ABSTRACT Against the background of a sharp decline in soybean planting, rising imports, and natural disasters in China, finding appropriate distribution areas for soybean has become a matter of urgency so that soybean planting policies can be formulated and soybean food security ensured. Among the numerous ecological niche models, the most suitable one for predicting potential distribution areas of soybean in the frigid region must be identified. Based on 65 soybean occurrence points and nine environmental variables, three ecological niche models, MaxEnt, BIOCLIM, and DOMAIN, were applied to the prediction of potential distribution areas for soybean. According to the analytical comparison, the three models predicted the potential distribution of soybean and, specifically, MaxEnt, stood out above the other two models as regards predicting the soybean distribution (Receiver Operating Characteristic curve, AUC = 0.916, Kappa = 0.685). The potential distribution areas (from low suitability to high suitability) predicted by MaxEnt was the largest and accounted for 59.5 % of the total area. The potential suitable distribution area of soybean was mainly concentrated in relatively flat terrain. The Sanjiang Plain and the Northeast Plain accounted for 9.4 % of the total area in the frigid region and are highly suitability for soybean. At the same time, annual mean temperature, elevation and Apr solar radiation were the key determinants affecting soybeans’ habitat. On the whole, the selection of ecological niche models and the prediction of soybean potential distribution can provide an essential reference for soybean planting and planning. Moreover, it would be a reliable example for the subsequent related research on soybean habitats.

Resumo Danos a edifícios são a principal causa de mortalidade nos terremotos. O método tradicional de detecção de construções danificadas por terremotos baseado em pixels pode ser afetado pelo ruído speckle. Neste artigo, apresenta-se um método de detecção de mudanças baseado em objetos para a detecção dos dano de terremotos usando dados de radar de abertura sintética (SAR). O método baseia-se em feições de textura em nível de objeto dos dados SAR. Em primeiro lugar, a análise da componente principal é usada para transformar as feições de textura ótimos em feições espaciais adequadas para extrair a mudança central. Posteriormente, as feições espaciais são agrupadas pelo algoritmo Watershed de segmentação, o qual introduz o conceito de orientação do objeto e desenvolve o cálculo do mapa de diferenças no nível do objeto. Tendo amostras de treinamento, é possível treinar os valores de limiar de classificação para diferentes graus de danos por terremoto, e consegue-se realizar a detecção dos edifícios danificados. O método proposto pode visualizar os danos por terremoto de forma eficiente usando imagens do satélite ALOS-1 (Advanced Land Observing Satellite-1). Seu desempenho foi avaliado na cidade de Jiegu, gravemente afetada pelo terremoto Yushu. Os resultados de validação cruzada mostram que a precisão geral é significativamente maior do que aquelas do TDCD e IDCD.

Abstract The damage of buildings is the major cause of casualties of from earthquakes. The traditional pixel-based earthquake damaged building detection method is prone to be affected by speckle noise. In this paper, an object-based change detection method is presented for the detection of earthquake damage using the synthetic aperture radar (SAR) data. The method is based on object-level texture features of SAR data. Firstly, the principal component analysis is used to transform the optimal texture features into a suitable feature space for extracting the key change. And then, the feature space is clustered by the watershed segmentation algorithm, which introduces the concept of object orientation and carries out the calculation of the difference map at the object level. Having training samples, the classification threshold values for different grade of earthquake damage can be trained, and the detection of damaged building is achieved. The proposed method could visualize the earthquake damage efficiently using the Advanced Land Observing Satellite-1 (ALOS-1) images. Its performance is evaluated in the town of jiegu, which was hit severely by the Yushu Earthquake. The cross-validation results shows that the overall accuracy is significantly higher than TDCD and IDCD.

Abstract Heparanase activity is involved in cancer growth and development in humans and single nucleotide polymorphisms (SNPs) in the heparanase gene (HPSE) have been shown to be associated with tumors. In this study, we investigated whether SNPs in HPSE were a risk factor for hepatocellular carcinoma (HCC) by undertaking a comprehensive haplotype-tagging, case-control study. For this, six haplotype-tagging SNPs (htSNPs) in HPSE were genotyped in 400 HCC patients and 480 controls by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis. A log-additive model revealed significant correlations between the HPSE polymorphisms rs12331678 and rs12503843 and the risk of HCC in the overall samples (p = 0.0046 and p = 0.0055). When the analysis was stratified based on hepatitis B virus (HBV) carrier status, significant interactions between rs12331678 and rs12503843 and HBV were observed. Conditional logistic regression analysis for the independent effect of one significant SNP suggested that rs12331678 or rs12503843 contributed an independent effect to the significant association with the risk of HCC, respectively. Our findings suggest that the SNPs rs12331678 and rs12503843 are HCC risk factors, although the potential functional roles of these two SNPs remain to be fully elucidated.