“Restorative-Repressive” perception on post-industrial parks based on artificial and natural scenarios: Difference and mediating effect

The development of post-industrial landscapes at industrial sites plays an important role to fill urban green spaces. However, current research on the use and redevelopment of post-industrial sites has mainly focused on ecological restoration, and studies combined with objective and subjective data to quantify public preferences remain poorly understood. In this study, deep learning was used to semantically segment the post-industrial landscape, and a multiple stepwise regression model was used to analyze the non-linear correlation between quantitative indicators and public “restorative-repressive” perception, and structural equation model (SEM) between quantitative indicators and public perception data were established. We investigated and found (1) Semantic segmentation models for machine learning combined with principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) analysis can categorize post-industrial parks into two groups dominated by artificial elements and natural elements. (2) Public perceptions varied more in the natural element-dominated group and less in the industrial element-dominated group. In addition, waterbody in the post-industrial landscape existed as a destabilizing factor. (3) There was a difference in the correlation between quantitative indicators and subjective perceptions in the two categories of parks. (4) Height of industrial building (HIB), function of industrial building(FIB), vegetation succession(VS) were significantly influenced public satisfaction. These findings informed that public satisfaction with post-industrial landscapes can be enhanced by taking full account of the different uses of natural and artificial elements and enabling researchers to analyze the redevelopment of post-industrial landscapes from a new perspective of evidence-based design.     

控制大白菜和白菜型油菜叶缘裂刻的QTL 定位及分析

 利用叶缘光滑无裂刻的大白菜[Brassica campestris L. ssp. pekinensis（Lour）Olsson]材料‘Z16’和叶缘深裂的白菜型油菜（Brassica campestris L. var. yellow sarson Prain）‘Yellow Sarson 143’构建的120 个株系的BC₂DH 群体及已构建的包含10 个连锁群的遗传图谱,利用JoinMap4 软件及MQM 作图法,对叶缘裂刻性状进行QTL 定位分析。3 次重复试验中,分别在第3、10 号染色体上的相同位置各检测到1 个控制叶缘裂刻性状的QTL,具有重复性。各QTL的LOD值在7.66 ~ 18.99 间,可解释26.4% ~ 44.7%的表型变异。通过大白菜与拟南芥注释基因比对,生物活性赤霉素（GA₁、GA₄）合成的关键基因BrGA20ox3位于第10 染色体的QTL 区域,对143 施加外源赤霉素GA₃ 能明显抑制叶缘裂刻表型,因此BrGA20ox3为控制叶片裂刻的可能候选基因。对BrGA20ox3 克隆测序获得与该基因共分离的特异标记（BrIDlobe-2）,对进一步研究叶缘裂刻性状及分子标记辅助育种具有重要的意义。     

云南不同花魔芋品种性状相关分析

对云南不同生态区域种植的魔花15、楚雄花魔芋2号、丽江花白魔芋、会泽花魔芋、大关花魔芋和富源花魔芋(CK)6个云南花魔芋地方品种的主要性状进行了相关性和聚类分析。分析结果表明,花魔芋种芋大小、干物质含量、球茎高等对球茎质量影响较大;干物质含量、球茎直径、球茎高等与葡甘露聚糖含量呈显著性相关;球茎质量、葡甘露聚糖、球茎直径等与干物质含量呈显著相关。云南花魔芋品种可聚为3个类群,富源花魔芋、会泽花魔芋、大关花魔芋和魔花15分布在第1个类群里,丽江花白魔芋、楚雄花魔芋2号分别聚成另外2个类群。     

27个木槿品种的数量分类和主成分分析

通过观测27个木槿品种的17个数量和质量性状,探究了木槿品种分类的等级和标准,便于为木槿品种的选育与资源研究等提供参考依据。首先对17个性状指标进行R型聚类分析和主成分分析,根据分析结果再对27个木槿品种进行Q型聚类分析。R型聚类分析结果表明,各性状选取是合理的;主成分分析结果表明,17个性状可综合为6个主成分,其累计贡献率达88.096%。Q型聚类结果表明,27个木槿品种根据有无丹心、花型、丹心基部形状、花瓣指数、花径、叶形、丹心线与丹心的关系可被分为2大类、3大类或8大类。16个性状对木槿品种的划分贡献较大。综合而言,有无丹心、花型、花瓣指数、丹心线与丹心的关系是木槿品种分类的主要标准和依据。     

