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Modeling bivariate (or multivariate) count data has received increased interest in recent years. The aim is to model the number of different but correlated counts taking into account covariate information. Bivariate Poisson regression models based on the shock model approach are widely used because of their simple form and interpretation. However, these models do not allow for overdispersion or negative correlation, and thus, other models have been proposed in the literature to avoid these limitations. The present paper proposes copula-based bivariate finite mixture of regression models. These models offer some advantages since they have all the benefits of a finite mixture, allowing for unobserved heterogeneity and clustering effects, while the copula-based derivation can produce more flexible structures, including negative correlations and regressors. In this paper, the new approach is defined, estimation through an EM algorithm is presented, and then different models are applied to a Spanish insurance claim count database.

Using a high-frequency framework, we show that the Auroba-Diebold-Scotti (ADS) daily business conditions index significantly increases the accuracy of U.S. unemployment nowcasts in real-time. This is of particular relevance in times of recession, such as the Global Financial Crisis and the Covid-19 pandemic, when the unemployment rate is prone to rise steeply. Based on our results, the ADS index presents itself as a better predictor than the financial indicators widely used by the literature and central banks, including both interest and credit spreads and the VXO.

HPGe detectors are commonly used in the laboratories to detect gamma-rays due to its high resolution and efficiency which is a good characteristic to distinguish low energy gamma radiation. In the present study a HPGe detector called Ge5 of LARA Laboratori d’anàlisi de radiactivitat ambiental (INTE-UPC) must be studied because it is intended to be used again to get the activities of several samples. The first step is to get the efficiency calibration for environmental samples which are small and large filters. Efficiency calibration can be obtained experimentally but the efficiency curves would not be reliable for a continuum energy range. Moreover, HPGe detector are limited because some of the radionuclides such as 60Co releases more than one gamma-ray at the same time and distorts the actual efficiency. Therefore, it is needed to get correction factors. Monte Carlo methods are used to get efficiency calibration so that an accurate efficiency curve can be obtained for a wide energy range. Moreover, the correction factors can be computed. PENELOPE/PenEasy is the program used to get the efficiency calibration for small and large filters and correct the efficiency of 60Co due to the true coincidence summing effect. This program will also allow to define the geometry of the Ge5 detector since the layout is unknown. In order to determine the geometry of the detector Ge5 a previous simulation is done with old environmental samples. After characterizing the geometry of the detector, a current experiment with a large filter is simulated to get a new efficiency calibration which is the main goal of the project.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948–2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

© 2019 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Objectives The objective of the study was to identify factors affecting the use of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement, specifically authors’ attitudes toward and experiences with it. Study Design and Setting An online survey was distributed to authors of observational studies recruited via social media, personal network snowballing, and mass mailings using targeted search strategies. Data on demographics, awareness, motivators, and usage were collected in conjunction with a modified Unified Theory of Acceptance and Use of Technology (UTAUT) scale on which confirmatory factor analysis (CFA) was performed. Results One thousand fifteen participants completed the survey. Of these, 185 (18.2%) indicated they had never heard of STROBE nor used it previously, 195 (19.2%) had heard of it but never used it, and 635 (62.6%) had used it. Journals promoting STROBE were both key motivators and awareness mechanisms; peers and educational workshops were also important influencing factors to a lesser degree. The internal consistency of the modified UTAUT scale was strong (Cronbach's alpha = 0.94). CFA supported a four-factor model with 23 questions. Conclusion The endorsement of STROBE by journals is key to authors’ awareness and use of the guideline. We tested and validated our scale which can guide future research on reporting guidelines. The authors would like to acknowledge the Methods in Research on Research (MiRoR) network for their support and guidance. The authors would also like to thank Dr. Michail Kovanis for assistance gathering author emails and Ms. Alice Biggane for her assistance with the recruitment strategy.

Objective: Patient recruitment in clinical trials is challenging with failure to recruit to time and target sample size common. This may be caused by unanticipated problems or by overestimation of the recruitment rate. This study is a systematic review of statistical models to predict recruitment at the design stage of clinical trials. Study Design and Setting: The Online Resource for Recruitment research in Clinical triAls database was searched to identify articles published between 2008 and 2016. Articles published before 2008 were identified from a relevant systematic review. Google search was used to find potential methods in gray literature. Results: Thirteen eligible articles were identified of which, 11 focused on stochastic approaches, one on deterministic models, and one included both stochastic and deterministic methods. Models varied considerably in the factors included and in their complexity. Key aspects included their ability to condition on time; whether they used average or center-specific recruitment rates; and assumptions around center initiation rates. Lack of flexibility of some models restricts their implementation. Conclusion: Deterministic models require specification of few parameters but are likely unrealistic although easy to implement. Increasingly, stochastic models require greater parameter specification, which, along with greater complexity may be a barrier to their implementation.

