Eliasenchristie0182

In recent years, there has been a scholarly debate regarding the decrease in automobile-related mobility indicators (car ownership, driving license holding, VMT, etc.). Broadly speaking, two theories have been put forward to explain this trend (1) economic factors whose impacts are well-understood in principle, but whose occurrence among young adults as a demographic sub-group had been overlooked, and (2) less well-understood shifts in cultural mores, values and sentiment towards the automobile. This second theory is devilishly difficult to study, due primarily to limitations in standard data resources such as the National Household Travel Survey and international peer datasets. In this study we first compiled a database of lyrics to popular music songs from 1956 to 2015 (defined by inclusion in the annual "top 40"), and subsequently identified references to automobiles within this corpus. We then evaluated whether there is support for theory #2 above within popular music, by looking at changes from the 1950s to the 2010s. We demonstrate that the frequency of references to automobility tended for many years to increase over time, however there has more recently been a decline after the late 2000s (decade). In terms of the sentiment of popular music lyrics that reference automobiles, our results are mixed as to whether the references are becoming increasingly positive or negative (machine analysis suggests increasing negativity, while human analysis did not find a significant association), however a consistent observation is that sentiment of automobile references have over time become more positive relative to sentiment of song lyrics overall. We also show that sentiment towards automobile references differs systematically by genre, e.g. automobile references within 'Rock' lyrics are in general more negative than similar references to cars in other music genres). The data generated on this project have been archived and made available open access for use by future researchers; details are in the full paper.[This corrects the article DOI 10.1007/s11135-021-01129-3.].In response to the global crisis resulted from the spread of the coronavirus in Brazil, many schools and universities suspended face-to-face classes and began to offer remote classes using digital resources. Z-VAD(OH)-FMK in vitro In this unprecedented crisis, educators, managers and families had to deal with unpredictability and look for new ways of learning and teaching. Based on the paradigm of transmission of mass distribution, remotely teaching has gained strength, with the teacher as the protagonist of the education process. Instead of interactivity, content is emphasized to the detriment of more participatory, dialogic, and collaborative methodologies. It is necessary to prepare teachers and students for the online education modality to face this critical moment. Thus, this article suggests online pedagogical actions supported by the multireferential research-formation and everyday school methodologies, which promote the teachers' and students' participation in an interactive and collaborative way.The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easily recorded. The Mel Frequency Cepstral Coefficient (MFCC) analysis of speech signal is one of the oldest but potential analysis tools. The performance of this analysis mainly depends on the use of conversion between normal frequency scale to perceptual frequency scale and the frequency range of the filters used. Traditionally, in speech recognition, these values are fixed. But the characteristics of speech signals vary from disease to disease. In the case of detection of COVID-19, mainly the coughing sounds are used whose bandwidth and properties are quite different from the complete speech signal. By exploiting these properties the efficiency of the COVID-19 detection can be improved. To achieve this objective the frequency range and the conversion scale of frequencies have been suitably optimized. Further to enhance the accuracy of detection performance, speech enhancement has been carried out before extraction of features. By implementing these two concepts a new feature called COVID-19 Coefficient (C-19CC) is developed in this paper. Finally, the performance of these features has been compared.Even though SARS-CoV-2's primary transmission pathway is person-to-person, the role played by surfaces and food contact materials in carrying viral RNA should be further explored. For this purpose, the study aimed to investigate the persistence of SARS-CoV-2 using the strain ATCC® VR-1986HK™ on flow pack polyethylene (FPP) and polystyrene food trays (PFT). Samples of FPP and PFT were contaminated with heat-inactivated SARS-CoV-2 and were incubated at a temperature of 24 ± 1 °C and at controlled relative humidity (RH 65%). The experimental design included analyses at the time 0, 3, 6, 12, 24, 36, 48 and after every 24 h until the viral RNA was no longer detectable. The results showed a significant decrease (P less then 0.001) in viral copy numbers on PFT within 3 h (24% reduction) and, at 72 h, the viral RNA had fallen below the limit of detection. Regarding the FPP, it was necessary to wait 24 h for a significant decrease (P = 0.015) in the viral load (14% reduction), while the detection threshold was reached at 96 h. These findings showed that the viral RNA persists longer on flow pack polyethylene samples than on polystyrene food trays, thus highlighting the importance of material characteristics in the persistence of SARS-CoV-2.The unprecedented quick spreading of newly emerged SARS-CoV-2, the virus responsible for causing COVID-19 has put the whole world in vast crisis. Several prophylactic interventions are being performed to discover the effective anti-COVID-19 agent. Thus, the present study aims to identify the cryptogamic secondary metabolites (CSMs) as potent inhibitors of two major targets of SARS-Cov2, namely 3-chymotrypsin-like protease (3CLpro) and receptor-binding domain (RBD) of spike glycoprotein (SGP), by implementing a computational approach. Molecular docking was carried out on Autodock 4.2 software with the 3CLpro (PDB ID6LU7) and RBD of SGP (PDB ID6W41) of the virus. Lopinavir and Arbidol were taken as positive controls to compare the efficacy of randomly selected 53 CSMs. The drug-likeness and pharmacokinetics properties of all metabolites were accessed to discern the anti-COVID 19 activity acting well at the physiological conditions. The docking results predicted that Marchantin E and Zeorin would potentially block the catalytic site of 3CLpro with the interaction energy values of -8.