In November 1981,an ancient female cadaver of Qidan nationality in wulanchatu League of InnerMongolia was excavated.This cadaver was estimated approximately 25 year of age.She has been buriedfor about 900 years.The cadaver was a mummified corpse,and preserved in a comparatively intact state. The hair was intact and dark brown in colour.All viseral organs were dreg-like and dark brown incolour.The individual organ could not be identified from its exernal appearance.Only lungs werepreserved,adhering to the back of the thoracic cavity and looked like dry leaves.The peritonem wasdry and translucent and contained a few blood vessels.The muscles were also dry and dark brown or brownishgray in colour.The alveoli and the parenchma of the lung as well as bacterial spores were identified.The histologicalstructure of tissues from the heart region looked like the cross section of cardiac fiber.Collagenous fiberswere seen.Other tissues were mostly autolyzed.The cadaver were group B.According to results of toxic analysis,larger amount of arsenic was demonstratedin tissues from the stomach region.So it was deduced that the lady might die of an arsenical poisoning.Factors involving in the well preservation of this case are discussed.

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.

The accelerating growth of the public microbial genomic data imposes substantial bur-den on the research community that uses such resources. Building databases for non-redundant ref-erence sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algo-rithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demon-strate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.

Objective:To explore the associations between perceived built environment attributes and adults’ leisure-time physical activity in four cities of China.Methods:Multistage cluster random sampling method was used to select adults aged 25 to 64 in Hangzhou, Suzhou, Chengdu, and Qingdao. Data were collected from June 2017 to July 2018. The perception of the urban built environment was assessed by the neighborhood environment walkability scale-abbreviated (NEWS-A), and the physical activity was assessed by the International Physical Activity Questionnaire. Generalized linear mixed models were used to explore the relationship between the perceived built environment and leisure-time physical activities.Results:A total of 3 789 participants were included in the analysis. After adjusting for potential confounders, better access to public services ( OR=1.34, 95% CI: 1.02-1.75) and higher aesthetic quality ( OR=1.37, 95% CI: 1.09-1.73) were positively associated with the possibility of engaging in leisure-time physical activity in the past week. Similarly, these two attributes were positively associated with leisure-time walking. Higher scores on the perception of street connectivity were positively associated with leisure-time walking [ exp( β)=1.09, 95% CI: 1.00-1.19]. Higher residential density [ exp( β)=1.000 4, 95% CI:1.000 0-1.000 8], better access to physical activity destinations[ exp( β)=1.09, 95% CI: 1.00-1.19], and better aesthetics [ exp( β)=1.11, 95% CI:1.00-1.22] were associated with higher leisure-time physical activity. Similarly, these three attributes were positively associated with the possibility of meeting the WHO recommendations. Conclusion:Changing some urban built environment attributes may increase leisure-time physical activity.