泌乳期中国荷斯坦牛转化孕酮浓度的影响因素分析及其表型预测

【目的】本研究基于试验测定的中国荷斯坦牛血液和乳汁中的孕酮浓度表型，探究了影响中国荷斯坦牛体内孕酮分泌的环境因素、孕酮浓度的变化趋势、孕酮浓度与乳成分的关联程度以及血乳中孕酮浓度的预测方法。【方法】试验于2021年8月在北京、山东两个牧场采集不同胎次、泌乳阶段和妊娠状态的中国荷斯坦泌乳牛的奶样、尾根血样，测定孕酮浓度，最终获得402条乳汁孕酮浓度和298条血液孕酮浓度表型用于数据分析。对孕酮浓度进行数据转化使其近似服从正态分布后，采用固定效应模型探究胎次、泌乳阶段、妊娠状态、牧场等固定效应对奶牛孕酮表型的影响，运用R语言cor函数计算孕酮与各乳成分间的关联，并利用偏最小二乘法和个体及乳成分信息对孕酮浓度进行预测，以建立孕酮浓度表型高通量获取手段。【结果】妊娠状态对转化乳汁孕酮浓度存在极显著影响（P<0.01），胎次、场效应对转化乳汁孕酮浓度均有显著影响（P<0.05），而转化血液孕酮浓度只受到妊娠状态影响（P<0.01）；全乳固体、乳脂率、乳蛋白率、脂蛋比与转化乳血孕酮浓度均存在显著或极显著的正相关关系（r=0.14~0.37，P<0.05；P<0.01）；基于本试验数据，乳成分与个体信息对转化乳血孕酮浓度的预测准确性不高（R²=0.030~0.17），但如果增加血液或乳汁的转化孕酮浓度对乳汁或血液的转化孕酮浓度进行预测，预测效果则有大幅提升（R²=0.40）。【结论】影响泌乳期中国荷斯坦牛转化孕酮浓度的因素除妊娠状态外，可能还包括饲养条件与胎次。此外转化孕酮浓度与乳脂率、乳蛋白率等乳成分呈极显著相关。基于乳成分信息与转化孕酮的关系，获得了对中国荷斯坦牛乳血转化孕酮浓度预测的可用策略，为今后牧场的繁殖辅助管理、奶牛育种新性状研发以及孕酮浓度的高通量获取等提供了新思路。     

Discrimination between Japanese and Chinese Products using trace elements concentration of kernel in pickled Japanese apricot

pp. 875–880

The trace-element composition of kernel in pickled Japanese apricot (Prunus mume Sieb. et Zucc.) was determined using an inductively coupled plasma optical emission spectrometer in order to distinguish between Japanese products and Chinese products.

Strontium and barium concentrations in the kernels of Chinese products were 10 or more times those of the Japanese ones. When based on 8.0 mg kg^?1 of strontium concentration in kernel, 93.2% of sample was distinguished as Japanese products or Chinese ones.

Applying principal component analysis using 9 elements (Mn, Zn, Fe, Ni, Ba, Sr, Cu, Co, Cr), the pickled Japanese apricots tend to separate into two countries. Linear discriminant analysis (LDA) using 9 elements allowed a reasonable classification of pickled Japanese apricots according to the country of production.

The result of the analysis of K-nearest neighbors (KNN) was better than that of LDA.     

基于PCA-ARIMA模型的高血压发病率预测

目的高血压发病率是政府和相关医学工作者预防和监测高血压的重要依据之一。方法利用主成分分析(principal component analysis,PCA)对因子进行线性筛选,获得保留因子后利用ARIMA进行建模预测,即为PCA-ARIMA多维时间序列组合预测模型。结果高血压发病率的拟合与独立预测结果表明,PCA-ARIMA优于PCA-MLR、ARIMA等参比模型。结论本文提出的基于主成分分析和ARIMA模型(PCA-ARIMA模型)的建模有助于提高模型的预测精度。     

Spatial optimization by simulated annealing and linear programming

Forest management planning comprises selection among treatment alternatives in management units. A traditional linear programming (LP) approach may effectively account for a profit maximization objective combined with sustainability constraints, e.g. on the temporal distribution of harvest volume flows, cash‐flow, and net present value development, but it fails to account for spatial constraints, especially those associated with final felling. By applying a simulated annealing adjacency model based on net present value maximization and combined with an LP consequence computation model, it is possible to delineate optimal strategies of final felling scheduling. Evaluation is made of the trade‐off between (1) the incremental cost (determined by use of the LP model) of an optimal adjacency model solution, and (2) the potential damage cost resulting from adjacency characteristics such as windthrow and bark injuries. The decision support system may contribute significantly to reduce damage costs and may improve the reliability of forest management planning.     

基于两层次线性混合效应模型的杉木林单木胸径生长量模型

基于两层次线性混合效应模型方法,建立江西省杉木人工林单木胸径生长量模型.研究所用数据来自于长期观测的固定样地数据,数据库包括82个区域、365个样地、5416株树木共计16248条记录.为了解决不同区域及不同样地之间的差异,本文构建的混合模型分别考虑样地层次、区域层次及两层次的随机参数效应.针对数据存在的重复测量及嵌套结构特性,在模拟时选择合适的异方差和自相关模型矩阵来解决此类问题.最后利用独立的抽样验证数据对模拟结果进行验证.结果表明:林分断面积、对象木胸径、林分内大于对象木的断面积之和与对象木胸径的比值以及海拔对单木胸径生长量有显著影响.与林业中常用的传统最小二乘方法相比,采用混合效应模型方法后模型的模拟精度和验证精度均有提高.选择适合的异方差和自相关函数后,模型比只考虑参数的随机效应有更好的适应性,并体现出了混合效应模型的灵活性和准确性.     

