A bayesian model for the analysis of lactation curves of dairy goats

This article aims to determine the effect of certain covariates, such as season of kidding, parity, and time of kidding on the characteristics of the lactation curve of Saanen dairy goats. Characteristics investigated are peak milk yield, time of peak milk yield, total milk production, persistency, and the relationship between fat and protein in milk composition, as well as between lactation curves of the same animal in successive years. The analysis is carried out using a hierarchical Bayesian approach, together with Wood’s model, to model lactation. Posterior distributions of quantities of interest are obtained by means of the Markov chain Monte Carlo (MCMC) methods. These clearly illustrate the significant effect of especially parity, but also season and time of kidding on the characteristics of the lactation curve. Total and peak milk yield increase with increasing parity up to about the third or fourth parity, while peak yield is later for first than for later parities. The analysis also enables estimation of lactation characteristics of untested animals, prediction of future characteristics and identification of exceptional animals.     

Affinity maturation of the humoral immune response: A Bayesian approach

This article considers statistical modeling of a simplified version of Darwinian evolution which involves only two genes encoding one molecule. In particular, we consider a process known as affinity maturation that is carried out in the germinal center reaction (GCR). We use a statistical model, developed earlier, that enables us to tease apart the effects of mutation rate and selection in B cell populations undergoing an immune response. Standard classical statistical approaches that use large-sample theory may lead to misleading conclusions when sample size is very small. The data collection in many biological systems, including the immune response of vertebrate organisms, is both time consuming and often results in relatively small datasets. Therefore, this article takes a Bayesian perspective that produces finite-sample estimates of the mutation rates and is flexible in extending the models to more complicated processes. The method is illustrated using sequence data obtained from Gen bank that carried the experiment over two years.     

Effects of N sources and methane concentrations on methane uptake potential of a typical coniferous forest and its adjacent orchard soil

After removal of the above-ground plant debris three different soil layers were taken from a typical coniferous forest and its adjacent orchard in Numata City, Japan. The potentials of soil CH₄ uptake at two initial CH₄ concentrations were studied under aerobic conditions in the laboratory, along with inhibition of soil CH₄ oxidation by urea-N or KNO₃-N addition. Due to long-term N inputs, the CH₄ uptake of the upper mineral layer of the orchard soil was 25.4% and 87.7% lower than that of the surface forest soil at 2.4 and 12.6 l l^–1 CH₄, respectively. Methane uptake of the forest soil decreased with increasing soil depths at two CH₄ levels. However, maximal CH₄-consuming activity occurred in the 9- to 23-cm depth of the orchard soil at 12.6 l l^–1 methane. Nitrogen additions in the form of KNO₃ or urea at the rate of 200 g N g^–1 soil substantially reduced soil CH₄ uptake in the upper and sub-surface mineral layers at both sites, except that the addition of KNO₃-N had no apparent inhibitory effect on the CH₄ uptake in the 9- to 23-cm depth of the orchard soil. A strong inhibitory effect of NO₃^– addition on the CH₄ uptake, in contrast to NH₄⁺, occurred in the surface forest soil. The use of KNO₃-N, as compared to urea or urea plus a nitrification inhibitor (dicyandiamide), resulted in a lower potential to cause inhibition of CH₄ oxidation in the 0- to 23-cm depth of the orchard soil.     

Do enchytraeid worms and habitat corridors facilitate the colonisation of habitat patches by soil microbes?

Due to their high abundance and ubiquitous existence, microbes are considered to be efficient colonisers of newly established habitats. To shed light on the dispersal mechanisms of soil microbes, a controlled microcosm experiment was established. In these microcosms, the dispersal of microbes from a source humus patch to originally sterile humus patches (embedded in a mineral soil matrix) was followed for 16 months, applying 16S and 18S ribosomal DNA-based PCR-DGGE molecular methods. Specifically, the role of enchytraeid worms and habitat (humus) corridors as possible facilitators of microbe dispersal was studied. The results showed that enchytraeid worms function efficiently as vectors for horizontal dispersal of saprophytic fungi in soil. Some of the fungi also proved to disperse through the corridors by vegetative growth, although this dispersal was inefficient as compared to dispersal with the enchytraeids. Virtually no saprophytic fungi were able to disperse through the mineral soil matrix in the absence of both enchytraeid worms and corridors. Unlike soil fungi, the dispersal of soil bacteria was not affected by any of the studied factors. The results of the present experiment provide direct evidence of the crucial role of soil fauna in aiding the horizontal dispersal of soil fungi. The role of enchytraeids as a functionally important species in boreal forest soils is further emphasized, since bringing microbes into contact with new resources is likely to enhance the rate of decomposition in soils.     

