Binary Regression Analysis with Pooled Exposure Measurements: A Regression Calibration Approach

Summary It has become increasingly common in epidemiological studies to pool specimens across subjects to achieve accurate quantitation of biomarkers and certain environmental chemicals. In this article, we consider the problem of fitting a binary regression model when an important exposure is subject to pooling. We take a regression calibration approach and derive several methods, including plug‐in methods that use a pooled measurement and other covariate information to predict the exposure level of an individual subject, and normality‐based methods that make further adjustments by assuming normality of calibration errors. Within each class we propose two ways to perform the calibration (covariate augmentation and imputation). These methods are shown in simulation experiments to effectively reduce the bias associated with the naive method that simply substitutes a pooled measurement for all individual measurements in the pool. In particular, the normality‐based imputation method performs reasonably well in a variety of settings, even under skewed distributions of calibration errors. The methods are illustrated using data from the Collaborative Perinatal Project.     

持续镇痛分娩对产妇分娩结局和新生儿评分的影响

目的:研究持续镇痛分娩对产妇分娩结局和新生儿评分的影响。方法:选择2018年7月～2019年7月中国医科大学航空总医院(本院)采取硬膜外分娩镇痛的101例产妇,将其随机分为两组。当产生确切的镇痛效果,进入第二产程后,观察组的51例产妇采用0.4μg/m L舒芬太尼以及0.08%罗哌卡因进行持续镇痛分娩;对照组的50例产妇则在宫口开全后,使用生理盐水替代泵内的局麻药物,直到分娩结束。比较两组产妇催产素的使用率,宫口扩张度和第一、第二产程按压硬膜外自控镇痛泵的次数,分娩方式,新生儿的体质量,脐动脉血pH值,出生后1 min和5 min Apgar评分,产妇修复会阴部时的视觉模拟评分(visual analogue scale, VAS)评分及产妇对于第二产程镇痛的满意度评分。结果:两组产妇催产素的使用率、宫口扩张度和第一、第二产程按压硬膜外自控镇痛泵的次数、分娩方式(剖宫产率、器械助产率、自然分娩率)、第一产程镇痛时间、第一以及第二产程时间相比均无显著差异(P0.05);两组新生儿的体质量,脐动脉血pH值,出生后1 min和5 min Apgar评分小于8分的新生儿所占的比例相比没有明显的差异(P0.05);观察组产妇修复会阴部时的VAS评分明显低于对照组(P0.05),产妇对于第二产程镇痛的满意度评分明显高于对照组(P0.05)。结论:持续镇痛分娩对产妇分娩结局和新生儿评分无明显的影响,但可显著提高产妇对第二产程镇痛和修复会阴部时镇痛的满意度。     

Analysis of Longitudinal Data of Epileptic Seizure Counts – A Two‐State Hidden Markov Regression Approach

This paper discusses a two‐state hidden Markov Poisson regression (MPR) model for analyzing longitudinal data of epileptic seizure counts, which allows for the rate of the Poisson process to depend on covariates through an exponential link function and to change according to the states of a two‐state Markov chain with its transition probabilities associated with covariates through a logit link function. This paper also considers a two‐state hidden Markov negative binomial regression (MNBR) model, as an alternative, by using the negative binomial instead of Poisson distribution in the proposed MPR model when there exists extra‐Poisson variation conditional on the states of the Markov chain. The two proposed models in this paper relax the stationary requirement of the Markov chain, allow for overdispersion relative to the usual Poisson regression model and for correlation between repeated observations. The proposed methodology provides a plausible analysis for the longitudinal data of epileptic seizure counts, and the MNBR model fits the data much better than the MPR model. Maximum likelihood estimation using the EM and quasi‐Newton algorithms is discussed. A Monte Carlo study for the proposed MPR model investigates the reliability of the estimation method, the choice of probabilities for the initial states of the Markov chain, and some finite sample behaviors of the maximum likelihood estimates, suggesting that (1) the estimation method is accurate and reliable as long as the total number of observations is reasonably large, and (2) the choice of probabilities for the initial states of the Markov process has little impact on the parameter estimates.     

