Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait, ～1,900,000 animals, and ～5,900 genotyped elite artificial insemination rams. For the ～8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare “partial” and “whole” predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the “official” (routine) evaluation, which is a mixture of both single and multiple trait, and “deletion” of data before 1990; and (2) several models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relationship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies estimated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdispersion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gamma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball effect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.     

Breeding schemes with optimum-contribution selection or truncation selection for beef cattle destined for use on dairy females

We tested the hypothesis that the size of a beef cattle population destined for use on dairy females is smaller under optimum-contribution selection (OCS) than under truncation selection (TRS) at the same genetic gain (ΔG) and the same rate of inbreeding (ΔF). We used stochastic simulation to estimate true ΔG realized at a 0.005 ΔF in breeding schemes with OCS or TRS. The schemes for the beef cattle population also differed in the number of purebred offspring per dam and the total number of purebred offspring per generation. Dams of the next generation were exclusively selected among the one-year-old heifers. All dams were donors for embryo transfer and produced a maximum of 5 or 10 offspring. The total number of purebred offspring per generation was: 400, 800, 1,600 or 4,000 calves, and it was used as a measure of population size. Rate of inbreeding was predicted and controlled using pedigree relationships. Each OCS (TRS) scheme was simulated for 10 discrete generations and replicated 100 (200) times. The OCS scheme and the TRS scheme with a maximum of 10 offspring per dam required approximately 783 and 1,257 purebred offspring per generation to realize a true ΔG of €14 and a ΔF of 0.005 per generation. Schemes with a maximum of 5 offspring per dam required more purebred offspring per generation to realize a similar true ΔG and a similar ΔF. Our results show that OCS and multiple ovulation and embryo transfer act on selection intensity through different mechanisms to achieve fewer selection candidates and fewer selected sires and dams than under TRS at the same ΔG and a fixed ΔF. Therefore, we advocate the use of a breeding scheme with OCS and multiple ovulation and embryo transfer for beef cattle destined for use on dairy females because it is favorable both from an economic perspective and a carbon footprint perspective.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

基于全相位预处理的时域多重信号分类波达方向估计方法

低信噪比下,针对宽带短脉冲情况下频域多重信号分类(MUSIC)中噪声子空间估计不稳定问题,提出一种基于全相位预处理的时域多重信号分类波达方向(DOA)估计方法。①对线列阵接收数据进行分组处理;②按搜索角度对各组数据进行相移预处理,并对各组数据预处理结果进行相加,得到一组新数据;③对线列阵接收数据在时域构建相移后的协方差矩阵,在更短数据长度下,稳定实现噪声子空间估计,并依据估计出的噪声子空间含有的正交特性,通过单位矩阵加法器得到相应空间谱估计值,实现波达方向估计。数值仿真和实测数据处理结果表明,相比频域MUSIC方法,该方法有效提高了线列阵接收数据协方差矩阵中信号含有量和信噪比,能够在更短数据长度情况下实现对噪声子空间的稳定估计,具有较好的稳定性和检测性能,提高了MUSIC方法在实际波达方向估计中的鲁棒性。     

海上稠油油田蒸汽吞吐产量确定新方法

针对目前蒸汽吞吐产量预测模型假设条件简单、普适性差等问题,一般采用测试法和类比法综合确定海上稠油油田蒸汽吞吐初期产量。由于目前海上油田通常只开展常规测试,无法直接获得热采开发初期产量。笔者提出海上稠油油田蒸汽吞吐初期产量确定新方法,建立蒸汽吞吐相对于常规开发的初期产量倍数预测模型,通过蒸汽吞吐产量倍数,将常规测试确定的产量转化为蒸汽吞吐产量。研究表明,蒸汽吞吐初期产量倍数主要受储集层渗透率、原油黏度、注入强度、蒸汽干度等因素影响,利用正交试验设计和多元回归等方法,建立海上稠油油田蒸汽吞吐初期产量倍数与油藏地质参数及注入参数之间的非线性预测模型,该模型经实际生产数据验证,预测误差小于5%,可靠性高,能够为海上稠油油田蒸汽吞吐初期产量的确定提供依据。     

