Extended truncated Tweedie-Poisson model

It has been argued that by truncating the sample space of the negative binomial and of the inverse Gaussian-Poisson mixture models at zero, one is allowed to extend the parameter space of the model. Here that is proved to be the case for the more general three parameter Tweedie-Poisson mixture model. It is also proved that the distributions in the extended part of the parameter space are not the zero truncation of mixed poisson distributions and that, other than for the negative binomial, they are not mixtures of zero truncated Poisson distributions either. By extending the parameter space one can improve the fit when the frequency of one is larger and the right tail is heavier than is allowed by the unextended model. Considering the extended model also allows one to use the basic maximum likelihood based inference tools when parameter estimates fall in the extended part of the parameter space, and hence when the m.l.e. does not exist under the unextended model. This extended truncated Tweedie-Poisson model is proved to be useful in the analysis of words and species frequency count data.

On the use of IRMS in forensic science: proposals for a methodological approach.

The flourishing number of publications on the use of isotope ratio mass spectrometry (IRMS) in forensicscience denotes the enthusiasm and the attraction generated by this technology. IRMS has demonstratedits potential to distinguish chemically identical compounds coming from different sources. Despite thenumerous applications of IRMS to a wide range of forensic materials, its implementation in a forensicframework is less straightforward than it appears. In addition, each laboratory has developed its ownstrategy of analysis on calibration, sequence design, standards utilisation and data treatment without aclear consensus.Through the experience acquired from research undertaken in different forensic fields, we propose amethodological framework of the whole process using IRMS methods. We emphasize the importance ofconsidering isotopic results as part of a whole approach, when applying this technology to a particularforensic issue. The process is divided into six different steps, which should be considered for a thoughtfuland relevant application. The dissection of this process into fundamental steps, further detailed, enablesa better understanding of the essential, though not exhaustive, factors that have to be considered in orderto obtain results of quality and sufficiently robust to proceed to retrospective analyses or interlaboratorycomparisons.

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

'Next generation' biogeography: towards understanding the drivers of species diversification and persistence

The drivers of species diversification and persistence are of great interest to current biogeography, especially in those global biodiversity hotspots' harbouring most of Earth's animal and plant life. Classical multispecies biogeographical work has yielded fascinating insights into broad-scale patterns of diversification, and DNA-based intraspecific phylogeographical studies have started to complement this picture at much finer temporal and spatial scales. The advent of novel next-generation sequencing (NGS) technologies provides the opportunity to greatly scale up the numbers of individuals, populations and species sampled, potentially merging intraspecific and interspecific approaches to biogeographical inference. Here, we outline these prospects and issues by using the example of an undisputed hotspot, the Cape of southern Africa. We outline the current state of knowledge on the biogeography of species diversification within the Cape, review the literature for phylogeographical evidence of its likely drivers and mechanisms, and suggest possible ways forward based on NGS approaches. We demonstrate the potential of these methods and current bioinformatic issues with the help of restriction-site-associated DNA (RAD) sequencing data for three highly divergent species of the Restionaceae, an important plant radiation in the Cape. A thorough understanding of the mechanisms that facilitate species diversification and persistence in spatially structured, species-rich environments will require the adoption of novel genomic and bioinformatic tools in biogeographical studies.

Usefulness of microsatellite typing in population genetic studies of Trypanosoma cruzi

Through microsatellite analysis of 53 monoclonal populations of Trypanosoma cruzi, we found a remarkable degree of genetic polymorphism with no single multilocus genotype being observed more than once. The microsatellite profile proved to be stable during 70 generations of the CL Brener clone in culture. The microsatellite profiling presented also high diagnostic sensitivity since DNA amplifications could be achieved with less than 100 fg DNA, corresponding to half parasite total DNA content. Based on these technical attributes the microsatellite assay turns out to be an important tool for direct typing T. cruzi in biological samples. By using this approach we were able to type T. cruzi in feces of artificially infected bugs and in single cells sorted by FACS. The microsatellites have shown to be excellent markers for T. cruzi phylogenetic reconstruction. We used maximum parsimony based on the minimum number of mutational steps to build an unrooted Wagner network, which confirms previous conclusions based on the analysis of the D7 domain of the LSU rDNA gene that T. cruzi is composed by two major groups. We also obtained evidence that strains belonging to rRNA group 2 are subdivided into two genetically distant clusters, and that one of these clusters is more related to rRNA group 1/2. These results suggest different origins for these strains.

