Personality Psychology and Economics

This paper explores the power of personality traits both as predictors and as causes of academic and economic success, health, and criminal activity. Measured personality is interpreted as a construct derived from an economic model of preferences, constraints, and information. Evidence is reviewed about the "situational specificity" of personality traits and preferences. An extreme version of the situationist view claims that there are no stable personality traits or preference parameters that persons carry across different situations. Those who hold this view claim that personality psychology has little relevance for economics. The biological and evolutionary origins of personality traits are explored. Personality measurement systems and relationships among the measures used by psychologists are examined. The predictive power of personality measures is compared with the predictive power of measures of cognition captured by IQ and achievement tests. For many outcomes, personality measures are just as predictive as cognitive measures, even after controlling for family background and cognition. Moreover, standard measures of cognition are heavily influenced by personality traits and incentives. Measured personality traits are positively correlated over the life cycle. However, they are not fixed and can be altered by experience and investment. Intervention studies, along with studies in biology and neuroscience, establish a causal basis for the observed effect of personality traits on economic and social outcomes. Personality traits are more malleable over the life cycle compared to cognition, which becomes highly rank stable around age 10. Interventions that change personality are promising avenues for addressing poverty and disadvantage.personality, behavioral economics, cognitive traits, wages, economic success, human development, person-situation debate

Characterizing the variation of propagation constants in multicore fibre

We demonstrate a numerical technique that can evaluate the core-to-core variations in propagation constant in multicore fibre. Using a Markov Chain Monte Carlo process, we replicate the interference patterns of light that has coupled between the cores during propagation. We describe the algorithm and verify its operation by successfully reconstructing target propagation constants in a fictional fibre. Then we carry out a reconstruction of the propagation constants in a real fibre containing 37 single-mode cores. We find that the range of fractional propagation constant variation across the cores is approximately $\pm2 \times 10^{-5}$.Comment: 17 pages; preprint format; 5 figures. Submitted to Optics Expres

Immunogenicity and protective efficacy against enterotoxigenic Escherichia coli colonization following intradermal, sublingual, or oral vaccination with EtpA adhesin

Enterotoxigenic Escherichia coli (ETEC) strains are a common cause of diarrhea. Extraordinary antigenic diversity has prompted a search for conserved antigens to complement canonical approaches to ETEC vaccine development. EtpA, an immunogenic extracellular ETEC adhesin relatively conserved in the ETEC pathovar, has previously been shown to be a protective antigen following intranasal immunization. These studies were undertaken to explore alternative routes of EtpA vaccination that would permit use of a double mutant (R192G L211A) heat-labile toxin (dmLT) adjuvant. Here, oral vaccination with EtpA adjuvanted with dmLT afforded significant protection against small intestinal colonization, and the degree of protection correlated with fecal IgG, IgA, or total fecal antibody responses to EtpA. Sublingual vaccination yielded compartmentalized mucosal immune responses with significant increases in anti-EtpA fecal IgG and IgA, and mice vaccinated via this route were also protected against colonization. In contrast, while intradermal (i.d.) vaccination achieved high levels of both serum and fecal antibodies against both EtpA and dmLT, mice vaccinated via the i.d. route were not protected against subsequent colonization and the avidity of serum IgG and IgA EtpA-specific antibodies was significantly lower after i.d. immunization compared to other routes. Finally, we demonstrate that antiserum from vaccinated mice significantly impairs binding of LT to cognate GM1 receptors and shows near complete neutralization of toxin delivery by ETEC in vitro. Collectively, these data provide further evidence that EtpA could complement future vaccine strategies but also suggest that additional effort will be required to optimize its use as a protective immunogen

Program Manager Competencies (Chapter 11 of Program Management for Improved Business Results)

The successful program manager is constantly seeking to learn and broaden his or her knowledge and experience in order to take on more complex and critical programs. The program management competency model was developed in order to address the breadth, depth, and complexity of the program management role. This chapter uses the program management competency model to detail the knowledge, skills, and abilities needed for program managers to continually grow as professionals and consistently succeed in their role. The various types of competencies that are discussed in the chapter are: customer and market competencies, business and financial competencies, process and project management competencies, and leadership competencies. Additionally, the chapter discusses the key organizational enablers needed to make the competency model fully effective and to adequately support the program management discipline within an organization

Natural Language Processing for Drug Discovery Knowledge Graphs: promises and pitfalls

Building and analysing knowledge graphs (KGs) to aid drug discovery is a topical area of research. A salient feature of KGs is their ability to combine many heterogeneous data sources in a format that facilitates discovering connections. The utility of KGs has been exemplified in areas such as drug repurposing, with insights made through manual exploration and modelling of the data. In this article, we discuss promises and pitfalls of using natural language processing (NLP) to mine unstructured text typically from scientific literature as a data source for KGs. This draws on our experience of initially parsing structured data sources such as ChEMBL as the basis for data within a KG, and then enriching or expanding upon them using NLP. The fundamental promise of NLP for KGs is the automated extraction of data from millions of documents a task practically impossible to do via human curation alone. However, there are many potential pitfalls in NLP-KG pipelines such as incorrect named entity recognition and ontology linking all of which could ultimately lead to erroneous inferences and conclusions.Comment: 17 pages, 7 figure

Interactions between metals and microbial communities in New Bedford Harbor, Massachusetts.

The fate of toxic metals in marine sediments depends on a combination of the physical, chemical, and biologic conditions encountered in any given environment. These conditions may vary dramatically, both spatially and temporally, in response to factors ranging from seasonal changes and storm events to human activities such as dredging or remediation efforts. This paper describes a program designed to evaluate the interrelationships between the microbial community and pollutants in the New Bedford Harbor, Massachusetts, area, a U.S. Environmental Protection Agency designated Superfund site. Research has focused on establishing distributional relationships between contaminant metals, fluxes of metals between sediments and the overlying water, changes in microbial diversity in response to metals, and potential use of the microbial community as a biomarker of contaminant availability. This research has shown that a significant flux of metals to the water column is mediated by benthic biologic activity, and that microbial communities may be a responsive marker of contaminant stress. A combination of biogeochemical studies and the use of molecular tools can be used to improve our understanding of the fate and effect of heavy metals released to aquatic systems

Statistical methods for automated drug susceptibility testing: Bayesian minimum inhibitory concentration prediction from growth curves

Determination of the minimum inhibitory concentration (MIC) of a drug that prevents microbial growth is an important step for managing patients with infections. In this paper we present a novel probabilistic approach that accurately estimates MICs based on a panel of multiple curves reflecting features of bacterial growth. We develop a probabilistic model for determining whether a given dilution of an antimicrobial agent is the MIC given features of the growth curves over time. Because of the potentially large collection of features, we utilize Bayesian model selection to narrow the collection of predictors to the most important variables. In addition to point estimates of MICs, we are able to provide posterior probabilities that each dilution is the MIC based on the observed growth curves. The methods are easily automated and have been incorporated into the Becton--Dickinson PHOENIX automated susceptibility system that rapidly and accurately classifies the resistance of a large number of microorganisms in clinical samples. Over seventy-five studies to date have shown this new method provides improved estimation of MICs over existing approaches.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS217 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org