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The real-time accurate measurement of the geomagnetic-field is the foundation to achieving high-precision geomagnetic navigation. The existing geomagnetic-field measurement models are essentially simplified models that cannot accurately describe the sources of measurement error. This paper, on the basis of systematically analyzing the source of geomagnetic-field measurement error, built a complete measurement model, into which the previously unconsidered geomagnetic daily variation field was introduced. This paper proposed an extended Kalman-filter based compensation method, which allows a large amount of measurement data to be used in estimating parameters to obtain the optimal solution in the sense of statistics. The experiment results showed that the compensated strength of the geomagnetic field remained close to the real value and the measurement error was basically controlled within 5nT. In addition, this compensation method has strong applicability due to its easy data collection and ability to remove the dependence on a high-precision measurement instrument. [ABSTRACT FROM AUTHOR]

In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment, the speed with which such solutions are found, and the ability to scale-up to more complex versions of the problem. The results indicate that the two original methods introduced in this paper and the Exponential Natural Evolutionary Strategy method largely outperform the other methods with respect to all considered criteria. The results collected in different experimental conditions also reveal the importance of regulating the selective pressure and the importance of exposing evolving agents to variable environmental conditions. The data collected and the results of the comparisons are used to identify the most effective methods and the most promising research directions. [ABSTRACT FROM AUTHOR]

The wealth of sensory data coming from different modalities has opened numerous opportunities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data processing. However, multimodal architectures must rely on models able to adapt to changes in the data distribution. Differences in the density functions can be due to changes in acquisition conditions (pose, illumination), sensors characteristics (number of channels, resolution) or different views (e.g. street level vs. aerial views of a same building). We call these different acquisition modes domains, and refer to the adaptation problem as domain adaptation. In this paper, instead of adapting the trained models themselves, we alternatively focus on finding mappings of the data sources into a common, semantically meaningful, representation domain. This field of manifold alignment extends traditional techniques in statistics such as canonical correlation analysis (CCA) to deal with nonlinear adaptation and possibly non-corresponding data pairs between the domains. We introduce a kernel method for manifold alignment (KEMA) that can match an arbitrary number of data sources without needing corresponding pairs, just few labeled examples in all domains. KEMA has interesting properties: 1) it generalizes other manifold alignment methods, 2) it can align manifolds of very different complexities, performing a discriminative alignment preserving each manifold inner structure, 3) it can define a domain-specific metric to cope with multimodal specificities, 4) it can align data spaces of different dimensionality, 5) it is robust to strong nonlinear feature deformations, and 6) it is closed-form invertible, which allows transfer across-domains and data synthesis. To authors’ knowledge this is the first method addressing all these important issues at once. We also present a reduced-rank version of KEMA for computational efficiency, and discuss the generalization performance of KEMA under Rademacher principles of stability. Aligning multimodal data with KEMA reports outstanding benefits when used as a data pre-conditioner step in the standard data analysis processing chain. KEMA exhibits very good performance over competing methods in synthetic controlled examples, visual object recognition and recognition of facial expressions tasks. KEMA is especially well-suited to deal with high-dimensional problems, such as images and videos, and under complicated distortions, twists and warpings of the data manifolds. A fully functional toolbox is available at . [ABSTRACT FROM AUTHOR]

We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome—asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniques, and in particular we focus on semi-supervised classification techniques since they enable us to use more of the Twitter data collected while only doing very minimal labelling. In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches. Additionally, we highlight the use of emojis and other special features capturing the tweet’s tone to improve the classification performance. Our results show that negative emojis and those that denote laughter provide the best classification performance in conjunction with a simple word-level n-gram approach. We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets and may be advantageous in the context of a weak signal. Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions. [ABSTRACT FROM AUTHOR]

This paper presents the results of two studies on the invariance of the Profile of Mood States questionnaire across response time frames and circumstances of administration. We applied Spanish versions of the instrument to gather data from 1146 athletes. In the first study (N = 700), we tested the factor structure of the questionnaire in training sessions by using two different time frames: ‘right now’ (n = 350) and ‘past week’ (n = 350). In the second study (N = 446), we compared the factor structure of the questionnaire with data collected using the instruction ‘right now’ at two different circumstances: ‘training’ (n = 223) and ‘competition’ (n = 223). Data analysis was similar in both studies. We conducted multi-group confirmatory factor analyses and applied the scaled difference chi-square statistic to examine whether discrepancies in successive constrained models were significant. We observed configural equivalence between the two time frames. Furthermore, we observed metric equivalence but not scalar invariance between the different circumstances of measurement. The findings highlight the need for studies of equivalence before using a single self-report with more than one set of instructions, or under diverse circumstances. Invariance of mood scores should be examined and taken into account when interpreting individual and group mood state assessments. [ABSTRACT FROM AUTHOR]

Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer. [ABSTRACT FROM AUTHOR]

Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management implications and extensions. [ABSTRACT FROM AUTHOR]

