Ventilation strategy has a major influence on remote ischaemic preconditioning in mice

Whether oxygen should be administered acutely during ST‐segment elevation myocardial infarction is debated. Despite this controversy, the possible influence of supplementary oxygen on animal models of ischaemia–reperfusion injury or cardioprotection is rarely considered. We used an in vivo mouse model of ischaemia and reperfusion to investigate the effect of ventilation with room air versus 100% oxygen. The coronary artery of anaesthetized mice was occluded for 40 min. followed by 2‐hrs reperfusion. Infarct size was measured by tetrazolium staining and expressed as a percentage of area at risk, determined using Evan's blue. Unexpectedly, infarct size in mice ventilated with 100% oxygen was significantly smaller than in those ventilated with room air (33 ± 5% versus 46 ± 3%; n = 6; P < 0.01). We tested a standard protocol of 3 × 5 min. cycles of remote ischaemic preconditioning (RIPC) and found this was unable to protect mice ventilated with 100% oxygen. RIPC protocols using 2.5‐ or 10‐min. occlusion were similarly ineffective in mice ventilated with oxygen. Similar disparate results were obtained with direct cardiac ischaemic preconditioning. In contrast, pharmacological protection using bradykinin administered at reperfusion was effective even in mice ventilated with 100% oxygen, reducing infarct size from 33 ± 5% to 21 ± 3% (n = 4–6; P < 0.01). Laser speckle contrast imaging of blood flow and direct pO₂ measurements were made in the hindlimb, but these measurements did not correlate with protection. In conclusion, ventilation protocol can have a major influence on infarct size and ischaemic preconditioning protocols in mice.     

轻小型无人机低空遥感及其在生态学中的应用进展

无人机低空遥感系统弥补了航天和航空遥感在影像分辨率、重访周期、云层影响以及高成本等方面的不足,为中观尺度的生态学研究提供了新方法.本文介绍了轻小型无人机低空遥感系统的组成,从物种、种群、群落和生态系统尺度综述了其在生态学中的应用现状,并指出目前存在的不足和未来的发展方向,以期为无人机生态学的后续研究提供参考.无人机生态学当前面临的挑战和未来发展的方向主要有物种形态和光谱特征库的建立、物种自动识别、光谱数据与植物生理生态过程之间关系的进一步挖掘、生态系统三维立体监测、多来源多尺度遥感数据融合等.随着无人机平台技术、传感器技术以及数据传输处理技术的成熟,以无人机低空遥感技术为基础的无人机生态学将迎来发展的机遇和曙光.     

亚热带典型区域水稻土氧化铁高光谱反演——以珠江三角洲为例

氧化铁是土壤中铁元素的主要存在形式,亚热带土壤中高含量的氧化铁形成了该区域重要的土壤附色成分针铁矿和赤铁矿等矿物,使得土壤颜色明显区别于其他气候带.以亚热带典型地区珠江三角洲为例,分析不同光谱形式与土壤氧化铁含量的相关性,提取特征光谱波段建立土壤氧化铁的反演模型.结果表明: 土壤氧化铁含量与反射光谱之间呈负相关,且敏感波段主要位于404、574、784、854和1204 nm等可见近红外区域.微分处理后的光谱与土壤氧化铁的相关性明显提高.在相关性显著波段的基础上采用逐步多元线性回归以及主成分分析剔除共线性波段,最后选择特征光谱波段作为反演模型的输入参数.比较反演模型的结果,得出该地区土壤氧化铁含量的最佳反演模型为BP人工神经网络(RMSEC=0.22,RMSEP=0.81,R²=0.93,RPD=12.20),该模型具有非常好的稳定性,适用于快速估测土壤中氧化铁含量,并且能够为测度土壤氧化铁的空间分布提供研究基础.     

