Factors of the epidemiological triad that influence the persistence of human papilloma virus infection in women with systemic lupus erythematosus

We studied the epidemiologic triad-related factors influencing human papilloma virus (HPV) persistence in Mexican women with systemic lupus erythematosus (SLE). Patients aged ?18 years with SLE (American College of Rheumatology criteria), with and without HPV persistence, were selected. Groups were analyzed by (1) host: clinical disease characteristics; (2) agent: (I) infectious (prevalence, incidence, HPV genotype and co-infections (?2 HPV genotypes or mycoplasmas)), (II) chemical (contraceptives and immunosuppressive drugs) and (III) physical (vitamin D deficiency) and (3) environment. A total of 121 SLE patients were selected over a two-year period. (1) Host: mean age 45.8 years and disease duration 12.7 years. (2) Agent: (I) infectious. HPV infection prevalence in the second sample was 26.4%, high-risk HPV genotypes 21.5% and co-infections 7.4%. HPV infection incidence was 13.2%, persistence 13.2% and clearance 15.7%. (II) Chemical: use of oral hormonal contraceptives 5% and immunosuppressive treatment 97.5%. (III) Physical: Vitamin D levels were similar in both groups. (3) Environment: (I) natural. A total of 60.6% of patients were residents of Puebla City. (II) Social: The mean education level was 10.9. Poverty levels were: III degree 52.4%, IV degree 28% and II degree 17%. (III) Cultural behavioral: Onset of sexual life was 20.5 years, 10% had ?3 sexual partners and 51.2% were postmenopausal. In conclusion, no factor of the epidemiologic triad was associated with HPV infection prevalence. © The Author(s) 2018

The KiVa antibullying program in primary schools in Chile, with and without the digital game component: study protocol for a randomized controlled trial.

BACKGROUND: Bullying is a major problem worldwide and Chile is no exception. Bullying is defined as a systematic aggressive behavior against a victim who cannot defend him or herself. Victims suffer social isolation and psychological maladjustment, while bullies have a higher risk for conduct problems and substance use disorders. These problems appear to last over time. The KiVa antibullying program has been evaluated in Finland and other European countries, showing preventive effects on victimization and self-reported bullying. The aims of this study are (1) to develop a culturally appropriate version of the KiVa material and (2) to test the effectiveness of the KiVa program, with and without the online game, on reducing experiences of victimization and bullying behavior among vulnerable primary schools in Santiago (Chile), using a cluster randomized controlled trial (RCT) design with three arms: (1) full KiVa program group, (2) partial KiVa (without online game) program group and (3) control group. METHODS AND DESIGN: This is a three-arm, single-blind, cluster randomized controlled trial (RCT) with a target enrolment of 1495 4th and 5th graders attending 13 vulnerable schools per arm. Students in the full and partial KiVa groups will receive universal actions: ten 2-h lessons delivered by trained teachers during 1 year; they will be exposed to posters encouraging them to support victims and behave constructively when witnessing bullying; and a person designated by the school authorities will be present in all school breaks and lunchtimes using a visible KiVa vest to remind everybody that they are in a KiVa school. KiVa schools also will have indicated actions, which consist of a set of discussion groups with the victims and with the bullies, with proper follow-up. Only full KiVa schools will also receive an online game which has the aim to raise awareness of the role of the group in bullying, increase empathy and promote strategies to support victimized peers. Self-reported victimization, bullying others and peer-reported bullying actions, psychological and academic functioning, and sense of school membership will be measured at baseline and 12 months after randomization. DISCUSSION: This is the first cluster RCT of the KiVa antibullying program in Latin America. TRIAL REGISTRATION: ClinicalTrials.gov, Identifier: NCT02898324 . Registered on 8 September 2016

Mendelian Randomization Analysis of the Relationship Between Native American Ancestry and Gallbladder Cancer Risk

