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

1. Download : Download high-res image (233KB)
2. Download : Download full-size image

     

Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.     

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...     

内蒙古部分地区骆驼和羊寄生蜱虫病毒组学分析

为了解内蒙古地区蜱虫病毒组学的本底数据,采用病毒宏基因组学方法对在内蒙古阿拉善盟左旗、右旗和四子王旗地区3个采样点采集骆驼和羊体表寄生的1789只蜱虫样品进行病毒宏基因组学分析,并对特定病毒进行巢式PCR扩增和测序,通过Clustal W和MEGA7.0等生物信息学软件对获得的病毒基因序列进行遗传进化分析.数据显示,蜱虫样品携带包括植物、脊椎动物和非脊椎动物等来源的17个病毒科和一些未分类的病毒;其中,2株弹状病毒具有丰富的遗传多样性,与新疆地区和长江地区的弹状病毒的同源性达到98.5％和96.26％,提示蜱虫弹状病毒可能是通过羊和骆驼等动物贸易导致了新疆和内蒙古地区,以及内地的跨区域传播;细小病毒仅在羊来源的蜱虫中检测到,与中国河北地区的山羊血清中的细小病毒形成同一进化分支,我们推测蜱虫细小病毒在国内不同地区间可跨区域传播,在进化分析过程中,发现这种病毒与多种的细小病毒的同源性都不低于50％,提示细小病毒可能具有遗传稳定性;Tamdy病毒与来自阿塞拜疆、乌兹别克斯坦和美国的Tamdy病毒均具有极高的同源性,结果显示该病毒在内蒙古地区已经出现,并存在潜在流行的可能,有必要对Tamdy病毒进行进一步的监测;在本研究中,我们鉴定的白蛉病毒与来自新疆的亚洲璃眼蜱所携带的博乐蜱虱病毒形成同一个进化分支,与新型布尼亚病毒和Heartland virus病毒的同源性达到50％以上,该结果提示,我们发现的蜱虫白蛉病毒可能具有潜在的致病性,需要对其流行情况和致病性进行监测和研究.本研究为完善内蒙古部分地区蜱虫病毒的多样性和本底情况提供了重要的基础数据.     

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.