煤样多元物性参数在煤与瓦斯突出防治中的应用研究

煤与瓦斯突出是一种复杂的动力现象，突出的类型不同、引起突出的危险因素不同，预测指标对其的敏感性也就不同，选择适应的突出危险性预测敏感指标及突出敏感指标的临界值，研究可靠的突出预测手段和方法来实现对突出危险性的准确预测，是指导突出防治工作的重要技术基础，而为了获得突出敏感指标的临界值就必须对煤的多元物性参数进行实验研究，掌握煤层的各项物化性质，是有效确定防治突出方法体系的关键基础。     

深水油区多油田组合开发及产量接替优化

对由多个油田组成的海外深水开发项目区块，高效开发的关键是针对不同油田的特点，统筹规划不同油田投产次序和产量接替的组合方式，实现有效开发时间内的整个开发项目的最大收益。文中基于深水油田特点及开发要求，通过3个方面的研究来实现深水油区不同油田组合的储量动用及产量接替优化。一是建立深水油田储量分级评价标准，并利用数学综合评价方法确定不同油田的储量等级；二是通过分析深水油田产量运行主控因素，建立不同等级储量的关键开发指标预测模型，可作为不同油田组合开发下产量运行计算的基础；三是建立满足不同限制条件、开发效益最大的数学模型，并利用过程控制理论进行计算，优化得到不同储量等级油田的组合方式和逐次动用程序。将本文方法应用在XF深水油区的开发部署中，基于油田实际数据，进行了不同条件下、多个油田储量动用及产量接替方式的计算，优化结果符合实际开发需求，提高了深水油田开发决策的科学性。     

多个质量控制图在大米总砷含量测定中的联合应用

目的研究多个质量控制图在大米中总砷含量测定的联合应用。方法收集大米中总砷含量测定数据,分析和计算质量控制图参数,绘制出多个质量控制图并分析联合应用效果。结果多个质量控制图法实现对大米中总砷含量测试结果控制,建立一种预警响应机制,实现问题快速查找之法。结论多个质量控制图联合应用为实验室技术要素管理和考核评价提供依据。     

活性肽-N对D-半乳糖致衰老小鼠的作用效果

为了探讨一种新型抗衰老活性因子——活性肽-N食用后的抗衰老作用,本研究通过给小鼠连续皮下注射D-半乳糖(200 mg/(kg·d),以体质量计,下同)建立衰老模型,造模的同时开始灌胃2.5、25.0、125.0μg/(kg·d)活性肽-N复乳,连续9周。分别采用旷场实验和Morris水迷宫实验检测小鼠的运动探究能力和学习记忆能力。上述行为学实验后,对小鼠血清中谷胱甘肽过氧化物酶(glutathione peroxidase,GSH-Px)活力、丙二醛(malondialdehyde,MDA)含量及脑组织中的乙酰胆碱酯酶(acetyl cholinesterase,ACh E)活力进行测定。结果表明,与衰老模型组相比,活性肽-N复乳高剂量组能显著提高衰老小鼠的运动探究能力,改善其学习记忆能力,提高血清中的GSH-Px活力28.38%(P0.05),降低MDA含量26.19%(P0.05),同时降低脑组织中的ACh E活力23.26%(P0.05)。综合结果表明该活性肽-N对D-半乳糖致衰老小鼠具有明显的抗衰老作用。     

一种新型细旦帘子布的生产工艺研究

对新型细旦浸胶帘子布进行试生产。在试生产过程中，针对其高经密、高总经根数的特点，增加了整经工序，并在此基础上，对整经工艺、织造工艺及浸胶工艺进行探索，确定了该新型细旦浸胶帘子布的工艺参数。从试验结果来看，此次试生产达到了预期目标，并初步验证了在帘子布织造行业使用整经工序来开发生产高端细旦产品的优势，为开发高经密、高总经根数产品积累了初步经验。