Usefulness of the murine model to study the immune response against Histoplasma capsulatum infection

The present paper is an overview of the primary events that are associated with the histoplasmosis immune response in the murine model. Valuable data that have been recorded in the scientific literature have contributed to an improved understanding of the clinical course of this systemic mycosis, which is caused by the dimorphic fungus Histoplasma capsulatum. Data must be analyzed carefully, given that misinterpretation could be generated because most of the available information is based on experimental host–parasite interactions that used inappropriate proceedings, i.e., the non-natural route of infection with the parasitic and virulent fungal yeast-phase, which is not the usual infective phase of the etiological agent of this mycosis.     

A spatial risk assessment of bighorn sheep extirpation by grazing domestic sheep on public lands

Bighorn sheep currently occupy just 30% of their historic distribution, and persist in populations less than 5% as abundant overall as their early 19th century counterparts. Present-day recovery of bighorn sheep populations is in large part limited by periodic outbreaks of respiratory disease, which can be transmitted to bighorn sheep via contact with domestic sheep grazing in their vicinity. In order to assess the viability of bighorn sheep populations on the Payette National Forest (PNF) under several alternative proposals for domestic sheep grazing, we developed a series of interlinked models. Using telemetry and habitat data, we characterized herd home ranges and foray movements of bighorn sheep from their home ranges. Combining foray model movement estimates with known domestic sheep grazing areas (allotments), a Risk of Contact Model estimated bighorn sheep contact rates with domestic sheep allotments. Finally, we used demographic and epidemiologic data to construct population and disease transmission models (Disease Model), which we used to estimate bighorn sheep persistence under each alternative grazing scenario. Depending on the probability of disease transmission following interspecies contact, extirpation probabilities for the seven bighorn sheep herds examined here ranged from 20% to 100%. The Disease Model allowed us to assess the probabilities that varied domestic sheep management scenarios would support persistent populations of free-ranging bighorn sheep.     

Zero-inflated models for identifying disease risk factors when case detection is imperfect: Application to highly pathogenic avian influenza H5N1 in Thailand

Logistic regression models integrating disease presence/absence data are widely used to identify risk factors for a given disease. However, when data arise from imperfect surveillance systems, the interpretation of results is confusing since explanatory variables can be related either to the occurrence of the disease or to the efficiency of the surveillance system. As an alternative, we present spatial and non-spatial zero-inflated Poisson (ZIP) regressions for modelling the number of highly pathogenic avian influenza (HPAI) H5N1 outbreaks that were reported at subdistrict level in Thailand during the second epidemic wave (July 3rd 2004 to May 5th 2005). The spatial ZIP model fitted the data more effectively than its non-spatial version. This model clarified the role of the different variables: for example, results suggested that human population density was not associated with the disease occurrence but was rather associated with the number of reported outbreaks given disease occurrence. In addition, these models allowed estimating that 902 (95% CI 881–922) subdistricts suffered at least one HPAI H5N1 outbreak in Thailand although only 779 were reported to veterinary authorities, leading to a general surveillance sensitivity of 86.4% (95% CI 84.5–88.4). Finally, the outputs of the spatial ZIP model revealed the spatial distribution of the probability that a subdistrict could have been a false negative. The methodology presented here can easily be adapted to other animal health contexts.     

Genetic parameters for direct and maternal calving ease in Walloon dairy cattle based on linear and threshold models

Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33 155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85 118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice.     

Model averaging for genome‐enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield

Predictive ability of yet‐to‐be observed litter size (pig) grain yield (wheat) records of several reproducing kernel Hilbert spaces (RKHS) regression models combining different number of Gaussian or t kernels was evaluated. Predictive performance was assessed as the average (over 50 replicates) predictive correlation in the testing set. Predictions from these models were combined using three different types of model averaging: (i) mean of predicted phenotypes obtained in each model, (ii) weighted average using mean squared error as weight or (iii) using the marginal likelihood as weight. (ii) and (iii) were obtained in a validation set with 5% of the data. Phenotypes consisted of 2598, 1604 and 1879 average litter size records from three commercial pig lines and wheat grain yield of 599 lines evaluated in four macro‐environments. SNPs from the PorcineSNP60 BeadChip and 1447 DArT markers were used as predictors for the pig and wheat data analyses, respectively. Gaussian and univariate t kernels led to same predictive performance. Multikernel RKHS regression models overcame shortcomings of single kernel models (increasing the predictive correlation of RKHS models by 0.05 where 3 Gaussian or t kernels were fitted in the RKHS models simultaneously). None of the proposed averaging strategies improved the predictive correlations attained with single models using multiple kernel fitting.     

生态农业经营模式浅析

生态农业经营是建立在生态环境发展基础上的经济性复合农业系统,不同地区的经营模式是由地域特色所决定的,生态农业经营模式具有多样性、效率性、综合性以及持续性等优势,在经营模式中能够将经济与生态的有效循环体现出来。为实现生态农业经营的发展与经济效益的最大化,现就生态农场的经营模式进行分析,并找出主导现代农业发展的因素与生态农业发展经营的方向。     

微米木纤维JGD型木橡胶减震器的建模理论研究

选用可再生资源---红松木材的加工剩余物和边角碎料切削而成的木纤维为原料,用数学建模方法构造JGD型木橡胶减震器的外部造型,通过MATLAB验证JGD型木橡胶减震器仿真模型的通用性和精确性。动态输入模型参数,得到不同模型所需的加工尺寸,制作与模型相对应的模具。在模具上完成微米木纤维JGD型木橡胶减震器的整个模压加工过程,既能保证木橡胶减震器外形尺寸,又降低其生产成本,可为微米木纤维JGD型木橡胶减震器的加工系统实际生产提供理论依据。     

用人工神经网络构建多形地位指数模型

以马尾松人工林132株优势木树干解析数据为训练样本,用145块标准地优势木平均高数据为检验样本,把林分年龄和地位指数或优势木平均高作为输入变量,将优势木平均高或地位指数作为输出变量,通过构建人工神经网络逆模型的途径,分别建立了多形地位指数曲线式和计算式模型。结果表明,多形地位指数曲线式的总体拟合精度为99.64％,总体预测精度达96％以上,比传统技术构建的多形地位指数模型能较真实地模拟各地位级的多形曲线;多形地位指数计算式的总体拟合精度为98.81％,用于计算地位指数,省去用迭代法计算地位指数的工作量。基于BP神经网络模型多形地位指数模型,对马尾松人工林地位指数测定提供指导作用,可为森林立地质量评价提供理论依据。     

基于FORSTAT的人工杨树二元材积模型参数估计应用

利用在内蒙古东部地区收集的1 570株人工杨树样木资料,通过FORSTAT软件对其二元材积模型进行拟合,比较各个模型的效果,选取最佳模型,以供参考。