Probit models for capture-recapture data subject to imperfect detection, individual heterogeneity and misidentification

As noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). However, current methods do not allow for individual variation in parameters, such as detection or survival probability. Here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. To facilitate Bayesian inference using our approach, we extend standard probit regression techniques to latent multinomial models where the dimension and zeros of the response cannot be observed. We also present a novel Metropolis-Hastings within Gibbs algorithm for fitting these models using Markov chain Monte Carlo. Using closed population abundance models for illustration, we re-visit a DNA capture-recapture population study of black bears in Michigan, USA and find evidence of misidentification due to genotyping error, as well as temporal, behavioral and individual variation in detection probability. We also estimate a salamander population of known size from laboratory experiments evaluating the effectiveness of a marking technique commonly used for amphibians and fish. Our model was able to reliably estimate the size of this population and provided evidence of individual heterogeneity in misidentification probability that is attributable to variable mark quality. Our approach is more computationally demanding than previously proposed methods, but it provides the flexibility necessary for a much broader suite of models to be explored while properly accounting for uncertainty introduced by misidentification and imperfect detection. In the absence of misidentification, our probit formulation also provides a convenient and efficient Gibbs sampler for Bayesian analysis of traditional closed population capture-recapture data.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS783 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Meeting Report: Hazard Assessment for Nanoparticles—Report from an Interdisciplinary Workshop

In this report we present the findings from a nanotoxicology workshop held 6–7 April 2006 at the Woodrow Wilson International Center for Scholars in Washington, DC. Over 2 days, 26 scientists from government, academia, industry, and nonprofit organizations addressed two specific questions: what information is needed to understand the human health impact of engineered nanoparticles and how is this information best obtained? To assess hazards of nanoparticles in the near-term, most participants noted the need to use existing in vivo toxicologic tests because of their greater familiarity and interpretability. For all types of toxicology tests, the best measures of nanoparticle dose need to be determined. Most participants agreed that a standard set of nanoparticles should be validated by laboratories worldwide and made available for benchmarking tests of other newly created nanoparticles. The group concluded that a battery of tests should be developed to uncover particularly hazardous properties. Given the large number of diverse materials, most participants favored a tiered approach. Over the long term, research aimed at developing a mechanistic understanding of the numerous characteristics that influence nanoparticle toxicity was deemed essential. Predicting the potential toxicity of emerging nanoparticles will require hypothesis-driven research that elucidates how physicochemical parameters influence toxic effects on biological systems. Research needs should be determined in the context of the current availability of testing methods for nanoscale particles. Finally, the group identified general policy and strategic opportunities to accelerate the development and implementation of testing protocols and ensure that the information generated is translated effectively for all stakeholders

Financial and Psychological Risk Attitudes Associated with Two Single Nucleotide Polymorphisms in the Nicotine Receptor (CHRNA4) Gene

With recent advances in understanding of the neuroscience of risk taking, attention is now turning to genetic factors that may contribute to individual heterogeneity in risk attitudes. In this paper we test for genetic associations with risk attitude measures derived from both the psychology and economics literature. To develop a long-term prospective study, we first evaluate both types of risk attitudes and find that the economic and psychological measures are poorly correlated, suggesting that different genetic factors may underlie human response to risk faced in different behavioral domains. We then examine polymorphisms in a spectrum of candidate genes that affect neurotransmitter systems influencing dopamine regulation or are thought to be associated with risk attitudes or impulsive disorders. Analysis of the genotyping data identified two single nucleotide polymorphisms (SNPs) in the gene encoding the alpha 4 nicotine receptor (CHRNA4, rs4603829 and rs4522666) that are significantly associated with harm avoidance, a risk attitude measurement drawn from the psychology literature. Novelty seeking, another risk attitude measure from the psychology literature, is associated with several COMT (catechol-O-methyl transferase) SNPs while economic risk attitude measures are associated with several VMAT2 (vesicular monoamine transporter) SNPs, but the significance of these associations did not withstand statistical adjustment for multiple testing and requires larger cohorts. These exploratory results provide a starting point for understanding the genetic basis of risk attitudes by considering the range of methods available for measuring risk attitudes and by searching beyond the traditional direct focus on dopamine and serotonin receptor and transporter genes

