As the largest ethnic group in the world, the Han Chinese population is nonetheless underrepresented in global efforts to catalogue the genomic variability of natural populations. Here, we developed the PGG.Han, a population genome database to serve as the central repository for the genomic data of the Han Chinese Genome Initiative (Phase I). In its current version, the PGG.Han archives whole-genome sequences or high-density genome-wide single-nucleotide variants (SNVs) of 114 783 Han Chinese individuals (a.k.a. the Han100K), representing geographical sub-populations covering 33 of the 34 administrative divisions of China, as well as Singapore. The PGG.Han provides: (i) an interactive interface for visualization of the fine-scale genetic structure of the Han Chinese population; (ii) genome-wide allele frequencies of hierarchical sub-populations; (iii) ancestry inference for individual samples and controlling population stratification based on nested ancestry informative markers (AIMs) panels; (iv) population-structure-aware shared control data for genotype-phenotype association studies (e.g. GWASs) and (v) a Han-Chinese-specific reference panel for genotype imputation. Computational tools are implemented into the PGG.Han, and an online user-friendly interface is provided for data analysis and results visualization. The PGG.Han database is freely accessible via http://www.pgghan.org or https://www.hanchinesegenomes.org.

Abstract. Acute ischemic stroke induces systemic inflammation, exhibited as changes in body temperature, white blood cell counts and C-reactive protein (CRP) levels. The aim of the present study was to observe the effects of intravenous thrombolytic therapy on inflammatory indices in order to investigate the hypothesis that post-stroke systemic inflammatory response occurs in response to the necrosis of brain tissues. In this study, 62 patients with acute cerebral infarction and indications for intravenous thrombolysis were divided into three groups on the basis of their treatment and response: Successful thrombolysis (n=36), failed thrombolysis (n=12) and control (n=14) groups. The body temperature, white blood cell counts and highsensitivity (hs)-CRP levels were recorded pre-treatment and on post-stroke days 1, 3, 5 and 7. Spearman's correlation analysis showed that the pre-treatment National Institutes of Health Stroke Scale (NIHSS) score positively correlated with body temperature, white blood cell count and hs-CRP levels. On day 3 of effective intravenous thrombolysis, the body temperature and white blood cell were decreased and on days 3 and 5, the serum levels of hs-CRP were reduced compared with those in the failed thrombolysis and control groups. The results indicate that the systemic inflammatory response following acute cerebral infarction was mainly caused by ischemic injury of local brain tissue; the more serious the stroke, the stronger the inflammatory response. Ultra-early thrombolytic therapy may inhibit the necrosis of brain tissue and thereby reduce the inflammatory response.

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Motivation Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining 3D structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecular particle images from thousands of low-contrast, high-noisy electron micrographs. Although machine-learning methods were developed to get rid of this bottleneck, it still lacks universal methods that could automatically picking the noisy cryo-EM particles of various macromolecules. Results Here, we present a deep-learning segmentation model that employs fully convolutional networks trained with synthetic data of known 3D structures, called PARSED (PARticle SEgmentation Detector). Without using any experimental information, PARSED could automatically segment the cryo-EM particles in a whole micrograph at a time, enabling faster particle picking than previous template/feature-matching and particle-classification methods. Applications to six large public cryo-EM datasets clearly validated its universal ability to pick macromolecular particles of various sizes. Thus, our deep-learning method could break the particle-picking bottleneck in the single-particle analysis, and thereby accelerates the high-resolution structure determination by cryo-EM. Availability and implementation The PARSED package and user manual for noncommercial use are available as Supplementary Material (in the compressed file: parsed_v1.zip). Supplementary information Supplementary data are available at Bioinformatics online.