SBML models and MathSBML

MathSBML is an open-source, freely-downloadable Mathematica package that facilitates working with Systems Biology Markup Language (SBML) models. SBML is a toolneutral,computer-readable format for representing models of biochemical reaction networks, applicable to metabolic networks, cell-signaling pathways, genomic regulatory networks, and other modeling problems in systems biology that is widely supported by the systems biology community. SBML is based on XML, a standard medium for representing and transporting data that is widely supported on the internet as well as in computational biology and bioinformatics. Because SBML is tool-independent, it enables model transportability, reuse, publication and survival. In addition to MathSBML, a number of other tools that support SBML model examination and manipulation are provided on the sbml.org website, including libSBML, a C/C++ library for reading SBML models; an SBML Toolbox for MatLab; file conversion programs; an SBML model validator and visualizer; and SBML specifications and schemas. MathSBML enables SBML file import to and export from Mathematica as well as providing an API for model manipulation and simulation

Moti-faction: retaining and engaging employees using motivation profile-based rewards

Organizations are searching for opportunities to increase job satisfaction, motivation for higher performance, and retaining their top talent. This study explores an assessment tool to aim rewards to individual motivation profiles so that companies can reach their potential. A survey exploring employee\u27s attitudes on these types of rewards and an assessment tool to determine employee\u27s individual motivation profiles was created and tested within a manufacturing corporate office through use of an online survey tool, Survey Monkey. Results showed that motivation profiles are evident and a relationship exists between rewarding based on the motivation profile\u27s reward preferences and employee satisfaction and performance. In conclusion, this study has made apparent a need for further research including a possible longitudinal study that explores how age groups, job titles, and change in personal desires over time can affect an employee\u27s motivational profile

Mugshot Exposure Effects: Retroactive Interference, Mugshot Commitment, Source Confusion, and Unconscious Transference

More than 25 years of research has accumulated concerning the possible biasing effects of mugshot exposure to eyewitnesses. Two separate metaanalyses were conducted on 32 independent tests of the hypothesis that prior mugshot exposure decreases witness accuracy at a subsequent lineup. Mugshot exposure both significantly decreased proportion correct and increased the false alarm rate, the effect being greater on false alarms. A mugshot commitment effect, arising from the identification of someone in a mugshot, was a substantial moderator of both these effects. Simple retroactive interference, where the target person is not included among mugshots and no one in a mugshot is present in the subsequent lineup, did not significantly impair target identification. A third metaanalysis was conducted on 19 independent tests of the hypothesis that failure of memory for facial source or context results in transference errors. The effect size was more than twice as large for “transference” studies involving mugshot exposure in proximate temporal context with the target than for “bystander” studies with no subsequent mugshot exposure

Evaluating megaprojects: from the “iron triangle” to network mapping

Evaluation literature has paid relatively little attention to the specific needs of evaluating large, complex industrial and infrastructure projects, often called ‘megaprojects’. The abundant megaproject governance literature, in turn, has largely focused on the so-called ‘megaproject pathologies’, i.e. the chronic budget overruns, and failure of such projects to keep to timetables and deliver the expected social and economic benefits. This article draws on these two strands of literature, identifies shortcomings, and suggests potential pathways towards an improved evaluation of megaprojects. To counterbalance the current overemphasis on relatively narrowly defined accountability as the main function of megaproject evaluation, and the narrow definition of project success in megaproject evaluation, the article argues that conceptualizing megaprojects as dynamic and evolving networks would provide a useful basis for the design of an evaluation approach better able to promote learning and to address the socio economic aspects of megaprojects. A modified version of ‘network mapping’ is suggested as a possible framework for megaproject evaluation, with the exploration of the multiple accountability relationships as a central evaluation task, designed to reconcile learning and accountability as the central evaluation functions. The article highlights the role of evaluation as an ‘emergent’ property of spontaneous megaproject ‘governing’, and explores the challenges that this poses to the role of the evaluator

Trust in the Jury System as a Predictor of Juror/Jury Decisions

To determine whether jurors’ attitudes are correlated with their verdicts and judgments at trial, the present experiments examined the relationship between individuals’ trust in the jury system, other legal attitudes, and their verdict judgments, at both the individual (juror) and group (jury) level. We used a binary logistic regression model to examine the factors—jury instructions and individual difference measures—that contribute to a juror’s verdict. The results indicate that jurors with higher PJAQ and JUST scores had a higher likelihood of voting guilty on a homicide trial involving a mercy killing. It was also found that the majority of juries in the second study took a verdict-based approach, and jurors with less trust in the jury system participated more in deliberation than high trust jurors

Reinstated episodic context guides sampling-based decisions for reward.

How does experience inform decisions? In episodic sampling, decisions are guided by a few episodic memories of past choices. This process can yield choice patterns similar to model-free reinforcement learning; however, samples can vary from trial to trial, causing decisions to vary. Here we show that context retrieved during episodic sampling can cause choice behavior to deviate sharply from the predictions of reinforcement learning. Specifically, we show that, when a given memory is sampled, choices (in the present) are influenced by the properties of other decisions made in the same context as the sampled event. This effect is mediated by fMRI measures of context retrieval on each trial, suggesting a mechanism whereby cues trigger retrieval of context, which then triggers retrieval of other decisions from that context. This result establishes a new avenue by which experience can guide choice and, as such, has broad implications for the study of decisions