This is the peer reviewed version of the following article: Forns, J. [et al.]. Validity of ICD-9 and ICD-10 codes used to identify acute liver injury: a study in three European data sources. "Pharmacoepidemiology and drug safety", 6 Juny 2019, vol. 28, núm. 7, p. 965-975, which has been published in final form at 10.1002/pds.4803. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." Purpose Validating cases of acute liver injury (ALI) in health care data sources is challenging. Previous validation studies reported low positive predictive values (PPVs). Methods Case validation was undertaken in a study conducted from 2009 to 2014 assessing the risk of ALI in antidepressants users in databases in Spain (EpiChron and SIDIAP) and the Danish National Health Registers. Three ALI definitions were evaluated: primary (specific hospital discharge codes), secondary (specific and nonspecific hospital discharge codes), and tertiary (specific and nonspecific hospital and outpatient codes). The validation included review of patient profiles (EpiChron and SIDIAP) and of clinical data from medical records (EpiChron and Denmark). ALI cases were confirmed when liver enzyme values met a definition by an international working group. Results Overall PPVs (95% CIs) for the study ALI definitions were, for the primary ALI definition, 84% (60%-97%) (EpiChron), 60% (26%-88%) (SIDIAP), and 74% (60%-85%) (Denmark); for the secondary ALI definition, 65% (45%-81%) (EpiChron), 40% (19%-64%) (SIDIAP), and 70% (64%-77%) (Denmark); and for the tertiary ALI definition, 25% (18%-34%) (EpiChron), 8% (7%-9%) (SIDIAP), and 47% (42%-52%) (Denmark). The overall PPVs were higher for specific than for nonspecific codes and for hospital discharge than for outpatient codes. The nonspecific code “unspecified jaundice” had high PPVs in Denmark. Conclusions PPVs obtained apply to patients using antidepressants without preexisting liver disease or ALI risk factors. To maximize validity, studies on ALI should prioritize hospital specific discharge codes and should include hospital codes for unspecified jaundice. Case validation is required when ALI outpatient cases are considered.

This is the peer reviewed version of the following article: Bofill, M.; Gómez Melis, G. A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components. "Statistics in medicine", 20 Maig 2019, vol. 38, núm. 11, p. 1935-1956, which has been published in final form at https://doi.org/10.1002/sim.8092. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Composite binary endpoints are increasingly used as primary endpoints in clinical trials. When designing a trial, it is crucial to determine the appropriate sample size for testing the statistical differences between treatment groups for the primary endpoint. As shown in this work, when using a composite binary endpoint to size a trial, one needs to specify the event rates and the effect sizes of the composite components as well as the correlation between them. In practice, the marginal parameters of the components can be obtained from previous studies or pilot trials; however, the correlation is often not previously reported and thus usually unknown. We first show that the sample size for composite binary endpoints is strongly dependent on the correlation and, second, that slight deviations in the prior information on the marginal parameters may result in underpowered trials for achieving the study objectives at a pre-specified significance level. We propose a general strategy for calculating the required sample size when the correlation is not specified and accounting for uncertainty in the marginal parameter values. We present the web platform CompARE to characterize composite endpoints and to calculate the sample size just as we propose in this paper. We evaluate the performance of the proposal with a simulation study and illustrate it by means of a real case study using CompARE.

Introduction: Community-acquired pneumonia (CAP) is treated with penicillin in some northern European countries. Objectives: To evaluate whether high-dose penicillin V is as effective as high-dose amoxicillin for the treatment of non-severe CAR. Design: Multicentre, parallel, double-blind, controlled, randomized clinical trial. Setting: 31 primary care centers in Spain. Participants: Patients from 18 to 75 years of age with no significant associated comorbidity and with symptoms of lower respiratory tract infection and radiological confirmation of CAP were randomized to receive either penicillin V 1.6 million units, or amoxicillin 1000 mg three times per day for 10 days. Main measurements: The main outcome was clinical cure at 14 days, and the primary hypothesis was that penicillin V would be non-inferior to amoxicillin with regard to this outcome, with a margin of 15% for the difference in proportions. EudraCT register 2012-003511-63. Results: A total of 43 subjects (amoxicillin: 28; penicillin: 15) were randomized. Clinical cure was observed in 10 (90.9%) patients assigned to penicillin and in 25 (100%) patients assigned to amoxicillin with a difference of -9.1% (95% CI, -41.3% to 6.4%; p=.951) for non-inferiority. In the intention-to-treat analysis, amoxicillin was found to be 28.6% superior to penicillin (95% CI, 7.3-58.1%; p =.009 for superiority). The number of adverse events was similar in both groups. Conclusions: There was a trend favoring high-dose amoxicillin versus high-dose penicillin in adults with uncomplicated CAR The main limitation of this trial was the low statistical power due to the low number of patients included., The project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded in the 2011 call under the Health Strategy Action 2013-2016, within the National Research Program oriented to Societal Challenges, within the Technical, Scientific and Innovation Research National Plan 2008-2011, with reference PI11/02471, co-funded with European Union ERDF funds. It was also supported by the Spanish Clinical Research Network (SCReN), PT13/0002/0030. The funding bodies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.

Background: From 2005 to 2010, we conducted 2 randomized studies on a journal (Medicina Clínica), where we took manuscripts received for publication and randomly assigned them to either the standard editorial process or to additional processes. Both studies were based on the use of methodological reviewers and reporting guidelines (RG). Those interventions slightly improved the items reported on the Manuscript Quality Assessment Instrument (MQAI), which assesses the quality of the research report. However, masked evaluators were able to guess the allocated group in 62% (56/90) of the papers, thus presenting a risk of detection bias. In this post-hoc study, we analyse whether those interventions that were originally designed for improving the completeness of manuscript reporting may have had an effect on the number of citations, which is the measured outcome that we used. Methods: Masked to the intervention group, one of us used the Web of Science (WoS) to quantify the number of citations that the participating manuscripts received up December 2016. We calculated the mean citation ratio between intervention arms and then quantified the uncertainty of it by means of the Jackknife method, which avoids assumptions about the distribution shape. Results: Our study included 191 articles (99 and 92, respectively) from the two previous studies, which all together received 1336 citations. In both studies, the groups subjected to additional processes showed higher averages, standard deviations and annual rates. The intervention effect was similar in both studies, with a combined estimate of a 43% (95% CI: 3 to 98%) increase in the number of citations. Conclusions: We interpret that those effects are driven mainly by introducing into the editorial process a senior methodologist to find missing RG items. Those results are promising, but not definitive due to the exploratory nature of the study and some important caveats such as: the limitations of using the number of citations as a measure of scientific impact; and the fact that our study is based on a single journal. We invite journals to perform their own studies to ascertain whether or not scientific repercussion is increased by adhering to reporting guidelines and further involving statisticians in the editorial process. DGA, JAG and EC belong to MiRoR (Methods in Research on Research), which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 676207. EC and JC are partially supported by the grants MTM2015–64465-C2–1-R (MINECO/FEDER, UE) from the Secretaría de Estado de Investigación, Desarrollo e Innovación del Ministerio de Economía y Competitividad (Spain) and through grant 2017 SGR 622 from the Departament d’Economia i Coneixement de la Generalitat de Catalunya (Spain). The funding bodies had no role in the study design, data collection and analysis, results interpretation, or in writing the manuscript.

