PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.

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Interindividual clinical variability in the course of SARS-CoV-2 infection is immense. We report that at least 101 of 987 patients with life-threatening COVID-19 pneumonia had neutralizing IgG auto-Abs against IFN-ω (13 patients), the 13 types of IFN-α (36), or both (52), at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1,227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 were men. A B cell auto-immune phenocopy of inborn errors of type I IFN immunity underlies life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men.

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PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

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About 8,000 years ago in the Fertile Crescent, a spontaneous hybridization of the wild diploid grass Aegilops tauschii (2n 5 14; DD) with the cultivated tetraploid wheat Triticum turgidum (2n 5 4x 5 28; AABB) resulted in hexaploid wheat (T. aestivum; 2n 5 6x 5 42; AABBDD) 1,2 . Wheat has since become a primary staple crop worldwide as a result of its enhanced adaptability to a wide range of climates and improved grain quality for the production of baker's flour 2 . Here we describe sequencing the Ae. tauschii genome and obtaining a roughly 90-fold depth of short reads from libraries with various insert sizes, to gain a better understanding of this genetically complex plant. The assembled scaffolds represented 83.4% of the genome, of which 65.9% comprised transposable elements. We generated comprehensive RNA-Seq data and used it to identify 43,150 protein-coding genes, of which 30,697 (71.1%) were uniquely anchored to chromosomes with an integrated high-density genetic map. Whole-genome analysis revealed gene family expansion in Ae. tauschii of agronomically relevant gene families that were associated with disease resistance, abiotic stress tolerance and grain quality. This draft genome sequence provides insight into the environmental adaptation of bread wheat and can aid in defining the large and complicated genomes of wheat species.We selected Ae. tauschii accession AL8/78 for genome sequencing because it has been extensively characterized genetically (Supplementary Information). Using a whole genome shotgun strategy, we generated 398 Gb of high-quality reads from 45 libraries with insert sizes ranging from 200 bp to 20 kb (Supplementary Information). A hierarchical, iterative assembly of short reads employing the parallelized sequence assembler SOAPdenovo 3 achieved contigs with an N50 length (minimum length of contigs representing 50% of the assembly) of 4,512 bp (Table 1). Paired-end information combined with an additional 18.4 Gb of Roche/454 long-read sequences was used sequentially to generate 4.23-Gb scaffolds (83.4% were non-gapped contiguous sequences) with an N50 length of 57.6 kb (Supplementary Information). The assembly represented 97% of the 4.36-Gb genome as estimated by K-mer analysis (Supplementary Information). We also obtained 13,185 Ae. tauschii expressed sequence tag (EST) sequences using Sanger sequencing, of which 11,998 (91%) could be mapped to the scaffolds with more than 90% coverage (Supplementary Information).To aid in gene identification, we performed RNA-Seq (53.2 Gb for a 117-Mb transcriptome assembly) on 23 libraries representing eight tissues including pistil, root, seed, spike, stamen, stem, leaf and sheath (Supplementary Information). Using both evidence-based and de novo gene predictions, we identified 34,498 high-confidence protein-coding loci. FGENESH 4 and GeneID models were supported by a 60% overlap with either our ESTs and RNA-Seq reads, or with homologous proteins. More than 76% of the gene models had a significant match (E value # 10 25; alignment length $ 60%) in the ...