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A performance-driven sustainable urban design is an important step in adapting to local climates and improving outdoor comfort level. This study proposed investigating an urban layout optimization method, validated in an urban area in Shenyang City, a cold region, that comprises nine urban blocks. This study selected an urban block type and street width as optimization input indices and defined the percentage of acceptable universal thermal comfort index (UTCI) range as the objective. The genetic algorithm is applied to optimize and generate an urban block to maximize the percentage of acceptable UTCI range at urban block level and street level. When the comfort level is set as −17–20 °C, optimization can achieve 87.7% of acceptable UTCI in the urban block level and 90.3% at urban street level. To attain an urban layout with higher than 85% acceptable UTCI range, results found, in a cold region, multistory blocks are more suitable than high-rise blocks and open spaces; it would be better to place multistory blocks with high building enclosures on the north side to block cold wind. [ABSTRACT FROM AUTHOR]

Accurate measurement of forest growing stem volume (GSV) is important for forest resource management and ecosystem dynamics monitoring. Optical remote sensing imagery has great application prospects in forest GSV estimation on regional and global scales as it is easily accessible, has a wide coverage, and mature technology. However, their application is limited by cloud coverage, data stripes, atmospheric effects, and satellite sensor errors. Combining multi-sensor data can reduce such limitations as it increases the data availability, but also causes the multi-dimensional problem that increases the difficulty of feature selection. In this study, GaoFen-2 (GF-2) and Sentinel-2 images were integrated, and feature variables and data scenarios were derived by a proposed adaptive feature variable combination optimization (AFCO) program for estimating the GSV of coniferous plantations. The AFCO algorithm was compared to four traditional feature variable selection methods, namely, random forest (RF), stepwise random forest (SRF), fast iterative feature selection method for k-nearest neighbors (KNN-FIFS), and the feature variable screening and combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (DC-FSCK). The comparison indicated that the AFCO program not only considered the combination effect of feature variables, but also optimized the selection of the first feature variable, error threshold, and selection of the estimation model. Furthermore, we selected feature variables from three datasets (GF-2, Sentinel-2, and the integrated data) following the AFCO and four other feature selection methods and used the k-nearest neighbors (KNN) and random forest regression (RFR) to estimate the GSV of coniferous plantations in northern China. The results indicated that the integrated data improved the GSV estimation accuracy of coniferous plantations, with relative root mean square errors (RMSErs) of 15.0% and 19.6%, which were lower than those of GF-2 and Sentinel-2 data, respectively. In particular, the texture feature variables derived from GF-2 red band image have a significant impact on GSV estimation performance of the integrated dataset. For most data scenarios, the AFCO algorithm gained more accurate GSV estimates, as the RMSErs were 30.0%, 23.7%, 17.7%, and 17.5% lower than those of RF, SRF, KNN-FIFS, and DC-FSCK, respectively. The GSV distribution map obtained by the AFCO method and RFR model matched the field observations well. This study provides some insight into the application of optical images, optimization of the feature variable combination, and modeling algorithm selection for estimating the GSV of coniferous plantations. [ABSTRACT FROM AUTHOR]

Chronic hepatitis C is a serious liver disease that often results in cirrhosis or hepatocellular carcinoma. The aim of this study was to assess the association of human leukocyte antigen-DP (HLA-DP) variants with risk of chronic hepatitis C virus (HCV) or anti-F antibody generation. We selected two single nucleotide polymorphisms (SNPs) in a region including HLA-DPA1 (rs3077) and HLA-DPB1 (rs9277534) and genotyped SNPs in 702 cases and 342 healthy controls from the Chinese population using TaqMan SNP genotyping assay. Moreover, the exon 2 of the HLA-DPA1 and HLA-DPB1 genes were amplified and determined by sequencing-based typing (SBT). The results showed that rs3077 significantly increased the risk of chronic HCV infection in additive models and dominant models (odds ratio (OR) = 1.32 and 1.53). The rs3077 also contributed to decrease the risk of anti-F antibody generation in additive models and dominant models (OR = 0.46 and 0.56). Subsequent analyses revealed the risk haplotypes (DPA1*0103-DPB1*0501 and DPA1*0103-DPB1*0201) and protective haplotypes (DPA1*0202-DPB1*0501 and DPA1*0202-DPB1*0202) to chronic HCV infection. Moreover, we also found that the haplotype of DPA1*0103-DPB1*0201 and DPA1*0202-DPB1*0202 were associated with the anti-F antibody generation. Our findings show that genetic variants in HLA-DP gene are associated with chronic HCV infection and anti-F antibody generation.