This paper describes the design, acquisition process and baseline evaluation of the new e-BioSign database, which includes dynamic signature and handwriting information. Data is acquired from 5 different COTS devices: three Wacom devices (STU-500, STU-530 and DTU-1031) specifically designed to capture dynamic signatures and handwriting, and two general purpose tablets (Samsung Galaxy Note 10.1 and Samsung ATIV 7). For the two Samsung tablets, data is collected using both pen stylus and also the finger in order to study the performance of signature verification in a mobile scenario. Data was collected in two sessions for 65 subjects, and includes dynamic information of the signature, the full name and alpha numeric sequences. Skilled forgeries were also performed for signatures and full names. We also report a benchmark evaluation based on e-BioSign for person verification under three different real scenarios: 1) intra-device, 2) inter-device, and 3) mixed writing-tool. We have experimented the proposed benchmark using the main existing approaches for signature verification: feature- and time functions-based. As a result, new insights into the problem of signature biometrics in sensor-interoperable scenarios have been obtained, namely: the importance of specific methods for dealing with device interoperability, and the necessity of a deeper analysis on signatures acquired using the finger as the writing tool. This e-BioSign public database allows the research community to: 1) further analyse and develop signature verification systems in realistic scenarios, and 2) investigate towards a better understanding of the nature of the human handwriting when captured using electronic COTS devices in realistic conditions. [ABSTRACT FROM AUTHOR]

Twitter is an extremely high volume platform for user generated contributions regarding any topic. The wealth of content created at real-time in massive quantities calls for automated approaches to identify the topics of the contributions. Such topics can be utilized in numerous ways, such as public opinion mining, marketing, entertainment, and disaster management. Towards this end, approaches to relate single or partial posts to knowledge base items have been proposed. However, in microblogging systems like Twitter, topics emerge from the culmination of a large number of contributions. Therefore, identifying topics based on collections of posts, where individual posts contribute to some aspect of the greater topic is necessary. Models, such as Latent Dirichlet Allocation (LDA), propose algorithms for relating collections of posts to sets of keywords that represent underlying topics. In these approaches, figuring out what the specific topic(s) the keyword sets represent remains as a separate task. Another issue in topic detection is the scope, which is often limited to specific domain, such as health. This work proposes an approach for identifying domain-independent specific topics related to sets of posts. In this approach, individual posts are processed and then aggregated to identify key tokens, which are then mapped to specific topics. Wikipedia article titles are selected to represent topics, since they are up to date, user-generated, sophisticated articles that span topics of human interest. This paper describes the proposed approach, a prototype implementation, and a case study based on data gathered during the heavily contributed periods corresponding to the four US election debates in 2012. The manually evaluated results (0.96 precision) and other observations from the study are discussed in detail. [ABSTRACT FROM AUTHOR]

Objective: Characterisation of anticoagulant control is fundamental to investigations of its association with clinical outcome. Anticoagulant control depends on several factors. This paper aims to illustrate the implications of different methods for measuring and analysing anticoagulant control in patients with second generation mechanical heart valve prostheses. Methods: International normalised ratio (INR) data collected during the 10-year follow-up of a randomised controlled trial were analysed. We considered the influence of: 3 different target INR ranges; anticoagulant control expressed as the proportion of INR readings (PoR) vs. anticoagulant control follow-up time (PoT); 3 ways of describing the profile of anticoagulant control over time. Results: Different target INR ranges dramatically influenced derived measures of anticoagulant control; the PoT within the target range varied from 88% for the widest to 28% for narrowest range. Overall distributions of PoR and PoT observations were similar but differed by up to ±20% for individuals; PoT exceeded PoR when control was good but was less than PoR when control was poor. Classifying PoT outside the target range showed that widely varying combinations of PoT too high and too low are possible across individuals. Conclusions: Researchers' choices about methods for measuring and quantifying anticoagulant control markedly influence the values derived from INR readings. The use of different methods across studies makes it difficult or impossible to compare findings and to establish an evidence base for clinical practice. Methods for quantifying anticoagulant control should be standardised. [ABSTRACT FROM AUTHOR]

Background: Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. Methodology/Principal findings: The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. Conclusion/Significance: The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates. [ABSTRACT FROM AUTHOR]

Reducing the loss of biodiversity is key to ensure the future well being of the planet. Indicators to measure the state of biodiversity should come from primary data that are collected using consistent field methods across several sites, longitudinal, and derived using sound statistical methods that correct for observation/detection bias. In this paper we analyze camera trap data collected between 2008 and 2012 at a site in Costa Rica (Volcan Barva transect) as part of an ongoing tropical forest global monitoring network (Tropical Ecology Assessment and Monitoring Network). We estimated occupancy dynamics for 13 species of mammals, using a hierarchical modeling approach. We calculated detection-corrected species richness and the Wildlife Picture Index, a promising new indicator derived from camera trap data that measures changes in biodiversity from the occupancy estimates of individual species. Our results show that 3 out of 13 species showed significant declines in occupancy over 5 years (lowland paca, Central American agouti, nine-banded armadillo). We hypothesize that hunting, competition and/or increased predation for paca and agouti might explain these patterns. Species richness and the Wildlife Picture Index are relatively stable at the site, but small herbivores that are hunted showed a decline in diversity of about 25%. We demonstrate the usefulness of longitudinal camera trap deployments coupled with modern statistical methods and advocate for the use of this approach in monitoring and developing global and national indicators for biodiversity change. [ABSTRACT FROM AUTHOR]

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages. [ABSTRACT FROM AUTHOR]