Evaluation of smallholder pig production systems in North Vietnam: Pig production management and pig performances

This study investigated the productive adaptability of pig breeds under different smallholder production conditions in Vietnam, comparing an indigenous with a Vietnamese improved breed. Fieldwork was conducted in four villages with different remoteness in North Vietnam from 2001 to 2002, in 64 households keeping the improved Mong Cai or indigenous Ban as sow breeds and progeny for fattening. Four visits per farm yielded 234 structured interviews. Reproductive performances were derived from 135 litters. Individual weights of pigs (n = 755) were obtained. Data were analysed by regression, linear and generalised linear, especially loglinear models.

Two distinct pig production systems were identified, that differed in remoteness, market access, resource availability, distribution of pig breeds and pig production intensity. Higher performances of 1.4 and 1.5 litters year^− 1, 8.4 and 8.4 piglets weaned litter^− 1, 66.6 and 93.0 kg piglets weaned sow^− 1 year^− 1, and 136 and 177 g day^− 1 ADG were found in two villages near town with semi-intensive production conditions and a high rate of improved Mong Cai sows and MC and LW × MC offspring in the observed population. Lower performances of 1.1 and 1.1 litters year^− 1, 4.6 and 5.5 piglets weaned litter^− 1, 20.5 and 30.9 kg piglets weaned sow^− 1 year^− 1, and 66 and 85 g day^− 1 ADG were observed in parallel to higher incidences of indigenous Ban pigs away from town under extensive conditions. Total live weight offtake per household per year was higher near town and in one village distant to town. MC and LW × MC remained even under demand-driven conditions below the performance potential reported for improved genotypes. Under resource-driven conditions with a saving-oriented production pattern, the higher-yielding but more demanding Mong Cai might not be an efficient production alternative as a further performance reduction can be assumed. A considerable live weight output was observed under resource-driven conditions and with a higher percentage of crossbred LW × Ban offspring in the population, possibly representing a promising production alternative. Factors influencing the performance expression of pigs, and the suitability of different methods for the assessment of performance parameters on-farm are discussed.     

利用广义线性模型预测家畜离散性状育种值

将广义线性混合模型(GLMM)引入动物离散性状的遗传分析及个体的遗传评定,初步比较了GLMM方法与一般线性方法(LM)的估计效果。模拟研究的性状为单阈值二项分类性状,选用的连接函数为对数连接μi=eη/(1+eη),方差函数为V(μi)=μ(i1-μi)/n,试验设计为全同胞-半同胞混合家系,参数估计采用Fisher迹法。结果表明:GLMM方法能较准确地估计公畜的个体育种值,在个体的遗传评定效果方面要明显优于常规的线性方法,其预测的育种值排序结果与真实育种值的排序之间存在极显著的相关性(P<0.001)。     

Postcards from the past: charting the landscape-scale conversion of tropical Australian savanna to closed forest during the 20th century

Repeated sequences of digitised and geo-referenced historical aerial photography provide a powerful means of understanding landscape change. We use this method to demonstrate a landscape wide expansion of closed forest (42% increase in total coverage) in the Australian monsoon tropics over the past five decades. Retrospective habitat suitability models (HSI) of closed forest derived using four landscape measures (drainage distance, slope angle, aspect and elevation) for imagery taken in 1947 correctly forecast the subsequent spatial distribution of the expansion, with topographic fire protection primarily determining the closed-forest distribution. The dynamics of the closed forest-savanna boundary were predicted accurately by generalised linear models, with closed-forest expansion in fire-protected sites along forest edges and regression in the more fire-prone areas. Two factors may plausibly explain the expansion of closed forests. First, eco-ethnographic records stress the skilful use of fire by Aboriginal people in protecting isolated and locally resource-rich closed-forest patches. Second, the recent global increase in atmospheric CO₂ may be changing the competitive balance between savanna and forest by enabling C₃ trees to grow fast enough to escape the fire trap presented by flammable C₄ grasses.     

Fisher大间距线性分类器

作为一种著名的特征抽取方法,Fisher线性鉴别分析的基本思想是选择使得Fisher准则函数达到最大值的向量(称为最优鉴别向量)作为最优投影方向,以便使得高维输入空间中的模式样本在该向量投影后,在类间散度达到最大的同时,类内散度最小。大间距线性分类器是寻找一个最优投影矢量(最优分隔超平面的法向量),它可使得投影后的两类样本之间的分类间距(Margin)最大。为了获得更佳的识别效果,结合Fisher线性鉴别分析和大间距分类器的优点,提出了一种新的线性投影分类算法——Fisher大间距线性分类器。该分类器的主要思想就是寻找最优投影矢量wbest(最优超平面的法向量),使得高维输入空间中的样本模式在wbest上投影后,在使类间间距达到最大的同时,使类内离散度尽可能地小。并从理论上讨论了与其他线性分类器的联系。在ORL人脸库和FERET人脸数据库上的实验结果表明,该线性投影分类算法的识别率优于其他分类器。     

AX=b在逆M-阵类中的反问题

将线性方程组 Ａ Ｘ＝ ｂ 分为５ 种情况（ Ｘ＞ ０、 Ｘ≥０、 Ｘ＜ ０、 Ｘ≤０、 Ｘ 为一般情况），通过构造矩阵的方法，讨论了该线性方程组的反问题在逆 Ｍ－ 阵类中有解的条件．在一般情况下，当给定的实向量 Ｘ 与ｂ 中相应的分量同号时，则线性方程组 Ａ Ｘ＝ ｂ 在逆 Ｍ － 阵类中的反问题有解．