The effects of bovine necrotic vulvo-vaginitis on reproductive and production performance of Israeli 1st calf heifers

Bovine necrotic vulvovaginitis (BNVV) is a syndrome unique to Israel characterized by necrotic lesion in the caudal vagina mainly in first calf heifers after calving, associated with Porphyromonas levii. The objectives of this study were to analyze the impact of BNVV on reproductive performance, milk production and survival in the heard of first calf dairy heifers in affected farms, and to verify if the effects of BNVV are severity-dependent. For assessment of the severity level a scale of 4 degrees was formed, and cows were scored 4 to 6 d after calving. Data were obtained from two dairy farms during 2006-07, consisting of 603 lactations. The incidence and the severity of BNVV declined between 2006 and 2007, and severe BNVV tended to be more prevalent in the summer. The odds to conceive in the first artificial insemination of BNVV cow tended to be lower than healthy cows (OR = 0.676, P = 0.052). Cows with BNVV had longer empty period (145.8 d vs. 135.1 d of healthy cows, P = 0.031), but only severe BNVV had a negative effect on the odds of the cow to be empty at 150 d in milk (DIM) (OR = 2.05, P = 0.052). Severe BNVV also affected the mean survival time to conception (155.9 d vs. 142.3 d, P = 0.042). All BNVV severity degrees had a negative effect on milk production. The effect on milk production was not limited only to the beginning of the lactation, cows with BNVV produced 338.1 kg milk less than healthy cows (P = 0.016) in 305 d corrected lactation. The effect on milk production was not severity depended. No effect on survival time in the herd was demonstrated.     

A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication

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Metabolic engineering of bread wheat improves grain iron concentration and bioavailability

Bread wheat (Triticum aestivum L.) is cultivated on more land than any other crop and produces a fifth of the calories consumed by humans. Wheat endosperm is rich in starch yet contains low concentrations of dietary iron (Fe) and zinc (Zn). Biofortification is a micronutrient intervention aimed at increasing the density and bioavailability of essential vitamins and minerals in staple crops; Fe biofortification of wheat has proved challenging. In this study we employed constitutive expression (CE) of the rice (Oryza sativa L.) nicotianamine synthase 2 (OsNAS2) gene in bread wheat to up‐regulate biosynthesis of two low molecular weight metal chelators – nicotianamine (NA) and 2′‐deoxymugineic acid (DMA) – that play key roles in metal transport and nutrition. The CE‐OsNAS2 plants accumulated higher concentrations of grain Fe, Zn, NA and DMA and synchrotron X‐ray fluorescence microscopy (XFM) revealed enhanced localization of Fe and Zn in endosperm and crease tissues, respectively. Iron bioavailability was increased in white flour milled from field‐grown CE‐OsNAS2 grain and positively correlated with NA and DMA concentrations.     

Parametric evaluation of forced expiration using a numerical model

Numerical calculations were performed to study the influence of several physiologic parameters on a forced expiration. It was found that the axial distribution of airway compliance produced profound changes in the detailed flow pattern, as characterized by the axial distributions of speed index and area ratio, but had little effect on the flow-volume curve. Similar results were obtained when the expression for frictional losses was changed to reflect new experimental results. In contrast, changes in airway size and geometry altered both the detailed flow pattern and the mean expiratory flow rate. The shape of the flow-volume curve remained unchanged.     

Characterization of Botrytis–plant interactions using PathTrack©—an automated system for dynamic analysis of disease development

The measurement of disease development is integral in studies on plant–microbe interactions. To address the need for a dynamic and quantitative disease evaluation, we developed PathTrack^©, and used it to analyse the interaction of plants with Botrytis cinerea. PathTrack^© is composed of an infection chamber, a photography unit and software that produces video files and numerical values of disease progression. We identified a previously unrecognized infection stage and determined numerical parameters of pathogenic development. Using these parameters, we identified differences in disease dynamics between seemingly similar B. cinerea pathogenicity mutants, and revealed new details on plant susceptibility to the fungus. We showed that the difference between the lesion expansion rate on leaves and colony spreading rate on artificial medium reflects the levels of the plant immune system, suggesting that this parameter can be used to quantify plant defence. Our results shed new light and reveal new details of the interaction between the model necrotrophic pathogen B. cinerea and plants. The concept that we present is universal and may be applied to facilitate the study of various types of plant–pathogen association.     

Tracing sub-structure in the European American population with PCA-informative markers

Genetic structure in the European American population reflects waves of migration and recent gene flow among different populations. This complex structure can introduce bias in genetic association studies. Using Principal Components Analysis (PCA), we analyze the structure of two independent European American datasets (1,521 individuals-307,315 autosomal SNPs). Individual variation lies across a continuum with some individuals showing high degrees of admixture with non-European populations, as demonstrated through joint analysis with HapMap data. The CEPH Europeans only represent a small fraction of the variation encountered in the larger European American datasets we studied. We interpret the first eigenvector of this data as correlated with ancestry, and we apply an algorithm that we have previously described to select PCA-informative markers (PCAIMs) that can reproduce this structure. Importantly, we develop a novel method that can remove redundancy from the selected SNP panels and show that we can effectively remove correlated markers, thus increasing genotyping savings. Only 150-200 PCAIMs suffice to accurately predict fine structure in European American datasets, as identified by PCA. Simulating association studies, we couple our method with a PCA-based stratification correction tool and demonstrate that a small number of PCAIMs can efficiently remove false correlations with almost no loss in power. The structure informative SNPs that we propose are an important resource for genetic association studies of European Americans. Furthermore, our redundancy removal algorithm can be applied on sets of ancestry informative markers selected with any method in order to select the most uncorrelated SNPs, and significantly decreases genotyping costs.