The current study aimed to devise eco-friendly, safe, and cost-effective strategies for enhanced degradation of low- and high-density polyethylene (LDPE and HDPE) using newly formulated thermophilic microbial consortia from cow dung and to assess the biodegradation end products. The plastic-degrading bacteria from cow dung samples gathered from highly plastic-acclimated environments were enriched by standard protocols. The degradation ability was comprehended by zone of clearance method, and the percentage of degradation was monitored by weight reduction process. The best isolates were characterized by standard microbiological and molecular biology protocols. The best isolates were employed to form several combinations of microbial consortia, and the degradation end products were analyzed. The stability of 16S ribosomal DNA (rDNA) was predicted by bioinformatics approach. This study identified 75 ± 2, 55 ± 2, 60 ± 3, and 43 ± 3% degradation for LDPE strips, pellets, HDPE strips, and pellets, respectively, for a period of 120 days (p < 0.05) at 55 °C by the formulated consortia of IS1-IS4, and the degradation efficiency was found to be better in comparison with other formulations. The end product analysis by Fourier transform infrared, scanning electron microscopy, energy-dispersive spectroscopy, and nuclear magnetic resonance showed major structural changes and formation of bacterial biofilm on plastic surfaces. These novel isolates were designated as Bacillus vallismortis bt-dsce01, Psuedomonas protegens bt-dsce02, Stenotrophomonas sp. bt-dsce03, and Paenibacillus sp.bt-dsce04 by 16S rDNA sequencing and suggested good gene stability with minimum Gibb's free energy. Therefore, this study imparts substantial information regarding the utilization of these thermophilic microbial consortia from cow dung for rapid polyethylene removal.

Ebola is a deadly virus that has recently emerged as an enormous public health concern which causes dangerous illness with high fatality rates of 90 %. The virus is not receptive to known antivirals, and hence, there is a promising need to identify novel inhibitors to combat the disease. The present study deals with identification of potential herbal leads that probably subdue the activity of four major drug targets of Ebola virus such as VP24, VP30, VP35 and VP40 by computer-aided virtual screening. The selection of receptors was performed based on their functional roles in the disease. The drug likeliness and ADMET parameters of 150 herbal ligands were computationally predicted. Those molecules that qualified these parameters were preferred for docking studies with the protein targets. An existing chemical antiviral drug, BCX4430 was also docked and its theoretical binding energy was scrutinized. The docking studies suggested that herbal ligand Limonin demonstrated high binding properties with VP24 and VP35 (binding energy -9.7 kcal/mol). Similarly, curcumin exhibited good binding with VP30 (binding energy -9.6 kcal/mol). Further, Mahanine displayed superior interaction with VP40 (binding energy -7.7 kcal/mol). These herbal leads demonstrated better binding potential than the known chemical analogue in the computational studies. This study serves to bestow paramount information for further experimental studies concerning the utility of herbal ligands as probable lead molecules against Ebola viral targets.

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Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.