Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias Tecnológicas

Vulnerability to adverse consequences of drinking and problem drinker status as predicted by risky drinking behaviours, drug use, sex differences and affect : a test of multiple models /

Although alcohol problems and alcohol consumption are related, consumption does not fully account for differences in vulnerability to alcohol problems. Therefore, other factors should account for these differences. Based on previous research, it was hypothesized that risky drinking behaviours, illicit and prescription drug use, affect and sex differences would account for differences in vulnerability to alcohol problems while statistically controlling for overall alcohol consumption. Four models were developed that were intended to test the predictive ability of these factors, three of which tested the predictor sets separately and a fourth which tested them in a combined model. In addition, two distinct criterion variables were regressed on the predictors. One was a measure of the frequency that participants experienced negative consequences that they attributed to their drinking and the other was a measure of the extent to which participants perceived themselves to be problem drinkers. Each of the models was tested on four samples from different populations, including fIrst year university students, university students in their graduating year, a clinical sample of people in treatment for addiction, and a community sample of young adults randomly selected from the general population. Overall, support was found for each of the models and each of the predictors in accounting for differences in vulnerability to alcohol problems. In particular, the frequency with which people become intoxicated, frequency of illicit drug use and high levels of negative affect were strong and consistent predictors of vulnerability to alcohol problems across samples and criterion variables. With the exception of the clinical sample, the combined models predicted vulnerability to negative consequences better than vulnerability to problem drinker status. Among the clinical and community samples the combined model predicted problem drinker status better than in the student samples.

Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

MOTIVATION: The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering or consensus based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ - a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilising the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead. RESULTS: The ModFOLDclustQ method is competitive with leading clustering based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over 5 times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction. AVAILABILITY: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from: http://www.reading.ac.uk/bioinf/downloads/ CONTACT: l.j.mcguffin@reading.ac.uk.

Clustering techniques applied to multiple-models structures

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

Dimension and coverage of multiple-models structures using clustering techniques

In this work a method for building multiple-model structures is presented. A clustering algorithm that uses data from the system is employed to define the architecture of the multiple-model, including the size of the region covered by each model, and the number of models. A heating ventilation and air conditioning system is used as a testbed of the proposed method.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

The ModFOLD server for the quality assessment of protein structural models

The reliable assessment of the quality of protein structural models is fundamental to the progress of structural bioinformatics. The ModFOLD server provides access to two accurate techniques for the global and local prediction of the quality of 3D models of proteins. Firstly ModFOLD, which is a fast Model Quality Assessment Program (MQAP) used for the global assessment of either single or multiple models. Secondly ModFOLDclust, which is a more intensive method that carries out clustering of multiple models and provides per-residue local quality assessment.

Superensembles of linear viscoelastic models of polymer melts

The idea of incorporating multiple models of linear rheology into a superensemble, to forge a consensus forecast from the individual model predictions, is investigated. The relative importance of the individual models in the so-called multimodel superensemble (MMSE) was inferred by evaluating their performance on a set of experimental training data, via nonlinear regression. The predictive ability of the MMSE model was tested by comparing its predictions on test data that were similar (in-sample) and dissimilar (out-of-sample) to the training data used in the calibration. For the in-sample forecasts, we found that the MMSE model easily outperformed the best constituent model. The presence of good individual models greatly enhanced the MMSE forecast, while the presence of some bad models in the superensemble also improved the MMSE forecast modestly. While the performance of the MMSE model on the out-of-sample training data was not as spectacular, it demonstrated the robustness of this approach.

Using P-GENESIS for Parallel Simulation of GENESIS Models

P-GENESIS is an extension to the GENESIS neural simulator that allows users to take advantage of parallel machines to speed up the simulation of their network models or concurrently simulate multiple models. P-GENESIS adds several commands to the GENESIS script language that let a script running on one processor execute remote procedure calls on other processors, and that let a script synchronize its execution with the scripts running on other processors. We present here some brief comments on the mechanisms underlying parallel script execution. We also offer advice on parallelizing parameter searches, partitioning network models, and selecting suitable parallel hardware on which to run P-GENESIS.

