Genes for the Major Structural Components of Thermotogales Species’ Togas Revealed by Proteomic and Evolutionary Analyses of OmpA and OmpB Homologs

The unifying structural characteristic of members of the bacterial order Thermotogales is their toga, an unusual cell envelope that includes a loose-fitting sheath around each cell. Only two toga-associated structural proteins have been purified and characterized in Thermotoga maritima: the anchor protein OmpA1 (or Ompα) and the porin OmpB (or Ompβ). The gene encoding OmpA1 (ompA1) was cloned and sequenced and later assigned to TM0477 in the genome sequence, but because no peptide sequence was available for OmpB, its gene (ompB) was not annotated. We identified six porin candidates in the genome sequence of T. maritima. Of these candidates, only one, encoded by TM0476, has all the characteristics reported for OmpB and characteristics expected of a porin including predominant β-sheet structure, a carboxy terminus porin anchoring motif, and a porin-specific amino acid composition. We highly enriched a toga fraction of cells for OmpB by sucrose gradient centrifugation and hydroxyapatite chromatography and analyzed it by LC/MS/MS. We found that the only porin candidate that it contained was the TM0476 product. This cell fraction also had β-sheet character as determined by circular dichroism, consistent with its enrichment for OmpB. We conclude that TM0476 encodes OmpB. A phylogenetic analysis of OmpB found orthologs encoded in syntenic locations in the genomes of all but two Thermotogales species. Those without orthologs have putative isofunctional genes in their place. Phylogenetic analyses of OmpA1 revealed that each species of the Thermotogales has one or two OmpA homologs. T. maritima has two OmpA homologs, encoded by ompA1 (TM0477) and ompA2 (TM1729), both of which were found in the toga protein-enriched cell extracts. These annotations of the genes encoding toga structural proteins will guide future examinations of the structure and function of this unusual lineage-defining cell sheath

Cytotoxicity of rhein, the active metabolite of sennoside laxatives, is reduced by multidrug resistance-associated protein 1

Anthranoid laxatives, belonging to the anthraquinones as do anthracyclines, possibly increase colorectal cancer risk. Anthracyclines interfere with topoisomerase II, intercalate DNA and are substrates for P-glycoprotein and multidrug resistance-associated protein 1. P-glycoprotein and multidrug resistance-associated protein 1 protect colonic epithelial cells against xenobiotics. The aim of this study was to analyse the interference of anthranoids with these natural defence mechanisms and the direct cytotoxicity of anthranoids in cancer cell lines expressing these mechanisms in varying combinations. A cytotoxicity profile of rhein, aloe emodin and danthron was established in related cell lines exhibiting different levels of topoisomerases, multidrug resistance-associated protein 1 and P-glycoprotein. Interaction of rhein with multidrug resistance-associated protein 1 was studied by carboxy fluorescein efflux and direct cytotoxicity by apoptosis induction. Rhein was less cytotoxic in the multidrug resistance-associated protein 1 overexpressing GLC4/ADR cell line compared to GLC4. Multidrug resistance-associated protein 1 inhibition with MK571 increased rhein cytotoxicity. Carboxy fluorescein efflux was blocked by rhein. No P-glycoprotein dependent rhein efflux was observed, nor was topoisomerase II responsible for reduced toxicity. Rhein induced apoptosis but did not intercalate DNA. Aloe emodin and danthron were no substrates for MDR mechanisms. Rhein is a substrate for multidrug resistance-associated protein 1 and induces apoptosis. It could therefore render the colonic epithelium sensitive to cytotoxic agents, apart from being toxic in itself

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Utilizing Image Processing and the YOLOv3 Network for Real-Time Traffic Light Control

In this study, different strategies used to count vehicles and people in different image areas at a street intersection were analyzed to obtain counts at appropriate times suitable for real-time control of a traffic light. To achieve this, video recordings of cameras placed at the intersection were used to test and verify image processing algorithms and deep learning using the YOLOv3 network implemented on a 4 GB RAM Jetson Nano card. We counted the vehicles and people that stopped and crossed the polygons to delimit the different areas of interest, with a maximum error of ±2 in the validation tests for all cases. In addition, as a strategy, we combined the images from both cameras into a single one, thereby allowing us to make a single detection and subsequently determine if they are inside or outside the polygons used in separating the areas of interest with the respective counts. Furthermore, this enabled us to obtain information on vehicles and people stopped and crossing in a time of 0.73 s on average. Hence, it was established that the inclusion of the control algorithm is appropriate for real-time control of traffic lights