基于提高鸟类多样性的北京城市线性公园绿地植物景观探析

植被覆盖率高的线性公园,可作为鸟类的迁徙途径和栖息之所,并猜测其可作为生态廊道的一部分,适宜的营造植物景观、提升植物种类的多样性对保护鸟类多样性有促进作用。因此,采用标准取样法对北京城市线性公园绿地进行研究,提取可能影响鸟类多样性的植物群落因素、植物生境因素,对提高鸟类多样性提出建设性的意见。研究结果主要表明,在线性公园绿地宽度30~200m内,随着宽度的增加,鸟类的数量有所提高;含有不同类型植物生境的线性公园绿地,对鸟类目标种的吸引力不同,植物生境类型数较多样的线性公园绿地具有较高的鸟类目标种吸引力;不同的鸟类目标种偏好在不同的树种上进行活动。     

Discovery of genomic regions and candidate genes controlling shelling percentage using QTL‐seq approach in cultivated peanut (Arachis hypogaea L.)

Cultivated peanut (Arachis hypogaea L.) is an important grain legume providing high‐quality cooking oil, rich proteins and other nutrients. Shelling percentage (SP) is the 2nd most important agronomic trait after pod yield and this trait significantly affects the economic value of peanut in the market. Deployment of diagnostic markers through genomics‐assisted breeding (GAB) can accelerate the process of developing improved varieties with enhanced SP. In this context, we deployed the QTL‐seq approach to identify genomic regions and candidate genes controlling SP in a recombinant inbred line population (Yuanza 9102 × Xuzhou 68‐4). Four libraries (two parents and two extreme bulks) were constructed and sequenced, generating 456.89–790.32 million reads and achieving 91.85%–93.18% genome coverage and 14.04–21.37 mean read depth. Comprehensive analysis of two sets of data (Yuanza 9102/two bulks and Xuzhou 68‐4/two bulks) using the QTL‐seq pipeline resulted in discovery of two overlapped genomic regions (2.75 Mb on A09 and 1.1 Mb on B02). Nine candidate genes affected by 10 SNPs with non‐synonymous effects or in UTRs were identified in these regions for SP. Cost‐effective KASP (Kompetitive Allele‐Specific PCR) markers were developed for one SNP from A09 and three SNPs from B02 chromosome. Genotyping of the mapping population with these newly developed KASP markers confirmed the major control and stable expressions of these genomic regions across five environments. The identified candidate genomic regions and genes for SP further provide opportunity for gene cloning and deployment of diagnostic markers in molecular breeding for achieving high SP in improved varieties.     

A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Marker‐based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F₁ hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F₁ hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.     

Prediction of lymph node metastasis in oral squamous cell carcinoma based on protein profile

Objective: Lymph node metastasis leads to high mortality rates of oral squamous cell carcinoma (OSCC). However, it is still controversial to define clinically negative neck (cN0) and positive neck (cN1-3).

Methods: We retrieved candidate biomarkers identified by proteomic analysis in OSCC from published works of literature. In training stage, immunohistochemistry (IHC) analysis was used to determine the expression of proteins and logistic regression models with stepwise variable selection were used to identify potential factors that might affect lymph node metastasis and life status. Furthermore, the prediction model was validated in validating stage.

Results: We screened eight highly expressed proteins related to lymph node metastasis in OSCC and found that the expression levels of SOD2, BST2, CAD, ITGB6, and PRDX4 were significantly elevated in patients with lymph node metastasis compared to the patients without lymph node metastasis. Furthermore, in training and validating stages, the prediction model base on the combination of CAD, SOD2 expression levels, and histopathologic grade was developed and validated in patients with OSCC.

Conclusions: Our findings showed that the developed model well predicts the lymph node metastasis and life status in patients with OSCC, independent of TNM stage.     

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.     

燕麦分子育种研究进展

燕麦是一种主要生长在温带冷凉地区的粮饲兼用作物,近年来燕麦的营养价值和降低胆固醇特性的发现使人们认识到燕麦及其制品是一种健康食品,促进了燕麦产业发展,对燕麦品种培育提出了更高要求。在此背景下,现代生物技术与常规育种技术结合是满足这一需求的重要途经。本文综述了国内外燕麦分子育种方面的研究进展:(1)我国燕麦种质资源的收集与遗传多样性研究。我国的燕麦种质资源收集工作起始于20世纪50年代,迄今收集并保存了5282份燕麦种质资源。对这些资源的遗传多样性分析研究表明,国内的燕麦种质资源中内蒙古和山西的资源多样性最高;(2)利用各种分子标记构建燕麦遗传连锁图谱研究;(3)一些重要农艺性状的QTL定位研究以及全基因组关联分析研究。包括产量、含油量、β-葡聚糖含量、蛋白质含量、抽穗期、抗病基因、抗冻性等重要农艺性状;(4)标记辅助基因组选择技术在燕麦中的应用;(5)燕麦遗传工程育种研究进展。同时,本文将国内的主要研究进展与国外相关的最新研究成果进行了比较,并对当前分子育种中存在的问题及今后的研究方向进行了探讨,希望能为今后通过生物技术手段培育燕麦新品种提供一定的参考。     

Generalized and species-specific prediction models for aboveground biomass in semi-steppe rangelands

Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting. Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales. Yet, only few prediction models are available for aboveground biomass in rangelands, as compared with forests. In addition to the development of prediction models, we tested whether such prediction models vary with plant growth forms and life spans, and with the inclusion of site and/or quadrat-specific factors.     

RAD‐sequencing for estimating genomic relatedness matrix‐based heritability in the wild: A case study in roe deer

Estimating the evolutionary potential of quantitative traits and reliably predicting responses to selection in wild populations are important challenges in evolutionary biology. The genomic revolution has opened up opportunities for measuring relatedness among individuals with precision, enabling pedigree‐free estimation of trait heritabilities in wild populations. However, until now, most quantitative genetic studies based on a genomic relatedness matrix (GRM) have focused on long‐term monitored populations for which traditional pedigrees were also available, and have often had access to knowledge of genome sequence and variability. Here, we investigated the potential of RAD‐sequencing for estimating heritability in a free‐ranging roe deer (Capreolous capreolus) population for which no prior genomic resources were available. We propose a step‐by‐step analytical framework to optimize the quality and quantity of the genomic data and explore the impact of the single nucleotide polymorphism (SNP) calling and filtering processes on the GRM structure and GRM‐based heritability estimates. As expected, our results show that sequence coverage strongly affects the number of recovered loci, the genotyping error rate and the amount of missing data. Ultimately, this had little effect on heritability estimates and their standard errors, provided that the GRM was built from a minimum number of loci (above 7,000). Genomic relatedness matrix‐based heritability estimates thus appear robust to a moderate level of genotyping errors in the SNP data set. We also showed that quality filters, such as the removal of low‐frequency variants, affect the relatedness structure of the GRM, generating lower h² estimates. Our work illustrates the huge potential of RAD‐sequencing for estimating GRM‐based heritability in virtually any natural population.