Measuring the overlap between two populations is, in principle, straightforward. Upon fully sampling both populations, the number of shared objects—species, taxonomical units, or gene variants, depending on the context—can be directly counted. In practice, however, only a fraction of each population’s objects are likely to be sampled due to stochastic data collection or sequencing techniques. Although methods exists for quantifying population overlap under subsampled conditions, their bias is well documented and the uncertainty of their estimates cannot be quantified. Here we derive and validate a method to rigorously estimate the population overlap from incomplete samples when the total number of objects, species, or genes in each population is known, a special case of the more general β-diversity problem that is particularly relevant in the ecology and genomic epidemiology of malaria. By solving a Bayesian inference problem, this method takes into account the rates of subsampling and produces unbiased and Bayes-optimal estimates of overlap. In addition, it provides a natural framework for computing the uncertainty of its estimates, and can be used prospectively in study planning by quantifying the tradeoff between sampling effort and uncertainty. [ABSTRACT FROM AUTHOR]

Secondary analyses of survey data collected from large probability samples of persons or establishments further scientific progress in many fields. The complex design features of these samples improve data collection efficiency, but also require analysts to account for these features when conducting analysis. Unfortunately, many secondary analysts from fields outside of statistics, biostatistics, and survey methodology do not have adequate training in this area, and as a result may apply incorrect statistical methods when analyzing these survey data sets. This in turn could lead to the publication of incorrect inferences based on the survey data that effectively negate the resources dedicated to these surveys. In this article, we build on the results of a preliminary meta-analysis of 100 peer-reviewed journal articles presenting analyses of data from a variety of national health surveys, which suggested that analytic errors may be extremely prevalent in these types of investigations. We first perform a meta-analysis of a stratified random sample of 145 additional research products analyzing survey data from the Scientists and Engineers Statistical Data System (SESTAT), which describes features of the U.S. Science and Engineering workforce, and examine trends in the prevalence of analytic error across the decades used to stratify the sample. We once again find that analytic errors appear to be quite prevalent in these studies. Next, we present several example analyses of real SESTAT data, and demonstrate that a failure to perform these analyses correctly can result in substantially biased estimates with standard errors that do not adequately reflect complex sample design features. Collectively, the results of this investigation suggest that reviewers of this type of research need to pay much closer attention to the analytic methods employed by researchers attempting to publish or present secondary analyses of survey data. [ABSTRACT FROM AUTHOR]

Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy. [ABSTRACT FROM AUTHOR]

The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also significant challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs). Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research. The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research. We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider. [ABSTRACT FROM AUTHOR]

Aim: This systematic review and meta-analysis characterizes the prevalence of hepatitis C virus (HCV) infection among intravenous drug users (IDUs) in upper middle-income countries. Methods: Five databases were searched from 1990–2016 for studies that took place in countries with a GDP per capita of $7,000 to $13,000 USD. The data extraction was performed based on information regarding prevalence, sample size, age of participants, duration of intravenous drug use (IDU), recruitment location, dates of data collection, study design, sampling scheme, type of tests used in identifying antibody reactivity to HCV, and the use of confirmatory tests. The synthesis was performed with a random effects model. The Cochrane statistical Q-test was used to evaluate the statistical heterogeneity of the results. Results: The 33 studies included in the analysis correspond to a sample of seven countries and 23,342 observations. The point prevalence value estimates and confidence intervals of the random effects model were 0.729 and 0.644–0.800, respectively for all seven countries, and were greatest for China (0.633; 0.522–0.732) as compared to Brazil (0.396; 0.249–0.564). Prevalence for Montenegro (0.416; 0.237–0.621) and Malaysia (0.475; 0.177–0.792) appear to be intermediate. Mexico (0.960) and Mauritania (0.973) had only one study with the largest prevalence. A clear association was not observed between age or duration of IDU and prevalence of HCV, but the data from some groups may indicate a possible relationship. The measures of heterogeneity (Q and I2) suggest a high level of heterogeneity in studies conducted at the country level and by groups of countries. Conclusions: In this systematic review and meta-analysis, we found that the pooled prevalence of HCV was high (0.729) among a group of seven upper middle income countries. However, there was significant variation in the prevalence of HCV observed in China (0.633) and Brazil (0.396). [ABSTRACT FROM AUTHOR]

Many contemporary neuroscience experiments utilize high-throughput approaches to simultaneously collect behavioural data from many animals. The resulting data are often complex in structure and are subjected to systematic biases, which require new approaches for analysis and normalization. This study addressed the normalization need by establishing an approach based on linear-regression modeling. The model was established using a dataset of visual motor response (VMR) obtained from several strains of wild-type (WT) zebrafish collected at multiple stages of development. The VMR is a locomotor response triggered by drastic light change, and is commonly measured repeatedly from multiple larvae arrayed in 96-well plates. This assay is subjected to several systematic variations. For example, the light emitted by the machine varies slightly from well to well. In addition to the light-intensity variation, biological replication also created batch-batch variation. These systematic variations may result in differences in the VMR and must be normalized. Our normalization approach explicitly modeled the effect of these systematic variations on VMR. It also normalized the activity profiles of different conditions to a common baseline. Our approach is versatile, as it can incorporate different normalization needs as separate factors. The versatility was demonstrated by an integrated normalization of three factors: light-intensity variation, batch-batch variation and baseline. After normalization, new biological insights were revealed from the data. For example, we found larvae of TL strain at 6 days post-fertilization (dpf) responded to light onset much stronger than the 9-dpf larvae, whereas previous analysis without normalization shows that their responses were relatively comparable. By removing systematic variations, our model-based normalization can facilitate downstream statistical comparisons and aid detecting true biological differences in high-throughput studies of neurobehaviour. [ABSTRACT FROM AUTHOR]

