A Quantile Regression Approach Can Reveal the Effect of Fruit and Vegetable Consumption on Plasma Homocysteine Levels

Introduction

A reduction in homocysteine concentration due to the use of supplemental folic acid is well recognized, although evidence of the same effect for natural folate sources, such as fruits and vegetables (FV), is lacking. The traditional statistical analysis approaches do not provide further information. As an alternative, quantile regression allows for the exploration of the effects of covariates through percentiles of the conditional distribution of the dependent variable.

Objective

To investigate how the associations of FV intake with plasma total homocysteine (tHcy) differ through percentiles in the distribution using quantile regression.

Materials and Methods

A cross-sectional population-based survey was conducted among 499 residents of Sao Paulo City, Brazil. The participants provided food intake and fasting blood samples. Fruit and vegetable intake was predicted by adjusting for day-to-day variation using a proper measurement error model. We performed a quantile regression to verify the association between tHcy and the predicted FV intake. The predicted values of tHcy for each percentile model were calculated considering an increase of 200 g in the FV intake for each percentile.

Results

The results showed that tHcy was inversely associated with FV intake when assessed by linear regression whereas, the association was different when using quantile regression. The relationship with FV consumption was inverse and significant for almost all percentiles of tHcy. The coefficients increased as the percentile of tHcy increased. A simulated increase of 200 g in the FV intake could decrease the tHcy levels in the overall percentiles, but the higher percentiles of tHcy benefited more.

Conclusions

This study confirms that the effect of FV intake on lowering the tHcy levels is dependent on the level of tHcy using an innovative statistical approach. From a public health point of view, encouraging people to increase FV intake would benefit people with high levels of tHcy.     

A Morpho-Density Approach to Estimating Neural Connectivity

Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF) describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining a greater understanding of the relationship between neural morphology and network connectivity.     

A Rational Approach to Estimating the Surgical Demand Elasticity Needed to Guide Manpower Reallocation during Contagious Outbreaks

Background

Emerging infectious diseases continue to pose serious threats to global public health. So far, however, few published study has addressed the need for manpower reallocation needed in hospitals when such a serious contagious outbreak occurs.

Aim

To quantify the demand elasticity of the major surgery types in order to guide future manpower reallocation during contagious outbreaks.

Materials and Methods

Based on a nationwide research database in Taiwan, we extracted the monthly volumes of major surgery types for the period 1998–2003, which covered the SARS period, in order to carry out a time series analysis. The demand elasticity of each surgery type was then estimated by autoregressive integrated moving average (ARIMA) analysis.

Results

During the study period, the surgical volumes of most selected surgery types either increased or remained steady. We categorized these surgery types into low-, moderate- and high-elastic groups according to their demand elasticity. Appendectomy, ‘open reduction of fracture with internal fixation’ and ‘free skin graft’ were in the low demand elasticity group. Transurethral prostatectomy and extracorporeal shockwave lithotripsy (ESWL) were in the high demand elasticity group. The manpower of the departments carrying out the surgeries with low demand elasticity should be maintained during outbreaks. In contrast, departments in charge of surgeries mainly with high demand elasticity, like urology departments, may be in a position to have part of their staff reallocated.

Conclusions

Taking advantage of the demand variation during the SARS period in 2003, we adopted the concept of demand elasticity and used a time series approach to figure out an effective index of demand elasticity for various types of surgery that could be used as a rational reference to carry out manpower reallocation during contagious outbreak situations.     

The Binary Regression Quantile Plot: Assessing the Importance of Predictors in Binary Regression Visually

We present a graphical measure of assessing the explanatory power of regression models with a binary response. The binary regression quantile plot and an area defined by it are used for the visual comparison and ordering of nested binary response regression models. The plot shows how well various models explain the data. Two data sets are analyzed and the area representing the fit of a model is shown to agree with the usual likelihood ratio test.     