A Population Accounting Approach to Assess Tourism Contributions to Conservation of IUCN-Redlisted Mammal Species

Over 1,000 mammal species are red-listed by IUCN, as Critically Endangered, Endangered or Vulnerable. Conservation of many threatened mammal species, even inside protected areas, depends on costly active day-to-day defence against poaching, bushmeat hunting, invasive species and habitat encroachment. Many parks agencies worldwide now rely heavily on tourism for routine operational funding: >50% in some cases. This puts rare mammals at a new risk, from downturns in tourism driven by external socioeconomic factors. Using the survival of individual animals as a metric or currency of successful conservation, we calculate here what proportions of remaining populations of IUCN-redlisted mammal species are currently supported by funds from tourism. This proportion is ≥5% for over half of the species where relevant data exist, ≥15% for one fifth, and up to 66% in a few cases. Many of these species, especially the most endangered, survive only in one single remaining subpopulation. These proportions are not correlated either with global population sizes or recognition as wildlife tourism icons. Most of the more heavily tourism-dependent species, however, are medium sized (>7.5 kg) or larger. Historically, biological concern over the growth of tourism in protected areas has centered on direct disturbance to wildlife. These results show that conservation of threatened mammal species has become reliant on revenue from tourism to a previously unsuspected degree. On the one hand, this provides new opportunities for conservation funding; but on the other, dependence on such an uncertain source of funding is a new, large and growing threat to red-listed species.     

A Fuzzy Logic Approach to Assess,Manage, and Communicate Carcinogenic Risk

A prospective approach to addressing carcinogen risk assessment is presented. Fuzzy reasoning is used to assess carcinogenic risk, characterize it, and control it. The approach is inspired by fuzzy control inference that deploys linguistic intelligence as input to a system described numerically through membership functions. Fuzzy-based reasoning to estimate carcinogenic risk provides several advantages as discussed here. The fuzzy reasoning approach has more capabilities than traditional models in dealing with risk agents that are probably carcinogens, possibly carcinogens, not classifiable as carcinogens, and probably not carcinogens. Input–output surfaces are presented for each hazard group to enable fast inferencing. Then, a hypothetical example is given to compare the results of traditional methods and the fuzzy-based approach to estimating the risk of a carcinogen to a human population. Results show similarity in risk characterization with less input information to the fuzzy-based approach. Fuzzy reasoning characterizes risk in more explicit and easy to grasp terms. Two outputs of the inferencing system are risk characterization and risk control or remediation.     

A Methodological Approach to Assess Cross-scale Relations and Interactions in Agricultural Ecosystems of Argentina

Agronomists, environmentalists, land managers, policy makers and development agents who make decisions at different levels (plot, farm, ecosystem, landscape, ecoregion, country) increasingly demand scientific information to understand cross-level links and interactions in systems that behave hierarchically. The purpose of this work is to study cross-scale relations and interactions in agricultural ecosystems of the Argentine Pampas. Based on public censuses and surveys from 1960 to 1996, data on crops cultivation, fossil energy (FE) consumption and energy productivity were used for the analysis. The whole region was divided into eight increasing geographic scales, and the impact of one scale on the broader ones was assessed through a simple analysis of energy productivity. Regression analysis was used to identify cross-scale linear slopes and to determine cross-scale interactions. We assume that non-parallel slopes in different years are attributable to cross-scale interactions. Given that the results show highly significant differences among inter-year slopes, we must accept that cross-scale interactions have occurred. Beyond any random behavior, it seems quite possible that decisions made at smaller scales may have a demonstrable effect on broader scales. Specific research is needed to elucidate key cause–effect relationships among scales. In principle, the possible interaction between intensification factors (for example, FE consumption) and spatial scales is a plausible hypothesis to be tested in the study area.     

A Comprehensive Genetic Approach for Improving Prediction of Skin Cancer Risk in Humans

Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.     

