Government College

About: Government College is a based out in . It is known for research contribution in the topics: Population & Ring (chemistry). The organization has 4481 authors who have published 5986 publications receiving 57398 citations.

Association between climatic variables and COVID-19 pandemic in National Capital Territory of Delhi, India.

Abstract: Globally, since the end of December 2019, coronavirus disease (COVID-19) has been recognized as a severe infectious disease. Therefore, this study has been attempted to examine the linkage between climatic variables and COVID-19 particularly in National Capital Territory of Delhi (NCT of Delhi), India. For this, daily data of COVID-19 has been used for the period March 14 to June 11, 2020, (90 days). Eight climatic variables such as maximum, minimum and mean temperature (°C), relative humidity (%), bright sunshine hours, wind speed (km/h), evaporation (mm), and rainfall (mm) have been analyzed in relation to COVID-19. To study the relationship among different climatic variables and COVID-19 spread, Karl Pearson's correlation analysis has been performed. The Mann-Kendall method and Sen's slope estimator have been used to detect the direction and magnitude of COVID-19 trends, respectively. The results have shown that out of eight selected climatic variables, six variables, viz. maximum temperature, minimum temperature, mean temperature, relative humidity, evaporation, and wind speed are positively associated with coronavirus disease cases (statistically significant at 95 and 99% confidence levels). No association of coronavirus disease has been found with bright sunshine hours and rainfall. Besides, COVID-19 cases and deaths have shown increasing trends, significant at 99% confidence level. The results of this study suggest that climatic conditions in NCT of Delhi are favorable for COVID-19 and the disease may spread further with the increasing temperature, relative humidity, evaporation and wind speed. This is the only study which has presented the analysis of COVID-19 spread in relation to several climatic variables for the most densely populated and rapidly growing city of India. Thus, considering the results obtained, effective policies and actions are necessary especially by identifying the areas where the spread rate is increasing rapidly in this megacity. The prevention and protection measures should be adopted aiming at to reduce the further transmission of disease in the city.

Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation

Abstract: Pathological color image segmentation is an exigent procedure due to the existence of imperceptibly correlated, and indistinct multiple regions of concern. Multi-level thresholding has been introduced as one of the most significant image segmentation procedures for pathological analysis. However, finding an optimal set of threshold values is an extremely time-consuming task, and crucially depends on the objective function criterion. In order to solve these problems, this paper presents a multi-level hematology color image thresholding approach with the assistance of a two-stage strategy called Eagle Strategy coupled with Whale Optimization Algorithm (ES-WOA), analyzing the performance over five well-known objective functions, namely; Kapur’s entropy, Fuzzy entropy, Tsallis’ entropy, Otsu’s method, and Cross entropy. A rigorous comparative study is performed among classical WOA and existing eagle strategy based optimization algorithms, considering a set of hematology color images, and common performance indexes evaluated by each objective function tested. Experimental results indicate that proposed ES-WOA in combination with Tsallis’ entropy outperforms the rest of tested algorithms in terms of computational effort, image segmentation quality, and robustness. For example, ES-WOA with Tsallis’ produces segmented images with average Peak Signal-to-Noise Ratio (PSNR) values 16.0371, 17.9975, 21.1353, and 23.0759 for threshold values 2, 3, 4, and 5, respectively, which are superior to other tested methods. Additionally, the numerical results are statistically validated using a nonparametric approach to eliminate the random effect in the obtained results.

Tumor Necrosis Factor and Alzheimer's Disease: A Cause and Consequence Relationship

Abstract: ABS TRACT: Tumor necrosis factor and alzheimer’s disease: a cause and consequence relationship Tumor necrosis factor alpha (TNF) was discovered more than a century ago as endotoxin-induced glycoprotein, which causes haemorrhagic necrosis of sarcomas. Originally described as a circulating factor that causes necrosis of tumours,it now appears that TNF has diverse and critical roles to play in the pathogenic progression of a number of chronic inflammatory disorders, including rheumatoid arthritis, Crohn’s disease, psoriasis, Alzheimer’s disease, ischemic stroke, Parkinson’s disease, amyotrophic lateral sclerosis, and multiple sclerosis. A pivotal role has emerged for TNF as an important contributor to Alzheimer’s disease pathology, as TNF appears to modulate several neuropathological mechanisms in Alzheimer’s disease. Evidence for the involvement of TNF in Alzheimer’s disease pathology and neuronal loss comes from studies of TNF over-expression, TNF localization studies, multiple relationships between TNF and amyloid β-peptide (Aβ), interactions between TNF and the microtubule-associated tau protein, TNF-mediated apoptotic cell death, and association of TNF with several neurotransmitters linked to Alzheimer’s pathology. This review presents TNF as a neuromodulator in pathological progression of Alzheimer’s disease by linking it with several endogenous mediators and advocates its status as a current therapeutic target in the quest to find a cure for Alzheimer’s disease.