Data warehouse for assessing animal health, welfare, risk management and –communication

The objective of this paper is to give an overview of existing databases in Denmark and describe some of the most important of these in relation to establishment of the Danish Veterinary and Food Administrations’ veterinary data warehouse. The purpose of the data warehouse and possible use of the data are described. Finally, sharing of data and validity of data is discussed. There are databases in other countries describing animal husbandry and veterinary antimicrobial consumption, but Denmark will be the first country relating all data concerning animal husbandry, -health and -welfare in Danish production animals to each other in a data warehouse. Moreover, creating access to these data for researchers and authorities will hopefully result in easier and more substantial risk based control, risk management and risk communication by the authorities and access to data for researchers for epidemiological studies in animal health and welfare

Use of information on disease diagnoses from databases for animal health economic, welfare and food safety purposes: strengths and limitations of recordings

Many animal health, welfare and food safety databases include data on clinical and test-based disease diagnoses. However, the circumstances and constraints for establishing the diagnoses vary considerably among databases. Therefore results based on different databases are difficult to compare and compilation of data in order to perform meta-analysis is almost impossible. Nevertheless, diagnostic information collected either routinely or in research projects is valuable in cross comparisons between databases, but there is a need for improved transparency and documentation of the data and the performance characteristics of tests used to establish diagnoses. The objective of this paper is to outline the circumstances and constraints for recording of disease diagnoses in different types of databases, and to discuss these in the context of disease diagnoses when using them for additional purposes, including research. Finally some limitations and recommendations for use of data and for recording of diagnostic information in the future are given. It is concluded that many research questions have such a specific objective that investigators need to collect their own data. However, there are also examples, where a minimal amount of extra information or continued validation could make sufficient improvement of secondary data to be used for other purposes. Regardless, researchers should always carefully evaluate the opportunities and constraints when they decide to use secondary data. If the data in the existing databases are not sufficiently valid, researchers may have to collect their own data, but improved recording of diagnostic data may improve the usefulness of secondary diagnostic data in the future

Influence of local wind speed and direction on wind power dynamics - Application to offshore very short-term forecasting

Wind power time series usually show complex dynamics mainly due to non-linearities related to the wind physics and the power transformation process in wind farms. This article provides an approach to the incorporation of observed local variables (wind speed and direction) to model some of these effects by means of statistical models. To this end, a benchmarking between two different families of varying-coefficient models (regime-switching and conditional parametric models) is carried out. The case of the offshore wind farm of Horns Rev in Denmark has been considered. The analysis is focused on one-step ahead forecasting and a time series resolution of 10 min. It has been found that the local wind direction contributes to model some features of the prevailing winds, such as the impact of the wind direction on the wind variability, whereas the non-linearities related to the power transformation process can be introduced by considering the local wind speed. In both cases, conditional parametric models showed a better performance than the one achieved by the regime-switching strategy. The results attained reinforce the idea that each explanatory variable allows the modelling of different underlying effects in the dynamics of wind power time series

Why do dogs (Canis familiaris) select the empty container in an observational learning task?

Many argue that dogs show unique susceptibility to human communicative signals that make them suitable for being engaged in complex co-operation with humans. It has also been revealed that socially provided information is particularly effective in influencing the behaviour of dogs even when the human’s action demonstration conveys inefficient or mistaken solution of task. It is unclear, however, how the communicative nature of the demonstration context and the presence of the human demonstrator affect the dogs’ object-choice behaviour in observational learning situations. In order to unfold the effects of these factors, 76 adult pet dogs could observe a communicative or a non-communicative demonstration in which the human retrieved a tennis ball from under an opaque container while manipulating another distant and obviously empty (transparent) one. Subjects were then allowed to choose either in the presence of the demonstrator or after she left the room. Results showed a significant main effect of the demonstration context (presence or absence of the human’s communicative signals), and we also found some evidence for the response-modifying effect of the presence of the human demonstrator during the dogs’ choice. That is, dogs predominantly chose the baited container, but if the demonstration context was communicative and the human was present during the dogs’ choice, subjects’ tendency to select the baited container has been reduced. In agreement with the studies showing sensitivity to human’s communicative signals in dogs, these findings point to a special form of social influence in observational learning situations when it comes to learning about causally opaque and less efficient (compared to what comes natural to the dog) action demonstrations

Leptogenesis and low energy observables in left-right symmetric models

In the context of left-right symmetric models we study the connection of leptogenesis and low energy parameters such as neutrinoless double beta decay and leptonic CP violation. Upon imposition of a unitarity constraint, the neutrino parameters are significantly restricted and the Majorana phases are determined within a narrow range, depending on the kind of solar solution. One of the Majorana phases gets determined to a good accuracy and thereby the second phase can be probed from the results of neutrinoless double beta decay experiments. We examine the contributions of the solar and atmospheric mass squared differences to the asymmetry and find that in general the solar scale dominates. In order to let the atmospheric scale dominate, some finetuning between one of the Majorana phases and the Dirac CP phase is required. In this case, one of the Majorana phases is determined by the amount of CP violation in oscillation experiments.Comment: 18 pages, 6 figures. Matches version to appear in PR

Leptogenesis and Neutrino Oscillations Within A Predictive G(224)/SO(10)-Framework

A framework based on an effective symmetry that is either G(224)= SU(2)_L x SU(2)_R xSU(4)^c or SO(10) has been proposed (a few years ago) that successfully describes the masses and mixings of all fermions including neutrinos, with seven predictions, in good accord with the data. Baryogenesis via leptogenesis is considered within this framework by allowing for natural phases (~ 1/20-1/2) in the entries of the Dirac and Majorana mass-matrices. It is shown that the framework leads quite naturally, for both thermal as well as non-thermal leptogenesis, to the desired magnitude for the baryon asymmetry. This result is obtained in full accord with the observed features of the atmospheric and solar neutrino oscillations, as well as with those of the quark and charged lepton masses and mixings, and the gravitino-constraint. Hereby one obtains a unified description of fermion masses, neutrino oscillations and baryogenesis (via leptogenesis) within a single predictive framework.Comment: Efficiency factor updated, some clarifications and new references added. 19 page

PLK1 facilitates chromosome biorientation by suppressing centromere disintegration driven by BLM-mediated unwinding and spindle pulling

Centromeres provide a pivotal function for faithful chromosome segregation. They serve as a foundation for the assembly of the kinetochore complex and spindle connection, which is essential for chromosome biorientation. Cells lacking Polo-like kinase 1 (PLK1) activity suffer severe chromosome alignment defects, which is believed primarily due to unstable kinetochore-microtubule attachment. Here, we reveal a previously undescribed mechanism named ‘centromere disintegration’ that drives chromosome misalignment in PLK1-inactivated cells. We find that PLK1 inhibition does not necessarily compromise metaphase establishment, but instead its maintenance. We demonstrate that this is caused by unlawful unwinding of DNA by BLM helicase at a specific centromere domain underneath kinetochores. Under bipolar spindle pulling, the distorted centromeres are promptly decompacted into DNA threadlike molecules, leading to centromere rupture and whole-chromosome arm splitting. Consequently, chromosome alignment collapses. Our study unveils an unexpected role of PLK1 as a chromosome guardian to maintain centromere integrity for chromosome biorientation

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time

KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness.

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10-9) and 3145 (P < 1 × 10-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness