Effect of iron supplementation during pregnancy on birthweight: evidence from Zimbabwe

BACKGROUND: Iron deficiency in pregnant women has been shown to reduce the oxygen supply to the fetus, cause intrauterine growth retardation, and increase the risk of premature delivery and reduced birthweight. Yet the effects of iron supplementation programs on pregnancy outcomes are not well documented for developing countries. OBJECTIVE: To examine the relation between iron supplementation of mothers during pregnancy and children's birthweight using data from a national population-based survey in Zimbabwe. METHODS: The analysis uses information on 3559 births during the five years preceding the 1999 Zimbabwe Demographic and Health Survey. The effect of iron supplementation during pregnancy on birthweight was estimated by multiple regression, controlling for potential confounding effects of prenatal care, child's sex and birth order, mother's education and nutritional status (measured by body-mass index), household living standard, smoke exposure, and other variables. RESULTS: Babies born to mothers who received iron supplementation during pregnancy were 103 g heavier (95% confidence interval, 42-164; p = .001), on average, than babies born to mothers who did not receive iron supplementation during pregnancy. The difference was 64 g (95% confidence interval, 2-125; p = .043) for children whose birthweights were taken from health cards and 163 g (95% confidence interval, 44-281; p = .008) for children whose birthweights were reported by their mothers. CONCLUSIONS: Iron supplementation during pregnancy is associated with significantly higher birthweight, independent of other pregnancy care factors, mother's nutritional status, smoke exposure, and a number of demographic and socioeconomic factors. Prenatal iron supplementation programs can improve pregnancy outcomes and promote child survival in developing countries.     

Accounting for pregnancy diagnosis in predicting days open

The system for estimating days open for cows with no subsequent lactation was examined to determine if estimates should vary depending on pregnancy diagnosis. Pregnancy diagnosis information was unavailable when the original prediction system was developed, but collection was begun in 2002. New prediction equations were estimated from nearly 1.1 million Holstein lactations for 20-d intervals from 110 to 250 days in milk (DIM). Use of pregnancy diagnosis improved accuracy compared with the original system. The improvement was particularly evident for lactations of cows confirmed to be open in the 130-to-149 DIM interval, where predicted days open increased by > 96 d. For lactations of cows with a confirmed pregnancy, predicted days open decreased by 18 d for the same interval. Prediction errors decreased with increasing DIM. Jersey lactations averaged fewer days open, but in most cases Holstein solutions provided adequate predictions. Specific adjustments were generated for Jersey lactations with no breedings reported. Those adjustments reduced the predicted days open averaged across parity by an amount that increased from 9 to 27 d with DIM interval. The new prediction equations were implemented for November 2004 evaluations for daughter pregnancy rate.     

一种新型补充矿物元素的载体——脱铁铁蛋白的研究进展

铁蛋白(ferritin)经脱铁处理后,内部空腔可装载多种矿物元素。鉴于这种蛋白可经胞饮作用,完整地被小肠上皮细胞吸收,因此有望作为新型矿物元素载体而发展。脱铁铁蛋白(apoferritin)的脱铁及矿物元素的装载过程可利用透射电镜(TEM)、能谱分析(EDS)、X射线粉末衍射(X-Ray)、质谱(MS)、核磁共振(NMR)、动态光散射(DLS)、紫外分光光度法(UV)等技术研究分析。本文综述了目前国内外利用铁蛋白装载矿物元素的成果及其在营养方面的应用。     

Experimental validation of models for predicting optimal surface quality sterilization temperatures

An experimental procedure was developed to validate experimentally the theoretical optimal process temperatures giving the maximum quality at the surface of conduction heating products for specified levels of sterility. the method involved filling a can with a 10% bentonite solution, placing glass capillaries containing a food quality indicator at the surface, and processing it at different temperatures for the necessary time in order to attain a specified sterility value at the least-lethality point. the acid catalysed sucrose hydrolysis reaction was used as a chemical time-temperature-integrator for a quality index of heat sterilized products. At a pH of 3.8, the kinetic parameters D_121.15°C ard z values of this reaction, were 214min and 31°C, respectively. Final surface retentions of sucrose compared well with theoretically predicted values from simulation modelling. Optimal temperatures calculated using either numerical computer optimization or generalized regression equations were in the range of experimentally determined optimal conditions.     

