Characterizing the spatial distribution of giant pandas (Ailuropoda melanoleuca) in fragmented forest landscapes

Aim To examine the effects of forest fragmentation on the distribution of the entire wild giant panda (Ailuropoda melanoleuca) population, and to propose a modelling approach for monitoring the spatial distribution and habitat of pandas at the landscape scale using Moderate Resolution Imaging Spectro‐radiometer (MODIS) enhanced vegetation index (EVI) time‐series data. Location Five mountain ranges in south‐western China (Qinling, Minshan, Qionglai, Xiangling and Liangshan). Methods Giant panda pseudo‐absence data were generated from data on panda occurrences obtained from the third national giant panda survey. To quantify the fragmentation of forests, 26 fragmentation metrics were derived from 16‐day composite MODIS 250‐m EVI multi‐temporal data and eight of these metrics were selected following factor analysis. The differences between panda presence and panda absence were examined by applying significance testing. A forward stepwise logistic regression was then applied to explore the relationship between panda distribution and forest fragmentation. Results Forest patch size, edge density and patch aggregation were found to have significant roles in determining the distribution of pandas. Patches of dense forest occupied by giant pandas were significantly larger, closer together and more contiguous than patches where giant pandas were not recorded. Forest fragmentation is least in the Qinling Mountains, while the Xiangling and Liangshan regions have most fragmentation. Using the selected landscape metrics, the logistic regression model predicted the distribution of giant pandas with an overall accuracy of 72.5% (κ = 0.45). However, when a knowledge‐based control for elevation and slope was applied to the regression, the overall accuracy of the model improved to 77.6% (κ = 0.55). Main conclusions Giant pandas appear sensitive to patch size and isolation effects associated with fragmentation of dense forest, implying that the design of effective conservation areas for wild giant pandas must include large and dense forest patches that are adjacent to other similar patches. The approach developed here is applicable for analysing the spatial distribution of the giant panda from multi‐temporal MODIS 250‐m EVI data and landscape metrics at the landscape scale.     

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.     

心肌带收缩率与急性心肌梗死患者左心室收缩功能关系

目的:急性前壁心肌梗死明显影响室间隔收缩率和左心室射血分数(left ventricular ejection fraction LVEF)。本文旨在探讨心肌带降段及升段收缩率与急性前壁心肌梗死患者LVEF的相关性。方法:收集2015年4月-2017年2月在心内科住院的急性前壁心肌梗死患者36例,正常对照组患者39例。所有患者取左心室长轴M型超声心动图,测量室间隔收缩率、升段收缩率及降段收缩率。心肌梗死左心室射血分数采用双平面Simpson's法计算。结果:与正常对照组相比,心肌梗死组患者舒张末期心肌带升段厚度没有统计学差异(P=0.69),收缩末期升段厚度(P=0.014)更薄、升段收缩率(P0.01)明显降低;心肌梗死组舒张末期降段厚度(P0.01)更薄、收缩末期降段厚度(P0.01)更薄、降段收缩率(P0.01)明显降低;心肌梗死组左心室射血分数与降段收缩率(r~2=0.13,P=0.026)、室间隔增厚率(r~2=0.19,P0.01)呈正相关,与升段收缩率没有相关性(P0.05)。正常对照组左心室射血分数与室间隔增厚率、降段增厚率及升段增厚率无相关性。经过相关分析,筛选出与心肌梗死LVEF的相关因素,进一步经逐步回归分析,得多元线性回归方程为LVEF=48.206+18.914*LVDD(cm)-25.414*LVSD(cm)。结论:急性前壁心肌梗死室间隔降段收缩率明显受损,与左心室射血分数降低有关。多元线性回归方程可估算前壁心肌梗死LVEF。     

暖温带乔木和灌木物候变化及对气候变化的响应

根据2003-2014年气象数据和暖温带3种乔木(辽东栎、五角枫和核桃楸)和3种灌木(土庄绣线菊、毛叶丁香和六道木)的物候观测数据资料, 采用气候倾向率和回归分析等方法, 观察乔木和灌木物候变化特征的差异, 分析温度、降水以及乔木、灌木的物候变化趋势, 同时对气象因子与乔木和灌木物候期的相关关系进行研究。结果表明: ①研究期间, 北京东灵山平均气温呈不显著的上升趋势, 气候倾向率为0.200℃·10a–1, 春季(3–5月)和夏季(6-8月)温度显著上升; 降水量呈下降趋势, 平均减少71.630 mm·10a–1, 总体呈暖、干的趋势。②3种乔木的生长季长度都缩短, 辽东栎、五角枫和核桃楸平均生长季长度分别缩短50.70 d·10 a–1、29.83 d·10a–1和22.36 d·10a–1。3种灌木的生长季长度也都缩短, 土庄绣线菊、毛叶丁香和六道木的平均生长季长度分别缩短42.55 d·10a–1、42.76 d·10a–1和38.15 d·10a–1。乔木和灌木的物候变化趋势相同, 整体表现为春季物候推迟, 秋季物候提前, 生长季长度都缩短且生长季长度相差不大。乔木和灌木都表现出芽期推迟最明显, 每10年推迟达19天以上。③乔木和灌木各物候期与气温总体表现为负相关, 即气温升高, 物候期提前, 其相关性显示出夏季(6-8月)温度对植被物候期影响较大, 夏季温度与各物候期表现为正相关, 即夏季温度升高, 物候期推迟。同时乔木和灌木与总体降水没有明显的相关关系, 但秋季物候与不同时段降水表现不同的相关性, 由此可知夏季温度变化对木本植物春季物候(出芽期、展叶期和首花期)的影响更大, 而秋季物候(叶变色期和落叶期)受温度和降水共同影响。