Increases in ocean temperatures and in the frequency and severity of hypoxic events are expected with climate change, and may become a challenge for cultured Atlantic salmon and negatively affect their growth, immunology and welfare. Thus, we examined how an incremental temperature increase alone (Warm & Normoxic-WN 12 → 20 °C; 1 °C week
  ), and in combination with moderate hypoxia (Warm & Hypoxic-WH ~ 70% air saturation), impacted the salmon's hepatic transcriptome expr\ession compared to control fish (CT 12 °C, normoxic) using 44 K microarrays and qPCR.
 
  Overall, we identified 2894 differentially expressed probes (DEPs, FDR < 5%), that included 1111 shared DEPs, while 789 and 994 DEPs were specific to WN and WH fish, respectively. Pathway analysis indicated that the cellular mechanisms affected by the two experimental conditions were quite similar, with up-regulated genes functionally associated with the heat shock response, ER-stress, apoptosis and immune defence, while genes connected with gal biomarker genes for improving our understanding of fish health and welfare.
  Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics.
 
  Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death ao understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected.
 
  This review was registered on PROSPERO as CRD42020177714 .
 This review was registered on PROSPERO as CRD42020177714 .
  Microsatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system.  https://www.selleckchem.com/products/bms-265246.html  Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size.
 
  We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model,ate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes.This correspondence responds to the critique by Butler et al. (BMC Genomics 22241, 2021) of our recent paper on transposable element (TE) persistence. We address the three main objections raised by Butler et al. After running a series of additional simulations that were inspired by the authors' criticisms, we are able to present a more nuanced understanding of the conditions that generate long-term persistence.
  Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more rhis work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.
  Salinity is a major threat to the agriculture industry due to the negative impact of salinity stress on crop productivity. In the present study, we isolated rhizobacteria and evaluated their capacities to promote crop growth under salt stress conditions.
 
  We isolated rhizospheric bacteria from sand dune flora of Pohang beach, Korea, and screened them for plant growth-promoting (PGP) traits. Among 55 bacterial isolates, 14 produced indole-3-acetic acid (IAA), 10 produced siderophores, and 12 produced extracellular polymeric and phosphate solubilization. Based on these PGP traits, we selected 11 isolates to assess for salinity tolerance. Among them, ALT29 and ALT43 showed the highest tolerance to salinity stress. Next, we tested the culture filtrate of isolates ALT29 and ALT43 for IAA and organic acids to confirm the presence of these PGP products. To investigate the effects of ALT29 and ALT43 on salt tolerance in soybean, we grew seedlings in 0 mM, 80 mM, 160 mM, and 240 mM NaCl treatments, inoculating half with the bacterial isolates.