Most research on consumer attitudes does not consider that attitudes are likely influenced by people with whom we have some relationship even though socioeconomic, psychological and political theories recognize the importance of referent individuals’ opinions in attitude formation. Knowledge on the role of referent individuals’ opinions in attitude formation could improve the understanding of consumer acceptance of foods frequently associated with health or other concerns. This article examines the association of attitudes towards genetically modified (GM) crops and foods between young adults and their referent individuals using data collected in 2016 via surveys from the Czech Republic, Russia and Ukraine. Loglinear models of cell counts in contingency tables reveal a positive association of GM food attitudes between young adults and their referent individuals. This association was stronger in Russia and the Czech Republic than it was in Ukraine and stronger between female young adults and their referent individuals than between males and their referent individuals. Concordance in GM food attitudes with mothers is significantly stronger than concordance with best friends but not significantly different from concordance with fathers. [ABSTRACT FROM AUTHOR]

Introduction: The objectives of this study was to investigate and compare levels of acceptability of pre-marital HIV testing; and intention to sero-sort future marital and its associated factors among unmarried adults in two cities in Cameroon. Methods: A population-based survey was conducted simultaneously in the cities of Kumba and Buea, located in the Southwest region of Cameroon. Data were collected from September to October 2016 by trained interviewers who administered questionnaires to eligible and consenting unmarried adults aged 21–35 years. Data were weighted and logistic regression analyses performed to identify significant predictors. The level of statistical significance was set at p< = 0.05. Results: A total of 1,406 respondents (767 in Kumba and 639 in Buea) participated in the study. In the pooled sample, the median age of respondents was 26 years (IQR = 23–29) and over half (54.8%) were males. Over 90% of respondents in both cities indicated their willingness to accept pre-marital HIV testing. Respondents who had previously tested for HIV in Kumba (AOR = 7.87; 95%CI, 4.02–15.44) were significantly more likely to accept premarital HIV testing than those who had never tested for HIV. In Kumba, older age (AOR = 0.42; 95%CI, 0.18–0.96) and those unemployed (AOR = 0.22; 95% CI, 0.06–0.76) were significantly less likely to accept pre-marital HIV testing. In Buea males (AOR = 0.64 95% CI, 0.45–0.89) who would test HIV negative would be significantly less likely to accept to marry an HIV positive partner. In Buea, respondents who indicated a moderate risk of contracting HIV (AOR = 1.71; 95%CI, 1.09–2.66, p = 0.018) were significantly more likely to accept to marry an HIV positive partner. The major limitation of the study was that a hypothetical situation was used to ask respondents about their willingness to accept pre-marital HIV testing rather than actual HIV test acceptance. Conclusions: Most respondents expressed their willingness to undergo pre-marital HIV testing. However, majority of respondents who would test HIV negative would refuse to marry their partner who tests HIV positive. These findings suggest that interventions to reduce HIV infection and fight against stigma and discrimination should be reinforced at community level. [ABSTRACT FROM AUTHOR]

The aim was to objectively identify key performance indicators in professional soccer that influence outfield players’ league status using an artificial neural network. Mean technical performance data were collected from 966 outfield players’ (mean SD; age: 25 ± 4 yr, 1.81 ±) 90-minute performances in the English Football League. ProZone’s MatchViewer system and online databases were used to collect data on 347 indicators assessing the total number, accuracy and consistency of passes, tackles, possessions regained, clearances and shots. Players were assigned to one of three categories based on where they went on to complete most of their match time in the following season: group 0 (n = 209 players) went on to play in a lower soccer league, group 1 (n = 637 players) remained in the Football League Championship, and group 2 (n = 120 players) consisted of players who moved up to the English Premier League. The models created correctly predicted between 61.5% and 78.8% of the players’ league status. The model with the highest average test performance was for group 0 v 2 (U21 international caps, international caps, median tackles, percentage of first time passes unsuccessful upper quartile, maximum dribbles and possessions gained minimum) which correctly predicted 78.8% of the players’ league status with a test error of 8.3%. To date, there has not been a published example of an objective method of predicting career trajectory in soccer. This is a significant development as it highlights the potential for machine learning to be used in the scouting and recruitment process in a professional soccer environment. [ABSTRACT FROM AUTHOR]

Clapping Music is a minimalist work by Steve Reich based on twelve phased variations of a rhythmic pattern. It has been reimagined as a game-based mobile application, designed with a dual purpose. First, to introduce new audiences to the Minimalist genre through interaction with the piece presented as an engaging game. Second, to use large-scale data collection within the app to address research questions about the factors determining rhythm production performance. The twelve patterns can be differentiated using existing theories of rhythmic complexity. Using performance indicators from the game such as tap accuracy we can determine which patterns players found most challenging and so assess hypotheses from theoretical models with empirical evidence. The app has been downloaded over 140,000 times since the launch in July 2015, and over 46 million rows of gameplay data have been collected, requiring a big data approach to analysis. The results shed light on the rhythmic factors contributing to performance difficulty and show that the effect of making a transition from one pattern to the next is as significant, in terms of pattern difficulty, as the inherent complexity of the pattern itself. Challenges that arose in applying this novel approach are discussed. [ABSTRACT FROM AUTHOR]

