Evaluating prognostic accuracy of biomarkers under competing risk

To develop more targeted intervention strategies, an important research goal is to identify markers predictive of clinical events. A crucial step toward this goal is to characterize the clinical performance of a marker for predicting different types of events. In this article, we present statistical methods for evaluating the performance of a prognostic marker in predicting multiple competing events. To capture the potential time-varying predictive performance of the marker and incorporate competing risks, we define time- and cause-specific accuracy summaries by stratifying cases based on causes of failure. Such definition would allow one to evaluate the predictive accuracy of a marker for each type of event and compare its predictiveness across event types. Extending the nonparametric crude cause-specific receiver operating characteristics curve estimators by Saha and Heagerty (2010), we develop inference procedures for a range of cause-specific accuracy summaries. To estimate the accuracy measures and assess how covariates may affect the accuracy of a marker under the competing risk setting, we consider two forms of semiparametric models through the cause-specific hazard framework. These approaches enable a flexible modeling of the relationships between the marker and failure times for each cause, while efficiently accommodating additional covariates. We investigate the asymptotic property of the proposed accuracy estimators and demonstrate the finite sample performance of these estimators through simulation studies. The proposed procedures are illustrated with data from a prostate cancer prognostic study.     

Identification and Quantification of a New Family of Peptide Endocannabinoids (Pepcans) Showing Negative Allosteric Modulation at CB1 Receptors

The α-hemoglobin-derived dodecapeptide RVD-hemopressin (RVDPVNFKLLSH) has been proposed to be an endogenous agonist for the cannabinoid receptor type 1 (CB₁). To study this peptide, we have raised mAbs against its C-terminal part. Using an immunoaffinity mass spectrometry approach, a whole family of N-terminally extended peptides in addition to RVD-Hpα were identified in rodent brain extracts and human and mouse plasma. We designated these peptides Pepcan-12 (RVDPVNFKLLSH) to Pepcan-23 (SALSDLHAHKLRVDPVNFKLLSH), referring to peptide length. The most abundant Pepcans found in the brain were tested for CB₁ receptor binding. In the classical radioligand displacement assay, Pepcan-12 was the most efficacious ligand but only partially displaced both [³H]CP55,940 and [³H]WIN55,212-2. The data were fitted with the allosteric ternary complex model, revealing a cooperativity factor value α < 1, thus indicating a negative allosteric modulation. Dissociation kinetic studies of [³H]CP55,940 in the absence and presence of Pepcan-12 confirmed these results by showing increased dissociation rate constants induced by Pepcan-12. A fluorescently labeled Pepcan-12 analog was synthesized to investigate the binding to CB₁ receptors. Competition binding studies revealed K_i values of several Pepcans in the nanomolar range. Accordingly, using competitive ELISA, we found low nanomolar concentrations of Pepcans in human plasma and ∼100 pmol/g in mouse brain. Surprisingly, Pepcan-12 exhibited potent negative allosteric modulation of the orthosteric agonist-induced cAMP accumulation, [³⁵S]GTPγS binding, and CB₁ receptor internalization. Pepcans are the first endogenous allosteric modulators identified for CB₁ receptors. Given their abundance in the brain, Pepcans could play an important physiological role in modulating endocannabinoid signaling.     

Multiple loci mapping via model-free variable selection

Despite recent flourish of proposals on variable selection, genome-wide multiple loci mapping remains to be challenging. The majority of existing variable selection methods impose a model, and often the homoscedastic linear model, prior to selection. However, the true association between the phenotypical trait and the genetic markers is rarely known a priori, and the presence of epistatic interactions makes the association more complex than a linear relation. Model-free variable selection offers a useful alternative in this context, but the fact that the number of markers p often far exceeds the number of experimental units n renders all the existing model-free solutions that require n > p inapplicable. In this article, we examine a number of model-free variable selection methods for small-n-large-p regressions in the context of genome-wide multiple loci mapping. We propose and advocate a multivariate group-wise adaptive penalization solution, which requires no model prespecification and thus works for complex trait-marker association, and handles one variable at a time so that works for n < p. Effectiveness of the new method is demonstrated through both intensive simulations and a comprehensive real data analysis across 6100 gene expression traits.     

A Dominant Negative Heterozygous G87R Mutation in the Zinc Transporter, ZnT-2 (SLC30A2), Results in Transient Neonatal Zinc Deficiency

