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New Statistical Approach (new + statistical_approach)
Selected AbstractsPeripheral coding of bitter taste in DrosophilaDEVELOPMENTAL NEUROBIOLOGY, Issue 2 2003Nicolas Meunier Abstract Taste receptors play a crucial role in detecting the presence of bitter compounds such as alkaloids, and help to prevent the ingestion of toxic food. In Drosophila, we show for the first time that several taste sensilla on the prothoracic legs detect bitter compounds both through the activation of specific taste neurons but also through inhibition of taste neurons activated by sugars and water. Each sensillum usually houses a cluster of four taste neurons classified according to their best stimulus (S for sugar, W for Water, L1 and L2 for salts). Using a new statistical approach based on the analysis of interspike intervals, we show that bitter compounds activate the L2 cell. Bitter-activated L2 cells were excited with a latency of at least 50 ms. Their sensitivity to bitter compounds was different between sensilla, suggesting that specific receptors to bitter compounds are differentially expressed among L2 cells. When presented in mixtures, bitter compounds inhibited the responses of S and W, but not the L1 cell. The inhibition was effective even in sensilla where bitter compounds did not activate the L2 cell, indicating that bitter compounds directly interact with the S and W cells. Interestingly, this inhibition occurred with latencies similar to the excitation of bitter-activated L2 cells. It suggests that the inhibition in the W and S cells shares similar transduction pathways with the excitation in the L2 cells. Combined with molecular approaches, the results presented here should provide a physiological basis to understand how bitter compounds are detected and discriminated. © 2003 Wiley Periodicals, Inc. J Neurobiol 56: 139,152, 2003 [source] INVESTIGATING EVOLUTIONARY TRADE-OFFS IN WILD POPULATIONS OF ATLANTIC SALMON (SALMO SALAR): INCORPORATING DETECTION PROBABILITIES AND INDIVIDUAL HETEROGENEITYEVOLUTION, Issue 9 2010Mathieu Buoro Evolutionary trade-offs among demographic parameters are important determinants of life-history evolution. Investigating such trade-offs under natural conditions has been limited by inappropriate analytical methods that fail to address the bias in demographic estimates that can result when issues of detection (uncertain detection of individual) are ignored. We propose a new statistical approach to quantify evolutionary trade-offs in wild populations. Our method is based on a state-space modeling framework that focuses on both the demographic process of interest as well as the observation process. As a case study, we used individual mark,recapture data for stream-dwelling Atlantic salmon juveniles in the Scorff River (Southern Brittany, France). In freshwater, juveniles face two life-history choices: migration to the ocean and sexual maturation (for males). Trade-offs may appear with these life-history choices and survival, because all are energy dependent. We found a cost of reproduction on survival for fish staying in freshwater and a survival advantage associated with the "decision" to migrate. Our modeling framework opens up promising prospects for the study of evolutionary trade-offs when some life-history traits are not, or only partially, observable. [source] Discrimination of dynamical system models for biological and chemical processesJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 8 2007Sönke Lorenz Abstract In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007 [source] Attribution of tumour lethality and estimation of the time to onset of occult tumours in the absence of cause-of-death InformationJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 2 2000H. Ahn A new statistical approach is developed for estimating the carcinogenic potential of drugs and other chemical substances used by humans. Improved statistical methods are developed for rodent tumorigenicity assays that have interval sacrifices but not cause-of-death data. For such experiments, this paper proposes a nonparametric maximum likelihood estimation method for estimating the distributions of the time to onset of and the time to death from the tumour. The log-likelihood function is optimized using a constrained direct search procedure. Using the maximum likelihood estimators, the number of fatal tumours in an experiment can be imputed. By applying the procedure proposed to a real data set, the effect of calorie restriction is investigated. In this study, we found that calorie restriction delays the tumour onset time significantly for pituitary tumours. The present method can result in substantial economic savings by relieving the need for a case-by-case assignment of the cause of death or context of observation by pathologists. The ultimate goal of the method proposed is to use the imputed number of fatal tumours to modify Peto's International Agency for Research on Cancer test for application to tumorigenicity assays that lack cause-of-death data. [source] An Analysis Paradigm for Investigating Multi-locus Effects in Complex Disease: Examination of Three GABAA Receptor Subunit Genes on 15q11-q13 as Risk Factors for Autistic Disorder.ANNALS OF HUMAN GENETICS, Issue 3 2006A. E. Ashley-Koch Summary Gene-gene interactions are likely involved in many complex genetic disorders and new statistical approaches for detecting such interactions are needed. We propose a multi-analytic paradigm, relying on convergence of evidence across multiple analysis tools. Our paradigm tests for main and interactive effects, through allele, genotype and haplotype association. We applied our paradigm to genotype data from three GABAA receptor subunit genes (GABRB3, GABRA5, and GABRG3) on chromosome 15 in 470 Caucasian autism families. Previously implicated in autism, we hypothesized these genes interact to contribute to risk. We detected no evidence of main effects by allelic (PDT, FBAT) or genotypic (genotype-PDT) association at individual markers. However, three two-marker haplotypes in GABRG3 were significant (HBAT). We detected no significant multi-locus associations using genotype-PDT analysis or the EMDR data reduction program. However, consistent with the haplotype findings, the best single locus EMDR model selected a GABRG3 marker. Further, the best pairwise genotype-PDT result involved GABRB3 and GABRG3, and all multi-locus EMDR models also selected GABRB3 and GABRG3 markers. GABA receptor subunit genes do not significantly interact to contribute to autism risk in our overall data set. However, the consistency of results across analyses suggests that we have defined a useful framework for evaluating gene-gene interactions. [source] | ||||||||||