Prevention and nutrition are key for healthy lifestyles and are crucial in the new paradigm of patient-centered medicine, as healthy diet protects against chronic diseases and helps to delay diseases in general population. Making healhty diets is becoming essential and there are no well-formalized support mechanisms for suporting nutritionists when designing diets of specific patients. The project Diet4You proposes an intelligent decision support system oriented to the adaptive and dynamic preparation of personalized diets for specific individuals from general population, with or without comorbidities. Diet4You is a hybrid system interacting several AI and data science components in a complex way to compose diets by taking into account all the information available on the person, including their characteristics, health conditions, personal and cultural habits and eventual drugs intake and their genomic information. Authors are not aware of other menus recommenders with such a holistic view. The system is highly configurable. It relies on an information system that can formalize all contextual knowledge to improve optimization, and the menus are composed over a food database containing prepared dishes with known nutritional decomposition. This work has been partially supported by project Diet4You (TIN2014-60557-R), and the Consolidated Research Group Grant from AGAUR (Generalitat de Catalunya, cataalan government) IDEAI-UPC (AGAUR SGR2017-574).

© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The objective of this study is to analyze the effect of different imaging modalities in treatment decision making in proximal humeral fractures. After evaluation of 116 consecutive proximal humeral fractures, observers were asked to give treatment recommendation (conservative vs. surgery). If surgery was proposed, they were told to select surgery of choice. When 3D imaging was added, complexity of fractures significantly increased (p¿

OBJECTIVE: To evaluate the effect of effervescent paracetamol on office and ambulatory blood pressure (BP) compared with noneffervescent paracetamol in hypertensive patients. DESIGN: This was a multicenter open crossover randomized clinical trial. SETTING: Primary care centers in Catalonia and the Basque Country. PARTICIPANTS: Inclusion criteria were office BP 150/95¿mmHg or less and daytime ambulatory BP 140/90¿mmHg or less, stable pharmacologic or nonpharmacologic antihypertensive treatment, and concomitant chronic osteoarticular pain. INTERVENTIONS: Baseline randomized assignment to 3-week periods of effervescent paracetamol (1¿g three times a day) first and noneffervescent paracetamol later, or inversely, during a 7-week study period. At the start and end of each treatment period, 24-h ambulatory BP monitoring was performed. MAIN OUTCOME MEASURES: Differences in 24-h SBP between baseline and end of both treatment periods. The main analyses were performed according to the intention-to-treat principle. RESULTS: In intention-to-treat analysis, 46 patients were analyzed, 21 were treated with paracetamol effervescent and noneffervescent later, and 25 followed the opposite sequence. The difference in 24-h SBP between the two treatments was 3.99¿mmHg (95% confidence interval 1.35-6.63; P¿=¿0.004), higher in the effervescent paracetamol treatment period. Similarly, the per-protocol analysis showed a difference in 24-h SBP between the two groups of 5.04¿mmHg (95% confidence interval 1.80-8.28; P¿=¿0.004), higher in the effervescent paracetamol treatment period. Self-reported pain levels did not differ between groups and did not vary by treatment period. No serious adverse events were reported in either study arm. CONCLUSION: Effervescent paracetamol tablets are responsible for a significant daytime and overall increase in ambulatory 24-h SBP. TRIAL REGISTRATION: NCT: 02514538 EudraCT: 2010-023485-53.

A method for the measurement of the size diversity based on the classical Shannon–Wiener expression was proposed as a proxy of the shape of the size distribution. The summatory of probabilities of a discrete variable (such as species relative abundances) in the original Shannon–Wiener expression was substituted by an integral of the probability density function of a continuous variable (such as body size). Here, we propose an update of this method by including the measurement of the size e-evenness, just dividing the exponential of the size diversity by its possible maximum for a given size range. Assuming a domain of the size range of (0,∞), for a given logarithmic mean ( math formula) and a logarithmic standard deviation math formula, the distribution with the highest diversity is the Log-Normal. The size e-evenness ranges between 0 and 1 because of the division by the maximum exponential diversity. Size e-evenness is useful to discriminate whether variations in size diversity are due to changes in the shape of the size distribution or caused by differences in size dispersion This work was supported by grants from the Generalitat de Catalunya (ref. 2014 SGR 484) and from the Ministerio de Ciencia e Innovaci on, Programa de Investigaci on Fundamental (CGL2011-23907)

