Perceptions regarding second wave of coronavirus disease 2019 (COVID-19) pandemic among Indian adults: A cross-sectional study.

BACKGROUND AND AIMS: A massive surge in coronavirus disease 2019 (COVID-19) cases and deaths occurred in India during March-April 2021, and this was considered as second wave of the pandemic in the country. This study was conducted to find out the perceptions about second wave of the COVID-19 pandemic among Indian adults. METHODS: An online-survey-based cross-sectional study was conducted over 3 weeks from April 21, 2021 to May 11, 2021. Information regarding sociodemographic profile, perceptions about COVID-19 during second wave, perceptions and practices related to COVID-19 vaccination, COVID-19 appropriate behavior, and government's response to the pandemic was collected. Descriptive analysis was performed. RESULTS: A total of 408 study participants were included. Mean age of the study participants was 29.2 ± 10.4 years. Around 92.6 percent (378) of respondents agreed that COVID-19 in 2021 is different from 2020. Perceived reasons for increased severity and cases were change in virus characteristics; social, religious, and political gatherings; and complacent behavior by people. Three-fourth (311, 76.2 percent) of the study participants agreed that vaccines have a positive role against COVID-19. Majority of the study participants (329, 80.6 percent) concurred that lockdown restrictions help in control of the pandemic. About 60.3 percent (246) of respondents had less trust on government post this pandemic compared to pre-COVID-19 times. CONCLUSION: The public perception about reasons for second wave in India acknowledges both human and virus factors and highlights the importance of shared responsibility between citizens and government for controlling the pandemic.

Perceptions regarding second wave of coronavirus disease 2019 (COVID-19) pandemic among Indian adults: A cross-sectional study.

BACKGROUND AND AIMS: A massive surge in coronavirus disease 2019 (COVID-19) cases and deaths occurred in India during March-April 2021, and this was considered as second wave of the pandemic in the country. This study was conducted to find out the perceptions about second wave of the COVID-19 pandemic among Indian adults. METHODS: An online-survey-based cross-sectional study was conducted over 3 weeks from April 21, 2021 to May 11, 2021. Information regarding sociodemographic profile, perceptions about COVID-19 during second wave, perceptions and practices related to COVID-19 vaccination, COVID-19 appropriate behavior, and government's response to the pandemic was collected. Descriptive analysis was performed. RESULTS: A total of 408 study participants were included. Mean age of the study participants was 29.2 ± 10.4 years. Around 92.6 percent (378) of respondents agreed that COVID-19 in 2021 is different from 2020. Perceived reasons for increased severity and cases were change in virus characteristics; social, religious, and political gatherings; and complacent behavior by people. Three-fourth (311, 76.2 percent) of the study participants agreed that vaccines have a positive role against COVID-19. Majority of the study participants (329, 80.6 percent) concurred that lockdown restrictions help in control of the pandemic. About 60.3 percent (246) of respondents had less trust on government post this pandemic compared to pre-COVID-19 times. CONCLUSION: The public perception about reasons for second wave in India acknowledges both human and virus factors and highlights the importance of shared responsibility between citizens and government for controlling the pandemic.

A Reappraisal of Dependency Length Minimization as a Linguistic Universal.

Dependency length minimization is widely regarded as a cross-linguistic universal reflecting syntactic complexity in natural languages. A typical way to operationalize dependency length in corpus-based studies has been to count the number of words between syntactically related words. However, such a formulation ignores the syntactic nature of the linguistic material that intervenes a dependency. In this work, we investigate if the number of syntactic heads (rather than the number of words) that intervene a dependency better captures the syntactic complexity across languages. We demonstrate that the dependency length minimization constraint in terms of the number of words could arise as a consequence of constraints on the intervening heads and the tree properties such as node arity. The current study highlights the importance of syntactic heads as central regions of structure building during processing. The results show that when syntactically related words are nonadjacent, increased structure building in the intervening region is avoided.

Ensemble of transfer learnt classifiers for recognition of cardiovascular tissues from histological images.

Recognition of tissues and organs is a recurrent step performed by experts during analyses of histological images. With advancement in the field of machine learning, such steps can be automated using computer vision methods. This paper presents an ensemble-based approach for improved classification of non-pathological tissues and organs in histological images using convolutional neural networks (CNNs). With limited dataset size, we relied upon transfer learning where pre-trained CNNs are re-used for new classification problems. The transfer learning was done using eleven CNN architectures upon 6000 image patches constituting training and validation subsets of a public dataset containing six cardiovascular categories. The CNN models were fine-tuned upon a much larger dataset obtained by augmenting training subset to obtain agreeable performance on validation subset. Lastly, we created various ensembles of trained classifiers and evaluate them on testing subset of 7500 patches. The best ensemble classifier gives, precision, recall, and accuracy of 0.876, 0.869 and 0.869, respectively upon test images. With an overall F1-score of 0.870, our ensemble-based approach outperforms previous approaches with single fine-tuned CNN, CNN trained from scratch, and traditional machine learning by 0.019, 0.064 and 0.183, respectively. Ensemble approach can perform better than individual classifier-based ones, provided the constituent classifiers are chosen wisely. The empirical choice of classifiers reinforces the intuition that models which are newer and outperformed in their native domain are more likely to outperform in transferred-domain, since the best ensemble dominantly consists of more lately proposed and better architectures.

Analysis of host protein interactions in plant viruses: an in silico study using Sesbania mosaic virus.

The dynamics of interactions of viral proteins with their host are pivotal in establishing a successful infection and ensuring systemic spread. To uncover these, an in silico analysis of the interactions between the coat protein (CP) of Sesbania mosaic virus (SeMV), a group IV virus with single-stranded positive-sense RNA genome was carried out with the known crystal structures of proteins belonging to the Fabaceae family, which is its natural host. SeMV is an isometric plant virus which infects Sesbania grandiflora, a member of Fabaceae, and causes mosaic symptoms. Earlier results have indicated that the assembly and disassembly events of SeMV favor the formation of CP dimers. Hence, the ability and strength of interactions of CP dimer with the host proteins were assessed using in silico protein-protein docking approaches. A set of 61 unique crystal structures of native proteins belonging to Fabaceae were downloaded from the Protein Data Bank (PDB) and docked with the CP dimer of SeMV. From the docking scores and interaction analysis, the host proteins were ranked according to their strength and significance of interactions with the CP dimers. The leads that were identified present themselves as strong candidates for developing antivirals against not only SeMV but also other related viruses that infect Fabaceae. The study is a prototype to understand host protein interactions in viruses and hosts.