Hazards of Healthy Living: Bottled Water and Salad Vegetables as Risk Factors for Campylobacter Infection

Campylobacter is the most common cause of bacterial gastroenteritis worldwide, yet the etiology of this infection remains only partly explained. In a retrospective cohort study, we compared 213 sporadic campylobacter case-patients with 1,144 patients with negative fecal samples. Information was obtained on food history, animal contact, foreign travel, leisure activities, medical conditions, and medication use. Eating chicken, eating food from a fried chicken outlet, eating salad vegetables, drinking bottled water, and direct contact with cows or calves were all independently associated with infection. The population-attributable fractions for these risk factors explained nearly 70% of sporadic campylobacter infections. Eating chicken is a well-established risk factor, but consuming salad and bottled water are not. The association with salad may be explained by cross-contamination of food within the home, but the possibility that natural mineral water is a risk factor for campylobacter infection could have wide public health implications

Reality and Rationality

This volume of articles (most published, some new) is a follow-up to the late Wesley C. Salmon's widely read collection Causality And Explanation (OUP 1998). It contains both published and unpublished articles, and focuses on two related areas of inquiry: First, is science a rational enterprise? Secondly, does science yield objective information about our world, even the aspects that we cannot observe directly? Salmon's own take is that objective knowledge of the world is possible, and his work in these articles centers around proving that this can be so. Salmon's influential standing in the field ensures that this volume will be of interest to both undergraduates and professional philosophers, primarily in the philosophy of science

Explainable software analytics

2018 Association for Computing Machinery. Software analytics has been the subject of considerable recent attention but is yet to receive significant industry traction. One of the key reasons is that software practitioners are reluctant to trust predictions produced by the analytics machinery without understanding the rationale for those predictions. While complex models such as deep learning and ensemble methods improve predictive performance, they have limited explainability. In this paper, we argue that making software analytics models explainable to software practitioners is as important as achieving accurate predictions. Explainability should therefore be a key measure for evaluating software analytics models.We envision that explainability will be a key driver for developing software analytics models that are useful in practice. We outline a research roadmap for this space, building on social science, explainable artificial intelligence and software engineering

Statistical Explanation

[Introduction] Generally speaking, scientific explanation has been a topic of lively discussion in twentieth-century philosophy of science; philosophers of science have endeavored to characterize rigorously a number of different types of explanation to be found in the various fields of scientific research. Given the indispensability of statistical concepts and techniques in virtually every branch of modern science, it is natural to ask whether some scientific explanations are essentially statistical or probabilistic in character. The answer would seem to be yes. For example, we can explain why atoms of carbon 14 have a 1/4 probability of surviving for 11,460 years because the half-life of that species is 5,730 years. As we shall see, explanations of this type are not especially problematic. As another example, we might explain why a particular weed withered by citing the fact that it received a dose of a herbicide, even though we know that the herbicide is not invariably effective. This means that the withering is related probabilistically to the herbicide treatment but is not necessitated by it. Explanations of this kind, by contrast, lead to severe difficulties

Logical Empirism Historical and contemporary perspectives

Buku ini mrp hasil penelitian dari banyak negara tentang logika empirisme. Senelas esainya berusaha membangun pengertian tentang logika empirisme, sejarah dan perkembangannya. Buku ini menawarkan pengertian yang baik terhadap perdebatan ilmu pengetahuan dari filsafat masa kini

On the Relevance of Statistical Relevance Theory

In Salmon\u27s discussion of his account of statistical relevance and statistical explanation there is a peculiarity in the selection of examples. Where he wishes to show that statistical accounts are reasonably treated as explanatory, he draws examples from the social sciences, such as juvenile delinquency. But when he explains the concept of ‘causal’ relevance, the examples are selected from the natural sciences. This conceals difficulties with Salmon\u27s account of causality in the face of multiple causes such as are characteristic of the social sciences. Salmon\u27s account is shown not to escape difficulties associated with Simon\u27s earlier approach