Hierarchical conditional random fields for parts based models matching

A parts based model is a parametrization of an object class using a collection of landmarks following the object structure. The matching of parts based models is one of the problems where pairwise Conditional Random Fields have been successfully applied. The main reason of their effectiveness is tractable inference and learning due to the simplicity of involved graphs, usually trees. However, these models do not consider possible patterns of statistics among sets of landmarks, and thus they sufffer from using too myopic information. To overcome this limitation, we propoese a novel structure based on a hierarchical Conditional Random Fields, which we explain in the first part of this memory. We build a hierarchy of combinations of landmarks, where matching is performed taking into account the whole hierarchy. To preserve tractable inference we effectively sample the label set. We test our method on facial feature selection and human pose estimation on two challenging datasets: Buffy and MultiPIE. In the second part of this memory, we present a novel approach to multiple kernel combination that relies on stacked classification. This method can be used to evaluate the landmarks of the parts-based model approach. Our method is based on combining responses of a set of independent classifiers for each individual kernel. Unlike earlier approaches that linearly combine kernel responses, our approach uses them as inputs to another set of classifiers. We will show that we outperform state-of-the-art methods on most of the standard benchmark datasets.

Creació d'una eina en Java que a partir d'OWL i JESS generi SCORM

Aquest projecte consisteix en generar un programa escrit en llenguatge Java, que utilitzant un motor d'inferència a través d'una llibreria anomenada JESS, pugui llegir un document en format OWL que és una representació de l'ontologia (representació del coneixement) sobre una assignatura, transformant-lo al format de triples que és capaç d'interpretar JESS.

Adult consequences of late adolescent alcohol consumption: a systematic review of cohort studies.

Alcohol is responsible for a significant portion of the global burden of disease. There is widespread concern reported in the media and other sources about drinking trends among young people, particularly heavy episodic or “binge” drinking. Prominent among policy responses, in the UK and elsewhere, have been attempts to manage antisocial behaviour related to intoxication in public spaces. Much less attention has been given to the longer term effects of excessive drinking in adolescence on later adult health and well-being. Some studies suggest that individuals “mature out” of late adolescent drinking behaviour, whilst others identify enduring effects on drinking and broader health and social outcomes in adulthood. If adolescent drinking does not cause later difficulties in adulthood then intervention approaches aimed at addressing the acute consequences of alcohol, such as unintentional injuries and anti-social behaviour, may be the most appropriate solution. If causal relationships do exist, however, this approach will not address the cumulative harms produced by alcohol, unless such intervention successfully modifies the long-term relationship with alcohol, which seems unlikely. To address this issue a systematic review of cohort studies was conducted, as this approach provides the strongest observational study design to evaluate evidence for causal inference.This resource was contributed by The National Documentation Centre on Drug Use.

Forensic science - A true science ?

While the US jurisprudence of the 1993 Daubert requires judges to question not only the methodology behind, but also the principles governing, a body of knowledge to qualify it as scientific, can forensic science, based on Locard's and Kirk's Principles, pretend to this higher status in the courtroom ? Moving away from the disputable American legal debate, this historical and philosophical study will screen the relevance of the different logical epistemologies to recognize the scientific status of forensic science. As a consequence, the authors are supporting a call for its recognition as a science of its own, defined as the science of identifying and associating traces for investigative and security purposes, based o its fundamental principles and the case assesment and interpretation process that follows with its specific and relevant mode of inference.

