Improved primary vertex finding for collider detectors

Primary vertex finding for collider experiments is studied. The efficiency and precision of finding interaction vertices can be improved by advanced clustering and classification methods, such as agglomerative clustering with fast pairwise nearest neighbor search, followed by Gaussian mixture model or k-means clustering.Comment: 12 pages, 10 figures, submitted to Nucl. Instrum. Meth.

Payao: a community platform for SBML pathway model curation

Summary: Payao is a community-based, collaborative web service platform for gene-regulatory and biochemical pathway model curation. The system combines Web 2.0 technologies and online model visualization functions to enable a collaborative community to annotate and curate biological models. Payao reads the models in Systems Biology Markup Language format, displays them with CellDesigner, a process diagram editor, which complies with the Systems Biology Graphical Notation, and provides an interface for model enrichment (adding tags and comments to the models) for the access-controlled community members

Pathological and ecological host consequences of infection by an introduced fish parasite

The infection consequences of the introduced cestode fish parasite Bothriocephalus acheilognathi were studied in a cohort of wild, young-of-the-year common carp Cyprinus carpio that lacked co-evolution with the parasite. Within the cohort, parasite prevalence was 42% and parasite burdens were up to 12% body weight. Pathological changes within the intestinal tract of parasitized carp included distension of the gut wall, epithelial compression and degeneration, pressure necrosis and varied inflammatory changes. These were most pronounced in regions containing the largest proportion of mature proglottids. Although the body lengths of parasitized and non-parasitized fish were not significantly different, parasitized fish were of lower body condition and reduced weight compared to non-parasitized conspecifics. Stable isotope analysis (δ15N and δ13C) revealed trophic impacts associated with infection, particularly for δ15N where values for parasitized fish were significantly reduced as their parasite burden increased. In a controlled aquarium environment where the fish were fed ad libitum on an identical food source, there was no significant difference in values of δ15N and δ13C between parasitized and non-parasitized fish. The growth consequences remained, however, with parasitized fish growing significantly slower than non-parasitized fish, with their feeding rate (items s−1) also significantly lower. Thus, infection by an introduced parasite had multiple pathological, ecological and trophic impacts on a host with no experience of the parasite

How mothers feel: validation of a measure of maternal mood

© 2019 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd Rationale: Low mood may affect developing relationships with a new baby, partner and family. Early identification of mood disturbance is crucial to improve outcomes for women perinatally. Instruments such as the Edinburgh Postnatal Depression Scale (EPDS) are used routinely, with evidence that some women do not feel comfortable with how they are asked about their mental health. Objective: To develop a mood checklist as a user-friendly, effective measure of well-being in post-partum women, for use by health professionals. Methods: Cognitive interviews with women who had recently given birth assessed response format and face validity of a prototype measure. A cross-sectional survey followed. A random split-half instrument development protocol was used. Exploratory factor analysis determined factor structure with the first sample,. The second sample confirmed factor structure and evaluationof key psychometric variables and known-groups discriminant validity (KGDV), requiring a supplementary between-subjects design with stratification based on case negative/case positive classification using EPDSscreening cut-off criteria. Results: Cognitive interview data confirmed the face validity of the measure. Exploratory factor analysis indicated an 18 item two-factor model with two (negatively) correlated factors. Factor 1 loaded with items reflecting positive mood and factor 2 negative items. Confirmatory factor analysis showed a good fit to the two-factor model across the full spectrum of fit indices. Statistically significant differences between groups were observed in relation to as EPDS caseness classification. Cronbach alpha coefficients for the positive and negative subscales revealed acceptable internal consistency of 0.79 and 0.72, respectively. Conclusion: The outcome checklist may be appropriate for use in clinical practice. It demonstrated effective psychometric properties and clear cross-validation with existing commonly used measures

Cross modal perception of body size in domestic dogs (Canis familiaris)

While the perception of size-related acoustic variation in animal vocalisations is well documented, little attention has been given to how this information might be integrated with corresponding visual information. Using a cross-modal design, we tested the ability of domestic dogs to match growls resynthesised to be typical of either a large or a small dog to size- matched models. Subjects looked at the size-matched model significantly more often and for a significantly longer duration than at the incorrect model, showing that they have the ability to relate information about body size from the acoustic domain to the appropriate visual category. Our study suggests that the perceptual and cognitive mechanisms at the basis of size assessment in mammals have a multisensory nature, and calls for further investigations of the multimodal processing of size information across animal species