Marker-assisted selection for complex trait in common bean (Phaseolus vulgaris L.) using QTL-based index

A procedure was developed for marker-assisted selection of complex traits for common bean (Phaseolus vulgarisL.) using an index based on QTL-linked markers and ultrametric genetic distances between lines and a target parent. A comparison of the mean seed yields of the top five lines selected by different schemes demonstrated that the highest yielding group was selected on the basis of a combination of phenotypic performance and a high QTL-based index,followed by groups identified by a high QTL-based-index, conventional selection,and a low QTL-based-index. This study demonstrated a simple way to use information obtained from QTL studies to make selection decisions. The study also showed that the use of the QTL-based-index in conjunction with the ultrametric genetic distance to the target parent would enablea plant breeder to select lines that retain important QTL in a desirable genetic background. Therefore, this type of MAS would be expected to be superior to the phenotypic selection. This revised version was published online in August 2006 with corrections to the Cover Date.     

利用双杂合位点标记资料构建芒果遗传图谱

为了建立芒果 (MangiferaindicaL )的分子标记遗传图 ,用 15对AFLP (AmplifiedFragmentLengthPolymorphism)引物组合扩增了芒果品种间杂交组合 (Keitt×Tommy Atkins)的 6 0个F1单株 ,获得了 191个多态性位点。它们的分离表现为双杂合 (Aa×Aa)和测交 (Aa×aa)分离两种类型 ,但前者占了 5 9 7%。为了充分利用双杂合位点分离所提供的遗传信息 ,我们根据群体中任意两个双杂合位点隐性个体出现的数目 ,利用二项式分布概率理论推断它们是否连锁以及它们彼此间的相引或相斥关系。在该芒果群体呈 3:1分离的 81个多态性标记中 ,39个被分为 14组 ,以此为基础构建了 15个连锁群 ;这些连锁群共覆盖了 35 4 1cM的芒果基因组。其中 ,最小与最大遗传距离分别为 3 7cM和 2 8 9cM。此外 ,对 18个 1∶1分离类型的标记 ,直接利用Mapmaker作图软件构建了两个芒果连锁群。本文对所提出的利用双杂合位点构建果树遗传图谱的策略进行了讨论。     

水稻籼爪交和粳爪交杂种优势的研究Ⅰ.杂种农艺性状的表现

研究了籼爪交和粳爪交杂种在长沙、三亚两地农艺性状和干物质产量的表现. 由引进爪哇稻品种所配的籼/爪和粳/爪交杂种F1代表现为植株高大, 生长茂盛, 具有强大的干物质产量优势. 绝对干物质产量(单株穗期干重)为: 籼爪杂种＞粳爪杂种＞对照(籼籼交杂种), 干物质生产率(单株日干物质产量)为: 籼爪杂种＞粳爪杂种＞对照. 供试籼     

Quantitative trait loci (QTL) affecting sugars, organic acids and other biochemical properties possibly contributing to flavor, identified in four advanced backcross populations of tomato

Although the Advanced Backcross strategy has proven very useful for QTL detection in tomato, it has been used mainly in identifying QTL for agronomic traits such as yield, color, etc. Tomato flavor is an important quality characteristic, yet it has been difficult to assess flavor or traits that affect it. In this study the AB-QTL strategy was applied to four advanced backcross populations to identify QTL for biochemical properties that may contribute to the flavor of processed tomatoes, such as sugars and organic acids. A total of 222 QTL were identified for 15 traits, including flavor as assessed by a taste panel. Correlations of certain biochemicals with flavor and possible methods of assessing and improving flavor are discussed. In particular, QTL with very significant effects associated with the ratio of sugars/glutamic acid, a trait highly correlated with improved flavor, have been identified as good targets for future work in improving the flavor of tomatoes. This revised version was published online in August 2006 with corrections to the Cover Date.