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The amino acid composition of collagens from different animal species was determined and their binding capacities to integrin α2β1 and HT1080 cells were studied by enzyme-linked immunosorbent assay (ELISA) and cell adhesion experiments. Furthermore, the effect of amino acid composition on collagen binding to α2β1 was discussed. The results showed that there were significant differences among the binding capacities of collagens from different animal species to integrin α2β1 and HT1080 cells, and the binding capacity of mammalian collagen was higher than that of fish collagen. The effects of amino acid composition on the binding capacities of collagen to integrin α2β1 and HT1080 cells were similar. The binding capacities were positively correlated with the contents of hydroxyproline, polar uncharged amino acids, imino acids and the degree of hydroxylation of proline, but negatively correlated with the contents of glutamate, glycine, polar charged amino acids and acidic amino acids (P < 0.01). Based on the content of informative amino acids at the high-affinity binding site between collagen and integrin α2β1, a mathematical model between the contents of hydroxyproline and arginine and collagen-integrin α2β1 binding was established by multivariate step-wise regression analysis, which had good predictive accuracy.

Background The worldwide COVID-19 pandemic has turned into a global catastrophic public health crisis, and the conclusion about the risk factors of hospital death in COVID-19 patients is not uniform. Objective To explore risk factors of in-hospital death in patients with COVID-19 by a meta-analysis. Methods Case-control studies about risk factors of in-hospital death in COVID-19 patients were searched from databases of the Cochrane Library, ScienceDirect, PubMed, Medline, Wanfang Data, CNKI and CQVIP from inception to October 1, 2021. Literature screening, data extraction and methodological quality assessment were conducted. Meta-analysis was performed using Stata 15.1. Meta-regression was used to explore the potential sources of heterogeneity. Results Eighty studies were included which involving 405 157 cases〔349 923 were survivors (86.37%) , and 55 234 deaths (13.63%) 〕, that were rated as being of high quality by the Newcastle-Ottawa Scale. Meta-analysis showed that being male〔OR=1.49, 95%CI (1.41, 1.57) , P

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

Abstract: Tropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation datasets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.25° (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper’s results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.

Background: To investigate the relationship between visfatin level in the peripheral blood of patients with acute myocardial infarction (AMI) patients and the severity of AMI, cardiovascular risk factors and atrial fibrillation after percutaneous coronary intervention (PCI). Methods: A total of 37 AMI patients diagnosed and treated in our hospital were selected as experimental group, and 35 patients with normal coronary angiography were enrolled as control group. The general pathological data and occurrence of atrial fibrillation after PCI of all the patients were recorded in detail, and the content of indexes related to the severity of AMI and visfatin was measured. Moreover, the correlations of visfatin with the severity of AMI, cardiovascular risk factors and atrial fibrillation after PCI were explored. Results: The differences in the levels of N-terminal pro-Btype natriuretic peptide (NT-proBNP), cardiac troponin I (cTnI), myoglobin (MYO) and creatine kinase isoenzyme MB mass (CK-MBm) in the peripheral blood, left ventricular end-systolic diameter (LVEDs), left ventricular end-diastolic volume (LVEDV) and left ventricular ejection fraction (LVEF) were statistically significant between experimental group and control group. Furthermore, experimental group had a remarkably higher visfatin level in the peripheral blood than control group. The visfatin level in the peripheral blood was positively correlated with the severity of AMI and the cardiovascular risk factors, but it was negatively associated with atrial fibrillation after PCI. Conclusion: The visfatin level in the peripheral blood of AMI patients has close correlations with the severity of AMI, cardiovascular risk factors and atrial fibrillation after PCI. This study suggests that measuring the visfatin level in the peripheral blood of AMI patients can provide useful information on the severity of AMI, cardiovascular risk factors, and the likelihood of atrial fibrillation after PCI. Therefore, visfatin may serve as a potential biomarker for risk stratification and personalized treatment of AMI patients.

Two flame retardants, melamine polyphosphate (MPP) and modified melamine cyanurate (MCA), were examined and added to a cellulose solution to fabricate flame-retardant Lyocell fibers Lyocell-MPP and Lyocell-MCA, respectively, via dry-wet spinning. Compared with the non-modified Lyocell fiber (without flame retardant), the flame-retardant fibers produced a higher amount of carbon residue during combustion, whereas the heat release capacity, peak heat release rate, and total heat release decreased, indicating that the flame-retardant fibers present a lower fire risk. The limit oxygen index values of Lyocell-MPP and Lyocell-MCA were 26.5% and 27.1%, respectively, complying with the flame-retardant standard. The crystallinity and mechanical properties of the flame-retardant fibers decreased relative to those of the nonmodified Lyocell fiber. Furthermore, the wearability of the flame-retardant fibers was studied--both flame-retardant fibers displayed good washing resistance properties and improved hygroscopicity but a lower dyeing performance than the non-modified Lyocell fiber. KEYWORDS dyeing, flame retardant, limiting oxygen index, Lyocell fiber, mechanical property, 1 | INTRODUCTION Lyocell fiber is a type of regenerated cellulose fiber, which has several advantages such as good air permeability, high dyeing rate, and good moisture absorption, and is [...]

Accurate estimates of aquifer parameters are necessary for effective groundwater management and for geotechnical engineering applications. Pumping tests may be employed to estimate the hydraulic conductivity in leaky aquifer/aquitard systems. This work introduces a hybrid algorithm with global search capacity (the Genetic algorithm, GA) and local search capacity—the Levenberg-Marquardt (LM) algorithm—coupled with a modified Neuman-Witherspoon solution for leaky aquifers to estimate the aquifer’s hydraulic parameters from pumping-test data. The GA is employed to determine the initial guesses of the aquifer parameter values. The optimal parameter values are then obtained with the LM algorithm, yielding a mixed GA/LM algorithm, herein named GALMA. Results show that the drawdown trends based on the estimated parameters agree well with measured drawdown. The proposed estimation algorithm identifies aquifer parameters with greater reliability than previous approaches. Verification of the GALMA is carried out based on three pumping tests in a layered aquifer in Tianjin, China, and on four historical case studies involving diverse hydrogeological settings. The excellent match between observed drawdown and GALMA-estimated parameters demonstrates the estimation accuracy and superior performance relative to previously reported estimation methods.

The correct email address for the corresponding author (Haizuo Zhou) is: hzzhou@tju.edu.cn.