The most suitable method for estimation of size diversity is investigated. Size diversity is computed on the basis of the Shannon diversity expression adapted for continuous variables, such as size. It takes the form of an integral involving the probability density function (pdf) of the size of the individuals. Different approaches for the estimation of pdf are compared: parametric methods, assuming that data come from a determinate family of pdfs, and nonparametric methods, where pdf is estimated using some kind of local evaluation. Exponential, generalized Pareto, normal, and log-normal distributions have been used to generate simulated samples using estimated parameters from real samples. Nonparametric methods include discrete computation of data histograms based on size intervals and continuous kernel estimation of pdf. Kernel approach gives accurate estimation of size diversity, whilst parametric methods are only useful when the reference distribution have similar shape to the real one. Special attention is given for data standardization. The division of data by the sample geometric mean is proposed as the most suitable standardization method, which shows additional advantages: the same size diversity value is obtained when using original size or log-transformed data, and size measurements with different dimensionality (longitudes, areas, volumes or biomasses) may be immediately compared with the simple addition of ln k where kis the dimensionality (1, 2, or 3, respectively). Thus, the kernel estimation, after data standardization by division of sample geometric mean, arises as the most reliable and generalizable method of size diversity evaluation This work was supported by the grants from the Ministerio de Ciencia y Tecnología of the Spanish Government, Programa Nacional de Biodiversidad, Ciencias de la Tierra y Cambio Global (ref. CGL2004- 05433/BOS) and the project MEASURE (MTM2006-03040/)

School is an essential context that contributes to children's communicative competence. Decision Support Systems (DSS) are tools that can help teachers to assess their own practice and to make decisions to improve it (Gregg, 2009). Our aim is the construction, implementation and validation of a digital Decision Support System (EVALOE-DSS), conceived as a teacher empowerment tool for self-assessment and decision making about their teaching practice concerning students' oral skills. Participants are 4 teachers and their groups of students of 4 diverse schools. The research procedure includes: 1) design and development of a DSS for different type of schools; 2) researcher assessment of one classroom observation using EVALOE-DSS; 3) weekly teacher self-assessment of classroom observation and decision making using EVALOE-DSS; 4) analysis of self-assessments, decision making and changes in teaching practices; 5) teacher-researcher assessment of the helpfulness of DSS and introduction of changes. EVALOE-DSS is a multimedia tool consisting of 30 items that assess 5 dimensions: teaching management, instructional design, communicative functions and teacher strategies, students' communicative functions, and students' management. It includes, also, a brief description of each item and a variety of resources to help teachers to make decisions and introduce the actions in their teaching practice.

It is currently well-known that diet plays an important role in the promotion of healthy lifestyle and the prevention of chronic diseases. The Diet4You project is conceived to support the creation of an intelligent decision support system that provides personalized menus fitting a nutritional plan and taking into account the characteristics, needs and preferences of the person. The system involves a background food database, recording a collection of foods and prepared dishes with their standard portions as well as their nutritional decomposition in different food families. This DB is used to search the best combination of dishes approaching the total intake of different nutrients specified in the prescribed nutritional plan. The available background databases, specify the quantities of standard portions of several foods based on different measurement units which are not standardized, and it happens that the weight specified by one cup of melon is different from that of one cup of berries, among others. This arises the need of applying variable conversion factors to the dish description, before assessing whereas the total quantities of a certain menu fit well to the prescription. In this paper, a knowledge based approach is presented to the automatically management. An annotated reference food ontology is built on the basis of additional documentation. However the granularity of the information provided is heterogeneous and non exhaustive. The ontology-based missing values imputation is presented to overcome this limitations.

Prevention and nutrition are key issues to guarantee a healthy lifestyle and are in the kernel of the new paradigm of patient-centered medicine. A healthy diet protects against risk factors of a large number of chronic diseases and contribute to delay the disease appearance in the general population. More and more, making right diets is becoming essential from the health point of view. However, nutritionists design diets on the specific needs of each patient, using their accumulated experience and there are no well-formalized support mechanisms for such activity. The project Diet4You proposes the creation of an intelligent decision support system oriented to the adaptive and dynamic preparation of personalized diets for the specific individuals of general population, having or not, one or more diseases, by taking into account all the information available on the person. The diets will be built by considering the characteristics of the person, their health conditions, their habits and eventual drugs intake and their genetic information, which until now were not taken into account. The Diet4You tool is a hybrid system interacting several components in a complex way. One of those components is a subsystem able to compose complete menus, for a certain period of time, based on the nutritional prescriptions given by the nutritionists, in terms of balance of different families of nutrients. This component is based on a a Case-Based Reasoning (CBR) engine that adaptively creates menus with breakfast, lunch and dinner on the bases of real food databases containing dishes with their nutritional composition. In this work, this component of the whole system is presented, described and tested.

This is the peer reviewed version of the following article: “Xampeny, R., Grima, P., Tort-Martorell, X. (2017) Estimating missing values from negligible interactions in factorial designs, (2017)1-13.” which has been published in final form at [doi: 10.1002/qre.2172]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." When a factorial design has some missing value but there are contrasts (at least as many as there are missing values) corresponding to interactions that can be considered negligible, equating the expressions of those contrasts to 0 allows the missing values to be deduced. This procedure allows estimating values that correspond to runs that have gone wrong or could not be performed as planned, as well as to reduce the experimental plan while saving some runs whose results can be estimated a posteriori. The problem is that the estimated values have a variance that is higher than that of the experimental runs, and the variance depends on which are the missing values and the contrasts used to make the estimate. This article performs a thorough analysis of the variances obtained by estimating up to 2 missing values in 8-run designs and up to 5 in 16-run designs, with tables included which provide the missing values and contrasts that should be used to obtain estimates that have minimal variability.

When applied to binary data, most classification algorithms behave well provided the dataset is balanced. However, when one single class includes the majority of cases, a good predictive performance for the minority class is not easy to achieve. We examine the strengths and weaknesses of three tree-based models when dealing with imbalanced data.We also explore sampling and cost sensitive methods as strategies for improving machine learning algorithms. An application to a large dataset of breath alcohol content tests performed in Catalonia (Spain) to detect drunk drivers is shown. The Random Forest method proved to be the model of choice if a high performance is required, while down- sampling strategies resulted in a significant reduction in computing time. When predicting alcohol impairment, the area of control (built-up or not), hour of day and drivers age were the most relevant variables for classification.