应用远程质量控制体系对CT性能监测的效果评价

目的 通过对CT远程质量控制体系应用前后其性能状态对比分析,检验CT远程质量控制模式的价值与效果。方法 收集3家医疗机构41份CT指标检测数据,对远程质量控制体系应用前、后CT的性能指标数据进行对比分析。结果 CT远程质量控制体系应用后,CT性能指标（噪声、空间分辨力)较前显著改善（P﹤0.01）,CT合格率明显提高（P﹤0.01）。结论 质量控制是提高临床质量,促进精准医疗发展的重要保障,远程质量控制体系可靠性好,为全国各医疗机构开展CT远程质量控制工作提供了借鉴依据,也为实现MRI、DSA、DR等大型设备远程质量控制开辟了可靠路径。     

水土流失区生态修复后植被健康的遥感判别

基于遥感技术和WorldView-2卫星影像,提出一个新型植被健康指数(VHI),以快速、大面积地监测与评价水土流失区的植被健康状况.该指数由归一化山地植被指数、氮素反射指数和黄光波段反射率3个因子构成,通过主成分变换将3个因子集成为VHI,以避免主观加权求和集成法所产生的偏差.将VHI应用于福建省长汀县河田盆地一带,对水土流失区的植被健康状况进行监测.结果表明: VHI可以很好地揭示植被的健康状况,其判别总精度可达91%.河田盆地健康等级为好、中、差的植被面积占植被总面积的比例分别为10.1%、49.2%和40.7%,说明研究区的植被健康总体状况仍不理想,主要因为水土流失区土壤贫瘠、新种植的植被生长不良.     

Application and comparison of remote sensing GPP models with multi-site data in China北大核心CSCD

Aims Estimation of gross primary productivity (GPP) of vegetation at the global and regional scales is important for understanding the carbon cycle of terrestrial ecosystems. Due to the heterogeneous nature of land surface, measurements at the site level cannot be directly up-scaled to the regional scale. Remote sensing has been widely used as a tool for up-saling GPP by integrating the land surface observations with spatial vegetation patterns. Although there have been many models based on light use efficiency and remote sensing data for simulating terrestrial ecosystem GPP, those models depend much on meteorological data; use of different sources of meteorological datasets often results in divergent outputs, leading to uncertainties in the simulation results. In this study, we examines the feasibility of using two GPP models driven by remote sensing data for estimating regional GPP across different vegetation types. Methods Two GPP models were tested in this study, including the Temperature and Greenness Model (TG) and the Vegetation Index Model (VI), based on remote sensing data and flux data from the China flux network (ChinaFLUX) for different vegatation types for the period 2003-2005. The study sites consist of eight ecological stations located in Xilingol (grassland), Changbaishan (mixed broadleaf-conifer forest), Haibei (shrubland), Yucheng (cropland), Damxung (alpine meadow), Qianyanzhou (evergreen needle-leaved forest), Dinghushan (evergreen broad-leaved forest), and Xishuangbanna (evergreen broad-leaved forest), respectively. Important findings All the remote sensing parameters employed by the TG and VI models had good relationships with the observed GPP, with the values of coefficient of determination, R2, exceeding 0.67 for majority of the study sites. However, the root mean square errors (RMSEs) varied greatly among the study sites: the RMSE of TG ranged from 0.29 to 6.40 g·m-2·d-1, and that of VI ranged from 0.31 to 7.09 g·m-2·d-1, respectively. The photosynthetic conversion coefficients m and a can be up-scaled to a regional scale based on their relationships with the annual average nighttime land surface temperature (LST), with 79% variations in m and 58% of variations in a being explainable in the up-scaling. The correlations between the simulated outputs of both TG and VI and the measured values were mostly high, with the values of correlation coefficient, r, ranging from 0.06 in the TG model and 0.13 in the VI model at the Xishuangbanna site, to 0.94 in the TG model and 0.89 in the VI model at the Haibei site. In general, the TG model performed better than the VI model, especially at sites with high elevation and that are mainly limited by temperature. Both models had potential to be applied at a regional scale in China.     

