Surface selection of PM-IRRAS was demonstrated by suppression of water and phosphate signals in buffers with monolayers of oleic acid. Phosphate signals were shown to reflect relative concentrations. Absorption peaks attributable to phospholipids were detected by PM-IRRAS on the human tear film surface and were augmented by the addition of phospholipid.
 
  The data provide strong evidence that phospholipids are present at the surface of tears.
 The data provide strong evidence that phospholipids are present at the surface of tears.Recent advances in light microscopy allow individual biological macromolecules to be visualized in the plasma membrane and cytosol of live cells with nanometer precision and ∼10-ms time resolution. This allows new discoveries to be made because the location and kinetics of molecular interactions can be directly observed in situ without the inherent averaging of bulk measurements. To date, the majority of single-molecule imaging studies have been performed in either unicellular organisms or cultured, and often chemically fixed, mammalian cell lines. However, primary cell cultures and cell lines derived from multi-cellular organisms might exhibit different properties from cells in their native tissue environment, in particular regarding the structure and organization of the plasma membrane. Here, we describe a simple approach to image, localize, and track single fluorescently tagged membrane proteins in freshly prepared live tissue slices and demonstrate how this method can give information about the movement and localization of a G protein-coupled receptor in cardiac tissue slices. In principle, this experimental approach can be used to image the dynamics of single molecules at the plasma membrane of many different soft tissue samples and may be combined with other experimental techniques.Cells exposed to heat shock induce a conserved gene expression program, the heat shock response (HSR), encoding protein homeostasis (proteostasis) factors. Heat shock also triggers proteostasis factors to form subcellular quality control bodies, but the relationship between these spatial structures and the HSR is unclear.  https://www.selleckchem.com/products/AZD2281(Olaparib).html  Here we show that localization of the J-protein Sis1, a cofactor for the chaperone Hsp70, controls HSR activation in yeast. Under nonstress conditions, Sis1 is concentrated in the nucleoplasm, where it promotes Hsp70 binding to the transcription factor Hsf1, repressing the HSR. Upon heat shock, Sis1 forms an interconnected network with other proteostasis factors that spans the nucleolus and the surface of the endoplasmic reticulum. We propose that localization of Sis1 to this network directs Hsp70 activity away from Hsf1 in the nucleoplasm, leaving Hsf1 free to induce the HSR. In this manner, Sis1 couples HSR activation to the spatial organization of the proteostasis network.
  Sequencing studies have identified causal genetic variants for distinct subtypes of heart failure (HF) such as hypertrophic or dilated cardiomyopathy. However, the role of rare, high-impact variants in HF, for which ischemic heart disease is the leading cause, has not been systematically investigated.
 
  To assess the contribution of rare variants to all-cause HF with and without reduced left ventricular ejection fraction.
 
  This was a retrospective analysis of clinical trials and a prospective epidemiological resource (UK Biobank). Whole-exome sequencing of patients with HF was conducted from the Candesartan in Heart Failure-Assessment of Reduction in Mortality and Morbidity (CHARM) and Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA) clinical trials. Data were collected from March 1999 to May 2003 for the CHARM studies and September 2003 to July 2007 for the CORONA study. Using a gene-based collapsing approach, the proportion of patients with HF and controls carrying rare and presumed ce that mendelian genetic conditions may represent an important subset of complex late-onset diseases such as HF, irrespective of the clinical presentation.
  Although optimal access is accepted as the key to quality care, an accepted methodology to ascertain potential disparities in surgical access has not been defined.
 
  To develop a systematic approach to detect surgical access disparities.
 
  This cross-sectional study used publicly available data from the Health Cost and Utilization Project State Inpatient Database from 2016. Using the surgical rate observed in the 5 highest-ranked counties (HRCs), the expected surgical rate in the 5 lowest-ranked counties (LRCs) in North Carolina were calculated. Patients 18 years and older who underwent an inpatient general surgery procedure and patients who underwent emergency inpatient cholecystectomy, herniorrhaphy, or bariatric surgery in 2016 were included. Data were collected from January to December 2016, and data were analyzed from March to July 2020.
 
  Health outcome county rank as defined by the Robert Wood Johnson Foundation.
 
  The primary outcome was the proportional surgical ratio (PSR), which was the disparity HRCs (P = .002). The rate of bariatric surgery in the 5 HRCs was 33.07 per 10 000 population with obesity. For the 5 LRCs, the PSR was 0.60 (95% CI, 0.51-0.69).
 
  The PSR is a systematic approach to define potential disparities in surgical access and should be useful for identifying, investigating, and monitoring interventions intended to mitigate disparities in surgical access that effects the health of vulnerable populations.
 The PSR is a systematic approach to define potential disparities in surgical access and should be useful for identifying, investigating, and monitoring interventions intended to mitigate disparities in surgical access that effects the health of vulnerable populations.
  We present NCBI-taxonomist - a command-line tool written in Python that collects and manages taxonomic data from the National Center for Biotechnology Information (NCBI). NCBI-taxonomist does not depend on a pre-downloaded taxonomic database but can store data locally. NCBI-taxonomist has six commands to map, collect, extract, resolve, import and group taxonomic data that can be linked together to create powerful analytical pipelines. Because many life science databases use the same taxonomic information, the data managed by NCBI-taxonomist is not limited to NCBI and can be used to find data linked to taxonomic information present in other scientific databases.
 
  NCBI-taxonomist is implemented in Python 3 (≥3.8) and available at https//gitlab.com/janpb/ncbi-taxonomist and via PyPi (https//pypi.org/project/ncbi-taxonomist/), as a Docker container (https//gitlab.com/janpb/ncbi-taxonomist/container_registry/) and Singularity (v3.5.3) image (https//cloud.sylabs.io/library/jpb/ncbi-taxonomist). NCBI-taxonomist is licensed under the GPLv3.