This is the peer reviewed version of the following article: Chacon, A., Mateu Jimenez, M., Langohr, K., Fermoselle, C., García Arumí, E., Andreu, A., Yelamos, J., Barreiro, E. Role of PARP activity in lung cancer-induced cachexia: effects on muscle oxidative stress, proteolysis, anabolic markers, and phenotype. "Journal of cellular physiology", Desembre 2017, vol. 232, núm. 12, p. 3744-3761, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/jcp.25851/pdf. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Strategies to treat cachexia are still at its infancy. Enhanced muscle protein breakdown and ubiquitin-proteasome system are common features of cachexia associated with chronic conditions including lung cancer (LC). Poly(ADP-ribose) polymerases (PARP), which play a major role in chromatin structure regulation, also underlie maintenance of muscle metabolism and body composition. We hypothesized that protein catabolism, proteolytic markers, muscle fiber phenotype, and muscle anabolism may improve in respiratory and limb muscles of LC-cachectic Parp-1-deficient (Parp-1-/-) and Parp-2-/- mice. In diaphragm and gastrocnemius of LC (LP07 adenocarcinoma) bearing mice (wild type, Parp-1-/-, and Parp-2-/-), PARP activity (ADP-ribose polymers, pADPr), redox balance, muscle fiber phenotype, apoptotic nuclei, tyrosine release, protein ubiquitination, muscle-specific E3 ligases, NF-¿B signaling pathway, markers of muscle anabolism (Akt, mTOR, p70S6K, and mitochondrial DNA) were evaluated along with body and muscle weights, and limb muscle force. Compared to wild type cachectic animals, in both respiratory and limb muscles of Parp-1-/- and Parp-2-/- cachectic mice: cancer induced-muscle wasting characterized by increased PARP activity, protein oxidation, tyrosine release, and ubiquitin-proteasome system (total protein ubiquitination, atrogin-1, and 20S proteasome C8 subunit) were blunted, the reduction in contractile myosin and atrophy of the fibers was attenuated, while no effects were seen in other structural features (inflammatory cells, internal or apoptotic nuclei), and markers of muscle anabolism partly improved. Activation of either PARP-1 or -2 is likely to play a role in muscle protein catabolism via oxidative stress, NF-¿B signaling, and enhanced proteasomal degradation in cancer-induced cachexia. Therapeutic potential of PARP activity inhibition deserves attention.

Report de Recerca aprovat per la Comissió de doctorat i de recerca del Departament d'EIO Conclusions from randomized clinical trials (RCT) rely primarily on the primary endpoint (PE) chosen at the design stage of the study. There should generally be only one PE which should be able to provide the most clinically relevant and scientific evidence regarding the potential eficacy of the new treatment. Therefore, it is of utmost importance to select it appropriately. Composite endpoints, consisting of the union of several endpoints, are often used as PE in RCT. Gomez and Lagakos (2013) develop a statistical methodology to evaluate the convenience of using a CE as opposed to one of its components. Their strategy is based on the asymptotic relative eficiency (ARE), relating the efi is based on the asymptotic relative eficiency (ARE), relating the eciency of using the logrank test based on the CE versus the eficiency based on one of its components. This paper introduces the freeware online platform CompARE that facilitates the study of the performance of different candidate endpoints which could be used as PE at the design stage of a trial. CompARE, through an intuitive interface, implements the novel ARE method.

One of the key questions in the use of mixture models concerns the choice of the number of components most suitable for a given data set. In this paper we investigate answers to this problem in the context of likelihood-based clustering of the rows of a matrix of ordinal data modelled by the ordered stereotype model. Two methodologies for selecting the best model are demonstrated and compared. The first approach fits a separate model to the data for each possible number of clusters, and then uses an information criterion to select the best model. The second approach uses a Bayesian construction in which the parameters and the number of clusters are estimated simultaneously from their joint posterior distribution. Simulation studies are presented which include a variety of scenarios in order to test the reliability of both approaches. Finally, the results of the application of model selection to two real data sets are shown. The authors are sincerely grateful to Prof. Shirley Pledger for making many valuablesuggestions and stimulating discussions about this work. We are grateful to two referees and an asso-ciate editor for helpful comments and references. Daniel Fernández acknowledges Victoria University ofWellington for providing financial support through Victoria Doctoral Scholarship program. This researchwas supported by the Marsden Fund Council from Government funding, administered by the Royal Societyof New Zealand, Grant E2317-2922

Down syndrome (DS) is an aneuploidy syndrome that is caused by trisomy for human chromosome 21 resulting in a characteristic cognitive and behavioral phenotype, which includes executive functioning and adaptive behavior difficulties possibly due to prefrontal cortex (PFC) deficits. DS also present a high risk for early onset of Alzheimer Disease-like dementia. The dopamine (DA) system plays a neuromodulatory role in the activity of the PFC. Several studies have implicated trait differences in DA signaling on executive functioning based on genetic polymorphisms in the genes encoding for the catechol-O-methyltransferase (COMTVal158Met) and the dopamine transporter (VNTR-DAT1). Since it is known that the phenotypic consequences of genetic variants are modulated by the genetic background in which they occur, we here explore whether these polymorphisms variants interact with the trisomic genetic background to influence gene expression, and how this in turn mediates DS phenotype variability regarding PFC cognition. We genotyped 69 young adults of both genders with DS, and found that VNTR-DAT1 was in Hardy-Weinberg equilibrium but COMTVal158Met had a reduced frequency of Met allele homozygotes. In our population, genotypes conferring higher DA availability, such as Met allele carriers and VNTR-DAT1 10-repeat allele homozygotes, resulted in improved performance in executive function tasks that require mental flexibility. Met allele carriers showed worse adaptive social skills and self-direction, and increased scores in the social subscale of the Dementia Questionnaire for People with Intellectual Disabilities than Val allele homozygotes. The VNTR-DAT1 was not involved in adaptive behavior or early dementia symptoms. Our results suggest that genetic variants of COMTVal158Met and VNTR-DAT1 may contribute to PFC-dependent cognition, while only COMTVal158Met is involved in behavioral phenotypes of DS, similar to euploid population. This work was supported by grants, donations and agreements from Fondation Jérôme Lejeune (Paris, France), Instituto de Salud Carlos III FEDER, (PI11/00744), MINECO (SAF2010-19434 and SAF2013-49129-C2-1-R), EU (Era Net Neuron PCIN-2013-060), DIUE de la Generalitat de Catalunya (SGR 2009/1450, 2014SGR 680, 2014 SGR 464, SGR 2009/718, and SGR 2014/1125). LdH is recipient of a pre-doctoral fellowship from ISCIII [FI12/00319]. CRG is Center of Excellence Severo Ochoa SEV-2012-0208.