Método multicritério como apoio a um processo de negociação numa empresa de serviços médicos

O processo de negociação tem ganho relevância como uma das formas de gestão de conflitos. Verifica-se que nas organizações a negociação é um processo omnipresente, que tem sido alvo de muito estudo e investigação, e as capacidades de negociação são consideradas determinantes para o sucesso. Em consequência dessas tendências, surgem propostas de modelos de negociação bastantes flexíveis e que visam colaboração entre as partes interessadas, modelos que se adequam aos contextos organizacionais em que predominam relações estáveis e de longo prazo. Estas propostas procuram a solução óptima para as partes interessadas. No entanto, faltam frequentemente os mecanismos e procedimentos que garantam um processo estruturado para elaborar e analisar os diversos cenários na negociação, considerando um conjunto de aspectos relevantes para ambas as partes. No presente trabalho de dissertação formula-se uma proposta baseada no modelo de negociação Win Win Quantitativa, em que foi utilizada uma abordagem do método multicritério Analitic Hierarchy Process (AHP) para seleccionar a melhor opção de serviço para uma determinada empresa. Para o caso de estudo, num contexto real, foi necessário desenvolver uma aplicação Excel que permitisse analisar, de uma forma clara, as diversas alternativas perante os critérios mencionados. A aplicação do método AHP permite aos clientes tomar uma decisão potencialmente mais acertada. A aplicação informática procura optimizar os custos inerentes à prestação de serviços, oferecendo aos clientes um custo reduzido e assim tornando a empresa mais competitiva e atractiva para os potenciais clientes.

Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

Convergent evolution of floral signals underlies the success of Neotropical orchids.

The great majority of plant species in the tropics require animals to achieve pollination, but the exact role of floral signals in attraction of animal pollinators is often debated. Many plants provide a floral reward to attract a guild of pollinators, and it has been proposed that floral signals of non-rewarding species may converge on those of rewarding species to exploit the relationship of the latter with their pollinators. In the orchid family (Orchidaceae), pollination is almost universally animal-mediated, but a third of species provide no floral reward, which suggests that deceptive pollination mechanisms are prevalent. Here, we examine floral colour and shape convergence in Neotropical plant communities, focusing on certain food-deceptive Oncidiinae orchids (e.g. Trichocentrum ascendens and Oncidium nebulosum) and rewarding species of Malpighiaceae. We show that the species from these two distantly related families are often more similar in floral colour and shape than expected by chance and propose that a system of multifarious floral mimicry-a form of Batesian mimicry that involves multiple models and is more complex than a simple one model-one mimic system-operates in these orchids. The same mimetic pollination system has evolved at least 14 times within the species-rich Oncidiinae throughout the Neotropics. These results help explain the extraordinary diversification of Neotropical orchids and highlight the complexity of plant-animal interactions.

TBS reflects trabecular microarchitecture in premenopausal women and men with idiopathic osteoporosis and low-traumatic fractures.

Transiliac bone biopsies, while widely considered to be the standard for the analysis of bone microstructure, are typically restricted to specialized centers. The benefit of Trabecular Bone Score (TBS) in addition to areal bone mineral density (aBMD) for fracture risk assessment has been documented in cross-sectional and prospective studies. The aim of this study was to test if TBS may be useful as a surrogate to histomorphometric trabecular parameters of transiliac bone biopsies. Transiliac bone biopsies from 80 female patients (median age 39.9years-interquartile range, IQR 34.7; 44.3) and 43 male patients (median age 42.7years-IQR 38.9; 49.0) with idiopathic osteoporosis and low traumatic fractures were included. Micro-computed tomography values of bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), trabecular separation (Tb.Sp), structural model index (SMI) as well as serum bone turnover markers (BTMs) sclerostin, intact N-terminal type 1 procollagen propeptide (P1NP) and cross-linked C-telopeptide (CTX) were investigated. TBS values were higher in females (1.282 vs 1.169, p< 0.0001) with no differences in spine aBMD, whereas sclerostin levels (45.5 vs 33.4pmol/L) and aBMD values at the total hip (0.989 vs 0.971g/cm(2), p<0.001 for all) were higher in males. Multiple regression models including: gender, aBMD and BTMs revealed TBS as an independent, discriminative variable with adjusted multiple R(2) values of 69.1% for SMI, 79.5% for Tb.N, 68.4% for Tb.Sp, and 83.3% for BV/TV. In univariate regression models, BTMs showed statistically significant results, whereas in the multiple models only P1NP and CTX were significant for Tb.N. TBS is a practical, non-invasive, surrogate technique for the assessment of cancellous bone microarchitecture and should be implemented as an additional tool for the determination of trabecular bone properties.