Quantifying evidence is an inherent aim of empirical science, yet the customary statistical methods in psychology do not communicate the degree to which the collected data serve as evidence for the tested hypothesis. In order to estimate the distribution of the strength of evidence that individual significant results offer in psychology, we calculated Bayes factors (BF) for 287,424 findings of 35,515 articles published in 293 psychological journals between 1985 and 2016. Overall, 55% of all analyzed results were found to provide BF > 10 (often labeled as strong evidence) for the alternative hypothesis, while more than half of the remaining results do not pass the level of BF = 3 (labeled as anecdotal evidence). The results estimate that at least 82% of all published psychological articles contain one or more significant results that do not provide BF > 10 for the hypothesis. We conclude that due to the threshold of acceptance having been set too low for psychological findings, a substantial proportion of the published results have weak evidential support. [ABSTRACT FROM AUTHOR]

Background: More countries are using verbal autopsy as a part of routine mortality surveillance. The length of time required to complete a verbal autopsy interview is a key logistical consideration for planning large-scale surveillance. Methods: We use the PHMRC shortened questionnaire to conduct verbal autopsy interviews at three sites and collect data on the length of time required to complete the interview. This instrument uses a novel checklist of keywords to capture relevant information from the open response. The open response section is timed separately from the section consisting of closed questions. Results: We found the median time to complete the entire interview was approximately 25 minutes and did not vary substantially by age-specific module. The median time for the open response section was approximately 4 minutes and 60% of interviewees mentioned at least one keyword within the open response section. Conclusions: The length of time required to complete the interview was short enough for large-scale routine use. The open-response section did not add a substantial amount of time and provided useful information which can be used to increase the accuracy of the predictions of the cause of death. The novel checklist approach further reduces the burden of transcribing and translating a large amount of free text. This makes the PHMRC instrument ideal for national mortality surveillance. [ABSTRACT FROM AUTHOR]

Objective: This prospective study is designed to detect changes in the treatment of ruptured intracranial aneurysms over a period of 17 years. Methods: We compared 361 treated cases of aneurysm occlusion after subarachnoid hemorrhage from 1997 to 2003 with 281 cases from 2006 to 2014. Specialists of neuroradiology and vascular neurosurgery decided over the modality assignment. We established a prospective data acquisition in both groups to detect significant differences within a follow-up time of one year. With this setting we evaluated the treatment methods over time and compared endovascular with microsurgical treatment. Results: When compared to the earlier group, microsurgical treatment was less frequently chosen in the more recent collective because of neck-configuration. Endovascular treatment was chosen more frequently over time (31.9% versus 48.8%). Occurrence of initial symptomatic ischemic stroke was significantly lower in the clipping group compared to the endovascular group and remained stable over time. The number of reinterventions due to refilled treated aneurysms significantly decreased in the endovascular group at one-year follow-up, but the significantly better occlusion- and reintervention-rate of the microsurgical group persisted. The rebleeding rate in the endovascular group at one year follow-up decreased from 6.1% to 2.2% and showed no statistically significant difference to the microsurgical group, anymore (endovascular 2.2% versus microsurgical 0.0%, p = 0.11). Conclusion: Microsurgical clipping still has some advantages, however endovascular treatment is improving rapidly. [ABSTRACT FROM AUTHOR]

Background: Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season. Methods: Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season. Results: 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5–7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen’s d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small. Conclusions: It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses. [ABSTRACT FROM AUTHOR]

It is commonly assumed that perfusion in a given cerebral territory can be inferred from Blood Flow Velocity (BFV) measurements in the corresponding stem artery. In order to test this hypothesis, we construct a cerebral blood flow (CBF) estimator based on transcranial Doppler (TCD) blood flow velocity and ten other easily available patient characteristics and clinical parameters. A total of 261 measurements were collected from 88 older patients. The estimator is based on local regression (Random Forest). Its performance is analyzed against baseline CBF from 3-D pseudocontinuous arterial spin labeling (pCASL) magnetic resonance imaging (MRI). Patient specific CBF predictions are of poor quality (r = 0.41 and p-value = 4.5 × 10−12); the hypothesis is thus not clearly supported by evidence. [ABSTRACT FROM AUTHOR]