To estimate treatment effects, trials are initiated by randomising patients to the interventions under study and finish by comparing patient evolution. In order to improve the trial report, the CONSORT statement provides authors and peer reviewers with a guide of the essential items that would allow research replication. Additionally, WebCONSORT aims to facilitate author reporting by providing the items from the different CONSORT extensions that are relevant to the trial being reported. WebCONSORT has been estimated to improve the proportion of reported items by 0.04 (95% CI, -0.02 to 0.10), interpreted as "no important difference", in accordance with the scheduled desired scenario of a 0.15 effect size improvement. However, in a non-scheduled analysis, it was found that, despite clear instructions, around a third of manuscripts selected for trials by the editorial staff were not actually randomised trials. We argue that surprises benefit science, and that further research should be conducted in order to improve the performance of editorial staff.Please see related research: http://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-016-0736-x.

The endocannabinoid (eCB) system can promote food intake by increasing odor detection in mice. The eCB system is over-active in human obesity. Our aim is to measure circulating eCB concentrations and olfactory capacity in a human sample that includes people with obesity and explore the possible interaction between olfaction, obesity and the eCB system. The study sample was made up of 161 females with five groups of body mass index sub-categories ranging from under-weight to morbidly obese. We assessed olfactory capacity with the “Sniffin´Sticks” test, which measures olfactory threshold-discrimination-identification (TDI) capacity. We measured plasma concentrations of the eCBs 2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine or anandamide (AEA), and several eCB-related compounds, 2-acylglycerols and N-acylethanolamines. 2-AG and other 2-acylglycerols fasting plasma circulating plasma concentrations were higher in obese and morbidly obese subjects. AEA and other N-acylethanolamine circulating concentrations were lower in under-weight subjects. Olfactory TDI scores were lower in obese and morbidly obese subjects. Lower TDI scores were independently associated with higher 2-AG fasting plasma circulating concentrations, higher %body fat, and higher body mass index, after controlling for age, smoking, menstruation, and use of contraceptives. Our results show that obese subjects have a lower olfactory capacity than non-obese ones and that elevated fasting plasma circulating 2-AG concentrations in obesity are linked to a lower olfactory capacity. In agreement with previous studies we show that eCBs AEA and 2-AG, and their respective congeners have a distinct profile in relation to body mass index. The present report is the first study in humans in which olfactory capacity and circulating eCB concentrations have been measured in the same subjects. The work was supported by the following: Instituto Salud Carlos III, http://www.isciii.es, FIS PI11/210, FIS14/290; Centro de Investigación Biomédica en Red. Fisiopatología de la Obesidad y Nutrición (CIBEROB), http://www.ciberobn.es; and Agència de Gestió d´Ajuts Universitaris i de Recerca (AGAUR), www.gencat.cat/agaur/, 2014 SGR 1672. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. SI

