Search for: All records

Blood coagulation is a vital physiological process involving a complex network of biochemical reactions, which converge to form a blood clot that repairs vascular injury. This process unfolds in three phases: initiation, amplification, and propagation, ultimately leading to thrombin formation. Coagulation begins when tissue factor (TF) is exposed on an injured vessel’s wall. The first step is when activated factor VII (VIIa) in the plasma binds to TF, forming complex TF:VIIa, which activates factor X. Activated factor X (Xa) is necessary for coagulation, so the regulation of its activation is crucial. Tissue Factor Pathway Inhibitor (TFPI) is a critical regulator of the initiation phase as it inhibits the activation of factor X. While previous studies have proposed two pathways—direct and indirect binding—for TFPI’s inhibitory role, the specific biochemical reactions and their rates remain ambiguous. Many existing mathematical models only assume an indirect pathway, which may be less effective under physiological flow conditions. In this study, we revisit datasets from two experiments focused on activated factor X formation in the presence of TFPI. We employ an adaptive Metropolis method for parameter estimation to reinvestigate a previously proposed biochemical scheme and corresponding rates for both inhibition pathways. Our findings show that both pathways are essential to replicate the static experimental results. Previous studies have suggested that flow itself makes a significant contribution to the inhibition of factor X activation. We added flow to this model with our estimated parameters to determine the contribution of the two inhibition pathways under these conditions. We found that direct binding of TFPI is necessary for inhibition under flow. The indirect pathway has a weaker inhibitory effect due to removal of solution phase inhibitory complexes by flow. 

Schematic representation of preferential uptake of good's buffer-coated PLGA nanoparticles into human breast cancer cells. Created with biorender.com. 

The emergence of plant pathogens is often associated with waves of unique evolutionary and epidemiological events. Xanthomonas hortorum pv. gardneri is one of the major pathogens causing bacterial spot disease of tomatoes. After its first report in the 1950s, there were no formal reports on this pathogen until the 1990s, despite active global research on the pathogens that cause tomato and pepper bacterial spot disease. Given the recently documented global distribution of X. hortorum pv. gardneri, our objective was to examine genomic diversification associated with its emergence. We sequenced the genomes of X. hortorum pv. gardneri strains collected in eight countries to examine global population structure and pathways of emergence using phylodynamic analysis. We found that strains isolated post-1990 group by region of collection and show minimal impact of recombination on genetic variation. A period of rapid geographic expansion in X. hortorum pv. gardneri is associated with acquisition of a large plasmid conferring copper tolerance by horizontal transfer and coincides with the burgeoning hybrid tomato seed industry through the 1980s. The ancestry of X. hortorum pv. gardneri is consistent with introduction to hybrid tomato seed production and dissemination during the rapid increase in trade of hybrid seeds. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license . 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.