Zinc is an essential mineral, and infants are particularly vulnerable to zinc deficiency as they require large amounts of zinc for their normal growth and development. We have recently described the first loss-of-function mutation (H54R) in the zinc transporter ZnT-2 (SLC30A2) in mothers with infants harboring transient neonatal zinc deficiency (TNZD). Here we identified and characterized a novel heterozygous G87R ZnT-2 mutation in two unrelated Ashkenazi Jewish mothers with infants displaying TNZD. Transient transfection of G87R ZnT-2 resulted in endoplasmic reticulum-Golgi retention, whereas the WT transporter properly localized to intracellular secretory vesicles in HC11 and MCF-7 cells. Consequently, G87R ZnT-2 showed decreased stability compared with WT ZnT-2 as revealed by Western blot analysis. Three-dimensional homology modeling based on the crystal structure of YiiP, a close zinc transporter homologue from Escherichia coli, revealed that the basic arginine residue of the mutant G87R points toward the membrane lipid core, suggesting misfolding and possible loss-of-function. Indeed, functional assays including vesicular zinc accumulation, zinc secretion, and cytoplasmic zinc pool assessment revealed markedly impaired zinc transport in G87R ZnT-2 transfectants. Moreover, co-transfection experiments with both mutant and WT transporters revealed a dominant negative effect of G87R ZnT-2 over the WT ZnT-2; this was associated with mislocalization, decreased stability, and loss of zinc transport activity of the WT ZnT-2 due to homodimerization observed upon immunoprecipitation experiments. These findings establish that inactivating ZnT-2 mutations are an underlying basis of TNZD and provide the first evidence for the dominant inheritance of heterozygous ZnT-2 mutations via negative dominance due to homodimer formation.     

A generalized model for overdispersed count data

Overdispersed count data are very common in ecology. The negative binomial model has been used widely to represent such data. Ecological data often vary considerably, and traditional approaches are likely to be inefficient or incorrect due to underestimation of uncertainty and poor predictive power. We propose a new statistical model to account for excessive overdisperson. It is the combination of two negative binomial models, where the first determines the number of clusters and the second the number of individuals in each cluster. Simulations show that this model often performs better than the negative binomial model. This model also fitted catch and effort data for southern bluefin tuna better than other models according to AIC. A model that explicitly and properly accounts for overdispersion should contribute to robust management and conservation for wildlife and plants.     

A two-dimension otolith growth inverse model

An inverse growth model was developed to describe fish otolith ontogenetic growth to find the most parsimonious growth field that drives the actual shape of an internal incremental structure to the actual external shape of the otolith. This approach is based in a pure physic model, and the single implicit assumption was that the otolith accretion occurred perpendicular to its external surface. The model could be used to define a general hypothesis situation, in the sense that it predicts the most plausible shape of the otolith at any time, assuming that no additional external forces have acted. Therefore, severe departures between the observed growth structure and the closest prediction to it could be used to identify anatomical constraints and physiological factors that modulate the pure physical expectations.     

新型发热材料在医疗辅助用品上的应用 安全型电热毯与电热敷

本文应用最新超安全型发热材料（电热PTC,NTC性能的多层加热线）于医疗辅助用品电热毯,电热敷,进一步提升了这一传统产品的安全系数和产品性能,并以此为基础跨人了超安全型电热毯,电热敷这一行业发展的方向性领域。     

The relative impact of chronic food restriction and acute food deprivation on plasma hormone levels and hypothalamic neuropeptide expression

Our understanding of the central regulation of food intake and body weight has increased tremendously through implication of a high number of neuropeptides. However, lack of all-embracing studies have made comparison difficult in the past. The objective of this study was to demonstrate the relative importance of the different neuropeptides in terms of involvement in appetite regulatory mechanisms. We quantified expression levels of 21 hypothalamic neuropeptides and circulating levels of leptin, insulin, corticosterone, adrenocorticotropic hormone, ghrelin and adiponectin in rats after acute food deprivation and chronic food restriction using validated quantitative real-time PCR and hormone measurements. Body weight, insulin and leptin were reduced whereas corticosterone was increased by both acute food deprivation and chronic food restriction. Our results confirmed the relative importance in body weight homeostasis of neuropeptide Y and proopiomelanocortin, which were increased and decreased as predicted. The expression of other neuropeptides previously attributed central roles in body weight homeostasis, e.g. melanin-concentrating hormone and orexin, appeared to be less affected by the treatments. Moreover, the expression of dynorphin, galanin-like peptide and neuropeptide B was dramatically reduced after both treatments. This suggests that the latter neuropeptides - although previously known to be involved in body weight homeostasis - may be of unexpected importance in states of negative energy balance.     

Sliced inverse regression with regularizations

Summary .   In high-dimensional data analysis, sliced inverse regression (SIR) has proven to be an effective dimension reduction tool and has enjoyed wide applications. The usual SIR, however, cannot work with problems where the number of predictors, p , exceeds the sample size, n , and can suffer when there is high collinearity among the predictors. In addition, the reduced dimensional space consists of linear combinations of all the original predictors and no variable selection is achieved. In this article, we propose a regularized SIR approach based on the least-squares formulation of SIR. The L ₂ regularization is introduced, and an alternating least-squares algorithm is developed, to enable SIR to work with   n < p   and highly correlated predictors. The L ₁ regularization is further introduced to achieve simultaneous reduction estimation and predictor selection. Both simulations and the analysis of a microarray expression data set demonstrate the usefulness of the proposed method.