifests the maintenance of DNA methylation mediated by the interaction between its SRA (SET and RING associated) domain and DNA methyltransferase-1 (DNMT1)-like epigenetic modulators. However, overexpression of UHRF1 epigenetically responds to the aberrant global methylation and promotes tumorigenesis. To date, no potential molecular inhibitor has been studied against the SRA domain. Therefore, this study focused on identifying the active natural drug-like candidates against the SRA domain. A comprehensive set of in silico approaches including molecular docking, molecular dynamics (MD) simulation, and toxicity analysis was performed to identify potential candidates. A dataset of 709 natural compounds was screened through molecular docking where chicoric acid and nystose have been found showing higher binding affinities to the SRA domain. The MD simulations also showed the protein ligand interaction stability of and in silico toxicity analysis has also showed chicoric acid as a safe and nontoxic drug. In addition, chicoric acid possessed a longer interaction time and higher LD50 of 5000 mg/kg. Moreover, the global methylation level (%5 mC) has been assessed after chicoric acid treatment was in the colorectal cancer cell line (HCT116) at different doses. The result showed that 7.5 µM chicoric acid treatment reduced methylation levels significantly. Thus, the study found chicoric acid can become a possible epidrug-like inhibitor against the SRA domain of UHRF1 protein.

Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage.

COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has 2.82% positive and 97.18% negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved 97.91%, 97.81%, and 73.37%, respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users' questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository.

HGSORF: Henry Gas Solubility Optimization-based Random Forest for C-Section prediction and XAI-based cause analysis.

A stable predictive model is essential for forecasting the chances of cesarean or C-section (CS) delivery, as unnecessary CS delivery can adversely affect neonatal, maternal, and pediatric morbidity and mortality, and can incur significant financial burdens. Limited state-of-the-art machine learning models have been applied in this area in recent years, and the current models are insufficient to correctly predict the probability of CS delivery. To alleviate this drawback, we have proposed a Henry gas solubility optimization (HGSO)-based random forest (RF), with an improved objective function, called HGSORF, for the classification of CS and non-CS classes. Real-world CS datasets can be noisy, such as the Pakistan Demographic and Health Survey (PDHS) dataset used in this study. The HGSO can provide fine-tuned hyperparameters of RF by avoiding local minima points. To compare performance, Gaussian Naive Bayes (GNB), linear discriminant analysis (LDA), K-nearest neighbors (KNN), gradient boosting classifier (GBC), and logistic regression (LR) have been considered in this research. The ADAptive SYNthetic (ADASYN) algorithm has been used to balance the model, and the proposed HGSORF has been compared with other classifiers as well as with other studies. The superior performance was achieved by HGSORF with an accuracy of 98.33% for the PDHS dataset. The hyperparameters of RF have also been optimized by using commonly used hyperparameter-optimization algorithms, and the proposed HGSORF provided comparatively better performance. Additionally, to analyze the causes of CS and their significance, the HGSORF is explained locally and globally using eXplainable artificial intelligence (XAI)-based tools such as SHapely Additive exPlanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). A decision support system has been developed as a potential application to support clinical staffs. All pre-trained models and relevant codes are available on: https://github.com/MIrazul29/HGSORF_CSection.

An Improved Machine-Learning Approach for COVID-19 Prediction Using Harris Hawks Optimization and Feature Analysis Using SHAP.

A healthcare monitoring system needs the support of recent technologies such as artificial intelligence (AI), machine learning (ML), and big data, especially during the COVID-19 pandemic. This global pandemic has already taken millions of lives. Both infected and uninfected people have generated big data where AI and ML can use to combat and detect COVID-19 at an early stage. Motivated by this, an improved ML framework for the early detection of this disease is proposed in this paper. The state-of-the-art Harris hawks optimization (HHO) algorithm with an improved objective function is proposed and applied to optimize the hyperparameters of the ML algorithms, namely HHO-based eXtreme gradient boosting (HHOXGB), light gradient boosting (HHOLGB), categorical boosting (HHOCAT), random forest (HHORF) and support vector classifier (HHOSVC). An ensemble technique was applied to these optimized ML models to improve the prediction performance. Our proposed method was applied to publicly available big COVID-19 data and yielded a prediction accuracy of 92.38% using the ensemble model. In contrast, HHOXGB provided the highest accuracy of 92.23% as a single optimized model. The performance of the proposed method was compared with the traditional algorithms and other ML-based methods. In both cases, our proposed method performed better. Furthermore, not only the classification improvement, but also the features are analyzed in terms of feature importance calculated by SHapely adaptive exPlanations (SHAP) values. A graphical user interface is also discussed as a potential tool for nonspecialist users such as clinical staff and nurses. The processed data, trained model, and codes related to this study are available at GitHub.

