Bull Groups and Relationships Among Sires in Best Linear Unbiased Prediction Sire Evaluation Models

Six methods of sire evaluation by best linear unbiased prediction were compared; two included grouping of bulls in addition to relationships among bulls. Groups did not add accuracy as judged by comparisons with modified contemporary comparisons which had significantly more data. Bull relationships alone appeared to be satisfactory, and proofs had smaller variances of errors of prediction than when groups of bulls were included.     

Prediction of Sire Effects for Probability of Survival to Fixed Ages with a Logistic Linear Model

A logistic linear model was used to predict sire effects on the underlying continuous scale for observed daughter survival rates to fixed ages. Results were compared with linear model prediction without logistic transformation. Fixed age categories of 41 and 54 mo had 720 and 615 sires with 18,941 and 16,437 sampling daughters. Sire variances were estimated on the underlying continuous scale from a data subset. The logistic linear model accounts for unequal subclass variances due to different mean survival rates in herd-year-seasons.Results with logistic linear model give heritabilities of .28 and .26 for 41 and 54 mo on underlying scale, corresponding to .12 and .15 on phenotypic scale. Genetic trend for daughter survival in sires was found for both fixed ages. Rank correlations between logistic and linear predictions were .61 and .70. Comparing top and bottom 5% of sires in each ranking indicated substantial differences; a few sires differed by 300 ranks out of a possible 615 for 54 mo.     

Estimation of Variance of Prediction Error for Best Linear Unbiased Prediction Models with Relationships Included

For mixed models, variance of prediction error of sire evaluations is required for computing Repeatabilities or confidence intervals. Although variance of prediction error can be computed by inverting the coefficient matrix, this is not practical because of prohibitive computational cost. Therefore, variance of prediction error is estimated. Three sire evaluation models were analyzed: 1) a multiparity model for single traits (i.e., multiple lactations of a cow were repeat samples of a single trait) without relationships included; 2) a multiparity model for single traits with relationships included; and 3) a multitrait model for the first three parities as correlated traits with relationships included. Data sets were constructed small enough that variances of prediction error could be computed by inversion and compared with estimates from various functions. Reciprocal of the diagonal element was an accurate estimate of variance of prediction error only for the multiparity model for single traits without relationships. However, variance of prediction error was estimated best by a cubic function of the reciprocal. For the multiparity model for single traits with relationships included, variance of prediction error was estimated best by a function with four terms: the reciprocal of the diagonal element of the coefficient matrix minus contributions of relatives, the square and cube of this term, and the reciprocal of the diagonal element of the inverse of the matrix of sire variance. For the multitrait model, all functions investigated resulted in less accuracy than desired for estimating variance of prediction error.     

Approximate Reliability of Best Linear Unbiased Prediction in Models with and Without Relationships

In sire summaries, attention is paid to the reliability of estimates of breeding values, often expressed as the squared correlation between estimated and true breeding values. In best linear unbiased prediction procedures, which account for relationships between sires, reliability is approximated by (n_e + ka^?1 ?k)/(n_e + ka^?1), where n_e is the effective number of daughters, i.e. the diagonal of the sire coefficient matrix after absorption of fixed effects; k is the ratio of residual and sire variance; and a^?1 is the diagonal element of the inverse of the numerator relationship matrix (A^?1). In simultated data this approximation compared with the true reliability, computed from direct inversion of the sire coefficient matrix including A^?1 after absorption of fixed effects, was biased upward for sire evaluations based on many effective daughters and few direct sire comparisons or few effective daughters and relationship to its sire. In field data, the effective number of daughters and the number of direct sire comparisons were correlated .97, and the formula gave good approximation (deviation <3%) when bulls were unrelated. However, when bulls were related, bias was considerable. Another approximation derived from selection index theory reduced the bias below 4%. This approximation takes the effective number of offspring of the bulls’ sire into account and reduces to n_e/(n_e + k) if a bulls’ sire has no daughter records. It can be applied easily and requires only small computational effort.     

利用代谢网络模型和线性规划法在线预测谷氨酸发酵中产物浓度

在好氧型的谷氨酸发酵实验中发现，溶解氧（DO）对发酵性能有很大的影响，谷氨酸的生成方式也因此有很大不同：较低的DO水平能够延长产酸期、提高谷氨酸的最终浓度，但是代谢副产物——乳酸也有较大程度的积蓄；而DO水平过高，虽然代谢副产物不会生成积蓄，但菌体消亡过快导致产酸期缩短、谷氨酸的最终浓度降低．同时，谷氨酸的生成方式与发酵过程中摄氧率（OUR）和CO2的释放率（CER）有着非常紧密的关联．作者利用代谢网络模型并结合使用线性规划优化法，通过在线测定OUR和CER，比较准确地在线推定出发酵过程中谷氨酸的质量浓度变化。与传统的非构造式动力学模型相比，上述预测方法具有建模简单、模型物理意义明确、通用性能好等优点，为后续过程的在线控制和优化提供一种全新和有效的途径。     

