77份裸燕麦品种籽粒相关性状分析北大核心CSCD

对77份裸燕麦品种籽粒品质等相关性状进行测定,运用聚类分析方法对测定指标进行分类比较,并采用灰色关联度进行综合评价,以期筛选出籽粒性状在青海表现良好的裸燕麦品种。主要研究结果如下:1)供试燕麦品种千粒重变异系数最高为49.29%,籽粒粗脂肪和粗蛋白含量变异系数相对较低,分别为12.23%和9.83%;千粒重与籽粒长度、宽度和直径呈极显著正相关;籽粒含水率与粗蛋白、淀粉含量呈极显著负相关,籽粒粗蛋白含量与粗脂肪含量呈极显著负相关;2)灰色关联分析表明77份裸燕麦品种籽粒性状综合评价较高的3个品种分别为5号(0.679)、73号(0.676)和26号(0.649);3)供试燕麦性状聚类分析表明,77份裸燕麦品种可被分成6个类群,其中类群Ⅵ中综合评价前10的品种占70%,包括综合评价较高的3个品种(5号、73号和26号),可考虑作为优异燕麦品种的选择区域。     

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data

The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R²) and intraclass correlation coefficient criteria. Results indicated that the RF model outperformed the SVM and MARS models in modeling used data and it can be utilized successfully utilized to diagnose the behaviour of brucellosis over time. Further research with application of such novel time series models in practice provides the most appropriate method in the control and prevention of future outbreaks for the epidemiologist.     

基于多光谱成像技术快速无损检测紫花苜蓿人工老化种子

种子老化是影响种子生产和储藏的重要因素,给农业生产带来了严重的经济损失,已成为影响种子活力中最具威胁性的因素之一。老化种子的检测以及种子老化后发芽情况的鉴定对种子生产具有重要意义,但目前常用的检测手段都是一次性、破坏性的。因此,一种快速、无损的种子老化和发芽检测方法不仅是研究的需要,也是种子行业进行种子检测分选所急需的。利用多光谱成像技术,采集紫花苜蓿种子的形态和光谱特征数据,利用LDA（线性判别分析）、SVM（支持向量机）和nCDA（归一化标准判别分析）3种多元分析方法,对不同老化程度苜蓿种子及其发芽情况分别进行分类和预测。结果表明,不同老化程度种子平均光谱反射率在470~660 nm处出现了明显的区别。LDA可以区分老化种子和未老化种子（准确度93.0%~97.7%）,也可以区分不同老化程度的种子（准确度75.3%~91.7%）,且均高于SVM的分类结果（准确度分别为92.4%~94.9%和68.7%~78.8%）;nCDA对老化种子进行区分的准确度高达88%~98%。同时,LDA可以准确预测发芽种子和不发芽种子,准确度可达98.7%,高于SVM的92.1%;nCDA预测老化种子发芽准确度达到了90%~99%。本研究证明了多光谱成像与分析技术不仅可以区分老化种子,也可以预测种子的发芽。上述结果证实多光谱成像技术结合多元分析为高效无损检测苜蓿种子活力提供了新途径,具有良好的应用前景。     

A comparison between multivariate Slash, Student's t and probit threshold models for analysis of clinical mastitis in first lactation cows

Robust threshold models with multivariate Student's t or multivariate Slash link functions were employed to infer genetic parameters of clinical mastitis at different stages of lactation, with each cow defining a cluster of records. The robust fits were compared with that from a multivariate probit model via a pseudo‐Bayes factor and an analysis of residuals. Clinical mastitis records on 36 178 first‐lactation Norwegian Red cows from 5286 herds, daughters of 245 sires, were analysed. The opportunity for infection interval, going from 30 days pre‐calving to 300 days postpartum, was divided into four periods: (i) ?30 to 0 days pre‐calving; (ii) 1–30 days; (iii) 31–120 days; and (iv) 121–300 days of lactation. Within each period, absence or presence of clinical mastitis was scored as 0 or 1 respectively. Markov chain Monte Carlo methods were used to draw samples from posterior distributions of interest. Pseudo‐Bayes factors strongly favoured the multivariate Slash and Student's t models over the probit model. The posterior mean of the degrees of freedom parameter for the Slash model was 2.2, indicating heavy tails of the liability distribution. The posterior mean of the degrees of freedom for the Student's t model was 8.5, also pointing away from a normal liability for clinical mastitis. A residual was the observed phenotype (0 or 1) minus the posterior mean of the probability of mastitis. The Slash and Student's t models tended to have smaller residuals than the probit model in cows that contracted mastitis. Heritability of liability to clinical mastitis was 0.13–0.14 before calving, and ranged from 0.05 to 0.08 after calving in the robust models. Genetic correlations were between 0.50 and 0.73, suggesting that clinical mastitis resistance is not the same trait across periods, corroborating earlier findings with probit models.     