El reconocimiento del Derecho Humano al Agua y Saneamiento (DHAS) a través de las resoluciones de la Asamblea General y del Consejo de Derechos Humanos de Naciones Unidas en 2010 representa un gran avance en el sector del Agua, Saneamiento e Higiene ya que otorga a todos los seres humanos el derecho a la provisión de unos estándares mínimos en relación a estos servicios. Tres hechos sientan las bases que motivan la investigación: i) las instituciones internacionales tienen la potestad de supervisar el cumplimiento del Derecho Humano por parte de los Estados pero las herramientas necesarias para esta tarea aún han sido definidas, ii) este hito influye en la gobernanza y la toma de decisiones a diferentes escalas y iii) la medición del acceso al agua en la nueva era de los Objetivos de Desarrollo Sostenible exige tomar en cuenta el marco de los derechos humanos. Esto supone por un lado que su contenido debe ser considerado a la hora de conceptualizar el nivel de servicio a través de indicadores adecuados y por el otro, exige dar seguimiento a la reducción de las desigualdades. Este trabajo contribuye de manera significativa a cada uno de los tres retos presentados. En primer lugar, se identifican las plataformas JMP WHO/UNICEF y GLAAS como mecanismos de especial interés para aquellos que tienen el mandato de vigilar el cumplimiento del derecho, lo cual contribuye de alguna forma al desafío planteado. En este sentido, la información que ofrecen ha sido analizada tomando en consideración el marco de los derechos humanos. Se ha construido una matriz para identificar específicamente de qué manera estos datos se podrían combinar para la vigilancia del DHAS en un sentido amplio. La nueva propuesta de JMP contribuye considerablemente con indicadores de resultados para medir el disfrute de los titulares de derechos y GLAAS añade indicadores estructurales y de proceso para medir la conducta de los titulares de deberes. Sin embargo, todavía quedarían vacíos de información y en este sentido la tesis contribuye con algunas ideas acerca de la forma en que se podrían abordar estas deficiencias. En segundo lugar, se propone una metodología para la medición y el seguimiento del acceso al agua en zonas rurales haciendo uso del marco conceptual del derecho humano al agua. Los diferentes criterios del derecho han sido incluidos en encuestas y entrevistas estructuradas que han sido implementadas en hogares y comités de agua rurales, respectivamente. Se analizan y discuten las ventajas y los retos relacionados con la utilización de este marco. Además, el enfoque proporciona elementos con potencial para la definición de políticas que han sido discutidos. Por último, esta investigación desarrolla y testea una metodología para medir disparidades a nivel intra-comunitario basadas en los criterios normativos del derecho humano al agua. Para ello se lleva a cabo un muestreo estratificado donde se definen dos tipologías de casas; aquellas que consumen agua gestionada por los comités de ahia potable y las que se abastece de agua por sus propios medios. Este enfoque implica tomar en consideración poblaciones muy reducidas y por lo tanto se debe prestar especial atención a los tamaños de muestra y las incertidumbres asociadas a las estimaciones. La metodología propuesta es práctica a la hora de localizar y caracterizar con precisión la situación de sectores minoritarios en el seno de las comunidades rurales y permite ir más allá de lo que ofrecen los estimadores de tendencia central. Esto implica mayores costes para la recolección de datos en terreno que los enfoques tradicionales. Éstos son asumibles dada la relevancia de la propuesta desde una perspectiva de derechos humanos, la cual exige desarrollar instrumentos adecuados para la definición de políticas con enfoque de equidad en el ámbito local. Además la investigación destaca cómo podrían utilizarse los resultados para los procesos de toma de decisiones, The recognition of the Human right to Water and Sanitation (HRWS) through UN General Assembly and Human rights Council Resolutions in 2010 represents great progress in the Water Sanitation and Hygiene (WASH) sector as it entitles everyone to the provision of minimum standards of these essential services. Much effort has gone into the recognition of these emerging human rights so it is essential now to shift discussion from legal and conceptual framework to practice. In this sense, three facts give the motivation to this thesis: First, international institutions have the authority to monitor States compliance with the Human right to Water and Sanitation (HRWS) but the necessary tools for this task are not ready yet. Secondly, this milestone influences governance and decision making processes at different scales. And finally, measuring access to water in the Sustainable Development Goals era involves taking into account the human rights framework. Therefore, its content should be considered to conceptualize the level of service through adequate indicators and to follow-up inequities reduction at global, national and local level. Accordingly, this work contributes significantly to each of one the three challenges presented. First, human development sector has a wider experience on using information about progress which provides a perfect opportunity to develop this further. WHO/UNICEF Joint Monitoring Programme (JMP) and UN Water GLAAS datasets could be used for those with a mandate to monitoring the right, contributing to this challenge. Consequently, the information they offer has been analysed through a human rights lens. A matrix has been constructed to specifically identify in which extend their datasets could be combined to monitoring HRWS in a broad sense. JMP-led post-2015 proposal considerably contributes with outcome indicators to measure right holders' enjoyment of the right and GLAAS adds structural and process ones to measure duty bearers' conduct. However, there are still some critical gaps if both UN Water platforms will be used to report progress on HRWS. The thesis forwards some ideas concerning the way these shortcomings could be addressed.Second, this work proposes a methodology for monitoring access to water in rural areas using the framework of this human right. The practicality of the approach is demonstrated by a case study carried out in Nicaragua. Different criteria of the right to water were included in surveys and structured interviews that were conducted in rural households and water committees, respectively. Discussion analyses advantages and challenges of using this framework. Finally, the approach provides elements for policy making that can be used by different stakeholders from development and human rights sectors. Finally, this research develops and tests a methodology to measure intra-community disparities based on human right to water normative criteria through a stratified sampling, splitting households served by community based organizations and those self-provided. This approach implies considering much reduced populations, thus special care needs to be taken with sample sizes and uncertainty of estimators. The proposed methodology is practical to locate and accurately characterize minority sectors within rural communities and allows moving beyond central-tendency estimators. It implies higher costs for field data collection than traditional approaches, but this can be assumed given the relevance of the approach from a human rights perspective, which calls for adequate tools for equity-oriented policy making at local level. The research point out how results might be used to shape decision-making processes

Sampling bias contained in data of biological surveys is very common. Bias is clearly a function of roads, cities, rivers, or other physical features that determines accessibility of collectors, and many data sets of species are presence-only. We set out to estimate spatial sampling bias in a region, based on presence-only data, by explicitly incorporating information on these accessibility factors, and by considering a target group of species that may share a common search pattern. In order to indirectly estimate the number of individuals, we also resort to the concept of species richness. A probabilistic (multinomial) model is proposed, enabling standard likelihood inference procedures to be implemented. Simulation scenarios for exploration of the model and experimentation with the estimation procedure are included. Illustrative examples over a region of Mexico with mammals and butterflies are also reported with insightful results. Our model is able to estimate the sampling bias in a region and enhance the inferences regarding presence-only data.

