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Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq

Undefeated super middleweight Zach Parker (22-0, 16 KOs) will take on veteran John Ryder (31-5, 17 KOs) for the vacant WBO super middleweight title at The O2 Arena in London on Saturday, November 26. What time is John Ryder vs. Zach Parker tonight? Ringwalks, running order, streaming, how to watch Ryder vs. Parker CLICK HERE TO WATCH LIVE FREE CLICK HERE TO WATCH LIVE FREE This dataset contains impact metrics and indicators for a set of publications that are related to the COVID-19 infectious disease and the coronavirus that causes it. It is based on:gdf Τhe CORD-19 dataset released by the team of Semantic Scholar1 anddg Τhe curated data provided by the LitCovid hub2.gd These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures: Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4. Influence_alt: Citation-based measure reflecting the total impact of a These data have been cleaned and integrated with data from COVID-19-TweetIDs and from other sources (e.g., PMC). The result was dataset of 500,314 unique articles along with relevant metadata (e.g., the underlying citation network). We utilized this dataset to produce, for each article, the values of the following impact measures:sdgfdh Influence: Citation-based measure reflecting the total impact of an article. This is based on the PageRank3 network analysis method. In the context of citation networks, it estimates the importance of each article based on its centrality in the whole network. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sdgd Influence_alt: Citation-based measure reflecting the total impact of an article. This is the Citation Count of each article, calculated based on the citation network between the articles contained in the BIP4COVID19 dataset.sdgf safs Popularity: Citation-based measure reflecting the current impact of an article. This is based on the AttRank5 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). AttRank alleviates this problem incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. This is why it is more suitable to capture the current "hype" of an article.asdsg sf Popularity alternative: An alternative citation-based measure reflecting the current impact of an article (this was the basic popularity measured provided by BIP4COVID19 until version 26). This is based on the RAM6 citation network analysis method. Methods like PageRank are biased against recently published articles (new articles need time to receive their first citations). RAM alleviates this problem using an approach known as "time-awareness". This is why it is more suitable to capture the current "hype" of an article. This measure was calculated using the PaperRanking (https://github.com/diwis/PaperRanking) library4.sfb Social Media Attention: The number of tweets related to this article. Relevant data were collected from the COVID-19-TweetIDs dataset. In this version, tweets between 23/6/22-29/6/22 have been considered from the previous dataset. We provide five CSV files, all containing the same information, however each having its entries ordered by a different impact measure. All CSV files are tab separated and have the same columns (PubMed_id, PMC_id, DOI, influence_score, popularity_alt_score, popularity score, influence_alt score, tweets count). gfdgdf gfdgr gytd fgdfsrg

Upregulated DEGs in hypothalamus. Raw DEG data for upregulated genes in hypothalamus. (XLSX 344 kb)

Face Mask detection using machine intelligence is one of the hot topic during the COVID-19 period. Variety of works are introduced to identify the policy violators in busy places however, the available image datasets are limited to build a generalized model that can be used in real-time applications with coarse-to-fine video frames. We present a real-time video/images dataset containing multiple subjects (with/without mask) walking within a University environment. Each annotated frame contains multiple instances (i.e. persons) with unique identifications, bounding boxes, and class/label information. The dataset and annotations can be used to train, validate and test the deep learning and computer vision based facial mask detection algorithms. Below are the details of dataset: Total video frames: 4357 Total bounding boxes: 21941 Boxes with Mask (MW): 8306 Boxes without Mask (NM): 13635 Image frames: This folder contains 4357 video frames (.png). Each frame contains multiple instances. Annotations: This folder contains the 4357 annotations (.xml) files for the above Image frames. Each .XML contains information about: Person ID: Unique identification for each person in a frame Bounding Box: The rectangular boundary around the person Class Names: Mask (MW), no Mask (NM) NOTE: Instances with mask and facing towards the camera are only labelled as being mask wearer (MW). All other subjects (i.e. instances) facing opposite to camera (i.e. backside towards the capturing device) are labelled as NM (i.e. without mask).

Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq

Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq

Bolsonarism: discursive strategies and passionate paths in the pandemic - This text examines the discursive disputes on Twitter around the pandemic of covid-19 from Bolsonaro’s emphatic affirmations made between 2020 and 2021. We analysed 660 messages, divided into thematic and discoursive groups. We discuss the nodal points which totalise the analyzed discourses, the subject positions and the passions, examining the consequences of these discourse articulations for the actual democratic and health crisis. The analyses adopt Laclau and Mouffe’s discourse theory and the passion semiotic theory, by Greimas and Fontanille. Essa base de dados dá origem ao texto: "A ‘gripezinha’ do Messias: estratégias discursivas e percursos passionais do bolsonarismo durante a pandemia", que examina as disputas discursivas no Twitter em torno da pandemia de covid-19 a partir de declarações enfáticas de Bolsonaro entre 2020 e 2021. O corpus contou com 660 tuítes que foram divididos em grupos temáticos e discursivos. Discutimos pontos nodais que totalizaram os discursos, posições de sujeito e paixões. Examinamos as consequências de tais articulações discursivas para o contexto democrático e relativo à crise sanitária. Adotamos na análise a teoria discursiva de Laclau e Mouffe e a semiótica das paixões de Greimas e Fontanille.

Data and code used for the analysis in Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland (Lemaitre et al., Swiss Medial Weekly 2020).

Similar to other RNA viruses, the emergence of Betacoronavirus relies on cross-species viral transmission, which requires careful health surveillance monitoring of protein-coding information as well as genome-wide analysis. Although the evolutionary jump from natural reservoirs to humans may be mainly traced-back by studying the effect that hotspot mutations have on viral proteins, it is largely unexplored if other impacts might emerge on the structured RNA genome of Betacoronavirus. In this survey, the protein-coding and viral genome architecture were simultaneously studied to uncover novel insights into cross-species horizontal transmission events. We analysed 1,252,952 viral genomes of SARS-CoV, MERS-CoV, and SARS-CoV-2 distributed across the world in bats, intermediate animals, and humans to build a new landscape of changes in the RNA viral genome. Phylogenetic analyses suggest that bat viruses are the most closely related to the time of most recent common ancestor of Betacoronavirus, and missense mutations in viral proteins, mainly in the S protein S1 subunit: SARS-CoV (G > T; A577S); MERS-CoV (C > T; S746R and C > T; N762A); and SARS-CoV-2 (A > G; D614G) appear to have driven viral diversification. We also found that codon sites under positive selection on S protein overlap with non-compensatory mutations that disrupt secondary RNA structures in the RNA genome complement. These findings provide pivotal factors that might be underlying the eventual jumping the species barrier from bats to intermediate hosts. Lastly, we discovered that nearly half of the Betacoronavirus genomes carry highly conserved RNA structures, and more than 90% of these RNA structures show negative selection signals, suggesting essential functions in the biology of Betacoronavirus that have not been investigated to date. Further research is needed on negatively selected RNA structures to scan for emerging functions like the potential of coding virus-derived small RNAs and to develop new candidate antiviral therapeutic strategies.