Finite state machine based SDL

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Identifying individual differences of fluoxetine response in juvenile rhesus monkeys by metabolite profiling

Fluoxetine is the only psychopharmacological agent approved for depression by the US Food and Drug Administration for children and is commonly used therapeutically in a variety of neurodevelopmental disorders. Therapeutic response shows high individual variability, and severe side effects have been observed. In the current study we set out to identify biomarkers of response to fluoxetine as well as biomarkers that correlate with impulsivity, a measure of reward delay behavior and potential side effect of the drug, in juvenile male rhesus monkeys. The study group was also genotyped for polymorphisms of monoamine oxidase A (MAOA), a gene that has been associated with psychiatric disorders. We used peripheral metabolite profiling of blood and cerebrospinal fluid (CSF) from animals treated daily with fluoxetine or vehicle for one year. Fluoxetine response metabolite profiles and metabolite/reward delay behavior associations were evaluated using multivariate analysis. Our analyses identified a set of plasma and CSF metabolites that distinguish fluoxetine-from vehicle-treated animals and metabolites that correlate with impulsivity. Some metabolites displayed an interaction between fluoxetine and MAOA genotype. The identified metabolite biomarkers belong to pathways that have important functions in central nervous system physiology. Biomarkers of response to fluoxetine in the normally functioning brain of juvenile nonhuman primates may aid in finding predictors of response to treatment in young psychiatric populations and in progress toward the realization of a precision medicine approach in the area of neurodevelopmental disorders

Computationally designed libraries of fluorescent proteins evaluated by preservation and diversity of function

To determine which of seven library design algorithms best introduces new protein function without destroying it altogether, seven combinatorial libraries of green fluorescent protein variants were designed and synthesized. Each was evaluated by distributions of emission intensity and color compiled from measurements made in vivo. Additional comparisons were made with a library constructed by error-prone PCR. Among the designed libraries, fluorescent function was preserved for the greatest fraction of samples in a library designed by using a structure-based computational method developed and described here. A trend was observed toward greater diversity of color in designed libraries that better preserved fluorescence. Contrary to trends observed among libraries constructed by error-prone PCR, preservation of function was observed to increase with a library's average mutation level among the four libraries designed with structure-based computational methods

Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting. (C) 2012 Elsevier Ltd. All rights reserved.Peer reviewe

Report on the Standardization Project ``Formal Methods in Conformance Testing''

This paper presents the latest developments in the “Formal Methods in Conformance Testing” (FMCT) project of ISO and ITU–T. The project has been initiated to study the role of formal description techniques in the conformance testing process. The goal is to develop a standard that defines the meaning of conformance in the context of formal description techniques. We give an account of the current status of FMCT in the standardization process as well as an overview of the technical status of the proposed standard. Moreover, we indicate some of its strong and weak points, and we give some directions for future work on FMCT

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

© 2016. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Ioannis Kioutsioukis, et al, ‘Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data’, Atmospheric Chemistry and Physics, Vol 16(24): 15629-15652, published 20 December 2016, the version of record is available at doi:10.5194/acp-16-15629-2016 Published by Copernicus Publications on behalf of the European Geosciences Union.Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.Peer reviewedFinal Published versio

Symmetry in temporal logic model checking

Temporal logic model checking involves checking the state-space of a model of a system to determine whether errors can occur in the system. Often this involves checking symmetrically equivalent areas of the state-space. The use of symmetry reduction to increase the efficiency of model checking has inspired a wealth of activity in the area of model checking research. We provide a survey of the associated literature

Children's Divergent Thinking Improves When They Understand False Beliefs

This research utilized longitudinal and cross sectional methods to investigate the relation between the development of a representational theory of mind and children's growing ability to search their own minds for appropriate problem solutions. In the first experiment 59 pre-school children were given three false-belief tasks and a divergent thinking task. Those children who passed false-belief tasks produced significantly more items, as well as more original items, in response to divergent thinking questions than those children who failed. This significant association persisted even when chronological age, verbal and nonverbal general ability were partialed out. In a second study 20 children who failed the false-belief tasks in the first experiment were re-tested three months later. Again, those who now passed the false-belief tasks were significantly better at the divergent thinking task than those who continued to fail. The associations between measures of divergent thinking and understanding false-beliefs remained significant when controlling for the covariates. Earlier divergent thinking scores did not predict false-belief understanding three months later. Instead, children who passed false-belief tasks on the second measure improved significantly in relation to their own earlier performance and improved significantly more than children who continued to fail. False-belief task performance was significantly correlated to the amount of intra-individual improvement in divergent thinking even when age, verbal and nonverbal skills were partialed out. These findings suggest that developments in common underlying skills are responsible for the improvement in understanding other minds and searching one's own. Changes in representational and executive skills are discussed as potential causes for the improvement

Age and skill bias of trade liberalisation? : heterogeneous employment effects of EU Eastern Enlargement

This study analyses the 2004 Eastern Enlargement to the European Union to obtain evidence on the employment effects of an increase in trade liberalisation. The Enlargement is thought to generate a trade-induced demand shock with no (or only limited) supply effects. Besides the variation over time induced by the Enlargement, identification of the effects is based on a Melitz (2003) type productivity term to differentiate firms by the extent of exposure to the demand shock. The idea is that the effects of the demand shock should be driven by differences in firm-level productivity from the period before the new member countries actually entered the EU. German linked employer-employee data allow to observe the relation of initial establishment productivity with employment changes over a long panel from 1995 to 2009. The estimates show that the Enlargement had a negative effect on establishment-level employment growth, which is driven by increased worker separations and increased job destruction. Besides the overall employment effect, the study focuses on effect heterogeneity across age and skill groups of the workforce. These estimates point to a skill bias in the effect of the Enlargement that disadvantages low- and medium-skilled workers in terms of higher worker separation and job destruction. In addition, lowskilled workers suffer fewer accessions by firms, where against medium-skilled workers enjoy increased accessions and creation of new jobs. Besides this indication for a skill bias, there are no clear indications that point to an age bias in the employment effect of the Eastern Enlargement