Regression Trees for Survival Data — an Approach to Select Discontinuous Split Points by Rank Statistics

Regression trees allow to search for meaningful explanatory variables that have a non linear impact on the dependent variable. Often they are used when there are many covariates and one does not want to restrict attention to only few of them. To grow a tree at each stage one has to select a cut point for splitting a group into two subgroups. The basis for this are the maxima of the test statistics related to the possible splits due to every covariate. They or the resulting P-values are compared as measure of importance. If covariates have different numbers of missing values, ties, or even different measurement scales the covariates lead to different numbers of tests. Those with a higher number of tests have a greater chance to achieve a smaller P-value if they are not adjusted. This can lead to erroneous splits even if the P-values are looked at informally. There is some theoretical work by Miller and Siegmund (1982) and Lausen and Schumacher (1992) to give an adjustment rule. But the asymptotic is based on a continuum of split points and may not lead to a fair splitting rule when applied to smaller data sets or to covariates with only few different values. Here we develop an approach that allows determination of P-values for any number of splits. The only approximation that is used is the normal approximation of the test statistics. The starting point for this investigation has been a prospective study on the development of AIDS. This is presented here as the main application.     

A Note on Maximum Likelihood Ratio Test of No Outliers in Regression Models

A derivation of the maximum likelihood ratio test for testing no outliers in regression models is given using the method of WETHERILL (1981, pp. 106–107) for estimating the regression parameters. This method is essentially similar to the one outlined in BARNETT and LEWIS (1978, p. 263), although by our detailed derivation it is easier to see that the maximum likelihood estimate of θ of model (3) under the hypothesis that the i^th observation in an outlier is the same as that obtained from model (1) when the i^th observation is removed.     

From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach

People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models—that is, an algorithm for using sensory signals to infer modality-independent representations. To evaluate this hypothesis, we instantiate it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit and a human hand simulator to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. We also report the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and show that the model provides a very accurate account of subjects’ ratings. Conceptually, this research significantly contributes to our understanding of modality invariance, an important type of perceptual constancy, by demonstrating how modality-independent representations can be acquired and used. Methodologically, it provides an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception.     

A Multi-biological Assay Approach to Assess Microbial Diversity in Arsenic (As) Contaminated Soils

Microbial genetic, structural and functional diversity was assessed in response to arsenic (As) pollution in a former gold mine soil. Ester-linked fatty acid methyl ester (EL-FAME), quantitative PCR (qPCR), denaturating gradient gel electrophoresis (DGGE), enzyme activities and MicroResp techniques were used.

Multivariate analysis showed that As bioavailability in soil was an important driver affecting microbial diversity. Microbial biomass assessed by EL-FAMEs and qPCR generally decreased under higher bioavailable As, as well as enzyme activities and C substrate utilization. Conversely, actinomycetes and fungal biomass increased along with total As content suggesting the selection of more resistant species.     

A Multidisciplinary Approach to Assess the Welfare of Weaned Pigs During Transport at Three Space Allowances

Transport can be a stressful experience for pigs, especially in pigs simultaneously experiencing weaning stress. The objective of this study was to use a multidisciplinary approach to assess the welfare of weaned pigs during transport at 3 space allowances. A commercial semitrailer, fitted with compartments, provided 0.05, 0.06, and 0.07 m²/pig. The study recorded frequency of standing, lying, sitting, and standing-rearing on another pig during the entire duration of transport. Blood samples, body weights, and lesion scores were collected from a subset of pigs (n = 48 per space allowance) in each experimental compartment. Transport time for the pigs was 148.0 ± 10.0 min to the wean-to-finishing site. Total white blood cell counts, cortisol, and several blood chemistry values increased (p < .05) after transport regardless of space allowance. Glucose and body weight decreased (p < .05) after transport regardless of space allowance. Space allowance influenced stand-rearing, sitting, standing, and lying behaviors in pigs. Combining behavioral and physiological measures of stress provides a robust picture of piglet welfare during transport at different space allowances.     