Background and Aim: Human epidermal growth factor receptor (HER) family plays an important role in gastric cancer (GC), especially HER2. Too much attention has been paid to HER2; however, the functions of HER3 and HER4 overexpression in GC are always ignored. The clinicopathological and prognostic roles of HER3 and HER4 in GC are controversial. In this study, a systematic review and meta-analysis was conducted to evaluate the use of HER3 or HER4 as a predictor of clinicopathology and survival time in GC patients. Methods: Eligible studies were searched on PubMed, Ovid, Web of Science, and Cochrane databases through multiple search strategies. Data collection and statistical analysis were carried out by the Revman 5.3 software. The Newcastle-Ottawa scale was used to assess the quality of included studies. Results: A total of 448 studies about HER3 overexpression and GC, and 398 studies about HER4 overexpression and GC were searched. Of these, 5 eligible studies about HER3 including 1016 GC patients and 3 eligible studies about HER4 including 793 GC patients met the inclusion criteria. The results showed that HER3 and HER4 overexpression were significantly associated with depth of tumor invasion (OR = 0.44, 95%CI 0.29–0.67, P = 0.0002 and OR = 0.50, 95%CI 0.38–0.86, P = 0.007) and lymph node metastasis (OR = 0.40, 95%CI 0.20–0.77, P = 0.007 and OR = 0.57, 95%CI 0.38–0.86, P = 0.007), and HER3 overexpression reveals a tendency of later tumor node metastases (TNM) stage (OR = 0.50, 95%CI 0.22–1.15, P = 0.10) and predicts a worse survival time (RR = 0.71, 95%CI 0.61–0.84, P<0.00001), while HER4 overexpression had no correlation with TNM stage (OR = 0.60, 95%CI 0.20–1.78) and survival time (RR = 1.09, 95%CI 0.91–1.30). Conclusions: This meta-analysis indicated that HER3 plays an essential role in the clinicopathology and prognosis of GC. However, HER4 may not be an ideal prognostic factor for GC. [ABSTRACT FROM AUTHOR]

Natural ecological variability and analytical design can bias the derived value of a biotic index through the variable influence of indicator body-size, abundance, richness, and ascribed tolerance scores. Descriptive statistics highlight this risk for 26 aquatic indicator systems; detailed analysis is provided for contrasting weighted-average indices applying the example of the BMWP, which has the best supporting data. Differences in body size between taxa from respective tolerance classes is a common feature of indicator systems; in some it represents a trend ranging from comparatively small pollution tolerant to larger intolerant organisms. Under this scenario, the propensity to collect a greater proportion of smaller organisms is associated with negative bias however, positive bias may occur when equipment (e.g. mesh-size) selectively samples larger organisms. Biotic indices are often derived from systems where indicator taxa are unevenly distributed along the gradient of tolerance classes. Such skews in indicator richness can distort index values in the direction of taxonomically rich indicator classes with the subsequent degree of bias related to the treatment of abundance data. The misclassification of indicator taxa causes bias that varies with the magnitude of the misclassification, the relative abundance of misclassified taxa and the treatment of abundance data. These artifacts of assessment design can compromise the ability to monitor biological quality. The statistical treatment of abundance data and the manipulation of indicator assignment and class richness can be used to improve index accuracy. While advances in methods of data collection (i.e. DNA barcoding) may facilitate improvement, the scope to reduce systematic bias is ultimately limited to a strategy of optimal compromise. The shortfall in accuracy must be addressed by statistical pragmatism. At any particular site, the net bias is a probabilistic function of the sample data, resulting in an error variance around an average deviation. Following standardized protocols and assigning precise reference conditions, the error variance of their comparative ratio (test-site:reference) can be measured and used to estimate the accuracy of the resultant assessment. [ABSTRACT FROM AUTHOR]

Purpose: Recent studies propose that the use of target stimuli within or close to complete spatial summation reveal larger threshold elevation in ocular disease. The Humphrey Visual Field Analyzer (HFA) is used to assess visual function yet the spatial summation characteristics are unexplored for the central macular region. We therefore wanted to establish the relationship between contrast sensitivity and stimulus size (spatial summation) within the central 20° visual field using the high sampling density of the 10–2 test grid. Methods: Thresholds were measured for one eye from 37 normal subjects using the HFA 10–2 test grid with all five Goldmann (G) targets (GI to GV). Subject data were converted to 50-year-old equivalent using published and calculated location-specific decade correction factors. Spatial summation curves were fitted for all data at all locations. The size of Ricco’s critical area (Ac) within which complete spatial summation operates (k = 1), and the slope of partial summation (k < 1: to characterize partial summation), was established. Results: The 50-year-old age normative data were determined for all Goldmann stimulus sizes for the 10–2 HFA test grid and showed a marked change in contrast sensitivity for small test stimuli (e.g. GI) and little change in larger test stimuli (e.g. GV). Both the Ac and k values did not vary with age allowing for the application of the age correction factors. Ac and k values increased with eccentricity with GI remaining within complete spatial summation and GII was close or within complete spatial summation. GIII or larger test sizes were always outside complete spatial summation operating within various levels of partial summation. Conclusions: The developed normative data now allows comparisons of data sets with high sampling density using the 10–2 grid irrespective of subject age. Test size is important when assessing ocular disease yet only GI or GII stimuli operate close to or within complete spatial summation in the macula. Current visual field testing protocols employ GIII which is always outside complete spatial summation and operates under various values of partial summation: GIII may not be the most suitable test size to assess ocular disease affecting the macula. [ABSTRACT FROM AUTHOR]

