基于三温模型的南宁市蒸散量估算及其影响因素

蒸散发是水文能量循环和气候系统的关键要素.研究蒸散发的时空变化特征及其响应气候、土地利用的变化规律,对理解城市流域水循环和生态过程效应具有重要意义.本研究基于三温模型和MODIS影像,估算并分析2001-2018年南宁市的蒸散量时空演变特征,并探讨了主要气候要素、土地利用类型对蒸散量的影响规律和驱动模式.结果表明:20...     

亚热带河口养殖塘沉积物有机碳矿化及其影响因子

亚热带河口的养殖塘是温室气体二氧化碳(CO2)的重要排放源,但其沉积物有机碳矿化动力学特征目前尚未厘清.以我国东南沿海亚热带闽江、木兰溪以及九龙江河口区的6个凡纳滨对虾(Penaeus vannamei)养殖塘为研究对象,对沉积物取样,并进行为期60 d的室内厌氧培养,对养殖塘沉积物CO2累积产量曲线利用一阶动力学模型...     

Pyroptosis in glioblastoma: A crucial regulator of the tumour immune microenvironment and a predictor of prognosis

Recent studies have shown that pyroptosis, an inflammatory form of cell death, has a dual role in tumorigenesis and tumour progression and affects the prognosis of patients; however, the role of pyroptosis in glioblastoma (GBM) is still unclear. In this study, based on GBM patients'' data from two independent cohorts, we performed a comprehensive analysis of the expression and prognostic value of 33 pyroptosis‐associated genes (PAGs) in GBM, as well as their role in the tumour immune microenvironment (TIME) of GBM. We identified 29 PAGs that were differentially expressed between GBM and normal brain tissue, 18 of which were upregulated in GBM tissue. Most of the 33 PAGs were strongly correlated with the levels of immune cell infiltration. Based on the 33 PAGs, the GBM samples can be divided into two clusters (C1‐C2), with C1 having a ‘hot’ but immunosuppressive TIME and C2 having a ‘cold’ TIME, suggesting different immunotherapeutic responses in the two clusters. In addition, we identified four PAGs that were strongly associated with GBM prognosis and constructed a risk model based on these four PAGs. This risk model is an independent prognostic factor for GBM patients, and there is a different immune status between high‐ and low‐risk groups. In conclusion, this study demonstrates that pyroptosis is closely associated with the prognosis and TIME of GBM and provides an important basis for further studies on the relationship between pyroptosis and GBM.     

基于鼠脑海马位置细胞与Q学习面向目标导航

生理学实验表明在鼠脑海马结构中存在的一种具有特异性放电特征的细胞,它在大鼠空间导航和环境认知中起着关键性作用,该特异性神经元被称之为位置细胞。本文将基于位置细胞、运动神经元来构建一种前馈神经网络模型,采用Q学习算法实现大鼠面向目标导航任务。实验结果表明该前馈神经网络模型能快速实现大鼠面向目标导航任务。     

基于肿瘤免疫微环境鉴定IDH突变型弥漫性胶质瘤的预后标志物

异柠檬酸脱氢酶(IDH)突变存在于大多数低级别胶质细胞瘤中,免疫逃逸是肿瘤标志性特征之一,免疫治疗在胶质瘤的治疗中的作用越来越重要。利用生信手段分析TCGA、CGGA、GEO数据集中IDH突变胶质瘤的770个免疫相关基因及其临床相关数据,从而获得每个患者的免疫风险评分(IMRS);结合IMRS和临床信息,筛选出6个差异表达基因(TRAF3、ATG10、BID、TAB1、MAP3K1、RPS6)组成IMRS模型并生成诺莫图对患者预后进行评估,发现低风险组患者的总生存期(OS)较高风险组均明显延长。此外,签名相关免疫细胞浸润分析发现肿瘤相关巨噬细胞浸润评分(TAM)与肿瘤相关T细胞浸润评分(TIS)呈明显的负相关,表明高IMRS富集了促肿瘤免疫浸润,而低IMRS则富集了相对较多的抗肿瘤免疫浸润。     