NuRV: A nuXmv Extension for Runtime Verification

We present NuRV, an extension of the nuXmv model checker for assumption-based LTL runtime verification with partial observability and resets. The tool provides some new commands for online/offline monitoring and code generations into standalone monitor code. Using the online/offline monitor, LTL properties can be verified incrementally on finite traces from the system under scrutiny. The code generation currently supports C, C++, Common Lisp and Java, and is extensible. Furthermore, from the same internal monitor automaton, the monitor can be generated into SMV modules, whose characteristics can be verified by Model Checking using nuXmv. We show the architecture, functionalities and some use scenarios of NuRV, and we compare the performance of generated monitor code (in Java) with those generated by a similar tool, RV-Monitor. We show that, using a benchmark from Dwyer's LTL patterns, besides the capacity of generating monitors for long LTL formulae, our Java-based monitors are about 200x faster than RV-Monitor at generation-time and 2–5x faster at runtime

Mirroring everyday clinical practice in clinical trial design: a new concept to improve the external validity of randomized double-blind placebo-controlled trials in the pharmacological treatment of major depression

Background: Randomized, double-blind, placebo-controlled trials constitute the gold standard in clinical research when testing the efficacy of new psychopharmacological interventions in the treatment of major depression. However, the blinded use of placebo has been found to influence clinical trial outcomes and may bias patient selection. Discussion: To improve clinical trial design in major depression so as to reflect clinical practice more closely we propose to present patients with a balanced view of the benefits of study participation irrespective of their assignment to placebo or active treatment. In addition every participant should be given the option to finally receive the active medication. A research agenda is outlined to evaluate the impact of the proposed changes on the efficacy of the drug to be evaluated and on the demographic and clinical characteristics of the enrollment fraction with regard to its representativeness of the eligible population. Summary: We propose a list of measures to be taken to improve the external validity of double-blind, placebocontrolled trials in major depression. The recommended changes to clinical trial design may also be relevant for other psychiatric as well as medical disorders in which expectations regarding treatment outcome may affect the outcome itself

Ensembles of jittered association rule classifiers

The ensembling of classifiers tends to improve predictive accuracy. To obtain an ensemble with N classifiers, one typically needs to run N learning processes. In this paper we introduce and explore Model Jittering Ensembling, where one single model is perturbed in order to obtain variants that can be used as an ensemble. We use as base classifiers sets of classification association rules. The two methods of jittering ensembling we propose are Iterative Reordering Ensembling (IRE) and Post Bagging (PB). Both methods start by learning one rule set over a single run, and then produce multiple rule sets without relearning. Empirical results on 36 data sets are positive and show that both strategies tend to reduce error with respect to the single model association rule classifier. A bias–variance analysis reveals that while both IRE and PB are able to reduce the variance component of the error, IRE is particularly effective in reducing the bias component. We show that Model Jittering Ensembling can represent a very good speed-up w.r.t. multiple model learning ensembling. We also compare Model Jittering with various state of the art classifiers in terms of predictive accuracy and computational efficiency.This work was partially supported by FCT project Rank! (PTDC/EIA/81178/2006) and by AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER), and also supported by Fundacao Ciencia e Tecnologia, FEDER e Programa de Financiamento Plurianual de Unidades de I & D. Thanks are due to William Cohen for kindly providing the executable code for the SLIPPER implementation. Our gratitude goes also to our anonymous reviewers who have helped to significantly improve this paper by sharing their knowledge and their informed criticism with the authors

AxPcoords & parallel AxParafit: statistical co-phylogenetic analyses on thousands of taxa

Background Current tools for Co-phylogenetic analyses are not able to cope with the continuous accumulation of phylogenetic data. The sophisticated statistical test for host-parasite co-phylogenetic analyses implemented in Parafit does not allow it to handle large datasets in reasonable times. The Parafit and DistPCoA programs are the by far most compute-intensive components of the Parafit analysis pipeline. We present AxParafit and AxPcoords (Ax stands for Accelerated) which are highly optimized versions of Parafit and DistPCoA respectively. Results Both programs have been entirely re-written in C. Via optimization of the algorithm and the C code as well as integration of highly tuned BLAS and LAPACK methods AxParafit runs 5–61 times faster than Parafit with a lower memory footprint (up to 35% reduction) while the performance benefit increases with growing dataset size. The MPI-based parallel implementation of AxParafit shows good scalability on up to 128 processors, even on medium-sized datasets. The parallel analysis with AxParafit on 128 CPUs for a medium-sized dataset with an 512 by 512 association matrix is more than 1,200/128 times faster per processor than the sequential Parafit run. AxPcoords is 8–26 times faster than DistPCoA and numerically stable on large datasets. We outline the substantial benefits of using parallel AxParafit by example of a large-scale empirical study on smut fungi and their host plants. To the best of our knowledge, this study represents the largest co-phylogenetic analysis to date. Conclusion The highly efficient AxPcoords and AxParafit programs allow for large-scale co-phylogenetic analyses on several thousands of taxa for the first time. In addition, AxParafit and AxPcoords have been integrated into the easy-to-use CopyCat tool