The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.

Analysis of the estrogenic components in kudzu root by bioassay and high performance liquid chromatography

The estrogenic activity of the Chinese herb kudzu root was investigated by a recombinant yeast screening assay (YES). Isoflavones are the main components in the plant, of which puerarin is the most abundant one. The kudzu root extract was separated into four fractions according to the polarity. The crude extract and its sub-fractions, except the water fraction, showed clear estrogenic activity and the potencies were in the range of 10(-3) to 10(-1) g/l. The ligand potency was used to compare the estrogenic activity of these fractions. The crude extract and its sub-fractions were further analyzed by high performance liquid chromatography (HPLC) to correlate the activity and the active components. Bioassay and chemical analysis showed that theoretical estrogenic activity expressed as equivalent 17 beta-estradiol concentration or the cumulative effects are comparable to that experimentally determined by YES. The results showed that the high content of isoflavones as well as the high estrogenic activity could make kudzu root extract an interesting candidate for hormone replacement therapy. (c) 2005 Elsevier Ltd. All rights reserved.

DACA: architecture to implement dynamic access control mechanisms on business tier components

Access control is a software engineering challenge in database applications. Currently, there is no satisfactory solution to dynamically implement evolving fine-grained access control mechanisms (FGACM) on business tiers of relational database applications. To tackle this access control gap, we propose an architecture, herein referred to as Dynamic Access Control Architecture (DACA). DACA allows FGACM to be dynamically built and updated at runtime in accordance with the established fine-grained access control policies (FGACP). DACA explores and makes use of Call Level Interfaces (CLI) features to implement FGACM on business tiers. Among the features, we emphasize their performance and their multiple access modes to data residing on relational databases. The different access modes of CLI are wrapped by typed objects driven by FGACM, which are built and updated at runtime. Programmers prescind of traditional access modes of CLI and start using the ones dynamically implemented and updated. DACA comprises three main components: Policy Server (repository of metadata for FGACM), Dynamic Access Control Component (DACC) (business tier component responsible for implementing FGACM) and Policy Manager (broker between DACC and Policy Server). Unlike current approaches, DACA is not dependent on any particular access control model or on any access control policy, this way promoting its applicability to a wide range of different situations. In order to validate DACA, a solution based on Java, Java Database Connectivity (JDBC) and SQL Server was devised and implemented. Two evaluations were carried out. The first one evaluates DACA capability to implement and update FGACM dynamically, at runtime, and, the second one assesses DACA performance against a standard use of JDBC without any FGACM. The collected results show that DACA is an effective approach for implementing evolving FGACM on business tiers based on Call Level Interfaces, in this case JDBC.

Elucidation of the components involved in the antioxidant activity of honey

Canadian honeys were analyzed for sugar concentration, honey colour, total phenolic content, the level of brown pigments, and antioxidant activity in order to elucidate the main components involved in the antioxidant activity of honey. By employing size-exclusion chromatography in combination with activity-guided fractionation, it was demonstrated that the antioxidant components are of high molecular weight (HMW), brown in colour and absorb at both 280nm and 450nm. The presence of brown HMW antioxidant components prompted an investigation on the influence of heattreatment on the Maillard reaction and the formation of melanoid ins. Heat-treatment of honey resulted in an increase in the level of phenolics in the melanoidin fractions which correlated with an increase in antioxidant activity. The preliminary results of this study suggest for the first time that honey melanoidins underlie the antioxidant activity of unheated and heat-treated honey, and that phenolic constituents are involved in the melanoidin structure and are likely incorporated by covalent or non-covalent interaction.

The genome of Rhizobium leguminosarum has recognizable core and accessory components

