870 research outputs found

    Learning mixed graphical models with separate sparsity parameters and stability-based model selection

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    Background: Mixed graphical models (MGMs) are graphical models learned over a combination of continuous and discrete variables. Mixed variable types are common in biomedical datasets. MGMs consist of a parameterized joint probability density, which implies a network structure over these heterogeneous variables. The network structure reveals direct associations between the variables and the joint probability density allows one to ask arbitrary probabilistic questions on the data. This information can be used for feature selection, classification and other important tasks. Results: We studied the properties of MGM learning and applications of MGMs to high-dimensional data (biological and simulated). Our results show that MGMs reliably uncover the underlying graph structure, and when used for classification, their performance is comparable to popular discriminative methods (lasso regression and support vector machines). We also show that imposing separate sparsity penalties for edges connecting different types of variables significantly improves edge recovery performance. To choose these sparsity parameters, we propose a new efficient model selection method, named Stable Edge-specific Penalty Selection (StEPS). StEPS is an expansion of an earlier method, StARS, to mixed variable types. In terms of edge recovery, StEPS selected MGMs outperform those models selected using standard techniques, including AIC, BIC and cross-validation. In addition, we use a heuristic search that is linear in size of the sparsity value search space as opposed to the cubic grid search required by other model selection methods. We applied our method to clinical and mRNA expression data from the Lung Genomics Research Consortium (LGRC) and the learned MGM correctly recovered connections between the diagnosis of obstructive or interstitial lung disease, two diagnostic breathing tests, and cigarette smoking history. Our model also suggested biologically relevant mRNA markers that are linked to these three clinical variables. Conclusions: MGMs are able to accurately recover dependencies between sets of continuous and discrete variables in both simulated and biomedical datasets. Separation of sparsity penalties by edge type is essential for accurate network edge recovery. Furthermore, our stability based method for model selection determines sparsity parameters faster and more accurately (in terms of edge recovery) than other model selection methods. With the ongoing availability of comprehensive clinical and biomedical datasets, MGMs are expected to become a valuable tool for investigating disease mechanisms and answering an array of critical healthcare questions

    Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

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    The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables

    Characteristics of men responding to an invitation to undergo testing for prostate cancer as part of a randomised trial

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    Background: Sociodemographic characteristics are associated with participating in cancer screening and trials. We compared the characteristics of those responding with those not responding to a single invitation for prostate-specific antigen (PSA) testing for prostate cancer as part of the Cluster randomised triAl of PSA testing for Prostate cancer (CAP). / Methods: Age, rurality and deprivation among 197,763 men from 271 cluster-randomised primary care centres in the UK were compared between those responding (n = 90,300) and those not responding (n = 100,953) to a prostate cancer testing invitation. / Results: There was little difference in age between responders and nonresponders. Responders were slightly more likely to come from urban rather than rural areas and were slightly less deprived than those who did not respond. / Conclusion: These data indicate similarities in age and only minor differences in deprivation and urban location between responders and nonresponders. These differences were smaller, but in the same direction as those observed in other screening trials. / Trial registration: ISRCTN92187251. Registered on 29 November 2004

    Robotic milking technologies and renegotiating situated ethical relationships on UK dairy farms

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    Robotic or automatic milking systems (AMS) are novel technologies that take over the labor of dairy farming and reduce the need for human-animal interactions. Because robotic milking involves the replacement of 'conventional' twice-a-day milking managed by people with a system that supposedly allows cows the freedom to be milked automatically whenever they choose, some claim robotic milking has health and welfare benefits for cows, increases productivity, and has lifestyle advantages for dairy farmers. This paper examines how established ethical relations on dairy farms are unsettled by the intervention of a radically different technology such as AMS. The renegotiation of ethical relationships is thus an important dimension of how the actors involved are re-assembled around a new technology. The paper draws on in-depth research on UK dairy farms comparing those using conventional milking technologies with those using AMS. We explore the situated ethical relations that are negotiated in practice, focusing on the contingent and complex nature of human-animal-technology interactions. We show that ethical relations are situated and emergent, and that as the identities, roles, and subjectivities of humans and animals are unsettled through the intervention of a new technology, the ethical relations also shift. © 2013 Springer Science+Business Media Dordrecht

    Monitoring Winter and Summer Abundance of Cetaceans in the Pelagos Sanctuary (Northwestern Mediterranean Sea) Through Aerial Surveys

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    Systematic long-term monitoring of abundance is essential to inform conservation measures and evaluate their effectiveness. To instigate such work in the Pelagos Sanctuary in the Mediterranean, two aerial surveys were conducted in winter and summer 2009. A total of 467 (131 in winter, 336 in summer) sightings of 7 species was made. Sample sizes were sufficient to estimate abundance of fin whales in summer (148; 95% CI = 87–254) and striped dolphins in winter (19,462; 95% CI = 12 939–29 273) and in summer (38 488; 95% CI = 27 447–53 968). Numbers of animals within the Sanctuary are significantly higher in summer, when human activities and thus potential population level impacts are highest. Comparisons with data from past shipboard surveys suggest an appreciable decrease in fin whales within the Sanctuary area and an appreciable increase in striped dolphins. Aerial surveys proved to be more efficient than ship surveys, allowing more robust estimates, with smaller CIs and CVs. These results provide essential baseline data for this marine protected area and continued regular surveys will allow the effectiveness of the MPA in terms of cetacean conservation to be evaluated and inform future management measures. The collected data may also be crucial in assessing whether ship strikes, one of the main causes of death for fin whales in the Mediterranean, are affecting the Mediterranean population
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