Purpose: A novel coronavirus, SARS-CoV-2 was identified as the cause of COVID-19 eventually led to the declaration of Public health emergency of international concern and a pandemic by WHO due to its exponential global spread. Present study was conducted to investigate the impact of second wave of pandemic on mental wellbeing and social behaviors among university students of Pakistan during this crucial period of COVID-19 infection. Methods: A cross-sectional analytical study was designed to evaluate the psychosocial impact during the current COVID-19 outbreak among the students of The Islamia University of Bahawalpur, Pakistan. Snowball sampling or chain referral sampling procedure was adopted to recruit the participants in the study. Verbal informed consent was taken from all participants before recruitment in the study irrespective of their gender, age and socioeconomic status. Results: Mental health of university students during COVID-19 epidemic was affected to a varying degree revealing that 26.66% were recorded to have mild, 27.15% moderate and 17.04% suffering from severe anxiety out of total 1029 students. Students who were residing in urban areas with parents and having a steady family income were negatively associated and found protective factors against anxiety. However, having a relative or an acquaintance infected with COVID-19 was an independent risk factor for experienced anxiety. Positively associated factors with the level of anxiety symptoms included economic stressors, effects on daily-life, and academic delays whereas social support was negatively correlated with anxiety in COVID-19 related stressors. Conclusion: Public health emergencies and such pandemic are exerting serious psychological impacts on university students. It is recommended that the higher authorities should plan better policies to reduce this impact for the provision of high quality and timely crisis-oriented psychological services to university students.

Bread wheat (Triticum aestivum L.) is staple of Pakistani people. However, its yield at farmer field is low as compared with its genetic potential. Integration of various crop and soil management strategies might be an option to enhance wheat productivity at farmer field. This 2-year experiment was conducted to check the influence of combine application of natural plant water extracts and biochar on the productvity of wheat during the winter season of 2015-16. The experiment consisted of seven treatment viz. (1) control (2) application of biochar (0.18 kg pot-1) alone, (3) application of sorghum water extract (SWE) alone, (4) application of moringa water extract (MWE) alone, (5) application of biochar + SWE, (6) application of biochar + MWE, (7) application of biochar + SWE+MWE. The results revealed that application of both crop water extracts in combination with biochar improved the growth and grain yield of wheat. Use of MWE in combination with biochar enhanced the grain weight, grain number and grain yield of wheat by 44, 14, and 24%, respectively than the control treatment. In crux, use of MWE in combination with biochar might be a viable option to improve the productivity of bread wheat.

Amyotrophic Lateral Sclerosis (ALS) is a prototypical neurodegenerative disease characterized by progressive degeneration of motor neurons to severely effect the functionality to control voluntary muscle movement. Most of the non additive genetic aberrations responsible for ALS make its molecular classification very challenging along with limited sample size, curse of dimensionality, class imbalance and noise in the data. Deep learning methods have been successful in many other related areas but have low minority class accuracy and suffer from the lack of explainailbilty when used directly with RNA expression features for ALS molecular classification. In this paper we propose a deep learning based molecular ALS classification and interpretation framework. Our framework is based on training a convolution neural network (CNN) on images obtained from converting RNA expression values into pixels based on DeepInsight similarity technique. Then we employed Shapley Additive Explanations (SHAP) to extract pixels with higher relevance to ALS classifications. These pixels were mapped back to the genes which made them up. This enabled us to classify ALS samples with high accuracy for a minority class along with identifying genes that might be playing an important role in ALS molecular classifications. Taken together with RNA expression images classified with CNN, our preliminary analysis of the genes identified by SHAP interpretation demonstrate the value of utilising Machine Learning to perform molecular classification of ALS and uncover disease-associated genes.

Coronavirus disease (COVID-19) is a new discovered strain where WHO officially declares the disease as COVID-19 while the virus responsible for it called Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2. The incubation period of this disease is between 14 days. Ordinary clinical symptoms that reported around the world include fever, cough, fatigue, diarrhoea and vomiting as well as asymptomatic for certain people. Infection is spread mainly through broad droplets. In early March 2020, WHO again has announced that COVID-19 is a pandemic with currently no specific treatment. The potential use of SARS-COV-2 proteome as a vaccine candidate by analysing through B-cell and T-cell antigenicity by using a immunoinformatics approach as a vaccine development early stage. In this study, we used consensus sequence for SARS-COV-2 proteome that was retrieved from NCBI database. VaxiJen 2.0 was mainly used to identify the antigenic property of SARS-COV-2 proteins. IEDB then used to analyse the B-cell epitope, the presence of T cell immunogenic epitope in SARS-COV-2 proteins was obtained by using compromise method of MHC class I and II tools that accessible respectively using ProPred-1 server and MHC II Binding Prediction in IEDB database. The best epitopes of B and T-cell epitopes were predicted with high antigencity and the information is disseminated through web-based database resource (https://covid-19.omicstutorials.com/epitopes/). This study will be useful to find a new epitope-based candidate for SARS-COV-2. However, further study needs to be done for the next stages of vaccine development.