Background: Rhizobium leguminosarum is an alpha-proteobacterial N-2-fixing symbiont of legumes that has been the subject of more than a thousand publications. Genes for the symbiotic interaction with plants are well studied, but the adaptations that allow survival and growth in the soil environment are poorly understood. We have sequenced the genome of R. leguminosarum biovar viciae strain 3841. Results: The 7.75 Mb genome comprises a circular chromosome and six circular plasmids, with 61% G+C overall. All three rRNA operons and 52 tRNA genes are on the chromosome; essential protein-encoding genes are largely chromosomal, but most functional classes occur on plasmids as well. Of the 7,263 protein-encoding genes, 2,056 had orthologs in each of three related genomes ( Agrobacterium tumefaciens, Sinorhizobium meliloti, and Mesorhizobium loti), and these genes were overrepresented in the chromosome and had above average G+C. Most supported the rRNA-based phylogeny, confirming A. tumefaciens to be the closest among these relatives, but 347 genes were incompatible with this phylogeny; these were scattered throughout the genome but were over-represented on the plasmids. An unexpectedly large number of genes were shared by all three rhizobia but were missing from A. tumefaciens. Conclusion: Overall, the genome can be considered to have two main components: a 'core', which is higher in G+C, is mostly chromosomal, is shared with related organisms, and has a consistent phylogeny; and an 'accessory' component, which is sporadic in distribution, lower in G+C, and located on the plasmids and chromosomal islands. The accessory genome has a different nucleotide composition from the core despite a long history of coexistence.

Integration of temporal and semantic components into the Geographic Information. Part II: Methodology

The overall objective of this research project is to enrich geographic data with temporal and semantic components in order to significantly improve spatio-temporal analysis of geographic phenomena. To achieve this goal, we intend to establish and incorporate three new layers (structures) into the core of the Geographic Information by using mark-up languages as well as defining a set of methods and tools for enriching the system to make it able to retrieve and exploit such layers (semantic-temporal, geosemantic, and incremental spatio-temporal). Besides these layers, we also propose a set of models (temporal and spatial) and two semantic engines that make the most of the enriched geographic data. The roots of the project and its definition have been previously presented in Siabato & Manso-Callejo 2011. In this new position paper, we extend such work by delineating clearly the methodology and the foundations on which we will base to define the main components of this research: the spatial model, the temporal model, the semantic layers, and the semantic engines. By putting together the former paper and this new work we try to present a comprehensive description of the whole process, from pinpointing the basic problem to describing and assessing the solution. In this new article we just mention the methods and the background to describe how we intend to define the components and integrate them into the GI.

Gaia-ESO Survey: Analysis of pre-main sequence stellar spectra

Context. The Gaia-ESO Public Spectroscopic Survey is obtaining high-quality spectroscopy of some 100 000 Milky Way stars using the FLAMES spectrograph at the VLT, down to V = 19 mag, systematically covering all the main components of the Milky Way and providing the first homogeneous overview of the distributions of kinematics and chemical element abundances in the Galaxy. Observations of young open clusters, in particular, are giving new insights into their initial structure, kinematics, and their subsequent evolution. Aims. This paper describes the analysis of UVES and GIRAFFE spectra acquired in the fields of young clusters whose population includes pre-main sequence (PMS) stars. The analysis is applied to all stars in such fields, regardless of any prior information on membership, and provides fundamental stellar atmospheric parameters, elemental abundances, and PMS-specific parameters such as veiling, accretion, and chromospheric activity. Methods. When feasible, different methods were used to derive raw parameters (e.g. line equivalent widths) fundamental atmospheric parameters and derived parameters (e.g. abundances). To derive some of these parameters, we used methods that have been extensively used in the past and new ones developed in the context of the Gaia-ESO survey enterprise. The internal precision of these quantities was estimated by inter-comparing the results obtained by these different methods, while the accuracy was estimated by comparison with independent external data, such as effective temperature and surface gravity derived from angular diameter measurements, on a sample of benchmarks stars. A validation procedure based on these comparisons was applied to discard spurious or doubtful results and produce recommended parameters. Specific strategies were implemented to resolve problems of fast rotation, accretion signatures, chromospheric activity, and veiling. Results. The analysis carried out on spectra acquired in young cluster fields during the first 18 months of observations, up to June 2013, is presented in preparation of the first release of advanced data products. These include targets in the fields of the ρ Oph, Cha I, NGC 2264, γ Vel, and NGC 2547 clusters. Stellar parameters obtained with the higher resolution and larger wavelength coverage from UVES are reproduced with comparable accuracy and precision using the smaller wavelength range and lower resolution of the GIRAFFE setup adopted for young stars, which allows us to provide stellar parameters with confidence for the much larger GIRAFFE sample. Precisions are estimated to be ≈120 K rms in Teff, ≈0.3 dex rms in log g, and ≈0.15 dex rms in [Fe/H] for the UVES and GIRAFFE setups.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.