The final publication is available at Springer via http://dx.doi.org/10.1007/s10439-014-0995-7 The objective of this work is to develop a remodeling model for biological matter coupling two different processes in a 3D framework: reorientation of the preferential direction of a given fibered structure and reorientation of the fibrils or filaments that make up such a structure. This work uses the microsphere-based approach to take into account the micro mechanics involved in biological fibered structures regarding both their passive behavior and the reorientation of their micro constituents. Moreover, the macro behavior of the material as a whole is obtained by means of homogenizing the underlying micro response. We associate the orientation space of the integration directions to the physical space of micro-fibrils. To approximate the directional distribution of the fibrils within each fiber bundle, a Bingham probability orientation density function is introduced into the Helmholtz energy function. With all these assumptions, the problem is studied from an energetic point of view, describing the dissipation inherent to remodeling processes, and the evolution equations for both reorientations (change in preferential direction of the network and change in shape of the fibril distribution) re obtained. The model is included in a finite element code which allows computing different geometries and boundary value problems. This results in a complete methodology for characterizing the reorientation evolution of different fibered biological structures, such as cells. Our results show remodeling of fibered structures in two different scales, presenting a qualitatively good agreement with experimental findings in cell mechanics. Hierarchical structures align in the direction of the maximum principal direction of the considered stimulus and narrow in the perpendicular direction. The dissipation rates follows predictable trends although there are no experimental findings to date for comparison. The incorporation of metabolic processes and an insight into cell-oriented mechano-sensing processes can help to overcome the limitations involved.

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e.,unrelated and related values), the type of variable (i.e., binary and continuous), and different Binomial distribution values and symmetry (i.e., symmetry and positive asymmetry). Iteratively reweighted least squares was used as the estimate method to fit the models in both the classical and Bayesian estimations. A weakly informative default distribution was chosen as the prior distribution for Bayesian estimation. The simulation results demonstrate that Bayesian estimations provide more stable distributions but are notable to solve problems generated by asymmetric distributions based on small samples. Additional research using different kinds of priors that is addressed at solving problems caused by asymmetry is needed.

Background: Roma ethnicity is greatly affected by tuberculosis (TB), AIDS, injecting drugs use (IDU) and imprisonment. Methods: We assessed the incidence of several health problems by means of a retrospective cohort study performed in Camp de la Bota, Barcelona (Spain). The 380 individuals included in the 1985 TB outbreak investigation were followed-up until 31 December 2008. One hundred ninety-two subjects (50.5%) were men and 188 (49.5%) women. Information sources included questionnaires taken at the time of this outbreak, a population census and other registries from Barcelona and Catalonia. Cox proportional hazards mixed models were employed in the multivariate survival analysis. Results: By the end of the follow-up, the survival rate was 79.4%; 50 persons (13.1%) had deceased and 28 (7.3%) had emigrated. The incidence of AIDS was 104 cases per 100 000 person-years of follow-up (pyf), IDU was 240 cases pyf, imprisonment was 642 cases pyf and that of TB was 91 cases pyf. Male survival was lower [hazard ratio (HR) 4.22], when the effect of family was taken into account, than when it was not taken into account (HR 3.67). Conclusions: High incidences of AIDS, TB, IDU, imprisonment and poor survival rates have been observed among Roma. Family was found to be an important factor influencing the survival rates: when not considered, the risk of death among men was underestimated.

The main objective of this paper is to detect the existence of financial contagion between the North American and European markets during the recent crises. To accomplish this, the relationships between the US and the Euro zone stock markets are considered, taking the daily equity prices of the Standard and Poor’s 500 as representative of the United States market and for the European market, the five most representative indexes. Time Series Factor Analysis (TSFA) procedure has allowed concentrating the information of the European indexes into a unique factor, which captures the underlying structure of the European return series. The relationship between the European factor and the US stock return series has been analyzed by means of the dynamic conditional correlation model (DCC). Once the DCC is estimated, the contagion between both markets is analyzed. Finally, in order to explain the sudden changes in dynamic US-EU correlation, a Markov switching model is fitted, using as input variables the macroeconomic ones associated with the monetary policies of the US as well as those related to uncertainty in the markets. The results show that there was contagion between the United States and European markets in the Subprime and Global Financial crises. The two-regime Markov switching model has helped to explain the variability of the pair-wise correlation. The first regime contains mostly the financially stable periods, and the dynamic correlations in this regime are explained by macroeconomic variables and other related with monetary policies in Europe and US. The second regime is explained mainly by the Federal Funds rate and the evolution of the Euro/US Exchange rate.

In the present study, we propose a general class of estimators for population mean of the study variable in the presence of non-response using auxiliary information under double sampling. The expression of mean squared error (MSE) of the proposed class of estimators is derived under double (two-stage) sampling. Some estimators are also derived from the proposed class by allocating the suitable values of constants used. Comparisons of the proposed strategy with the usual unbiased estimator and other estimators are carried out. The results obtained are illustrated numerically using an empirical sample considered in the literature.

Inverse sampling and formal sequential designs may prove useful in reducing the sample size in studies where a small population proportion p is compared with a hypothesized reference proportion p0. These methods are applied to the design of a cytogenetic study about chromosomal abnormalities in men with a daughter affected by Turner's syndrome. First it is shown how the calculated sample size for a classical design depends on the parameterization used. Later this sample size is compared with the required sample size in an inverse sampling design and a triangular sequential design using four different parameterizations (absolute differences, log-odds ratio, angular transform and Sprott's transform). The expected savings in sample size, when the alternative hypothesis is true, are 20% of the fixed sample size for the inverse sampling design and 40% for the triangular sequential design.

This paper presents an empirical investigation of the performance of five strategies for estimating the finite population mean using parameters such as mean or variance or both of an auxiliary variable. The criteria used for the choices of these strategies are bias, efficiency and approach to normality (asymmetry).

Linear regression models with random effects are applied to estimating the population means of indirectly measured variables in small areas. The proposed method, a hybrid with design- and model-based elements, takes account of the area-level variation and of the uncertainty about the fitted regression model and the area-level population means of the covariates. The method is illustrated on data from the U.S. Department of Labor Literacy Surveys and is informally validated on two states, Mississippi and Oregon, for which statewide surveys have been conducted.

In this paper, we compare six almost unbiased ratio estimators with respect to bias and efficiency for (i) finite populations, and (ii) infinite populations in which the joint distribution of the characters under study is bivariate normal.