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.     

Evaluation of back-calculated elastic moduli of unreinforced and geocell-reinforced unbound granular material from full-scale field tests

This paper presents a field-scale experimental track over a poor subgrade with an unreinforced section and a geocell-reinforced section subjected to in-situ performance tests. Plate load tests and Benkelman beam tests were carried out distributed in several unreinforced and reinforced layers. The objective was to: (1) examine the variability of the elastic modulus of unbound granular material (UGM) due the influence of its thickness and the presence of poor subgrade in its base, (2) evaluate the modulus improvement factor (MIF) generated by the geocell reinforcement in the UGM and (3) verify the most appropriate condition to apply the MIF to transport infrastructure design. The results showed that there is a significant influence of the thickness of the UGM layer on its elastic modulus when the layer is supported directly over a soft subgrade. The MIF values obtained in field suggest that its determination is mostly related to the UGM maximum elastic modulus rather than its decreased values (by virtue of poor subgrade or reduced thicknesses), and that the analytical formulation presented for MIF calculation has good predictive capability to be applied to pavement design.     

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).     

Synthesis of Diamond@SiO2@Ag composite materials for high SERS effect

Among various carbon materials, diamond stands out due to excellent physical and chemical properties. In this work, we designed Dia@SiO₂@Ag composites combining diamond micropowder and Ag nanoparticles by a simple chemical method and obtained stable substrate for surface enhanced Raman scattering (SERS) owing to its high surface-to-volume ratio, low density, as well as close bond between diamond and Ag. As-prepared Dia@SiO₂@Ag presented high activity to detect crystal violet and rhodamine 6G molecules, which was demonstrated by significantly enhanced SERS spectra and high enhancement factor values (10⁸-10⁹). Moreover, Dia@SiO₂@Ag also showed desired sensitivity, which was investigated by detection limit. Therefore, our study provided more theoretical support and broadened the functional applications of diamond, particularly in Raman detection.     

核电厂数字化主控室操纵员行为形成因子评价模型

由于人因可靠性分析(HRA)方法中用于量化基本人因失误概率(HEP)的行为形成因子（PSF）数目众多，且一般是通过专家评判，从而带有主观性、模糊性和不确定性。本文提出一种利用相关系数矩阵、图的距离分类和主成分分析法相结合的方法构建核电厂数字化主控室操纵员PSF的评价模型，其目的是识别不同类型的人因事件中主要影响人因绩效的PSF，以供决策减少人因失误。对某核电厂的179起人因事件报告进行实验，结果表明该评价模型能对核电厂数字化主控室操纵员的PSF进行有效评价。     

认知双向中继网络在κ-μ衰落信道下的中断性能分析

针对认知双向中继网络在进行数据传输时面临的复杂无线信道场景问题,采用广义κ-μ分布构建认知双向中继网络中的视距和非视距无线传输信道,推导次网络在κ-μ衰落信道下的统一中断概率,并分析次网络在多种单一和混合衰落信道情况下的中断性能。仿真结果表明,无线信道的衰落程度对次网络的中断概率影响显著,依据衰落信道类型合理设置网络参数将有助于提升次网络中断性能。     

抗FGF-2纳米抗体抑制碱烧伤诱导大鼠角膜血管新生

目的：研究抗成纤维细胞生长因子（FGF-2）纳米抗体对碱烧伤诱导的大鼠角膜血管生成的治疗作用。方法：将SD大鼠分为：假手术组（Sham），模型组（Model，直径为3 mm的浸有1 mol/L NaOH溶液圆形滤纸贴于大鼠眼角膜中央处30 s，制备大鼠碱烧伤血管生成模型）和治疗组（Treatment，术后7天至21天用3 mg/mL的抗FGF-2纳米抗体溶液滴眼，每日3次，每次10 μL，共14天）。通过体视显微镜和CD31免疫组织化学染色计算大鼠角膜血管生成情况。实时荧光定量PCR、酶联免疫吸附测定和免疫组织化学染色3种方法检测抗血管内皮生长因子（VEGF）和FGF-2的mRNA和蛋白表达水平。结果：（1）血管：治疗组较模型组的面积显著减少，血管管腔较窄（P<0.05），在药物干预14天后，差异最为显著；（2）FGF-2的mRNA和蛋白表达水平：模型组与治疗组的结果相近（P>0.05）；（3）VEGF的mRNA和蛋白表达水平：治疗组显著高于模型组（P<0.05）。此外，假手术组的持续给药也使得VEGF表达显著增加（P<0.05）。 结论：抗FGF-2纳米抗体可抑制由碱烧伤诱导的角膜血管新生，但也使得正常大鼠角膜或病理大鼠角膜的VEGF表达水平代偿性升高。     

双波长褪色分光光度法测定盐酸美西律

建立了一种快速、灵敏测定药物中盐酸美西律的双波长分光光度法。在弱碱性溶液中,虎红与盐酸美西律反应生成离子缔合物,使溶液发生褪色现象,光谱曲线上呈现2个较强的负吸收峰,它们分别位于472和560 nm,在此2个波长处,盐酸美西律的线性范围为0.04~2.6 mg/L,表观摩尔吸光系数(κ)分别为5.87×10⁴(472 nm)和3.59×10⁴ L/(mol·cm)(560 nm),检出限为0.033(472 nm)和0.035 mg/L(560 nm)。用双波长法测定时,其表观摩尔吸光系数(κ)达9.46×10⁴ L/(mol·cm),检出限为0.017 mg/L。双波长法用于盐酸美西律药片测定,加标回收率为97.7%~103%,相对标准偏差(RSD)为2.2%~2.6%。     

Testing EGFR with Idylla on Cytological Specimens of Lung Cancer: A Review

The current standard of care for advanced non-small-cell lung cancer is based on detecting actionable mutations that can benefit from targeted therapy. Comprehensive genetic tests can have long turn-around times, and because EGFR mutations are the most prevalent actionable mutation, a quick detection would enable a prompt initiation of targeted therapy. Furthermore, the scarcity of diagnostic material means that sometimes only cytologic material is available. The Idylla™ EGFR assay is a real-time PCR–based method able to detect 51 EGFR mutations in 2.5 h. Idylla is validated for use only on FFPE sections, but some researchers described their experiences with cytological material. We reviewed the relevant literature, finding four articles describing 471 cases and many types of cytological input material: smears, cell-block sections, suspensions, and extracted DNA. The sensitivity, specificity, and limit of detection appear comparable to those obtained with histological input material, with one exception: the usage of scraped stained smears as input may reduce the accuracy of the test. In conclusion, usage of cytological material as input to the Idylla EGFR test is possible. A workflow where common mutations are tested first and fast, leaving rarer mutations for subsequent comprehensive profiling, seems the most effective approach.