Nearest Neighbor Classification Rule for Mixtures of Discrete and Continuous Random Variables

In this paper very simple nonparametric classification rule for mixtures of discrete and continuous random variables is described. It is based on the method of nearest neighbor proposed by Cover and Hart (1967). The bounds on the limit of the nearest neighbor rule risks are given. Both lower and upper bound depend on the Bayes risk and the loss function. Finally the method is compared with other existing methods on some practical data set.     

Effects of a participatory approach,with systematic impact matrix analysis in herd health planning in organic dairy cattle herds

The animal health and welfare status in European organic dairy production does not in all aspects meet the organic principles and consumers’ expectations and needs to be improved. To achieve this, tailored herd health planning, targeted to the specific situation of individual farms could be of use. The aim of this study was to apply herd health planning in a structured participatory approach, with impact matrix analysis, not previously used in this context, in European organic dairy farms and to assess changes in animal health and welfare. Herd health planning farm visits were conducted on 122 organic dairy farms in France, Germany and Sweden. The farmer, the herd veterinarian and/or an advisor took part in the farm discussions. The researcher served as facilitator. Baseline data on the animal health status of the individual farm, collected from national milk recording schemes, were presented as an input for the discussion. Thereafter a systematic impact matrix analysis was performed. This was to capture the complexity of individual farms with the aim to identify the farm-specific factors that could have a strong impact on animal health. The participants (i.e. farmer, veterinarian and advisor) jointly identified areas in need of improvement, taking the health status and the interconnected farm system components into account, and appropriate actions were jointly identified. The researcher took minutes during the discussions, and these were shared with the participants. No intervention was made by the researcher, and further actions were left with the participants. The number of actions per farm ranged from 0 to 22. The change in mortality, metabolic diseases, reproductive performance and udder health was assessed at two time points, and potential determinators of the change were evaluated with linear regression models. A significant association was seen between change in udder health, as measured by the somatic cell count, and country. At the first follow-up, a significant association was also found between change in the proportion of prolonged calving interval and the farmers’ desire to improve reproductive health as well as with an increase in herd size, but this was not seen at the second follow-up. The degree of implementation of the actions was good (median 67%, lower quartile 40%, upper quartile 83%). To conclude, the degree of implementation was quite high, improvement of animal health could not be linked to the herd health planning approach. However, the approach was highly appreciated by the participants and deserves further study.     

Focus: Addiction: Relapse Prevention and the Five Rules of Recovery

There are four main ideas in relapse prevention. First, relapse is a gradual process with distinct stages. The goal of treatment is to help individuals recognize the early stages, in which the chances of success are greatest. Second, recovery is a process of personal growth with developmental milestones. Each stage of recovery has its own risks of relapse. Third, the main tools of relapse prevention are cognitive therapy and mind-body relaxation, which are used to develop healthy coping skills. Fourth, most relapses can be explained in terms of a few basic rules. Educating clients in these rules can help them focus on what is important: 1) change your life (recovery involves creating a new life where it is easier to not use); 2) be completely honest; 3) ask for help; 4) practice self-care; and 5) don’t bend the rules.     

Species co‐occurrence patterns and dietary resource competition in primates

Diamond (Assembly of species communities. In: Cody ML, Diamond JM, editors. Ecology and evolution of communities. Cambridge: Belknap. p 342–444 ( 1975 )) argued that interspecific competition between species occupying similar niches results in a nonrandom pattern of species distributions. In particular, some species pairs may never be found in the same community due to competitive exclusion. Rigorous analytical methods have been developed to investigate the possible role that interspecific competition has on the evolution of communities. Many studies that have implemented these methods have shown support for Diamond's assembly rules, yet there are numerous exceptions. We build on this previous research by examining the co‐occurrence patterns of primate species in 109 communities from across the world. We used EcoSim to calculate a checkerboard (C) score for each region. The C score provides a measure of the proportion of species pairs that do not co‐occur in a set of communities. High C scores indicate that species are nonrandomly distributed throughout a region, and interspecific competition may be driving patterns of competitive exclusion. We conducted two sets of analyses. One included all primate species per region, and the second analysis assigned each species to one of four dietary guilds: frugivores, folivores, insectivores, and frugivore‐insectivores. Using all species per region, we found significantly high C scores in 9 of 10 regions examined. For frugivores, we found significantly high‐C scores in more than 50% of regions. In contrast, only 23% of regions exhibited significantly high‐C scores for folivores. Our results suggest that communities are nonrandomly structured and may be the result of greater levels of interspecific competition between frugivores compared to folivores. Am J Phys Anthropol, 2010. © 2010 Wiley‐Liss, Inc.