赤水河外来鱼类尖头(鱥)和董氏须鳅的引种溯源及生态适应性分析

历史上,赤水河流域干流及支流的鱼类物种组成中未曾出现过尖头(鱥)(Rhynchocypris oxycephalus)和董氏须鳅(Barbatula toni).但是,近年来在赤水河支流白沙河的鱼类资源调查中发现了这两个物种的分布,其来源及未来的生存可能性受到关注.本研究比较了尖头(鱥)赤水河野外种群与养殖种群形态上的...     

黑龙江省2010～2020年猩红热发病与气象因素的关联性研究

分析黑龙江省气象因素与猩红热发病的关系,建立时间序列模型,为今后制定更科学有效的猩红热防控策略提供参考依据。收集黑龙江省2010～2020年猩红热月发病数据以及同期气温、气压等气象资料,应用广义相加模型分析气象因素与猩红热发病之间的关联程度和形式。结果发现: 猩红热全年均有发病而且呈现出较为典型的双峰型特征,在春季的4～5月份和冬季的11～12月份发病数达到高峰;月平均气压、月平均相对湿度、月日照时数和月平均风速的P值均小于0.05,表明具有统计学意义。同时,RR(相对危险度Risk Ratio)值均小于1,即猩红热发病与四个气象因素呈负相关。黑龙江省猩红热发病每年存在两个流行高峰,主要以冬季为主,发病数随着月平均相对湿度、月日照时数、月平均风速与月平均气压的升高而降低。     

基于HP模型的蛋白质折叠问题的研究

基于蛋白质二维HP模型提出改进的遗传算法对真实蛋白质进行计算机折叠模拟。结果显示疏水能量函数最小值的蛋白质构象对应含疏水核心的稳定结构,疏水作用在蛋白质折叠中起主要作用。研究表明二维HP模型在蛋白质折叠研究中是可行的和有效的并为进一步揭示蛋白质折叠机理提供重要参考信息。     

The effect of past changes in inter‐annual temperature variability on tree distribution limits

Aim The northern limits of temperate broadleaved species in Fennoscanndia are controlled by their requirements for summer warmth for successful regeneration and growth as well as by the detrimental effects of winter cold on plant tissue. However, occurrences of meteorological conditions with detrimental effects on individual species are rare events rather than a reflection of average conditions. We explore the effect of changes in inter‐annual temperature variability on the abundances of the tree species Tilia cordata, Quercus robur and Ulmus glabra near their distribution limits using a process‐based model of ecosystem dynamics. Location A site in central Sweden and a site in southern Finland were used as examples for the ecotone between boreal and temperate forests in Fennoscandia. The Finnish site was selected because of the availability of varve‐thickness data. Methods The dynamic vegetation model LPJ‐GUESS was run with four scenarios of inter‐annual temperature forcing for the last 10,000 years. In one scenario the variability in the thickness of summer and winter varves from the annually laminated lake in Finland was used as a proxy for past inter‐annual temperature variability. Two scenarios were devised to explore systematically the effect of stepwise changes in the variance and shape parameter of a probability distribution. All variability scenarios were run both with and without the long‐term trend in Holocene temperature change predicted by an atmospheric general circulation model. Results Directional changes in inter‐annual temperature variability have significant effects on simulated tree distribution limits through time. Variations in inter‐annual temperature variability alone are shown to alter vegetation composition by magnitudes similar to the magnitude of changes driven by variation in mean temperatures. Main conclusions The varve data indicate that inter‐annual climate variability has changed in the past. The model results show that past changes in species abundance can be explained by changes in the inter‐annual variability of climate parameters as well as by mean climate. Because inter‐annual climatic variability is predicted to change in the future, this component of climate change should be taken into account both when making projections of future plant distributions and when interpreting vegetation history.