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries.

Background: Hypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances. Methods: We conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors. Results: Of the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values <6%. Conclusion: ML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.

Computational Identification of Druggable Bioactive Compounds from Catharanthus roseus and Avicennia marina against Colorectal Cancer by Targeting Thymidylate Synthase.

Colorectal cancer (CRC) is the second most common cause of death worldwide, affecting approximately 1.9 million individuals in 2020. Therapeutics of the disease are not yet available and discovering a novel anticancer drug candidate against the disease is an urgent need. Thymidylate synthase (TS) is an important enzyme and prime precursor for DNA biosynthesis that catalyzes the methylation of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP) that has emerged as a novel drug target against the disease. Elevated expression of TS in proliferating cells promotes oncogenesis as well as CRC. Therefore, this study aimed to identify potential natural anticancer agents that can inhibit the activity of the TS protein, subsequently blocking the progression of colorectal cancer. Initially, molecular docking was implied on 63 natural compounds identified from Catharanthus roseus and Avicennia marina to evaluate their binding affinity to the desired protein. Subsequently, molecular dynamics (MD) simulation, ADME (Absorption, Distribution, Metabolism, and Excretion), toxicity, and quantum chemical-based DFT (density-functional theory) approaches were applied to evaluate the efficacy of the selected compounds. Molecular docking analysis initially identified four compounds (PubChem CID: 5281349, CID: 102004710, CID: 11969465, CID: 198912) that have better binding affinity to the target protein. The ADME and toxicity properties indicated good pharmacokinetics (PK) and toxicity ability of the selected compounds. Additionally, the quantum chemical calculation of the selected molecules found low chemical reactivity indicating the bioactivity of the drug candidate. The global descriptor and HOMO-LUMO energy gap values indicated a satisfactory and remarkable profile of the selected molecules. Furthermore, MD simulations of the compounds identified better binding stability of the compounds to the desired protein. To sum up, the phytoconstituents from two plants showed better anticancer activity against TS protein that can be further developed as an anti-CRC drug.

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes.

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00168-2.

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data.

The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.

Potency of spirulina (Spirulina platensis) on arsenic-induced lipid peroxidation in rat.

OBJECTIVE: Natural substances found in dietary sources and medicinal plants have attracted considerable attention in recent years as chemopreventive agents. Spirulina is a blue-green alga that possesses chemopreventive properties. The purpose of this study was to determine the effect of spirulina on rats with inorganic arsenic (As) [sodium arsenite (NaAsO2)]-induced lipid peroxidation. MATERIALS AND METHODS: 120 rats were randomly assigned to 10 groups and designated T0, T1, T2, T3, T4, T5, T6, T7, T8, and T9. One group was kept as a control (T0) that received no treatment. The seven groups received 3.0 mg of NaAsO2/kg body weight in drinking water and were given spirulina ad libitum. T1 was treated with NaAsO2 but not with spirulina. Two groups of rats (T2 and T3), on the other hand, were treated with spirulina without receiving any As (NaAsO2). T2 received agro-based spirulina (Ab-Sp; grown in 1.5% soybean meal media and harvested on day 12 of seed inoculation) at 2.0 gm/kg feed, whereas T3 received commercially available spirulina (Com-Sp) at 2.0 gm/kg feed. T4, T5, and T6 were concurrently treated with Ab-Sp at 1.0, 1.5, and 2.0 gm/kg of feed. On the other hand, T7, T8, and T9 induced by NaAsO2 were concurrently treated with Com-Sp at 1.0, 1.5, and 2.0 gm/kg feed. All groups received treatment for 90 days. RESULTS: The efficacy of both spirulina in preventing lipid peroxidation caused by As was determined quantitatively by measuring the rats' serum malondialdehyde (MDA). The results indicated that As supplementation increased serum MDA levels, whereas both types of spirulina significantly decreased them. The highest dose of Ab-Sp (2.0 gm/kg feed) was found to be the most effective in preventing lipid peroxidation in rats treated with inorganic As. CONCLUSION: Ab-Sp could be a natural, cost-effective, and safe measure to mitigate As toxicity.