An application of linear regression technique for predicting bulgur yield and quality of wheat cultivars

Grains of 26 Turkish wheat cultivars and advanced breeding lines were used in this study. Simple correlations between a number of quality parameters to predict bulgur yield and bulgur cooking quality were determined. Highly significant correlations between bulgur yield and each of the thousand-kernel weight and the sum of the grain over 2.8 + 2.5 mm sieves were obtained for both durum and bread wheat samples (p < 0.01). The regression equations showed that the models involving two variables (the thousand-kernel weight and the thickness of the grain for durum wheat samples; the thousand-kernel weight and the length of the grain for bread wheat samples) resulted in the highest R2 values. For an assessment of the influence of all factors on bulgur cooking properties (total organic matter: TOM and colorimetric test values), simple and multiple regression analyses were used to find equations that predict best the relationship between various quality parameters and bulgur cooking properties. The models involving two variables; the vitreousness and the dry gluten contents for the durum wheat samples and SDS sedimentation test value and wheat protein content for the bread wheat samples resulted in the highest R2 for the TOM value.     

Product unit neural network models for predicting the growth limits of Listeria monocytogenes

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.     

Assessment of mathematical models for predicting Staphylococcus aureus growth in cooked meat products

The growth of Staphylococcus aureus in commercially available vacuum-packaged cooked ham, turkey breast meat, and chicken breast meat stored at 2.3, 6.5, 10, 13.5, and 17.7 degrees C was studied. Growth rates observed in these food products were compared with those predicted on the basis of various growth models found in the literature and with those generated by the Pathogen Modeling Program and the Food MicroModel software using graphical and mathematical analysis for performance evaluation. In general, the models studied overestimated the growth of S. aureus. The Dengremont and Membré model most closely matched the observed behavior of S. aureus in ham and chicken breast meat, with bias factors of 1.56 and 1.09, respectively. The Eifert et al. model accurately described the growth of S. aureus in turkey breast meat, with a bias factor of 1.51. The remaining models provided safe predictions of the growth rate of S. aureus, but with poor accuracy. Predictive microbiology models have an immediate practical application in improving microbial food safety and quality and are very useful decision support tools, but they should not be used as the sole determinant of product safety.     

热黏合土工布应力松弛力学性能预测模型研究

采用四种力学模型对三种不同类型热黏合土工布的应力松弛力学性能进行拟合研究。结果表明,使用力学模型对热黏合土工布的应力松弛进行预测是可行的,广义麦克斯韦变化模型二对三种试样均具有最佳的拟合效果,拟合能力优于欧林模型,显示了良好的品种适应性和预测能力。     

Comparing predicting models for heat inactivation of Listeria monocytogenes and Pseudomonas aeruginosa at different pH

Under the same experimental conditions it has been demonstrated that whereas survival curves of Listeria monocytogenes in the range of temperatures from 54 to 62 °C followed a first-order kinetic, those of Pseudomonas aeruginosa in the range of temperatures from 50 to 56 °C were not linear showing a shoulder followed by a linear region. The first order kinetic model did not describe survival curves of P. aeruginosa. A model based on the Weibull distribution (Log₁₀(N_t/N₀)=(1/−2.303)*(t/b)ⁿ)) accurately described the inactivation kinetics of both microorganisms at the three pHs of 4, 5.5, 7.4 investigated. For both microorganisms, the b value depended on the treatment temperature and the pH of the treatment medium. Whereas for L. monocytogenes the n value was independent of the treatment conditions, for P. aeruginosa the n value depended on the pH of the treatment medium.

The model based on the Weibull distribution was capable of accurately predicting the treatment time to inactivate five Log₁₀ cycles of both microorganisms at the three pHs investigated.     