The orphan nuclear receptor NR5A2 regulates peri-ovulatory events and their consequent luteinization in mice

Le récepteur nucléaire Nr5a2, également connu sous le nom de liver receptor homolog-1 (Lrh-1), est exprimé au niveau de l’ovaire chez la souris, exclusivement dans les cellules lutéales et de la granulosa. La perturbation de Nr5a2, spécifique aux cellules de la granulosa chez la souris à partir des follicules primaires dans la trajectoire du développement folliculaire a démontré que Nr5a2 est un régulateur clé de l’ovulation et de la fertilité chez la femelle. Notre hypothèse veut que Nr5a2 régule les évènements péri- et post-ovulatoires dans une séquence temporelle lors de la folliculogénèse. Afin d'étudier l’implication de Nr5a2 lors de l’ovulation et de la lutéinisation à différents stades du développement folliculaire, nous avons généré deux modèles de souris knockout spécifiques aux cellules de la granulosa pour Nr5a2: 1) Nr5a2Amhr2-/-, avec une réduction de Nr5a2 à partir des follicules primaires et subséquents; 2) Nr5a2Cyp19-/-, avec une réduction de Nr5a2 débutant au stade antral de développement en progressant. L’absence de Nr5a2 à partir des follicules antraux a résulté en une infertilité chez les femelles Nr5a2Cyp19-/-, de même qu’en des structures non-fonctionnelles similaires aux structures lutéales au niveau des ovaires, en une réduction des niveaux de progestérone synthétisée ainsi qu’en un échec dans le support d’une pseudo-gestation. La synthèse de progestérone a été entravée suite à l’absence de Nr5a2 par l’entremise d’une régulation à la baisse des gènes reliés au transport du cholestérol, Scarb1, StAR et Ldlr, démontré par qPCR. Les complexes cumulus-oocytes des femelles Nr5a2Cyp19-/- immatures super-stimulées ont subi une expansion in vivo, mais l’ovulation a été perturbée, possiblement par une régulation à la baisse du gène du récepteur de la progestérone (Pgr). Un essai d’expansion du cumulus in vitro a démontré une expansion défectueuse du cumulus chez les Nr5a2Amhr2-/-, associée à un dérèglement de la protéine des jonctions communicantes (Gja1; Cx43). Cependant, l’expansion du cumulus chez les Nr5a2Cyp19-/- n’a pas été autant affectée. Des résultats obtenus par qPCR ont démontré une régulation à la baisse dans l’expression des gènes Areg, Ereg, Btc et Tnfaip6 chez les deux modèles de cellules ovariennes knockout à 2h et 4h post hCG. Nous avons observé que 85% des oocytes, chez les deux génotypes mutants, peuvent subir une rupture de la vésicule germinative, confirmant leur capacité de maturation in vivo. La technique d’injection intra-cytoplasmique de spermatozoïdes a prouvé que les oocytes des deux génotypes mutants sont fertilisables et que 70% des embryons résultants ont poursuivi leur développement vers le stade de blastocyste, et ce, indépendamment du génotype. En conclusion, Nr5a2 régule la fertilité chez les femelles tout au long du processus du développement folliculaire. Il a été démontré que Nr5a2 est essentiel à la lutéinisation et que sa perturbation dans les cellules somatiques ovariennes ne compromet pas la capacité des oocytes à être fertilisés. En vue d’ensemble, nous avons fourni une investigation inédite et complète, utilisant de multiples modèles et techniques afin de déterminer les mécanismes par lesquels Nr5a2 régule les importants processus que sont l’expansion du cumulus, l’ovulation ainsi que la formation du corps jaune.