Building phenotype networks to improve QTL detection: a comparative analysis of fatty acid and fat traits in pigs

Models in QTL mapping can be improved by considering all potential variables, i.e. we can use remaining traits other than the trait under study as potential predictors. QTL mapping is often conducted by correcting for a few fixed effects or covariates (e.g. sex, age), although many traits with potential causal relationships between them are recorded. In this work, we evaluate by simulation several procedures to identify optimum models in QTL scans: forward selection, undirected dependency graph and QTL-directed dependency graph (QDG). The latter, QDG, performed better in terms of power and false discovery rate and was applied to fatty acid (FA) composition and fat deposition traits in two pig F2 crosses from China and Spain. Compared with the typical QTL mapping, QDG approach revealed several new QTL. To the contrary, several FA QTL on chromosome 4 (e.g. Palmitic, C16:0; Stearic, C18:0) detected by typical mapping vanished after adjusting for phenotypic covariates in QDG mapping. This suggests that the QTL detected in typical mapping could be indirect. When a QTL is supported by both approaches, there is an increased confidence that the QTL have a primary effect on the corresponding trait. An example is a QTL for C16:1 on chromosome 8. In conclusion, mapping QTL based on causal phenotypic networks can increase power and help to make more biologically sound hypothesis on the genetic architecture of complex traits.     

基于GIS的降水多元回归模型在黑河干流山区的应用

结合GIS和统计学方法,利用21个站点气象数据和DEM（基于5个因子：高程、坡度、坡向、经度和纬度）在黑河干流山区构建一种多元非线性回归模型,用以模拟研究区降水量空间分布,并分析了全年、湿季和干季3种情景及3种不同空间分辨率数据相互耦合下的雨量变化。结果表明,该模型可解释研究区74.5%的年降水空间变异,对湿季降水量解释效果要好于全年和干季两种情景;100 m分辨率下的3种降水模型效果均为最好;降水量空间分布不均匀,100 m分辨率下,由西北部不足200 mm增加至东南部700 mm左右,降水量分界线呈西北-东南走向;500 m分辨率的降水量分界呈带状,有一定程度上移;1 000 m分辨率的降水量分布误差大。本研究采用的建模方法有较强移植性,可在其他山区开发类似模型,利用其模拟结果进行更深入的研究,今后在建模中加入空间化的风速变量有可能进一步提高模型精度。     

The nearest-plant method is robust and powerful enough for different survey teams to monitor change in mesic grassland species composition

A long-term rangeland monitoring programme needs to employ a field survey technique that is practicable, precise, powerful enough to distinguish change, not prone to worker bias, and able to distinguish real change from operator error arising from staff turnover. These criteria were used to evaluate a widely used grassland sampling technique in South Africa – the nearest-plant (with 200 points) method (NP) – against common alternatives, namely NP excluding forbs (NP-nf), the plant number scale (PNS; a cover-abundance method) and quadrat frequency (QF), using multivariate ordination and permutation tests. Four trained teams surveyed four grasslands using each method. PNS took more than twice as long as the other techniques, which were similarly rapid. Estimates of composition using NP methods were the most precise and PNS was least repeatable, with QF intermediate. Compositional differences between sites were most finely distinguished using NP-nf, followed by NP and QF. PNS was able to detect only marked differences and had the greatest potential for surveyor bias. The NP method, with or without including forbs, is therefore recommended for monitoring the species composition of mesic, dense grassland. Some suggestions for monitoring agencies on how best to use multivariate methods are presented.     

基于多元统计分析的茄衣烟叶生产气候资源配置

为探究茄衣烟叶生产中的气候资源配置情况,以“云雪1号”品种为试验材料,通过调整移栽期以改变烟株生育期内的气候指标,采用多元统计分析法研究茄衣烟叶的气候资源配置及其对烟株农艺性状及发酵后烟叶化学成分的影响。相关性分析结果显示,烟株大田期的积温、≥10 ℃有效积温、总降水量与总温差对烟株农艺性状各指标的影响较大,其相关系数分布范围为0.478~0.939,而积温、≥10 ℃有效积温、平均气温对烟叶总糖、烟碱、总氮、蛋白质含量影响较大,其相关系数分布范围为-0.955~-1.000。采用因子分析的方法在8项气候指标中提取出气候积累因子与湿度因子2个主因子,进一步通过灰色关联分析得出上述二者分别是影响烟株农艺性状和烟叶常规化学成分的主因子。以上结果表明,确定茄衣烟株移栽期的主要气候因子依据为大田生育期的气温、降水量和相对湿度的综合配置。