Snapin promotes HIV‐1 transmission from dendritic cells by dampening TLR8 signaling

HIV‐1 traffics through dendritic cells (DCs) en route to establishing a productive infection in T lymphocytes but fails to induce an innate immune response. Within DC endosomes, HIV‐1 somehow evades detection by the pattern‐recognition receptor (PRR) Toll‐like receptor 8 (TLR8). Using a phosphoproteomic approach, we identified a robust and diverse signaling cascade triggered by HIV‐1 upon entry into human DCs. A secondary siRNA screen of the identified signaling factors revealed several new mediators of HIV‐1 trans‐infection of CD4⁺ T cells in DCs, including the dynein motor protein Snapin. Inhibition of Snapin enhanced localization of HIV‐1 with TLR8⁺ early endosomes, triggered a pro‐inflammatory response, and inhibited trans‐infection of CD4⁺ T cells. Snapin inhibited TLR8 signaling in the absence of HIV‐1 and is a general regulator of endosomal maturation. Thus, we identify a new mechanism of innate immune sensing by TLR8 in DCs, which is exploited by HIV‐1 to promote transmission.     

Climate variability drives recent tree mortality in Europe

Tree mortality is an important process in forest ecosystems, frequently hypothesized to be highly climate sensitive. Yet, tree death remains one of the least understood processes of forest dynamics. Recently, changes in tree mortality have been observed in forests around the globe, which could profoundly affect ecosystem functioning and services provisioning to society. We describe continental‐scale patterns of recent tree mortality from the only consistent pan‐European forest monitoring network, identifying recent mortality hotspots in southern and northern Europe. Analyzing 925,462 annual observations of 235,895 trees between 2000 and 2012, we determine the influence of climate variability and tree age on interannual variation in tree mortality using Cox proportional hazard models. Warm summers as well as high seasonal variability in precipitation increased the likelihood of tree death. However, our data also suggest that reduced cold‐induced mortality could compensate increased mortality related to peak temperatures in a warming climate. Besides climate variability, age was an important driver of tree mortality, with individual mortality probability decreasing with age over the first century of a trees life. A considerable portion of the observed variation in tree mortality could be explained by satellite‐derived net primary productivity, suggesting that widely available remote sensing products can be used as an early warning indicator of widespread tree mortality. Our findings advance the understanding of patterns of large‐scale tree mortality by demonstrating the influence of seasonal and diurnal climate variation, and highlight the potential of state‐of‐the‐art remote sensing to anticipate an increased likelihood of tree mortality in space and time.     

Attribution of seasonal leaf area index trends in the northern latitudes with “optimally” integrated ecosystem models

Significant increases in remotely sensed vegetation indices in the northern latitudes since the 1980s have been detected and attributed at annual and growing season scales. However, we presently lack a systematic understanding of how vegetation responds to asymmetric seasonal environmental changes. In this study, we first investigated trends in the seasonal mean leaf area index (LAI) at northern latitudes (north of 30°N) between 1982 and 2009 using three remotely sensed long‐term LAI data sets. The most significant LAI increases occurred in summer (0.009 m² m^?2 year^?1, p < .01), followed by autumn (0.005 m² m^?2 year^?1, p < .01) and spring (0.003 m² m^?2 year^?1, p < .01). We then quantified the contribution of elevating atmospheric CO₂ concentration (eCO₂), climate change, nitrogen deposition, and land cover change to seasonal LAI increases based on factorial simulations from 10 state‐of‐the‐art ecosystem models. Unlike previous studies that used multimodel ensemble mean (MME), we used the Bayesian model averaging (BMA) to optimize the integration of model ensemble. The optimally integrated ensemble LAI changes are significantly closer to the observed seasonal LAI changes than the traditional MME results. The BMA factorial simulations suggest that eCO₂ provides the greatest contribution to increasing LAI trends in all seasons (0.003–0.007 m² m^?2 year^?1), and is the main factor driving asymmetric seasonal LAI trends. Climate change controls the spatial pattern of seasonal LAI trends and dominates the increase in seasonal LAI in the northern high latitudes. The effects of nitrogen deposition and land use change are relatively small in all seasons (around 0.0002 m² m^?2 year^?1 and 0.0001–0.001 m² m^?2 year^?1, respectively). Our analysis of the seasonal LAI responses to the interactions between seasonal changes in environmental factors offers a new perspective on the response of global vegetation to environmental changes.