Expression of transforming growth factor-β in local bony callus in traumatic brain injury combined with extremity fracture in rats

Objective To investigate gene expression of transforming growth factor-β(TGF-β)in local bony callus in tracumatic brain in jury combined with extremity long bone fracture in rats.Methods Eighty male SD rats were randomized into 2 even

Assessment of Central Asian heat extremes by statistical downscaling:Validation and future projection for 2015-2100

Increasing heatwaves and extreme temperatures have recently been observed across Central Asia(CA).Accurately assessing and projecting the changing climate extremes at the local(station)scale required for climate risk management are therefore highly important.However,global and regional climate models often fail to represent the statistical distributions of observed daily extreme variables and hence extremes in complex terrain.In this work,we developed a statistical downscaling(SD)model to project summer daily maximum temperature(Tmax)and heatwave indices for 65 meteorological stations in CA toward 2100.The SD model involves first-order autoregression and multiple linear regression using large-scale Tmax and circulation indices(Cis)as predictors,and the model is cross-validated against historical observations.The local Tmax and heatwave indices are then projected for 2015-2100 driven by the output of a global climate model(CNRM-CM6-1)under four Shared Socioeconomic Pathways(SSP126,SSP245,SSP370,and SSP585).The application of the SD model significantly improves forecasting of the probability distribution(10th/90th percentiles)of Tmax at stations,particularly across mountainous regions.The model also captures interannual variability and the long-term trend in Tmax,consistent with synoptic-scale inputs.SD projections demonstrate strong warming trends of summer Tmax in CA toward 2100 with rates between 0.35-0.64℃ per decade based on the SSP245 and SSP370 seenarios.Consequently,heatwave occurrence is projected to rise by 1.0-5.0 and 2.0-7.0 d per decade under the SSP245 and SSP370 scenarios,respectively,by 2100.Duration,intensity,and amplitude of heatwaves rise at greater rates under higher-emission scenarios,particularly in southeastern CA.The proposed SD model serves as a useful tool for assessing local climate extremes,which are needed for regional risk management and policymaking for adaption to climate change.

Effects of corner modifications on wind loads and local pressures on walls of tall buildings

In this study, the aerodynamic characteristics of tall buildings with corner modifications (e.g., local wind force coefficients, mean pressure distributions, normalized power spectrum density, and extreme local pressure) were examined. Wind tunnel experiments were conducted to measure the wind pressures on building models with different heights and recessed corners of different ratios. At a wind direction of a = 0° (i.e., wind blowing on the front of a building), corner modifications effectively reduced wind forces in all cases. Specifically, small corner modification ratios reduced wind forces more effectively than their larger counterparts. However, corner modifications resulted in extreme local pressure on building surfaces. In addition, for small corner modification ratios, the probability of extreme local pressure occurring at a = 0° was high. This probability was also high for large corner modification ratios at a = 15° (i.e., wind blowing slightly obliquely on the front of a building) because wind blowing obliquely creates substantial vortex shedding on one side surface and extreme negative pressure over one building side surface. Results of computational fluid dynamic modeling were adopted to determine details of the aerodynamic characteristics of tall buildings with corner modifications.

Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia(CA)is highly sensitive and vulnerable to changes in precipitation due to global warming,so the projection of precipitation extremes is essential for local climate risk assessment.However,global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes,especially in areas with complex terrain.In this study,we proposed a statistical downscaling(SD)model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models(GCMs)for present and future(2081-2100)periods under two shared socioeconomic pathways(SSP245 and SSP585).The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity(SDII)and maximum 1-day precipitation(RX1DAY)and overestimate the number of wet days(R1MM)and maximum consecutive wet days(CWD)at stations across CA.However,the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations.Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA.In addition,it is skilled in capturing the spatial patterns of the observed precipitation indices.Obviously,SDII and RX1DAY are improved by the SD model,especially in the southeastern mountainous area.Under the intermediate scenario(SSP245),our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation(R95PTOT)of 0.5 mm d^(-1) and 19.7 mm,respectively,over CA at the end of the 21st century(2081-2100)compared to the present values(1995-2014).More pronounced increases in indices R95PTOT,SDII,number of very wet days(R10MM),and RX1DAY are projected under the higher emission scenario(SSP585),particularly in the mountainous southeastern region.The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081-2100.The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.