Ethylene synthesis is accelerated in response to various environmental stresses like salinity. Ten rhizobacterial strains isolated from wheat rhizosphere taken from different salt affected areas were screened for growth promotion of wheat under axenic conditions at 1, 5, 10 and 15 dS m(-1). Three strains, i.e., Pseudomonas putida (N21), Pseudomonas aeruginosa (N39) and Serratia proteamaculans (M35) showing promising performance under axenic conditions were selected for a pot trial at 1.63 (original), 5, 10 and 15 dS m(-1). Results showed that inoculation was effective even in the presence of higher salinity levels. P. putida was the most efficient strain compared to the other strains and significantly increased the plant height, root length, grain yield, 100-grain weight and straw yield up to 52, 60, 76, 19 and 67%, respectively, over uninoculated control at 15 dS m(-1). Similarly, chlorophyll content and K(+)/Na(+) of leaves also increased by P. putida over control. It is highly likely that under salinity stress, 1-aminocyclopropane-1-carboxylic acid-deaminase activity of these microbial strains might have caused reduction in the synthesis of stress (salt)-induced inhibitory levels of ethylene. The results suggested that these strains could be employed for salinity tolerance in wheat; however, P. putida may have better prospects in stress alleviation/reduction.

Covid-19 proved and pandemic that has affected the whole world on a large scale. Every walk of life got disturbed by this pandemic. Educational institutions not only in Pakistan but all over the globe remain close, which causes a loss of study for the students of all Grades, notably Higher education (Postgraduate Level), which directly affected education, learners,  and teachers in terms of learning, time, and economically. Virtual Teaching (VT) is proving an emerging method of teaching in the field of education all over the world. Developed countries have opted for this method of teaching much before. In Pakistan, universities under the directions of HEC started Virtual Teaching VT (Online Teaching) for the students, which was an attempt to cover the loss on an experimental basis. This study is conducted to know the impact of VT on ESL students' behavior. For this purpose among 100 students of KFUEIT, RYK University distributed a questionnaire to measure their behavior level. Students' participation was inspiriting, and their response found positive in this new field of Teaching.

It has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were

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Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.