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Das Intensivregister (www.intensivregister.de) ist eine digitale Plattform zur Echtzeiterfassung von intensivmedizinischen Behandlungs- und Bettenkapazitäten sowie den Fallzahlen intensivmedizinisch behandelter COVID-19-Patient:innen der etwa 1.300 Akut-Krankenhäuser Deutschlands. Bis 12 Uhr ist die tägliche Meldung laut Verordnung für die Krankenhäuser verpflichtend. Darüber hinaus kann beliebig oft gemeldet werden, sodass Veränderungen in den Kapazitäten aktuell berichtet werden können. Damit ermöglicht das Intensivregister in der Pandemie sowie darüber hinaus, Engpässe in der intensivmedizinischen Versorgung im regionalen und zeitlichen Vergleich zu erkennen. Es schafft somit eine wertvolle Grundlage zur Reaktion und zur datengestützten Handlungssteuerung in Echtzeit. Zur Aufrechterhaltung der Krankenhausversorgung bietet das DIVI-Intensivregister demnach ein Portal zur Suche freier Intensivbetten und zur Kontaktaufnahme zwischen Fachkolleg*innen, um die gegenseitige Unterstützung bei Behandlungsfragen im intensivmedizinischen Kontext zu erleichtern. Ebenso liefert es eine maßgebliche Informationsgrundlage für staatliche Steuerungs- und Planungsmaßnahmen sowie für die lokale und überregionale Steuerung und Koordinierung der Bettenbelegung.

Additional file 1: Figure S1. Correlation between the expression of CXCL9–11, CXCR3 and immune cells. Immune cells include B cell, CD8+T cell, CD4+T cell, macrophage, neutrophil and dendritic cell. a CXCL9. b CXCL10. c CXCL11. d CXCR3.

Additional file 7: Supplementary figure 7. Subgroup analysis of HCC patients stratified by distinct clinicopathological factors. Kaplan-Meier curves of OS between high and low EME HCC patients in each subgroup of (A, B) age ≥65 and <65, (C, D) female and male, (E, F) G1-2 and G3-4, (G, H) stage I-II and stage III-IV, (I, J) T1-2 and T3-4, and (K, L) HBV infection and non-infection.

Im Datensatz 'SARS-CoV-2 Infektionen in Deutschland' werden die tagesaktuellen Fallzahlen, der nach den Vorgaben des Infektionsschutzgesetzes - IfSG - von den Gesundheitsämtern in Deutschand gemeldeten positiven SARS-Cov-2 Infektionen, Todes- und Genesungsfälle bereitgestellt.

Additional file 6: Supplementary Table S6. Number of tRNAs and anticodons of different amino acids in the eight red algae (S6a-S6h).

Abstract Background BRASSINAZOLE-RESISTANT (BZR) is a class of specific transcription factor (TFs) involved in brassinosteroid (BR) signal transduction. The regulatory mechanism of target genes mediated by BZR has become one of the key research areas in plant BR signaling networks. However, the functions of the BZR gene family in cucumber have not been well characterized. Results In this study, six CsBZR gene family members were identified by analyzing the conserved domain of BES1 N in the cucumber genome. The size of CsBZR proteins ranges from 311 to 698 amino acids and are mostly located in the nucleus. Phylogenetic analysis divided CsBZR genes into three subgroups. The gene structure and conserved domain showed that the BZR genes domain in the same group was conserved. Cis-acting element analysis showed that cucumber BZR genes were mainly involved in hormone response, stress response and growth regulation. The qRT-PCR results also confirmed CsBZR response to hormones and abiotic stress. Conclusion Collectively, the CsBZR gene is involved in regulating cucumber growth and development, particularly in hormone response and response to abiotic stress. These findings provide valuable information for understanding the structure and expression patterns of BZR genes.

The erosivity data in Brazil. It has a spatial resolution of 30 seconds (~ 1 km²). The data set grid is in GeoTIFF format and corresponds perfectly to WorldClim. It uses the geographic coordinate reference system, with WGS84 projection (EPSG: 4326). Soil is a most important non-renewable natural resource for sustaining life. The rates of soil loss have been increasing. The strength of storms can become a disturbing factor, this water energy is known as rain erosivity, and is a major cause of the loss of sediment and nutrients worldwide. The method of obtaining these values is not simple and is usually one-off and uses the USLE or RUSLE equation. Point values cannot be applied in areas that need to estimate soil losses. And traditional spatialization techniques like kriging, IDW or Thiessen polygons do not represent the variability that actually occurs. Thus, the objective of this article was to model a map of rainfall erosivity for Brazil, with spatial resolution of 30 seconds of arc (~ 1 km²). Using products made available by other articles, GIS techniques and machine learning modeling. Of the 31 pre-selected covariates 8 were used in the modeling, in order of importance, they were: Longitude, Solar Radiation, Annual precipitation (BIO12), Precipitation of the coldest quarter (BIO19), Wind speed, Precipitation of the warmest quarter (BIO18 ) and the annual reference evapotranspiration. After 400 trainings and validations, the model with the best performance indicators was the Random Forest, using the medians, the indices were: NSE of 0.5823, RMSE of 1567.17 MJ.mm/ha.h.ano, MAE of 1135.90 MJ.mm / ha.h.year, nRMSE of 58.50%, ME of -17.76 MJ.mm/ha.h.year and D of 0.8487. The article was submitted for publication. Dados_Erosividade_BR.csv - Data used to model the models. eros_cubist.tif - Erosivity image generated by the cubist model eros_gbm.tif - Image of erosivity generated by the gbm model eros_lm.tif - Erosivity image generated by the linear model eros_rf.tif - Erosivity image generated by the random forest model We are grateful for the financial support (doctoral scholarship) granted by the Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) under process number: 88882.450196 / 2019-01 and Financial Code 001. We are also grateful to the Graduate Program in Agronomy of the UEPG, to the Group of Studies and Research in Water and Soil Management (GEMAS) of the State University of Ponta Grossa-PR (UEPG).

This dataset contains the field measurements collected and used in the scientific study “Optical properties and simple forcing efficiency of the organic aerosols and black carbon emitted by residential wood burning in rural Central Europe” by Andrea Cuesta-Mosquera et al.

Abstract Background The cattle sector is the most important economic production unit of the Irish farming and agri-food sector. Despite its relevance, there has been limited quantitative information about the structure of differing cattle production types and of the connections between them. This paper addresses this gap by providing, for the first time, an overview of the Irish cattle population structured by enterprise type. Methods & Results We collected data from the cattle register for the period 2015 to 2019 and assigned registered herds to one of 18 different herd types using a recently published herd type classification approach. This allows, for the first time, to exploring changes in enterprise types and subtypes over time, and describing the movements between these subtypes and from these subtypes to slaughter. Conclusions The overview and associated classification presented in this study will form the basis for a number of future comparative studies, including cross-sectoral assessments of profitability, estimation of the extent of animal health losses on Irish cattle farms or structural analysis of Greenhouse Gas (GHG) emissions across production systems.