Register-based predictors of violations of animal welfare legislation in dairy herds

The assessment of animal welfare can include resource-based or animal-based measures. Official animal welfare inspections in Denmark primarily control compliance with animal welfare legislation based on resource measures (e.g. housing system) and usually do not regard animal response parameters (e.g. clinical and behavioural observations). Herds selected for welfare inspections are sampled by a risk-based strategy based on existing register data. The aim of the present study was to evaluate register data variables as predictors of dairy herds with violations of the animal welfare legislation (VoAWL) defined as occurrence of at least one of the two most frequently violated measures found at recent inspections in Denmark, namely (a) presence of injured animals not separated from the rest of the group and/or (b) animals in a condition warranting euthanasia still being present in the herd. A total of 25 variables were extracted from the Danish Cattle Database and assessed as predictors using a multivariable logistic analysis of a data set including 73 Danish dairy herds, which all had more than 100 cows and cubicle loose-housing systems. Univariable screening was used to identify variables associated with VoAWL at a P-value<0.2 for the inclusion in a multivariable logistic regression analysis. Backward selection procedures identified the following variables for the final model predictive of VoAWL: increasing standard deviation of milk yield for first lactation cows, high bulk tank somatic cell count (⩾250 000 cells/ml) and suspiciously low number of recorded veterinary treatments (⩽25 treatments/100 cow years). The identified predictors may be explained by underlying management factors leading to impaired animal welfare in the herd, such as poor hygiene, feeding and management of dry or calving cows and sick animals. However, further investigations are required for causal inferences to be established.     

How to estimate variability in affinity relationships in partially observed groups of domestic herbivores?

Animal sociability measurements based on inter-individual distances or nearest-neighbour distributions can be obtained automatically with telemetry collars. So far, all the indices that have been used require the whole group to be observed. Here, we propose an index of the variability in affinity relationships in groups of domestic herbivores, whose definition does not depend on group size and that can be used even if some data are missing. This index and its estimators are based on a function that measures how frequently an animal is closer than another one from a third animal. When no data are missing, we show that our estimator and the variance of the sociability matrixsensu Sibbald (considered as the reference method) are strongly correlated. We then consider two cases of missing data. In the first case, some animals are randomly missing, that is, to account for random breakdown of telemetry collars. Our estimator is unbiased by such missing data and its variance decreases as the number of observation dates increases. In the second case, the same animals are missing at all observation dates, that is, in large herds where there are more individuals to be observed than available telemetry collars. Our estimator of affinity variance within a group is biased by such missing data. Thus, it requires changing animals equipped with telemetry collars regularly during the experiment. Conversely, the estimator remains unbiased at the population level, that is, if several independent groups are being analysed. We finally illustrate how this estimator can be used by investigating changes in the variability of affinities according to group size in grazing heifers.     

MPAgenomics: an R package for multi-patient analysis of genomic markers

Background

Last generations of Single Nucleotide Polymorphism (SNP) arrays allow to study copy-number variations in addition to genotyping measures.

Results

MPAgenomics, standing for multi-patient analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation and (ii) selection of genomic markers from multi-patient copy number and SNP data profiles. It provides wrappers from commonly used packages to streamline their repeated (sometimes difficult) manipulation, offering an easy-to-use pipeline for beginners in R.The segmentation of successive multiple profiles (finding losses and gains) is performed with an automatic choice of parameters involved in the wrapped packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given outcome.

Conclusions

MPAgenomics provides an easy tool to analyze data from SNP arrays in R. The R-package MPAgenomics is available on CRAN.     

A novel approach for multi-domain and multi-gene family identification provides insights into evolutionary dynamics of disease resistance genes in core eudicot plants

Background

Recent advances in DNA sequencing techniques resulted in more than forty sequenced plant genomes representing a diverse set of taxa of agricultural, energy, medicinal and ecological importance. However, gene family curation is often only inferred from DNA sequence homology and lacks insights into evolutionary processes contributing to gene family dynamics. In a comparative genomics framework, we integrated multiple lines of evidence provided by gene synteny, sequence homology and protein-based Hidden Markov Modelling to extract homologous super-clusters composed of multi-domain resistance (R)-proteins of the NB-LRR type (for NUCLEOTIDE BINDING/LEUCINE-RICH REPEATS), that are involved in plant innate immunity.