真空包装狮子头货架期预测模型的建立

为建立真空包装狮子头货架期预测模型,分析不同温度贮藏期间狮子头中菌落总数的变化情况,分别用线性模型、修正的Gompertz模型、修正的Logistic模型和Baranyi模型对狮子头中菌落总数进行一级模型的拟合,在此基础上使用平方根模型建立二级模型。通过比较各模型的评价参数选择最优模型,并进一步建立货架期预测模型。结果表明在一级模型中,修正的Gompertz模型对真空包装狮子头中菌落总数生长曲线的拟合优度最高;基于修正的Gompertz模型建立的平方根模型可较好地描述温度对狮子头最大比生长速率和迟滞期的影响。在4、10、15、20、25℃条件下贮藏狮子头的货架期分别为80.79、45.22、10.96、4.96、4.01 d,货架期实测值与预测值的相对误差值均在10%以内,表明建立的模型可以较准确地对贮藏在4~25℃条件下的狮子头进行货架期预测。     

水产品货架期预测模型的研究进展

水产品货架期预测的研究为监测和控制水产品安全提供了理论基础。水产品是一类极易腐败变质的产品,利用货架期预测可以对水产品贮藏、运输、销售等流通环节下的品质进行及时监控,从理论上预测水产品剩余货架期。本文分析了影响水产品货架期的主要因素,包括微生物作用、水产品化学反应、水产品物理变化以及环境温度的作用等;提出了2种水产品货架期的建模思路;并总结了几类常用水产品货架期预测模型(基于水产品品质损失的动力学模型、基于温度变化的模型、基于统计学的模型和人工神经网络模型等);最后分析了水产品货架期预测目前存在的问题,为未来研究提供思路。     

Linear Programming in Blending Various Components of Surimi Seafood

Shear stress, shear strain, whiteness of surimi gels made with high and low grades of Alaska pollock and Pacific whiting were investigated to determine linearity of their relationships. A canonical form of least square linear model in mixture design showed a greater linearity (r²0.99) for blending high and low grade surimi lots. Due to linearity without interaction (p<0.001), a least cost linear program provided optimum blending for surimi lots based on value, constraints, and decision variables. Linear programming to determine optimum formulation of surimi with starches and water content was not feasible due to interactions between surimi and starch, or starch and water.     

烟草主要化学成分的NIR定量模型传递

考察了烟草主要化学成分NIR预测模型在两台同型号NIR仪器间的转移效果.结果表明:总植物碱、总糖、还原糖、总氮、钾、总挥发酸、总挥发碱、石油醚提取物及其中性物总量、多酚、硫酸根、烟草pH值的NIR模型可以在两台同型号NIR仪器间直接传递,而氯、淀粉、纤维素、灰分、水溶性灰分碱度的NIR模型不可直接传递;利用斜率/截距算法和多样品标准化法修正后的氯、淀粉、纤维素、灰分、水溶性灰分碱度模型的预测标准差分别为0. 061、0. 523、1. 250、1. 195、0. 206和0. 062、0. 512、1. 129、1. 261、0. 205,模型预测能力提高百分比分别为75.20%、55.37%、36.19%、37.37%、71.23%和74.80%、57.48%、42.37%、33.91%、71.37%.两种方法修正的模型预测精度均与主仪器的相当,且两种方法修正的模型预测精度之间没有显著性差异.     

线性聚合物制高性能纤维综述

文章从芳族聚酰胺、凝胶纺丝高性能聚乙烯纤维、熔体纺丝全芳香族聚酯纤维（DB）、PBO和相关聚合物（RTY和CLS）、PDPI或“M5”硬杆聚合物（DJS）、芳香族俄罗斯纤维（KEP）等线性聚合物制高性能纤维的基本特征和性能的分析入手,对高性能纤维的纺丝工艺步骤和特点作简要的介绍与分析。     

叶组配方卷烟烟气预测模型的建立

利用多元回归分析方法建立了叶组配方焦油量、烟气烟碱量、一氧化碳量和抽吸口数的预测模型,并筛选出了与这4项指标关系密切的化学成分,包括总糖、还原糖、总氮、烟碱、挥发碱、钾、氯、硫酸根等。利用所建模型对4种卷烟产品叶组配方的烟气指标进行了预测,结果表明,其焦油量、烟气烟碱量、一氧化碳量和抽吸口数的预测误差范围分别为0.04～1.13mg/支、0.13～0.50mg/支、0.11～2.95mg/支和0.37～1.15口/支。     