Olfactory ensheathing cell (OEC) transplantation is a candidate cellular treatment approach for human spinal cord injury (SCI) due to their unique regenerative potential and autologous origin. The objective of this study was, through a meta-epidemiologic approach, (i) to assess the efficacy of OEC transplantation on locomotor recovery after traumatic experimental SCI and (ii) to estimate the likelihood of reporting bias and/or missing data. A study protocol was finalized before data collection. Embedded into a systematic review and meta-analysis, we conducted a literature research of databases including PubMed, EMBASE, and ISI Web of Science from 1949/01 to 2014/10 with no language restrictions, screened by two independent investigators. Studies were included if they assessed neurobehavioral improvement after traumatic experimental SCI, administrated no combined interventions, and reported the number of animals in the treatment and control group. Individual effect sizes were pooled using a random effects model. Details regarding the study design were extracted and impact of these on locomotor outcome was assessed by meta-regression. Missing data (reporting bias) was determined by Egger regression and Funnel-plotting. The primary study outcome assessed was improvement in locomotor function at the final time point of measurement. We included 49 studies (62 experiments, 1,164 animals) in the final analysis. The overall improvement in locomotor function after OEC transplantation, measured using the Basso, Beattie, and Bresnahan (BBB) score, was 20.3% (95% CI 17.8–29.5). One missing study was imputed by trim and fill analysis, suggesting only slight publication bias and reducing the overall effect to a 19.2% improvement of locomotor activity. Dose-response ratio supports neurobiological plausibility. Studies were assessed using a 9-point item quality score, resulting in a median score of 5 (interquartile range [IQR] 3–5). In conclusion, OEC transplantation exerts considerable beneficial effects on neurobehavioral recovery after traumatic experimental SCI. Publication bias was minimal and affirms the translational potential of efficacy, but safety cannot be adequately assessed. The data justify OECs as a cellular substrate to develop and optimize minimally invasive and safe cellular transplantation paradigms for the lesioned spinal cord embedded into state-of-the-art Phase I/II clinical trial design studies for human SCI. [ABSTRACT FROM AUTHOR]

Over the past half century, thousands of quantitative trait loci (QTL) have been identified by using animal models and plant populations. However, the none-reliability and imprecision of the genomic regions of these loci have remained the major hurdle for the identification of the causal genes for the correspondent traits. We used a none-experimental strategy of strain number reduction for testing accuracy and ascertainment of the candidate region for QTL. We tested the strategy in over 400 analyses with data from 47 studies. These studies include: 1) studies with recombinant inbred (RI) strains of mice. We first tested two previously mapped QTL with well-defined genomic regions; We then tested additional four studies with known QTL regions; and finally we examined the reliability of QTL in 38 sets of data which are produced from relatively large numbers of RI strains, derived from C57BL/6J (B6) X DBA/2J (D2), known as BXD RI mouse strains; 2) studies with RI strains of rats and plants; and 3) studies using F2 populations in mice, rats and plants. In these cases, our method identified the reliability of mapped QTL and localized the candidate genes into the defined genomic regions. Our data also suggests that LRS score produced by permutation tests does not necessarily confirm the reliability of the QTL. Number of strains are not the reliable indicators for the accuracy of QTL either. Our strategy determines the reliability and accuracy of the genomic region of a QTL without any additional experimental study such as congenic breeding. [ABSTRACT FROM AUTHOR]

Background: Computerized adaptive testing (CAT) utilizes latent variable measurement model parameters that are typically assumed to be equivalently applicable to all people. Biased latent variable scores may be obtained in samples that are heterogeneous with respect to a specified measurement model. We examined the implications of sample heterogeneity with respect to CAT-predicted patient-reported outcomes (PRO) scores for the measurement of pain. Methods: A latent variable mixture modeling (LVMM) analysis was conducted using data collected from a heterogeneous sample of people in British Columbia, Canada, who were administered the 36 pain domain items of the CAT-5D-QOL. The fitted LVMM was then used to produce data for a simulation analysis. We evaluated bias by comparing the referent PRO scores of the LVMM with PRO scores predicted by a “conventional” CAT (ignoring heterogeneity) and a LVMM-based “mixture” CAT (accommodating heterogeneity). Results: The LVMM analysis indicated support for three latent classes with class proportions of 0.25, 0.30 and 0.45, which suggests that the sample was heterogeneous. The simulation analyses revealed differences between the referent PRO scores and the PRO scores produced by the “conventional” CAT. The “mixture” CAT produced PRO scores that were nearly equivalent to the referent scores. Conclusion: Bias in PRO scores based on latent variable models may result when population heterogeneity is ignored. Improved accuracy could be obtained by using CATs that are parameterized using LVMM. [ABSTRACT FROM AUTHOR]

Background: Mental illnesses worldwide are accompanied by another pandemic, that of stigma and discrimination. Public understanding about mental illnesses and attitudes towards people with mental illness play a paramount role in the prevention and treatment of mental illness and the rehabilitation of people with mental illness. Objective: To assess community attitude and associated factors towards people with mental illness. Methods: Community based cross-sectional study was conducted from April 28 to May 28, 2014. Quantitative data were collected through interview from 435 adults selected using simple random sampling. Data were collected using community attitude towards mentally ill (CAMI) tool to assess community attitude towards people with mental illness and associated factors. Multiple linear regression analysis was performed to identify predictors of community attitude towards people with mental illness and the level of significance association was determined by beta with 95% confidence interval and P less than 0.05. Results: The highest mean score was on social restrictiveness subscale (31.55±5.62). Farmers had more socially restrictive view (β = 0.291, CI [0.09, 0.49]) and have less humanistic view towards mentally ill (β = 0.193, CI [-0.36, -0.03]). Having mental health information had significantly less socially restrictive (β = -0.59, CI [-1.13, -0.05]) and less authoritarian (β = -0.10, CI [-1.11, -0.06]) view towards mentally ill but respondents who are at university or college level reported to be more socially restrictive (β = 0.298, CI [0.059, 0.54]). Respondents whose age is above 48 years old had significantly less view of community mental health ideology (β = -0.59, CI [-1.09, -0.08]). Conclusion and Recommendation: Residents of Worabe town were highly socially restrictive but less authoritarian. There was high level of negative attitude towards people with mental illness along all the subscales with relative variation indicating a need to develop strategies to change negative attitude attached to mental illness in Worabe town at community level. [ABSTRACT FROM AUTHOR]

