TFPIα Interacts with FVa and FXa to Inhibit Prothrombinase During the Initiation of Coagulation

Tissue factor pathway inhibitor α (TFPIα) inhibits prothrombinase, the thrombin-generating complex of factor Xa (FXa) and factor Va (FVa), during the initiation of coagulation. This inhibition requires binding of a conserved basic region within TFPIα to a conserved acidic region in FXa-activated and platelet-released FVa. In this study, the contribution of interactions between TFPIα and the FXa active site and FVa heavy chain to prothrombinase inhibition were examined to further define the inhibitory biochemistry. Removal of FXa active site binding by mutation or by deletion of the second Kunitz domain (K2) of TFPIα produced 17- or 34-fold weaker prothrombinase inhibition, respectively, establishing that K2 binding to the FXa active site is required for efficient inhibition. Substitution of the TFPIα basic region uncharged residues (Leu252, Ile253, Thr255) with Ala (TFPI-AAKA) produced 5.8-fold decreased inhibition. This finding was confirmed using a basic region peptide (Leu252-Lys261) and Ala substitution peptides, which established that the uncharged residues are required for prothrombinase inhibitory activity but not for binding the FVa acidic region. This suggests that the uncharged residues mediate a secondary interaction with FVa subsequent to acidic region binding. This secondary interaction seems to be with the FVa heavy chain, because the FV Leiden mutation weakened prothrombinase inhibition by TFPIα but did not alter TFPI-AAKA inhibitory activity. Thus, efficient inhibition of prothrombinase by TFPIα requires at least 3 intermolecular interactions: (1) the TFPIα basic region binds the FVa acidic region, (2) K2 binds the FXa active site, and (3) Leu252-Thr255 binds the FVa heavy chain

Effects of the gastric juice injection pattern and contraction frequency on the digestibility of casein powder suspensions in an in vitro dynamic rat stomach made with a 3D printed model

Previously, we have prepared a version of the dynamic in vitro rat stomach system (DIVRS-II or Biomimic Rat II). It was constructed and tested by showing similar digestive behaviors with those occurred in vivo. In the present work, a 3D-printed plastic mold was employed to create highly repeatable silicone rat stomach model. It has been seen to have shortened the time to handcraft a model like that used in DIVRS-II. The maximum mechanical force of the current stomach model generated by rolling extrusion is found to be more stable probably due to the more uniform wall thickness of the new model. Then the effects of the simulated gastric secretion patterns and contraction frequency of the system on the in vitro digestibility of casein powder suspensions were investigated. The results have shown that the location of the gastric secretion injection has an impact on experimental digestibility. The position of rolling-extrusion area, established at the central part of glandular portion (stomach B), displayed the highest digestibility compared to that at the other locations. Furthermore, the extent of digestion was positively correlated with the contraction frequency of the model stomach system, with the maximum frequency of 12cpm giving the highest digestibility. This highest digestibility is almost the same as the average value found in vivo. The better digestive performance produced by optimizing the gastric secretion pattern and contraction frequency may be both resulted from the improved mixing efficiency of the food matrix with digestive juice. This study shows that it is possible to achieve what in vivo in a simulated digestion device, which may be used for future food and nutrition studies in vitro

The impact of the atmospheric turbulence-development tendency on new particle formation : a common finding on three continents

A new mechanism of new particle formation (NPF) is investigated using comprehensive measurements of aerosol physicochemical quantities and meteorological variables made in three continents, including Beijing, China; the Southern Great Plains site in the USA; and SMEAR II Station in Hyytiala, Finland. Despite the considerably different emissions of chemical species among the sites, a common relationship was found between the characteristics of NPF and the stability intensity. The stability parameter (zeta = Z/L, where Z is the height above ground and L is the Monin-Obukhov length) is found to play an important role; it drops significantly before NPF as the atmosphere becomes more unstable, which may serve as an indicator of nucleation bursts. As the atmosphere becomes unstable, the NPF duration is closely related to the tendency for turbulence development, which influences the evolution of the condensation sink. Presumably, the unstable atmosphere may dilute pre-existing particles, effectively reducing the condensation sink, especially at coarse mode to foster nucleation. This new mechanism is confirmed by model simulations using a molecular dynamic model that mimics the impact of turbulence development on nucleation by inducing and intensifying homogeneous nucleation events.Peer reviewe