单增李斯特菌的生长特性及其在冷藏牛奶中的预测模型

通过测定单增李斯特菌菌株在不同培养条件下菌液的OD600值,绘制生长曲线,分析培养基成分、温度、p H、Na Cl浓度、接种量以及转速对单增李斯特菌生长的影响,并选取Gompertz方程、Logistic方程和Hill方程建立单增李斯特菌在冷藏牛奶中的生长模型。结果表明:菌株在TSB-YE和MRS培养基中生长良好;能够耐受0.5%³%的Na Cl浓度范围;最适生长温度为37℃,最适生长p H为8,对氧气的需求量表现不明显。菌株可在4℃冷藏的全脂牛奶中缓慢生长,达到稳定期的菌数有2 lg CFU/m L的增长,Gompertz和Logistic方程模型可用于预测其生长情况。      

基于机器学习的密集烘烤过程烟叶失水率预测模型对比

为准确预测密集烘烤过程烟叶失水率,以精准调控烘烤工艺参数,基于机器学习建立烟叶失水率预测模型。以翠碧一号中部叶为材料,实时采集烘烤过程中烟叶图像和失水率;利用图像处理技术提取烟叶的10种颜色特征和10种纹理特征,通过变量聚类和皮尔逊相关性分析优选出2种颜色特征(a^*/b^*、R)和2种纹理特征(梯度熵、梯度分布不均匀性);将训练集图像的4种优选特征和烟叶失水率作为输入变量,分别对建立的网格式支持向量机(GS-SVM)、遗传算法优化的BP神经网络(GA-BP)、极限学习机(ELM)3种预测模型进行训练。利用3种预测模型对测试集图像进行烟叶失水率预测并与实际失水率比较。结果表明,3种预测模型均能够较为准确地预测密集烘烤过程烟叶失水率(均方根误差RMSE≤0.014 0,决定系数R2≥0.996 1),对变黄期(0～40 h)和定色期(40～100 h)的预测误差小于干筋期(100～140 h)。该技术可为烟叶烘烤智能调控系统的研发提供支持。     

Heat transfer models for predicting Salmonella enteritidis in shell eggs through supply chain distribution

ABSTRACT:  Egg and egg preparations are important vehicles for Salmonella enteritidis infections. The influence of time–temperature becomes important when the presence of this organism is found in commercial shell eggs. A computer-aided mathematical model was validated to estimate surface and interior temperature of shell eggs under variable ambient and refrigerated storage temperature. A risk assessment of S. enteritidis based on the use of this model, coupled with S. enteritidis kinetics, has already been reported in a companion paper published earlier in JFS . The model considered the actual geometry and composition of shell eggs and was solved by numerical techniques (finite differences and finite elements). Parameters of interest such as local ( h ) and global ( U ) heat transfer coefficient, thermal conductivity, and apparent volumetric specific heat were estimated by an inverse procedure from experimental temperature measurement. In order to assess the error in predicting microbial population growth, theoretical and experimental temperatures were applied to a S. enteritidis growth model taken from the literature. Errors between values of microbial population growth calculated from model predicted compared with experimentally measured temperatures were satisfactorily low: 1.1% and 0.8% for the finite difference and finite element model, respectively.     

基于BP神经网络建立烤烟感官质量的预测模型

烟叶化学成分是影响烟叶质量的物质基础,烟叶化学成分与评吸质量的关系探究一直是非常重要的研究领域.传统的数理统计方法无法直接给出烟叶感官质量的预测.BP神经网络具有很强的非线性映射能力,但是容易出现"过拟合".由于烟叶样本数据的噪声较大,为了提高预测模型的泛化性能,本文在应用BP神经网络建模时,采用"权值衰减"策略和样本验证策略进行训练.结果表明,设计合理的BP网络可以较好的对烟叶的常规化学成分进行感官质量预测.     