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

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Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands

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Highlights

• •Curation of 2066 phosphorylated HLA class I peptides from immunopeptidomics data.
• •Determination of 22 HLA class I binding motifs for phosphorylated peptides.
• •Observation of a higher frequency of phosphorylated ligands binding HLA-C molecules.
• •Development of a predictor of phosphorylated peptide interactions with HLA class I.

     

A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction

Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT). Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2) required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006). Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001). Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.     

A Whole Cell Bioreporter Approach to Assess Transport and Bioavailability of Organic Contaminants in Water Unsaturated Systems

Bioavailability of contaminants is a prerequisite for their effective biodegradation in soil. The average bulk concentration of a contaminant, however, is not an appropriate measure for its availability; bioavailability rather depends on the dynamic interplay of potential mass transfer (flux) of a compound to a microbial cell and the capacity of the latter to degrade the compound. In water-unsaturated parts of the soil, mycelia have been shown to overcome bioavailability limitations by actively transporting and mobilizing organic compounds over the range of centimeters. Whereas the extent of mycelia-based transport can be quantified easily by chemical means, verification of the contaminant-bioavailability to bacterial cells requires a biological method. Addressing this constraint, we chose the PAH fluorene (FLU) as a model compound and developed a water unsaturated model microcosm linking a spatially separated FLU point source and the FLU degrading bioreporter bacterium Burkholderia sartisoli RP037-mChe by a mycelial network of Pythium ultimum. Since the bioreporter expresses eGFP in response of the PAH flux to the cell, bacterial FLU exposure and degradation could be monitored directly in the microcosms via confocal laser scanning microscopy (CLSM). CLSM and image analyses revealed a significant increase of the eGFP expression in the presence of P. ultimum compared to controls without mycelia or FLU thus indicating FLU bioavailability to bacteria after mycelia-mediated transport. CLSM results were supported by chemical analyses in identical microcosms. The developed microcosm proved suitable to investigate contaminant bioavailability and to concomitantly visualize the involved bacteria-mycelial interactions.     

A Multiple Imputation Approach to Regression Analysis for Doubly Censored Data with Application to AIDS Studies

Sun, Liao, and Pagano (1999) proposed an interesting estimating equation approach to Cox regression with doubly censored data. Here we point out that a modification of their proposal leads to a multiple imputation approach, where the double censoring is reduced to single censoring by imputing for the censored initiating times. For each imputed data set one can take advantage of many existing techniques and software for singly censored data. Under the general framework of multiple imputation, the proposed method is simple to implement and can accommodate modeling issues such as model checking, which has not been adequately discussed previously in the literature for doubly censored data. Here we illustrate our method with an application to a formal goodness-of-fit test and a graphical check for the proportional hazards model for doubly censored data. We reanalyze a well-known AIDS data set.     

A Stochastic Regression Model for General Trend Analysis of Longitudinal Continuous Data

A predictive continuous time model is developed for continuous panel data to assess the effect of time‐varying covariates on the general direction of the movement of a continuous response that fluctuates over time. This is accomplished by reparameterizing the infinitesimal mean of an Ornstein–Uhlenbeck processes in terms of its equilibrium mean and a drift parameter, which assesses the rate that the process reverts to its equilibrium mean. The equilibrium mean is modeled as a linear predictor of covariates. This model can be viewed as a continuous time first‐order autoregressive regression model with time‐varying lag effects of covariates and the response, which is more appropriate for unequally spaced panel data than its discrete time analog. Both maximum likelihood and quasi‐likelihood approaches are considered for estimating the model parameters and their performances are compared through simulation studies. The simpler quasi‐likelihood approach is suggested because it yields an estimator that is of high efficiency relative to the maximum likelihood estimator and it yields a variance estimator that is robust to the diffusion assumption of the model. To illustrate the proposed model, an application to diastolic blood pressure data from a follow‐up study on cardiovascular diseases is presented. Missing observations are handled naturally with this model.