The educational system across the world has immensely been affected due to outbreak of COVID-19; it forced the shut down of educational institutions, which adversely affected student fraternity across the globe. Due to its contagious nature, COVID-19 demanded containment and enforced isolation that tremendously affected personal interaction of teachers and students. In the absence of traditional classroom teaching and one-to-one interaction, computer-based learning has emerged as closest substitute for off-line teaching. Against such a backdrop, it is pertinent to examine the students’ perception and readiness about online-learning system adopted at the university level during the ongoing COVID-19 pandemic. For the present study, the quantitative approach has been adopted and responses from 184 university students of National Capital Territory (NCT) of Delhi, India namely Delhi University, Jamia Millia Islamia (Central University) and Guru Gobind Singh Indraprastha University are collected through online questionnaire. This research study was conducted during June–August 2020. The findings of the study reveal students’ positive perception towards e-learning and thus acceptance of this new learning system. It has also empirically demonstrated the significance of e-learning in the time of COVID-19 crisis. In fact, e-learning has emerged as a new way of enhancing the learning process where social media may further improve the learning output. The findings of the study will facilitate educational institutions and policy makers to take this online-learning process to the next level in a better way.

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Supplementary data are available at Bioinformatics online.

Background: The knowledge of subcellular location of proteins is essential to the comprehension of numerous protein functions. Objective: Accurate as well as computationally efficient and reliable automated analysis of protein localization imagery greatly depend on the calculation of features from these images. Methods: In the current work, a novel method termed as MD-LBP is proposed for feature extraction from fluorescence microscopy protein images. For a given neighborhood, the value of central pixel is computed as the difference of global and local means of the input image that is further used as threshold to generate a binary pattern for that neighborhood. Results: The performance of our method is assessed for 2D HeLa dataset using 5-fold crossvalidation protocol. The performance of MD-LBP method with RBF-SVM as base classifier, is superior to that of standard LBP algorithm, Threshold Adjacency Statistics, and Haralick texture features. Conclusion: Development of specialized systems for different kinds of medical imagery will certainly pave the path for effective drug discovery in pharmaceutical industry. Furthermore, biological and bioinformatics based procedures can be simplified to facilitate pharmaceutical industry for drug designing.

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The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

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