Suitability of the linear relationships for predicting tryptophan of feedstuffs from nitrogen content: A critical study of literature data

The tryptophan contents of 20 samples of various feedstuffs, determined by using assay procedures which were proved to give quantitative results, were compared with those predicted from the relationships between nitrogen and tryptophan published by three laboratories. The ratio of predicted tryptophan to the observed tryptophan, expressed as percentage, ranged from 65% (pea) to 137% (fishmeal). These differences were shown to be related to a systematic underestimation of tryptophan regarding data from two laboratories associated with a variability of tryptophan content for a given nitrogen content and a given type of feedstuff.     

Cytological endometritis at artificial insemination in dairy cows: Prevalence and effect on pregnancy outcome

The aims of our field study in dairy cows were (1) to consolidate cytotape (CT) as a valid technique to diagnose cytological endometritis (CYTO) during artificial insemination (AI); (2) to establish a cutoff point concerning the polymorphonuclear cells (PMN) proportion to diagnose CYTO at AI; (3) to assess the prevalence of CYTO at AI; and (4) to evaluate the effect of CYTO on the pregnancy outcome of that AI. The investigation was performed using 1,625 AI-CT samples harvested from 873 Holstein-Friesian cows from 18 dairy farms in the Flemish region of Belgium. The CT device consisted of adapting a 1.5-cm piece of paper tape on the top of a conventional AI catheter covered with a double guard sheet, allowing an endometrial cytology sample to be taken when performing an AI. A receiving operator characteristic curve was built to assess the threshold level above which the PMN proportion significantly affected the AI success. Multilevel generalized mixed-effect models were built to identify factors affecting the pregnancy outcome of the AI under investigation. Only 7 samples (0.4%) harvested in 5 cows were discarded because of low-quality parameters. The cutoff point for CYTO at AI was set at ≥1% PMN (sensitivity = 33.8%, specificity = 88.6%). Prevalence of CYTO at AI was 27.8%. The conception rate for CYTO-positive samples was 32.7%, whereas it was 47% for CYTO-negative samples. A CYTO-negative AI had 1.8 [odds ratio (OR)] more chances to become pregnant than a CYTO-positive one. Other factors identified as detrimental for the pregnancy outcome were body condition score ≤1.5 (OR = 0.6), relative 305-d milk yield (OR = 0.9), dystocia (OR = 0.3), parity ≥2 (OR = 0.7), and warm months of the year. In conclusion, CT is a consolidated technique to diagnose CYTO at AI, PMN 1% is the threshold level to diagnose CYTO at AI, around one-quarter of inseminated uteri suffer from CYTO, and affected uteri having a significantly lower chance to become pregnant from that insemination.     

Comparison of the neural network model and linear regression model for predicting the intermingled yarn breaking strength and elongation

In this study, the effects of selected intermingling process parameters on yarn breaking strength and elongation were predicted using artificial neural network. For this aim, partially oriented polyester yarn with 283 dtex linear density and three different numbers of filaments (34, 68, and 100) were used for producing interlaced yarn under different process parameters (speed and pressure). Yarns’ elongation and strength values measured with Uster Tensorapid test device and the number of filaments are input variables of the artificial neural networks. Feed forward neural network (FFNN) is used as the network structure. All FFNN computations were performed by MATLAB software package. The comparison results show that the FFNN has a better prediction performance than linear regression.     

Short communication: Alteration of priors for random effects in Gaussian linear mixed models

Linear mixed models, for which the prior multivariate normal distributions of random effects are assumed to have a mean equal to 0, are commonly used in animal breeding. However, some statistical analyses (e.g., the consideration of a population under selection into a genomic scheme breeding, multiple-trait predictions of lactation yields, and Bayesian approaches integrating external information into genetic evaluations) need to alter both the mean and (co)variance of the prior distributions and, to our knowledge, most software packages available in the animal breeding community do not permit such alterations. Therefore, the aim of this study was to propose a method to alter both the mean and (co)variance of the prior multivariate normal distributions of random effects of linear mixed models while using currently available software packages. The proposed method was tested on simulated examples with 3 different software packages available in animal breeding. The examples showed the possibility of the proposed method to alter both the mean and (co)variance of the prior distributions with currently available software packages through the use of an extended data file and a user-supplied (co)variance matrix.