Results

To assess the diversity of R-proteins within and between species, we screened twelve eudicot plant genomes including six major crops and found a total of 2,363 NB-LRR genes. Our curated R-proteins set shows a 50% average for tandem duplicates and a 22% fraction of gene copies retained from ancient polyploidy events (ohnologs). We provide evidence for strong positive selection and show significant differences in molecular evolution rates (Ka/Ks-ratio) among tandem- (mean = 1.59), ohnolog (mean = 1.36) and singleton (mean = 1.22) R-gene duplicates. To foster the process of gene-edited plant breeding, we report species-specific presence/absence of all 140 NB-LRR genes present in the model plant Arabidopsis and describe four distinct clusters of NB-LRR “gatekeeper” loci sharing syntenic orthologs across all analyzed genomes.

Conclusion

By curating a near-complete set of multi-domain R-protein clusters in an eudicot-wide scale, our analysis offers significant insight into evolutionary dynamics underlying diversification of the plant innate immune system. Furthermore, our methods provide a blueprint for future efforts to identify and more rapidly clone functional NB-LRR genes from any plant species.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-966) contains supplementary material, which is available to authorized users.     

Using genotyping data to assign markers to their chromosome type and to infer the sex of individuals: a Bayesian model‐based classifier

The recent democratization of next‐generation‐sequencing‐based approaches towards nonmodel species has made it cost‐effective to produce large genotyping data sets for a wider range of species. However, when no detailed genome assembly is available, poor knowledge about the organization of the markers within the genome might hamper the optimal use of this abundant information. At the most basic level of genomic organization, the type of chromosome (autosomes, sex chromosomes, mitochondria or chloroplast in plants) may remain unknown for most markers which might be limiting or even misleading in some applications, particularly in population genetics. Conversely, the characterization of sex‐linked markers allows molecular sexing of the individuals. In this study, we propose a Bayesian model‐based classifier named detsex, to assign markers to their chromosome type and/or to perform sexing of individuals based on genotyping data. The performance of detsex is further evaluated by a comprehensive simulation study and by the analysis of real data sets from various origins (microsatellite and SNP data derived from genotyping assay designs and NGS experiments). Irrespective of the origin of the markers or the size of the data set, detsex was proved efficient (i) to identify the sex‐linked markers, (ii) to perform molecular sexing of the individuals and (iii) to perform basic quality check of the genotyping data sets. The underlying structure of the model also allows to consider each of these potential applications either separately or jointly.     

代谢组学信息获取与数据预处理瓶颈问题探讨

经过近15 年的快速发展,代谢组学已逐步成熟并渗透到生命科学和医学研究领域的各个层面。随着认识的不断深入,各种影响代谢组学研究的细节因素和技术瓶颈逐步被揭示。从生物样品采集与制备、原始数据采集、数据预处理和方法学验证等方面系统探讨了影响代谢组学研究的主要瓶颈问题,以期推动代谢组学研究的精细化和规范化发展。     

Eco‐efficiency Based on Social Performance and its Relationship with Financial Performance

As corporate responsibility for environmental management has gained attention, eco‐efficiency has become recognized as an important concept for improving the social performance of the business sector as well as that of the public sector. Improving eco‐efficiency is widely accepted not only as a means of increasing economic value, but also as a means of reducing environmental effects. However, managing for eco‐efficiency should take into consideration the differences among industries, because the impact of eco‐efficiency on financial and social performance varies among industries. To explore this variation, we conducted a cross‐industry analysis of eco‐efficiency based on social performance using data envelopment analysis (DEA). DEA measures relative efficiency and is a useful tool for taking into account the relative importance of industry‐specific characteristics. Using DEA, eco‐efficiency scores were derived based on the ratio of two factors of social performance: (1) value‐added inducing and production‐inducing economic spillover effects and (2) the amount of greenhouse gases emitted and energy used. Then, we identified the relationships between our eco‐efficiency score and financial performance, which is a measure of the firm's stability. The case study is based on 272 firms in 16 industries in South Korea. Results show that firms in product manufacturing and service‐intensive industries tend to have higher eco‐efficiency scores than those in raw material or chemical‐intensive industries. In addition, most of the industries reveal no relationship between traditional financial performance metrics and eco‐efficiency scores. A handful of industries had significant relationships with one or more financial performance metrics; in some cases, these relationships were negative, whereas in others they were positive. Surprisingly, almost all industries have no significant relationships between eco‐efficiency and financial performance. This result implies that government support for policies that reward firms that attempt to be eco‐efficient are needed, or that other nonfinancial metrics that influence eco‐efficiency, such as employment and brand reputation, should be considered. This article is expected to support policy makers as they formulate industry‐specific environmental strategies.