Naturally acquired antibodies to merozoite surface protein (MSP)-1(19) and cumulative exposure to Plasmodium falciparum and Plasmodium vivax in remote populations of the Amazon Basin of Brazil

To infer recent patterns of malaria transmission, we measured naturally acquired IgG antibodies to the conserved 19-kDa C-terminal region of the merozoite surface protein (MSP)-1 of both Plasmodium vivax (PvMSP-1(19)) and Plasmodium falciparum (PfMSP-1(19)) in remote malaria-exposed populations of the Amazon Basin. Community-based cross-sectional surveys were carried out between 2002 and 2003 in subjects of all age groups living along the margins of the Unini and Jaú rivers, Northwestern Brazil. We found high prevalence rates of IgG antibodies to PvMSP-1(19) (64.0 - 69.6%) and PfMSP-1(19) (51.6 - 52.0%), with significant differences in the proportion of subjects with antibodies to PvMSP-1(19) according to age, place of residence and habitual involvement in high-risk activities, defining some groups of highly exposed people who might be preferential targets of malaria control measures. In contrast, no risk factor other than age was significantly associated with seropositivity to PfMSP-1(19). Only 14.1% and 19.3% of the subjects tested for antibodies to PvMSP-1(19) and PfMSP-1(19) in consecutive surveys (142 - 203 days apart) seroconverted or had a three fold or higher increase in the levels of antibodies to these antigens. We discuss the extent to which serological data correlated with the classical malariometric indices and morbidity indicators measured in the studied population at the time of the seroprevalence surveys and highlight some limitations of serological data for epidemiological inference.

Total hemoglobin mass--a new parameter to detect blood doping?

PURPOSE: All kinds of blood manipulations aim to increase the total hemoglobin mass (tHb-mass). To establish tHb-mass as an effective screening parameter for detecting blood doping, the knowledge of its normal variation over time is necessary. The aim of the present study, therefore, was to determine the intraindividual variance of tHb-mass in elite athletes during a training year emphasizing off, training, and race seasons at sea level. METHODS: tHb-mass and hemoglobin concentration ([Hb]) were determined in 24 endurance athletes five times during a year and were compared with a control group (n = 6). An analysis of covariance was used to test the effects of training phases, age, gender, competition level, body mass, and training volume. Three error models, based on 1) a total percentage error of measurement, 2) the combination of a typical percentage error (TE) of analytical origin with an absolute SD of biological origin, and 3) between-subject and within-subject variance components as obtained by an analysis of variance, were tested. RESULTS: In addition to the expected influence of performance status, the main results were that the effects of training volume (P = 0.20) and training phases (P = 0.81) on tHb-mass were not significant. We found that within-subject variations mainly have an analytical origin (TE approximately 1.4%) and a very small SD (7.5 g) of biological origin. CONCLUSION: tHb-mass shows very low individual oscillations during a training year (<6%), and these oscillations are below the expected changes in tHb-mass due to Herythropoetin (EPO) application or blood infusion (approximately 10%). The high stability of tHb-mass over a period of 1 year suggests that it should be included in an athlete's biological passport and analyzed by recently developed probabilistic inference techniques that define subject-based reference ranges.

Polymorphism analysis of the CTLA-4 gene in paracoccidioidomycosis patients

The CTLA-4 protein is expressed in activated T cells and plays an essential role in the immune response through its regulatory effect on T cell activation. Polymorphisms of the CTLA-4 gene have been correlated with autoimmune, neoplastic and infectious illnesses. This work aimed to verify possible associations between single nucleotide polymorphisms (SNPs) in CTLA-4, -318C/T in the promoter and +49A/G in exon 1 and paracoccidioidomycosis (PCM) caused by Paracoccidioides brasiliensis. For this purpose, 66 chronic form PCM patients and 76 healthy controls had their allele, genotype and haplotype frequencies determined. The genetic admixture structure of the patients and controls was evaluated to eliminate ancestral bias. The comparison of frequencies indicated no significant differences between patients and controls that could link the SNPs to PCM. Groups were admixture matched with no difference observed in population ancestry inference, indicating that the absence of association between CTLA-4 polymorphisms and PCM could not be attributed to ancestral bias. This study showed that there was no association between the CTLA-4 SNPs -318 and +49 and the resistance or susceptibility to PCM.