Oral supplementation of medium-chain fatty acids during the dry period supports the neutrophil viability of peripartum dairy cows

A randomised clinical trial was conducted to explore the effect of orally supplemented medium-chain fatty acids (MCFA) to heifers and cows starting 6-8 weeks prior to expected calving date on blood and milk polymorphonuclear neutrophilic leucocyte (PMNL) apoptosis between 1 and 3 d in milk (DIM). The effects of MCFA-supplementation on the likelihood of intramammary infections (IMI) in early lactation, and test-day somatic cell count (SCC) and average daily milk yield (MY) during the first 4 months of lactation were evaluated as well. Twenty-two animals were included of which half were orally supplemented with MCFA starting 6-8 weeks prior to calving and half served as non-supplemented controls. The PMNL viability in both blood and milk was quantified using dual-colour flow cytometry with fluorescein-labelled annexin and propidium iodide. In non-supplemented animals, % blood PMNL apoptosis significantly increased between start of supplementation and early lactation, reflecting a potential reduction in innate immune capacity, whereas this was not true in the MCFA-supplemented animals. Similar results were seen in milk PMNL apoptosis. Overall, the % apoptotic milk PMNL between 1 and 3 DIM was significantly lower in the MCFA-supplemented group compared with the non-supplemented group. There was no substantial effect of oral MCFA-supplementation on the likelihood of quarter IMI nor on the composite test-day milk SCC or average daily MY. In conclusion, oral MCFA-supplementation starting 6-8 weeks before expected calving date supported the blood and milk neutrophil viability in early lactating dairy cows. Still, this was not reflected in an improvement of udder health nor MY in early and later lactation. The results should trigger research to further unravel the mechanisms behind the observed immunomodulating effect, and the potential relevance for the cows' performances throughout lactation

Coagulase-negative staphylococci: contagious or environmental?: outline of two ongoing studies

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Co-clustering of Fuzzy Lagged Data

The paper focuses on mining patterns that are characterized by a fuzzy lagged relationship between the data objects forming them. Such a regulatory mechanism is quite common in real life settings. It appears in a variety of fields: finance, gene expression, neuroscience, crowds and collective movements are but a limited list of examples. Mining such patterns not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal fuzzy lagged co-cluster is an NP-complete problem. We thus present a polynomial-time Monte-Carlo approximation algorithm for mining fuzzy lagged co-clusters. We prove that for any data matrix, the algorithm mines a fuzzy lagged co-cluster with fixed probability, which encompasses the optimal fuzzy lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values and overlapping patterns. The algorithm was extensively evaluated using both artificial and real datasets. The results not only corroborate the ability of the algorithm to efficiently mine relevant and accurate fuzzy lagged co-clusters, but also illustrate the importance of including the fuzziness in the lagged-pattern model.Comment: Under consideration for publication in Knowledge and Information Systems. The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-014-0758-

Timing Interruptions for Better Human-Computer Coordinated Planning

The high operations tempo and growing complexity of planning (and re-planning) in various mission-critical domains suggest an approach in which systems act as primary planners rather than assisting the user in planning. We present a high-level overview of our design of a Coordination Autonomy (CA) module as part of such planning system, responsible to intelligently initiate and manage the necessary interactions with the user for enhancing the system's performance.Engineering and Applied Science

Sharing Experiences to Learn User Characteristics in Dynamic Environments with Sparse Data

This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agent system, has no a priori information about the structure of the distribution of parameter values. The agent must be able to produce estimations even when it may have made only a small number of direct observations, and thus it must be able to operate with sparse data. The paper describes a mechanism that enables the agent to significantly improve its estimation by augmenting its direct observations with those obtained by other agents with which it is coordinating. To avoid undesirable bias in relatively heterogeneous environments while effectively using relevant data to improve its estimations, the mechanism weighs the contributions of other agents' observations based on a real-time estimation of the level of similarity between each of these agents and itself. The "coordination autonomy" module of a coordination-manager system provided an empirical setting for evaluation. Simulation-based evaluations demonstrated that the proposed mechanism outperforms estimations based exclusively on an agent's own observations as well as estimations based on an unweighted aggregate of all other agents' observations.Engineering and Applied Science