鸡蛋卵转铁蛋白过敏原B细胞线性表位的预测研究

为预测鸡蛋过敏原卵转铁蛋白的B细胞线性表位,以GenBank中提供的鸡蛋卵转铁蛋白氨基酸序列为基础,分别用Jameson-Wolf法、Kyte-Doolittle法、Emini法和Karplus-Schulz法对鸡蛋卵转铁蛋白的抗原指数、亲水性、蛋白表面可及性及柔韧性进行预测,并用Chou-Fasman法和Deleage-Roux法预测其二级结构,综合分析以上预测结果,得出鸡蛋卵转铁蛋白可能的B细胞线性表位区。结果表明鸡蛋卵转铁蛋白存在7个潜在表位区,包括蛋白第174～181、215～222、231～240、288～294、332～344、415～429、547～555位可能为B细胞线性表位优势区域。该结果将有助于鸡蛋卵转铁蛋白B细胞表位的进一步精确定位。     

Use of Average Molecular Weights for Product Categories to Predict Freezing Characteristics of Foods

ABSTRACT:  In the design of food freezing process, food property parameters, initial freezing temperature ( T_Fi ), and frozen water fraction ( X_I ) are required. The predictive approaches of these 2 parameters have been developed based on mass fractions and molecular weights of specific food components such as proteins, carbohydrates, minerals, and acids/bases. In this study, the molecular weights of the key mineral and acid/base components were successfully represented using average molecular weights (     ) and 4  T_Fi  and  X_I  calculation approaches were proposed. Based on an analysis of 212 food products, the absolute differences (AD) between the experimental and predicted  T_Fi  values for the 4 approaches were small. The prediction for the food model category was excellent with average AD (     ) values as low as ± 0.03 °C. For the other food categories, the prediction efficiency was impressive with     values between ± 0.22 and ± 0.38 °C. The predicted relationship between temperature and  X_I  for all analyzed food products provided close agreements with experimental data.     

基于Solid Edge的三维模型通用参数化程序设计方法

阐述了基于三维CAD系统的三维模型参数化设计方法,提出了用设计变量作为三维模型样板与参数化设计程序相联系的纽带,从而大大提高了程序设计效率和程序的通用性.以SolidEdge三维软件为例,采用VB开发技术,实现了三维模型的通用参数化程序设计.     

低温杀菌黄焖鸡中菌落总数生长预测模型的比较和货架期预测

为比较不同生长预测模型对低温杀菌黄焖鸡中菌落总数生长情况的拟合效果,使用修正的Gompertz模型、修正的Logistic模型和Baranyi模型描述其在4、15、25 ℃贮藏期间菌落总数的变化情况,使用Belehradek模型和Arrhenius模型描述菌落总数生长参数与贮藏温度之间的关系,通过计算各模型拟合所得的参数值及回归相关系数R2、均方误差平方根、赤池信息准则和贝叶斯信息准则等指标评价模型的拟合优度,以最优组合建立产品的货架期预测模型。结果表明：在一级模型中,修正的Logistic模型拟合所得的生长参数值最接近实测值,模型的评价指标最优;在二级模型中,Arrhenius模型的拟合优度最高,其R2均在0.97以上;对修正的Logistic模型的偏差因子、准确因子和Arrhenius模型的残差值进行分析,表明建立的一级、二级模型可被接受;以此为基础建立低温杀菌黄焖鸡的货架期预测模型,经过验证,模型预测值与实测值的相对误差值均在±10%以内,表明所建立的货架期预测模型能够比较准确地预测低温杀菌黄焖鸡在4～25 ℃范围内的货架期。     

Evaluation of Mathematical Models for Prediction of Thin-Layer Drying of Banana Slices

Drying characteristics of bananas were experimentally determined. The drying experiments were carried out in a hot air dryer at four inlet temperatures of 50, 60, 70 and 80°C, at a constant air velocity of 2.4 m/s and relative humidity of 4–25%. The experimental results were fitted to five thin-layer drying models and it was found that the Page and Logarithmic models gave better fit that the other models. Values of the effective diffusivity ranged from 7.374 × 10^?11 to 2.148 × 10^?10 m²/s. Activation energy for moisture diffusion of the banana slices was found to be 32.65 kJ/mol.     

Additive Versus Multiplicative Precorrections of Dairy Records for Some Environmental Effects in Sire Evaluation

Additive and multiplicative correction factors were compared for precorrection of milk production records for effects of age at calving, month of calving, and pasture, using the data on 168,480 progeny of 70 proven sires of the Simmental breed. Criteria used for the comparison were unremoved variation, interactions of the mentioned effects with herd-year, residual variance ignoring interactions, and the bias in sire predictions. For age at calving and month of calving both types of correction factors were equally efficient in removing the variation in yields of milk and fat. Pasture effects were removed more effectively with additive than with multiplicative factors. The variances due to herd-year × month of calving and herd-year × pasture interactions were larger with multiplicative factors. The herd-year × age at calving interaction variances were similar with both types of corrections. Different criteria of the comparison showed that the differences in the efficiency of both types of correction factors were unimportant. A model of sire evaluation was found equally accurate with additive or multiplicative factors.     

肉糜加工中亚硝酸盐的变化及预测模型建立?

以肉糜为材料,研究腌制、常压蒸煮、高压蒸煮单元加工条件及NaNO2 添加量对肉糜中NaNO2 残留量的影响。考察建立NaNO2 添加量、腌制时间、煮制时间、蒸煮温度对NaNO2 残留量影响的预测模型,并对模型进行验证。结果表明：腌制阶段NaNO2 残留量与NaNO2 添加量呈显著正相关,与腌制时间呈显著负相关;蒸煮阶段NaNO2 残留量与NaNO2 添加量呈显著正相关,与蒸煮温度与时间呈负相关;动力学模型分析表明,高压蒸煮对NaNO2 残留量下降速率的影响比常压蒸煮大,不同温度对NaNO2 残留量的影响极其显著(P ＜ 0.01);80℃与121℃条件下建立的预测模型R2 分别为0.939、0.888。预测模型试验值与回归值符合得比较好,相对误差小于10%。     

日本沼虾原肌球蛋白线性表位预测研究

本文旨在预测日本沼虾原肌球蛋白的线性表位,为相关食物过敏的识别与检测提供依据,同时为基于抗原表位的致敏性消减提供靶标。采用DNAStar软件、SOPMA、BepiPred 1.0 Server及ABCpred在线网站预测日本沼虾原肌球蛋白的B细胞线性表位,应用SYFPEITHI、NetMHCII 2.3 Server与NetMHCIIpan 3.2 Server预测T细胞表位。综合分析以上预测结果,表明日本沼虾原肌球蛋白可能的B细胞线性表位有²¹RADTLEQQNKEANN³⁴、³⁷EKTEEEIRTTQKKMQQ⁵²、⁷¹LEEKEKA⁷⁷、⁹⁹LERSEERLN¹⁰⁷、¹¹⁹AADESER¹²⁵、¹³⁴SLSDEER¹⁴⁰、¹⁵⁸ADRKYDE¹⁶⁴、¹⁷⁷ERAEERAETG¹⁸⁶、²¹⁰SEEKANQREEAYKE²²³、²⁶²NEKEKYK²⁶⁸,可能的T细胞表位有⁸²EGEVAALNRRIQLL⁹⁵、¹⁰⁵RLNTATTKLAEAS¹¹⁷、¹⁶⁵VARKLAMVEADLE¹⁷⁷、¹⁹⁵EELRVVGNNLKSLE²⁰⁸、²²²KEQIKTLTNKLKAA²³⁵。该结果可为继续深入开展日本沼虾过敏原基础性研究提供更精准的靶标。     

盐池滩羊肉脂肪含量的高光谱预测模型构建

本文利用可见-近红外高光谱成像技术预测冷鲜滩羊肉脂肪含量,优选最佳预测模型。测定90个滩羊背最长肌的脂肪含量并采集其光谱图像,对原始光谱进行不同种预处理后,构建了全波段下的偏最小二乘回归(PLSR)和主成分回归(PCR)的光谱预测模型。为减少模型运算次数,在预处理效果最优的全波段模型上采用连续投影算法(SPA)、应用竞争性自适应重加权(CARS)、变量组合集群分析(VCPA)和波长空间迭代收缩(IVISSA)方法提取特征波长,构建脂肪含量的光谱预测模型。结果表明:采用归一化(Normlize)预处理后光谱构建的PLSR全波段模型效果最好,校正集模型相关系数(Rc)达到0.921;采用多元散射校正(MSC)预处理后光谱构建的PCR全波段模型效果最好,其校正集模型相关系数(Rc)达到0.850;在4种提取特征波长过程中,IVISSA算法所提取特征波长的交互验证均方根误差(RMSECV)最低,为0.0072;Normlize-IVISSA-PLSR模型较其他3种算法所构建的PLSR模型效果最优,其校正集相关系数(Rc)和预测集相关系数(Rp)值分别为0.931和0.754,表明利用高光谱技术对盐池滩羊肉脂肪含量进行预测是可行的。研究成果为冷鲜滩羊肉品质在线光谱快速无损检测系统开发提供理论依据。