aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli.

BACKGROUND: There are several antibiotic resistance genes (ARG) for the Escherichia coli (E. coli) bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in E. coli in gene expression data. METHODOLOGY: The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models. RESULTS: The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 (p < 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR. CONCLUSIONS: aiGeneR successfully detected the E. coli genes validating our four hypotheses.

Assessing the status of changing regimes of water bodies in Gorakhpur District, Uttar Pradesh, India.

Water bodies play a very important role in maintaining and restoring the ecological balance, but they are one of the most threatened habitats in the world. Anthropogenic intervention is changing the regimes of wetlands almost everywhere particularly in the developing countries. Gorakhpur District is dotted with many big and small flood plain-related water bodies like rivers, streams, tanks, dead arms, oxbow lakes, etc. Some of these water bodies are worst affected and are degraded by encroachment for agriculture and other economic and developmental activities. Channel migration, aggravated by human intervention, on alluvial plain is also very frequent, which has direct impact on the nature of water bodies and land use transformations of the region. In this paper, the authors have made an attempt to (a) bring current geographical and historical background of water bodies/wetlands for the district. It aims to assess long-term (1917-2018) and short-term (pre- and post-monsoon) changes in the water bodies of Gorakhpur District; (b) provide changes in the regime of water bodies/wetlands and their conversion to different types of land use/land cover classes due to human intervention and due to annual rainy season, which inundates a large extent of the area every year; (C) assess the channel characteristics and morphometric analysis of main rivers of the region during the last hundred years. Remote sensing and Geographical Information System (GIS) have been used to prepare the inventory and to perform change detection, using land use/land cover maps. The floodplain areas of water bodies have almost changed their morphological characters due to encroachment by the nearby areas. Canals, drainage channels, and lakes are the most affected water bodies in the region, which have recorded - 65.38% and 43.37% loss in their area. Even permanent rivers have recorded a decrease of - 16.96% in the area. As per the seasonal change, agriculture land suffered the greatest conversion (18.33%) due to floodwater inundation. The study provides a platform to planners to chalk out their policies and also for monitoring the water bodies. Furthermore, analysis on channel migration will help predict the future course of the main rivers.

Novel Compound from Flowers of Moringa oleifera Active Against Multi- Drug Resistant Gram-negative Bacilli.

BACKGROUND: The need for suitable antibacterial agents effective against Multi-drug resistant Gram-negative bacteria is acknowledged globally. The present study was designed to evaluate the possible antibacterial potential of an extracted compound from edible flowers of Moringa oleifera. METHODS: Five different solvents were used for preparing dried flower extracts. The most effective extract was subjected to fractionation and further isolation of the active compound with the highest antibacterial effect was obtained using TLC, Column Chromatography and reverse phase- HPLC. Approaches were made for characterization of the isolated compound using FTIR, NMR and Mass spectrometry. Antibacterial activity was evaluated according to the CLSI guidelines. RESULTS: One fraction of aqueous acetic acid extract of M. oleifera flower was found highly effective and more potent than conventional antibiotics of different classes against Multi-drug resistant Gram-negative bacilli (MDR-GNB) when compared. The phytochemical analysis of the isolated compound revealed the presence of hydrogen-bonded amine and hydroxyl groups attributable to unsaturated amides. CONCLUSION: The present study provided data indicating a potential for use of the flowers extract of M. oleifera in the fight against infections caused by lethal MDR-GNB. RECOMMENDATIONS: Aqueous acetic acid flower extract of M. oleifera is effective, in-vitro, against Gram-negative bacilli. This finding may open a scope in pharmaceutics for the development of new classes of antibiotics.

ECG beat classification using empirical mode decomposition and mixture of features.

Computer-aided analysis is useful in predicting arrhythmia conditions of the heart by analysing the recorded ECG signals. In this work, we proposed a method to detect, extract informative features to classify six types of heartbeat of ECG signals obtained from the MIT-BIH Arrhythmia database. The powerful discrete wavelet transform (DWT) is used to eliminate different sources of noises. Empirical mode decomposition (EMD) with adaptive thresholding has been used to detect precise R-peaks and QRS complex. The significant features consists of temporal, morphological and statistical were extracted from the processed ECG signals and combined to form a set of features. This feature set is classified with probabilistic neural network (PNN) and radial basis function neural network (RBF-NN) to recognise the arrhythmia beats. The process achieved better result with sensitivity of 99.96%, and positive predictivity of 99.81 with error rate of 0.23% in detecting the QRS complex. In class-oriented scheme, the arrhythmia conditions are classified with accuracy of 99.54%, 99.89% using PNN and RBF-NN classifier respectively. The obtained result confirms the superiority of the proposed scheme compared to other published results cited in literature.