ABSTRACT
Objectives: To find the impact of COVID-19 on the sports industry and how the industry will adapt to the changes. Methodology: Secondary data was collected from several websites related to various sports' revenues, broadcasting, and sponsorships. Primary data was collected in the form of public opinion to analyze the change in the coming years. A qualitative method was used for the analysis. Findings: After analyzing the data following results are indicated concerning the changes in the sports industry: a) Sports industry is set to take a massive loss in terms of revenue, b) The public would not attend any sports event until early next year or until a vaccine is developed, whichever is the earliest. Limitations: The limitations of the research could be the revenue data which is likely to change with the resumption of sporting activities. Public opinion might vary as the situation improves, and the idea will differ from one country to another. Application: The study will be helpful for all sports enthusiasts as they would get to know the overall industry scenario and for students who would want to explore the sports industry from the outside.
ABSTRACT
Covid-19 pandemic forced academic Institutes to switch from 100% offline to 100% online delivery mode in very short amount of time. Most of academic Institutes were not ready for this change in terms of required faculty training. Also many Institutes lagged essential computing hardware, software, and Internet bandwidth support for effective implementation of ICT based education. Due to pressure from apex bodies like AICTE, peer Institutes, students and parents almost all academic Institutes started implementation of the online academic delivery in hurry. However, this unprepared start caused increased level of frustration among students and faculty community. Very soon a need of effective implementation of online academic delivery was recognized by the different Institutes. The paper presents how our Institute implemented and ensured effective implementation of online academic delivery using Innovative Practice League (IPL) competition. The paper explains methodology adopted by the Institute in details for improving quality of online academic delivery. The Institute undertook various initiatives at faculty-level, department-level, and Institute-level for the same;the paper discusses the same in details. The paper also presents details of IPL competition and discusses how IPL helped to have awareness of quality issues related with online academic delivery. © 2022, Rajarambapu Institute Of Technology. All rights reserved.
ABSTRACT
qRT-PCR still remains the most widely used method for quantifying gene expression levels, although newer technologies such as next generation sequencing are becoming increasingly popular. A critical, yet often underappreciated, problem when analysing qRT-PCR data is the selection of suitable reference genes. This problem is compounded in situations where up to 25% of all genes may change (e.g., due to leukocyte invasion), as is typically the case in ARDS. Here, we examined 11 widely used reference genes for their suitability in commonly used models of acute lung injury (ALI): ventilator-induced lung injury (VILI), in vivo and ex vivo, lipopolysaccharide plus mechanical ventilation (MV), and hydrochloric acid plus MV. The stability of reference gene expression was determined using the NormFinder, BestKeeper, and geNorm algorithms. We then proceeded with the geNorm results because this is the only algorithm that provides the number of reference genes required to achieve normalisation. We chose interleukin-6 (Il-6) and C-X-C motif ligand 1 (Cxcl-1) as the genes of interest to analyse and demonstrate the impact of inappropriate normalisation. Reference gene stability differed between the ALI models and even within the subgroup of VILI models, no common reference gene index (RGI) could be determined. NormFinder, BestKeeper, and geNorm produced slightly different, but comparable results. Inappropriate normalisation of Il-6 and Cxcl1 gene expression resulted in significant misinterpretation in all four ALI settings. In conclusion, choosing an inappropriate normalisation strategy can introduce different kinds of bias such as gain or loss as well as under- or overestimation of effects, affecting the interpretation of gene expression data.