Jessore University of Science & Technology

About: Jessore University of Science & Technology is a based out in . It is known for research contribution in the topics: Adsorption & Population. The organization has 811 authors who have published 974 publications receiving 10471 citations.

Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders.

Abstract: Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

Chrysanthemum Production in Bangladesh: Significance the Insect Pests and Diseases Management: a Review

Abstract: Chrysanthemum is a floricultural, ornamental and medicinal plant with tremendous income cash crop which is cultivated chiefly in winter. The crop charity as nerve sedative, anti-oxidant, anti-inflammatory, anti-mutagenic, anti-microbial, anti-fungal, anti-angiogenic, anti-atherosclerosis and nematocidal goods. Chrysanthemum has been used for hundreds of years in manufacturing medicine. It is used to treat respiratory complications, high blood pressure, and hyperthyroidism and reduce inflammation. Leaves remedies for colds, headaches, bronchitis, rheumatism, and swellings. The plant has aesthetic significance as well. Nevertheless, outbreaks of pests and diseases have hold back Chrysanthemum cultivation in Bangladesh. Insect pest damages the plant directly by sucking cell sap and indirectly spread out of virus diseases as act of vector. Aphid resistance variety might be the effective tool for successful production of chrysanthemum plant. Sustainable crop disease management for Chrysanthemum needs a multitude of consideration of the impacts of management methods on economics, sociology and ecology by wholly understanding the mechanisms of crop disease epidemics and the functioning of normal agroecosystems. The findings of this study present clear insights into efforts that will encourage farmers to adopt effective and ecological methods of pest and disease management for successful chrysanthemum production in Bangladesh.

An Automated Dermatological Images Segmentation Based on a New Hybrid Intelligent ACO-GA Algorithm and Diseases Identification Using TSVM Classifier

Abstract: Dermatological diseases are the common and most extensive diseases worldwide. Diagnosis of dermatological diseases as well as skin cancer diseases is extremely difficult during its early to mid-stages by visual examination. Current researches propose efficient approach which can identify maximum 3- 9 types of skin diseases. It is necessary to develop an automated system which can identify more than 9 types of skin diseases. In this paper, we propose a new automated system for various kinds of dermatological diseases detection. In this automated system, a novel hybrid intelligent ACO-GA algorithm including ACO Algorithm, GA and tabu list is used for different kinds of skin lesion segmentation of dermatological images and Transductive Support Vector Machine (TSVM) is used for dermatological diseases identification. Ant Colony Optimization Algorithm and Genetic Algorithm work simultaneously to inquire most optimal cluster centers from the problem set. After image segmentation, pixels are divided into different classes. In testing phase, the testing samples are compared with training samples using TSVM classifier and features extraction which can give the accurate disease detection result. The result of the proposed system successfully finds out 24 different patterns of dermatological diseases with an accuracy rate of 95%.