PROBIT MODELS FOR CAPTURE-RECAPTURE DATA SUBJECT TO IMPERFECT DETECTION, INDIVIDUAL HETEROGENEITY AND MISIDENTIFICATION1

As noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). However, current methods do not allow for individual variation in parameters, such as detection or survival probability. Here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. To facilitate Bayesian inference using our approach, we extended standard probit regression techniques to latent multinomial models where the dimension and zeros of the response cannot be observed. We also present a novel Metropolis-Hastings within Gibbs algorithm for fitting these models using Markov chain Monte Carlo. Using closed population abundance models for illustration, we re-visit a DNA capture-recapture population study of black bears in Michigan, USA and find evidence of misidentification due to genotyping error, as well as temporal, behavioral and individual variation in detection probability. We also estimate a salamander population of known size from laboratory experiments evaluating the effectiveness of a marking technique commonly used for amphibians and fish. Our models was able to reliably estimate the size of this population and provided evidence of individual heterogeneity in misidentification probability that is attributable to variable mark quality. Our approach is more computationally demanding than previously proposed methods, but it provides the flexibility necessary for a much broader suite of models to be explored while properly accounting for uncertainty introduced by misidentification and imperfect detection. In the absence of misidentification, our probit formulation also provides a convenient and efficient Gibbs sampler for Bayesian analysis of traditional closed population capture-recapture data

PROBIT MODELS FOR CAPTURE-RECAPTURE DATA SUBJECT TO IMPERFECT DETECTION, INDIVIDUAL HETEROGENEITY AND MISIDENTIFICATION1

As noninvasive sampling techniques for animal populations have become more popular, there has been increasing interest in the development of capture-recapture models that can accommodate both imperfect detection and misidentification of individuals (e.g., due to genotyping error). However, current methods do not allow for individual variation in parameters, such as detection or survival probability. Here we develop misidentification models for capture-recapture data that can simultaneously account for temporal variation, behavioral effects and individual heterogeneity in parameters. To facilitate Bayesian inference using our approach, we extended standard probit regression techniques to latent multinomial models where the dimension and zeros of the response cannot be observed. We also present a novel Metropolis-Hastings within Gibbs algorithm for fitting these models using Markov chain Monte Carlo. Using closed population abundance models for illustration, we re-visit a DNA capture-recapture population study of black bears in Michigan, USA and find evidence of misidentification due to genotyping error, as well as temporal, behavioral and individual variation in detection probability. We also estimate a salamander population of known size from laboratory experiments evaluating the effectiveness of a marking technique commonly used for amphibians and fish. Our models was able to reliably estimate the size of this population and provided evidence of individual heterogeneity in misidentification probability that is attributable to variable mark quality. Our approach is more computationally demanding than previously proposed methods, but it provides the flexibility necessary for a much broader suite of models to be explored while properly accounting for uncertainty introduced by misidentification and imperfect detection. In the absence of misidentification, our probit formulation also provides a convenient and efficient Gibbs sampler for Bayesian analysis of traditional closed population capture-recapture data

Palladium-Catalyzed Enantioselective Arylation of Aryl Sulfenate Anions: A Combined Experimental and Computational Study

A novel approach to produce chiral diaryl sulfoxides from aryl benzyl sulfoxides and aryl bromides via an enantioselective arylation of aryl sulfenate anions is reported. A (JosiPhos)Pd-based catalyst successfully promotes the asymmetric arylation reaction with good functional group compatibility. A wide range of enantioenriched diaryl, aryl heteroaryl, and even diheteroaryl sulfoxides were generated. Many of the sulfoxides prepared herein would be difficult to prepare via classic enantioselective oxidation of sulfides, including Ph(Ph-d5)SO (90% ee, 95% yield). A DFT-based computational study suggested that chiral induction originates from two primary factors: (i) both a kinetic and a thermodynamic preference for oxidative addition that places the bromide trans to the JosiPhos-diarylphosphine moiety and (ii) Curtin-Hammett-type control over the interconversion between O- and S-bound isomers of palladium sulfenate species following rapid interconversion between re- and si-bound transmetalation products, re/si-Pd-OSPh (re/si-PdO-trans).Fil: Jia, Tiezheng. University of Pennsylvania; Estados UnidosFil: Zhang, Mengnan. University of Pennsylvania; Estados UnidosFil: McCollom, Samuel P.. University of Pennsylvania; Estados UnidosFil: Bellomo Peraza, Ana Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Bionanociencias ; ArgentinaFil: Montel, Sonia. University of Pennsylvania; Estados UnidosFil: Mao, Jianyou. Nanjing Tech University; República de ChinaFil: Dreher, Spencer D.. Merck & Company; Estados UnidosFil: Welch, Christopher J.. Merck & Company; Estados UnidosFil: Regalado, Erik L.. Merck & Company; Estados UnidosFil: Williamson, R. Thomas. Merck & Company; Estados UnidosFil: Manor, Brian C.. Merck & Company; Estados UnidosFil: Tomson, Neil C.. University of Pennsylvania; Estados UnidosFil: Walsh, Patrick J.. University of Pennsylvania; Estados Unido