Multikingdom interactions govern the microbiome in subterranean cultural heritage sites

9 páginas.- 5 figuras.- 66 referencias.- Data Availability. The amplicon sequences, shotgun metagenomics, and screened Actinobacteria strain sequences reported in this article have been deposited in the NCBI BioProject and GenBank databases (accession nos. PRJNA721777, PRJNA745276, and OL444665 to OL444682, respectively). All other study data are included in the article and/or supporting informationMicrobial biodeterioration is a major concern for the conservation of historical cultural relics worldwide. However, the ecology involving the origin, composition, and establishment of microbiomes on relics, once exposed to external environments, is largely unknown. Here, we combined field surveys with physiological assays and biological interaction experiments to investigate the microbiome in the Dahuting Han Dynasty Tomb, a Chinese tomb with more than 1,800 y of history, and its surrounding environments. Our investigation finds that multikingdom interactions, from mutualism to competition, drive the microbiome in this subterranean tomb. We reveal that Actinobacteria, Pseudonocardiaceae are the dominant organisms on walls in this tomb. These bacteria produce volatile geosmin that attracts springtails (Collembola), forming an interkingdom mutualism, which contributes to their dispersal, as one of the possible sources into the tomb from surrounding environments. Then, intrakingdom competition helps explain why Pseudonocardiaceae thrive in this tomb via the production of a mixture of cellulases, in combination with potential antimicrobial substances. Together, our findings show that multikingdom interactions play an important role in governing the microbiomes that colonize cultural relics. This knowledge is integral to understanding the ecological and physiological features of relic microbiomes and to supporting the relics’ long-term conservation.This work was supported by the National Key R&D Program (2019YFC1520700), the National Natural Science Foundation of China (42177297), Chinese Academy of Sciences (CAS) Strategic Priority Research Program Grant XDA28010302, and the Youth Innovation Promotion Association, CAS (Member No. 2014271). M.D.-B. is supported by a Ramón y Cajal Grant (RYC2018-025483-I), a project from the Spanish Ministry of Science and Innovation (PID2020-115813RA-I00), and Project Plan Andaluz de Investigación, Desarrollo e Innovación 2020 from the Junta de Andalucía (P20_00879).Peer reviewe

Runoff simulation driven by multi-source satellite data based on hydrological mechanism algorithm and deep learning network

Study region: Four sub-basins of the Songhua River basin, northeast China. Study focus: Conventional runoff models typically require in-depth knowledge of the hydrological and physical processes and are costly to construct and compute. Moreover, these models predominantly rely on ground site data, where incomplete or delayed data might introduce simulation uncertainty. Therefore, it is imperative to provide a scientifically rigorous and rational approach for simulating the runoff process, effectively addressing the limitations of existing methods. Combining a long short-term memory (LSTM) network with a modified Michel soil conservation service (MMSCS) algorithm, this study proposed the LSTM-MMSCS runoff simulation scheme. New hydrological insights for the region: The LSTM-MMSCS model was constructed by adjusting and optimizing the difference characteristics of the LSTM runoff simulation by establishing regression relationships according to the MMSCS-calculated runoff depth. LSTM-MMSCS adopted the coupling method of hydrological mechanism and deep learning to establish a simulation framework with adaptive feedback and adjustment between observed and simulated data. This scheme incorporated satellite meteorological products, solving the problem of inaccuracies caused by standard models' ineffective mining of temporal series information. LSTM-MMSCS reduced overall runoff error (RMSE was reduced from 50.07 mm to 24.47 mm) and effectively alleviated the problem of peak runoff underestimation (the relative error was reduced from 30.39% to 13.39%) compared to LSTM. Using satellite meteorological data to drive LSTM-MMSCS enabled runoff change trends visualization and aids in abnormal runoff localization