High Altitude Frogs (Rana kukonoris) Adopt a Diversified Bethedging Strategy in the Face of Environmental Unpredictability

Environmental unpredictability can influence strategies of maternal investment among eggs within a clutch. Models predict that breeding females should adopt a diversified bet-hedging strategy in unpredictable environments, but empirical field evidence from Asia is scarce. Here we tested this hypothesis by exploring spatial patterns in egg size along an altitudinal gradient in a frog species(Rana kukunoris) inhabiting the Tibetan Plateau. Within-clutch variability in egg size increased as the environment became variable(e.g., lower mean monthly temperature and mean monthly rainfall at higher altitudes), and populations in environments with more unpredictable rainfall produced eggs that were smaller and more variable in size. We provide support for a diversified bet-hedging strategy in high-altitude environments, which experience dynamic weather patterns and therefore are of unpredictable environmental quality. This strategy may be an adaptive response to lower environmental quality and higher unpredictable environmental variance. Such a strategy should increase the likelihood of breeding success and maximize maternal lifetime fitness by producing offspring that are adapted to current environmental conditions. We speculate that in high-altitude environments prone to physical disturbance, breeding females are unable to consistently produce the optimal egg size due to physiological constraints imposed by environmental conditions(e.g., duration of the active season, food availability). Species and populations whose breeding strategies are adapted to cope with uncertain environmental conditions by adjusting offspring size and therefore quality show a remarkable degree of ability to cope with future climatic changes.     

电针镇痛时人脑脊液中β-内啡肽样免疫活性物质的含量与痛阈的关系

用β-内啡肽放射免疫分析法测定10例无疼痛主诉的患者电针前后侧脑室脑脊液中B-内啡肽样免疫活性物质(B-EPIS)的含量,其中6例用弹簧棒测定了痛阈和耐痛阈。结果表明,每 ml 脑脊液中β-EPIS 的含量比针前增加126.7 fmol/ml(P<0.02),病人的痛阈比针前升高29.5%,耐痛阈升高28.1%(P<0.05)。β-EPIS 含量的增加量与痛阈、耐痛阈的升高值呈直线相关,r_1=0.776,r_2=0.741(P<0.05)。表明电针能促使脑内释放β-内啡肽,释放增加的β-内啡肽参与镇痛作用,这可能是电针镇痛机制的重要环节之一。     

Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma

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Diversity of endophytic bacteria and fungi in seeds of Elymus nutans growing in four locations of Qinghai Tibet Plateau,China

Plant and Soil - Seeds are involved in the transmission of microorganisms from one plant generation to the next, acting as initial inoculum for the plant microbiome, therefore provide a key source...     

N-糖蛋白去糖基化酶（PNGase）的研究进展

N-糖蛋白去糖基化酶（PNGase）是一种广泛存在于真菌、植物、哺乳动物中的去糖基化酶，可以水解N-糖蛋白或 N-糖肽上天冬酰胺与寡糖链连接的化学键，并释放出完整的N-寡糖。PNGase在生物体内参与蛋白质降解、器官发育、个体生长等过程。人PNGase基因功能缺陷会导致先天性去糖基化障碍，小鼠PNGase缺陷会导致胚胎致死性，线虫PNGase缺陷使其寿命下降。本文对PNGase在不同物种的分布、蛋白质结构、酶学功能及生物学功能进行阐述，为PNGase的生理病理功能及致病机制的基础研究提供思路，为PNGase作为糖生物学工具酶或药物开发的创新应用研究奠定基础。     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.