Background A strong association between the proportion of Native American ancestry and the risk of gallbladder cancer (GBC) has been reported in observational studies. Chileans show the highest incidence of GBC worldwide, and the Mapuche are the largest Native American people in Chile. We set out to investigate the causal association between Native American Mapuche ancestry and GBC risk, and the possible mediating effects of gallstone disease and body mass index (BMI) on this association. Methods Markers of Mapuche ancestry were selected based on the informativeness for assignment measure and then used as instrumental variables in two-sample mendelian randomization (MR) analyses and complementary sensitivity analyses. Result We found evidence of a causal effect of Mapuche ancestry on GBC risk (inverse variance-weighted (IVW) risk increase of 0.8% for every 1% increase in Mapuche ancestry proportion, 95% CI 0.4% to 1.2%, p = 6.6×10-5). Mapuche ancestry was also causally linked to gallstone disease (IVW risk increase of 3.6% per 1% increase in Mapuche proportion, 95% CI 3.1% to 4.0%, p = 1.0×10-59), suggesting a mediating effect of gallstones in the relationship between Mapuche ancestry and GBC. In contrast, the proportion of Mapuche ancestry showed a negative causal effect on BMI (IVW estimate -0.006 kg/m2 per 1% increase in Mapuche proportion, 95% CI -0.009 to -0.003, p = 4.4×10-5). Conclusions The results presented here may have significant implications for GBC prevention and are important for future admixture mapping studies. Given that the association between Mapuche ancestry and GBC risk previously noted in observational studies appears to be causal, primary and secondary prevention strategies that take into account the individual proportion of Mapuche ancestry could be particularly efficient

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Enhanced discriminative models with tree kernels and unsupervised training for entity detection

This work explores two approaches to improve the discriminative models that are commonly used nowadays for entity detection: tree-kernels and unsupervised training. Feature-rich classifiers have been widely adopted by the Natural Language processing (NLP) community because of their powerful modeling capacity and their support for correlated features, which allow separating the expert task of designing features from the core learning method. The first proposed approach consists in leveraging the fast and efficient linear models with unsupervised training, thanks to a recently proposed approximation of the classifier risk, an appealing method that provably converges towards the minimum risk without any labeled corpus. In the second proposed approach, tree kernels are used with support vector machines to exploit dependency structures for entity detection, which relieve designers from the burden of carefully design rich syntactic features manually. We study both approaches on the same task and corpus and show that they offer interesting alternatives to supervised learning for entity recognition

Bayesian inverse reinforcement learning for modeling conversational agents in a virtual environment

This work proposes a Bayesian approach to learn the behavior of human characters that give advice and help users to complete tasks in a situated environment. We apply Bayesian Inverse Reinforcement Learning (BIRL) to infer this behavior in the context of a serious game, given evidence in the form of stored dialogues provided by experts who play the role of several conversational agents in the game. We show that the proposed approach converges relatively quickly and that it outperforms two baseline systems, including a dialogue manager trained to provide "locally" optimal decisions. © 2014 Springer-Verlag Berlin Heidelberg

Weakly supervised discriminative training of linear models for natural language processing

This work explores weakly supervised training of discriminative linear classifiers. Such features-rich classifiers have been widely adopted by the Natural Language processing (NLP) community because of their powerful modeling capacity and their support for correlated features, which allow separating the expert task of designing features from the core learning method. However, unsupervised training of discriminative models is more challenging than with generative models. We adapt a recently proposed approximation of the classifier risk and derive a closed-form solution that greatly speeds-up its convergence time. This method is appealing because it provably converges towards the minimum risk without any labeled corpus, thanks to only two reasonable assumptions about the rank of class marginal and Gaussianity of class-conditional linear scores. We also show that the method is a viable, interesting alternative to achieve weakly supervised training of linear classifiers in two NLP tasks: predicate and entity recognition

An end-to-end evaluation of two situated dialog systems

We present and evaluate two state-of-the art dialogue systems developed to support dialog with French speaking virtual characters in the context of a serious game: one hybrid statistical/symbolic and one purely statistical. We conducted a quantitative evaluation where we compare the accuracy of the interpreter and of the dialog manager used by each system; a user based evaluation based on 22 subjects using both the statistical and the hybrid system; and a corpus based evaluation where we examine such criteria as dialog coherence, dialog success, interpretation and generation errors in the corpus of Human-System interactions collected during the user-based evaluation. We show that although the statistical approach is slightly more robust, the hybrid strategy seems to be better at guiding the player through the game