Figure 1: High resolution (384 × 384) results of STGAN for facial attribute editing, and more results are given in the suppl. AbstractArbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoderdecoder usually gives rise to blurry and low quality editing result. And adding skip connections improves image quality at the cost of weakened attribute manipulation ability. Moreover, existing methods exploit target attribute vector to guide the flexible translation to desired target domain. In this work, we suggest to address these issues from selective transfer perspective. Considering that specific editing task is certainly only related to the changed attributes instead of all target attributes, our model selectively takes the difference between target and source attribute vectors as input. Furthermore, selective transfer units are incorporated with encoder-decoder to adaptively select and modify encoder feature for enhanced attribute editing. Experiments show that our method (i.e., STGAN) simultaneously improves attribute manipulation accuracy as well as perception quality, and performs favorably against state-of-the-arts in arbitrary facial attribute editing and season translation.

No abstract

To explore the contribution of functional coding variants to psoriasis, we analyzed nonsynonymous single-nucleotide variants (SNVs) across the genome by exome sequencing in 781 psoriasis cases and 676 controls and through follow-up validation in 1,326 candidate genes by targeted sequencing in 9,946 psoriasis cases and 9,906 controls from the Chinese population. We discovered two independent missense SNVs in IL23R and GJB2 of low frequency and five common missense SNVs in LCE3D, ERAP1, CARD14 and ZNF816A associated with psoriasis at genome-wide significance. Rare missense SNVs in FUT2 and TARBP1 were also observed with suggestive evidence of association. Single-variant and gene-based association analyses of nonsynonymous SNVs did not identify newly associated genes for psoriasis in the regions subjected to targeted resequencing. This suggests that coding variants in the 1,326 targeted genes contribute only a limited fraction of the overall genetic risk for psoriasis.

BackgroundMore extensive use of metagenomic shotgun sequencing in microbiome research relies on the development of high-throughput, cost-effective sequencing. Here we present a comprehensive evaluation of the performance of the new high-throughput sequencing platform BGISEQ-500 for metagenomic shotgun sequencing and compare its performance with that of 2 Illumina platforms.FindingsUsing fecal samples from 20 healthy individuals, we evaluated the intra-platform reproducibility for metagenomic sequencing on the BGISEQ-500 platform in a setup comprising 8 library replicates and 8 sequencing replicates. Cross-platform consistency was evaluated by comparing 20 pairwise replicates on the BGISEQ-500 platform vs the Illumina HiSeq 2000 platform and the Illumina HiSeq 4000 platform. In addition, we compared the performance of the 2 Illumina platforms against each other. By a newly developed overall accuracy quality control method, an average of 82.45 million high-quality reads (96.06% of raw reads) per sample, with 90.56% of bases scoring Q30 and above, was obtained using the BGISEQ-500 platform. Quantitative analyses revealed extremely high reproducibility between BGISEQ-500 intra-platform replicates. Cross-platform replicates differed slightly more than intra-platform replicates, yet a high consistency was observed. Only a low percentage (2.02%–3.25%) of genes exhibited significant differences in relative abundance comparing the BGISEQ-500 and HiSeq platforms, with a bias toward genes with higher GC content being enriched on the HiSeq platforms.ConclusionsOur study provides the first set of performance metrics for human gut metagenomic sequencing data using BGISEQ-500. The high accuracy and technical reproducibility confirm the applicability of the new platform for metagenomic studies, though caution is still warranted when combining metagenomic data from different platforms.