The GTEx Consortium reported that hierarchical clustering of RNA profiles from 25 unique tissue types among 1641 individuals accurately distinguished the tissue types, but a multidimensional scaling failed to generate a 2D projection of the data that separates tissue-subtypes. In this study we show that a projection by t-Distributed Stochastic Neighbor Embedding is in line with the cluster analysis which allows a more detailed examination and visualization of human tissue relationships. [ABSTRACT FROM AUTHOR]

Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts that the amount of integrated information in the brain should reflect levels of consciousness. Empirical evaluation of this theory requires computing integrated information from neural data acquired from experiments, although difficulties with using the original measure Φ precludes such computations. Although some practical measures have been previously proposed, we found that these measures fail to satisfy the theoretical requirements as a measure of integrated information. Measures of integrated information should satisfy the lower and upper bounds as follows: The lower bound of integrated information should be 0 and is equal to 0 when the system does not generate information (no information) or when the system comprises independent parts (no integration). The upper bound of integrated information is the amount of information generated by the whole system. Here we derive the novel practical measure Φ* by introducing a concept of mismatched decoding developed from information theory. We show that Φ* is properly bounded from below and above, as required, as a measure of integrated information. We derive the analytical expression of Φ* under the Gaussian assumption, which makes it readily applicable to experimental data. Our novel measure Φ* can generally be used as a measure of integrated information in research on consciousness, and also as a tool for network analysis on diverse areas of biology. [ABSTRACT FROM AUTHOR]

Background: Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. Methodology: We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. Results: The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). Conclusions: According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process. [ABSTRACT FROM AUTHOR]

Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player's wealth at every point in time, but also all actions over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in Western countries. In particular we find an approximate exponential distribution for low wealth levels and a power-law tail for high levels. The Gini index is found to be , which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degrees in the trade network, relatively low nearest-neighbor degrees, and low clustering coefficients. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies. We find that players that are not organized within social groups are significantly poorer on average. We observe that “political” status and wealth go hand in hand. [ABSTRACT FROM AUTHOR]

The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA) in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds) were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd’s incidence rate of lameness to influence its overall reproductive performance) using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period), PSA revealed that, when viewed in the context of a realistic clinical situation, a herd’s lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd) rather than individual level. [ABSTRACT FROM AUTHOR]

The number of co-infections of a pathogen (multiplicity of infection or MOI) is a relevant parameter in epidemiology as it relates to transmission intensity. Notably, such quantities can be built into a metric in the context of disease control and prevention. Having applications to malaria in mind, we develop here a maximum-likelihood (ML) framework to estimate the quantities of interest at low computational and no additional costs to study designs or data collection. We show how the ML estimate for the quantities of interest and corresponding confidence-regions are obtained from multiple genetic loci. Assuming specifically that infections are rare and independent events, the number of infections per host follows a conditional Poisson distribution. Under this assumption, we show that a unique ML estimate for the parameter () describing MOI exists which is found by a simple recursion. Moreover, we provide explicit formulas for asymptotic confidence intervals, and show that profile-likelihood-based confidence intervals exist, which are found by a simple two-dimensional recursion. Based on the confidence intervals we provide alternative statistical tests for the MOI parameter. Finally, we illustrate the methods on three malaria data sets. The statistical framework however is not limited to malaria. [ABSTRACT FROM AUTHOR]

This study characterized the soft tissue insertion morphometrics on the tibial plateau and their inter-relationships as well as variabilities. The outlines of the cruciate ligament and meniscal root insertions along with the medial and lateral cartilage on 20 cadaveric tibias (10 left and 10 right knees) were digitized and co-registered with corresponding CT-based 3D bone models. Generalized Procrustes Analysis was employed in conjunction with Principal Components Analysis to first create a geometric consensus based on tibial cartilage and then determine the means and variations of insertion morphometrics including shape, size, location, and inter-relationship measures. Step-wise regression analysis was conducted in search of parsimonious models relating the morphometric measures to the tibial plateau width and depth, and basic anthropometric and gender factors. The analyses resulted in statistical morphometric representations for Procrustes-superimposed cruciate ligament and meniscus insertions, and identified only a few moderate correlations (R2: 0.37–0.49). The study provided evidence challenging the isometric scaling based on a single dimension frequently employed in related morphometric studies, and data for evaluating cruciate ligament reconstruction strategies in terms of re-creating the native anatomy and minimizing the risk of iatrogenic injury. It paved the way for future development of computer-aided personalized orthopaedic surgery applications improving the quality of care and patient safety, and biomechanical models with a better population or average representation. [ABSTRACT FROM AUTHOR]

Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to any arbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxes extracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape. [ABSTRACT FROM AUTHOR]

Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient’s unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient’s unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions. [ABSTRACT FROM AUTHOR]