Evidence of N<Subscript>2</Subscript>O emission and gaseous nitrogen losses through nitrification-denitrification induced by rice plants (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A greenhouse experiment was conducted with a specially designed apparatus consisting of an upper and lower chamber where the treatment with rice was carried out (treatment 1). The apparatus also had a single chamber where treatment 2, without rice plants, was carried out. The scope of this study was to elucidate the influence of rice plant growth on gaseous N losses as N₂ and N₂O produced by nitrification-denitrification in a flooded soil fertilized with (NH₄)₂SO₄ (with 56.50 atom% ¹⁵N). Gas samples were withdrawn weekly and analyzed for (N₂ + N₂O)-¹⁵N losses by mass spectrometer and for N₂O by gas chromatograph. The gaseous (N₂ + N₂O)-¹⁵N losses of the treatment with rice plants were significantly (P =0.01) higher than those of the treatment without rice plants, as were the amounts of N₂O emitted. Rice plants facilitate the efflux of N₂ and N₂O from soil to atmosphere, as about half of the total gaseous ¹⁵N loss as N₂ and N₂O was found in the upper chamber. The proportion of N₂O-¹⁵N to (N₂ + N₂O)-¹⁵N in the upper chamber was 10.56%, much higher than that of the lower chamber in treatment 1 and the headspace of treatment 2.     

Virtual Studio System Technology

Virtual studio system (VSS), a new technology based on virtual reality, develops fast in recent years. Virtual studio concept replaces real background sets with computer generated 3D synthetic and combines real foreground action, with computer generated backgrounds. As VSS need not construct real background sets and has little limitness of time and space, it is regarded as a revolution in TV programs. This paper mainly discusses about the background, technology feature, application and future.     

盖子红七彩神仙形态指标分析及其雌雄鉴别模型建立

测定和分析了138尾盖子红七彩神仙的形态学性状,并建立了雌雄个体的判别模型。对22项直观测量性状的主成分分析和21项标准化比例性状的R聚类分析,两者均显示盖子红七彩神仙的个体差异主要集中在体型大小、头部宽度、鱼鳍长度和宽度方面。对21项标准化比例性状进行T-检验,结果表明在雌雄间这些性状均差异不显著。以21项标准化比例性状为预选参数,通过逐步判别,建立了判别方程,对群体识别的正确率为80.2%。用32尾盖子红七彩神仙对这一判别方程进行识别验证,识别的正确率为84.4%。     

生长猪大米蛋白消化能、代谢能的评定及预测模型研究

本试验通过采集我国多个地区大米蛋白样品,分析大米蛋白常规成分,测定生长猪消化能(DE)、代谢能(ME),建立大米蛋白常规成分与生长猪DE、ME之间的关系。试验选用体重(33.0±1.3)kg的"杜×长×大"三元杂交健康去势公猪12头,采用2个6×6拉丁方试验设计,包括1种基础饲粮和11种大米蛋白替代15%基础饲粮的试验饲粮。采用全收粪法和套算法结合测定大米蛋白猪DE、ME,并将大米蛋白的常规成分与猪DE、ME进行相关和回归分析,建立大米蛋白猪DE、ME预测模型。结果表明,11种大米蛋白风干基础下猪DE为(18.13±1.12)M J/kg,M E为(16.44±1.59)M J/kg。由此得出,大米蛋白猪DE最佳预测模型(绝干基础)为DE=22.17-0.51NDF(R2=0.50,RSD=0.93),DE=18.58-0.49 CF+0.31 EE(R2=0.70,RSD=0.77);M E最佳预测模型(绝干基础)为ME=21.42-0.74 NDF(R2=0.52,RSD=1.30)。NDF为大米蛋白猪DE、M E最佳预测因子。