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias.

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

A Novel Ultra-Low Loss Rectangle-Based Porous-Core PCF for Efficient THz Waveguidance: Design and Numerical Analysis.

A novel, rectangle-based, porous-core photonic crystal fiber (PCF) has been modeled for the efficient propagation of a THz wave. The performance of the anticipated model has been assessed using the finite element method (FEM) in the range of 0.5-1.5 THz. Both the fiber core and cladding are modeled with rectangular air holes. Numerical analysis for this model reveals that the model has a lower amount of dispersion of about 0.3251 ps/THz/cm at 1.3 THz. Compared to the other THz waveguides, the model offers an ultra-lower effective material loss of 0.0039 cm-1 at the same frequency. The confinement loss is also lower for this model. Moreover, this model has a high-power fraction of about 64.90% at the core in the x-polarization mode. However, the effective area, birefringence, and numerical aperture have also been evaluated for this model. Maintenance of standard values for all the optical parameters suggests that the proposed PCF can efficiently be applied in multichannel communication and several domains of the THz technology.

EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review.

OBJECTIVES: Hypoxic ischaemic encephalopathy is a significant cause of mortality and morbidity in the term infant. Electroencephalography (EEG) is a useful tool in the assessment of newborns with HIE. This systematic review of published literature identifies those background features of EEG in term neonates with HIE that best predict neurodevelopmental outcome. METHODS: A literature search was conducted using the PubMed, EMBASE and CINAHL databases from January 1960 to April 2014. Studies included in the review described recorded EEG background features, neurodevelopmental outcomes at a minimum age of 12 months and were published in English. Pooled sensitivities and specificities of EEG background features were calculated and meta-analyses were performed for each background feature. RESULTS: Of the 860 articles generated by the initial search strategy, 52 studies were identified as potentially relevant. Twenty-one studies were excluded as they did not distinguish between different abnormal background features, leaving 31 studies from which data were extracted for the meta-analysis. The most promising neonatal EEG features are: burst suppression (sensitivity 0.87 [95% CI (0.78-0.92)]; specificity 0.82 [95% CI (0.72-0.88)]), low voltage (sensitivity 0.92 [95% CI (0.72-0.97)]; specificity 0.99 [95% CI (0.88-1.0)]), and flat trace (sensitivity 0.78 [95% CI (0.58-0.91)]; specificity 0.99 [95% CI (0.88-1.0)]). CONCLUSION: Burst suppression, low voltage and flat trace in the EEG of term neonates with HIE most accurately predict long term neurodevelopmental outcome. SIGNIFICANCE: This structured review and meta-analysis provides quality evidence of the background EEG features that best predict neurodevelopmental outcome.

Arsenic exposure to dairy cows in Bangladesh.

Food-chain contamination by arsenic (As) is a newly uncovered disaster. Effects of As-contaminated drinking water and paddy straw on the excretion of As through milk, urine, and dung of dairy cows (n = 240) were studied in As-prone areas of Bangladesh. Mean (±SEM) total As (inorganic plus organic) concentration in drinking water, paddy straw [dry weight dw)], cow's urine (specific gravity adjusted to 1.035), dung (dw), and milk (wet weight) were 89.6 ± 6.5 µg/l, 1,114.4 ± 57.3 µg/kg, 123.6 ± 7.6 µg/l, 1,693.0 ± 65.1 µg/kg, and 26.2 ± 2.8 µg/l, respectively. Significantly (p < 0.01) greater As was in Boro straw (1,386.9 ± 71.8 µg/kg) than Aus (702.4 ± 67.1 µg/kg) and Aman (431.7 ± 28.8 µg/kg) straw and in straw irrigated with shallow (1,697.3 ± 81.9 µg/kg) than deep well water (583.6 ± 62.7 µg/kg) and surface water (511.8 ± 30.0 µg/kg). Significant (p < 0.01) positive correlations were found between As contents of cow's urine and drinking water (r = 0.92) as well as cow dung and straw (r = 0.82). Concentrations of As in cow urine, dung, and milk were increased with the relative increment of As in drinking water and/or straw. These results provide evidence that dairy cows excrete ingested As mainly through urine and dung; thus, As biotransformation through milk remains low. This low concentration of As in milk may be of concern when humans are exposed to multiple sources of As simultaneously. Moreover, As in cow dung could be an environmental issue in Bangladesh.