A Novel Approach to Estimating Heterozygosity from Low-Coverage Genome Sequence

High-throughput shotgun sequence data make it possible in principle to accurately estimate population genetic parameters without confounding by SNP ascertainment bias. One such statistic of interest is the proportion of heterozygous sites within an individual’s genome, which is informative about inbreeding and effective population size. However, in many cases, the available sequence data of an individual are limited to low coverage, preventing the confident calling of genotypes necessary to directly count the proportion of heterozygous sites. Here, we present a method for estimating an individual’s genome-wide rate of heterozygosity from low-coverage sequence data, without an intermediate step that calls genotypes. Our method jointly learns the shared allele distribution between the individual and a panel of other individuals, together with the sequencing error distributions and the reference bias. We show our method works well, first, by its performance on simulated sequence data and, second, on real sequence data where we obtain estimates using low-coverage data consistent with those from higher coverage. We apply our method to obtain estimates of the rate of heterozygosity for 11 humans from diverse worldwide populations and through this analysis reveal the complex dependency of local sequencing coverage on the true underlying heterozygosity, which complicates the estimation of heterozygosity from sequence data. We show how we can use filters to correct for the confounding arising from sequencing depth. We find in practice that ratios of heterozygosity are more interpretable than absolute estimates and show that we obtain excellent conformity of ratios of heterozygosity with previous estimates from higher-coverage data.     

A System Approach to Fluorescence Induction in Green Leaves

Intact green oat leaves (Avena saliva) were irradiated with modulated exciting light. Ai the application of initial square pulses of exciting light, the fluorescence induction pattern will change, but after some repeated pulses the fluorescence response pattern stabilizes and becomes identical. This periodic stimulation gave a better resolution of the two peaks constituting the fast fluorescence transient (the DPS region) compared to a single light pulse. The first peak could be depressed by weak light of wavelengths up to about 740 nm, given between the intense light phases of the pulse, while this treatment did not alter the second peak. The intensity of the higher light level of the pulse as well as the frequency were systematically varied. The results are tentatively rationalized in the form of sequential relaxation processes within the frame of the Hill-Bendull scheme, leading to the possibility of two substances, one belonging to Photosystem II and the other to Photosystem I. underlying the two peaks in the DPS region of the fluorescence induction curve.     

The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths.Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal / temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1).The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2)This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.     

Estimating Variable Effective Population Sizes from Multiple Genomes: A Sequentially Markov Conditional Sampling Distribution Approach

Throughout history, the population size of modern humans has varied considerably due to changes in environment, culture, and technology. More accurate estimates of population size changes, and when they occurred, should provide a clearer picture of human colonization history and help remove confounding effects from natural selection inference. Demography influences the pattern of genetic variation in a population, and thus genomic data of multiple individuals sampled from one or more present-day populations contain valuable information about the past demographic history. Recently, Li and Durbin developed a coalescent-based hidden Markov model, called the pairwise sequentially Markovian coalescent (PSMC), for a pair of chromosomes (or one diploid individual) to estimate past population sizes. This is an efficient, useful approach, but its accuracy in the very recent past is hampered by the fact that, because of the small sample size, only few coalescence events occur in that period. Multiple genomes from the same population contain more information about the recent past, but are also more computationally challenging to study jointly in a coalescent framework. Here, we present a new coalescent-based method that can efficiently infer population size changes from multiple genomes, providing access to a new store of information about the recent past. Our work generalizes the recently developed sequentially Markov conditional sampling distribution framework, which provides an accurate approximation of the probability of observing a newly sampled haplotype given a set of previously sampled haplotypes. Simulation results demonstrate that we can accurately reconstruct the true population histories, with a significant improvement over the PSMC in the recent past. We apply our method, called diCal, to the genomes of multiple human individuals of European and African ancestry to obtain a detailed population size change history during recent times.     

A Life History Approach to Understanding Youth Time Preference

Following from life history and attachment theory, individuals are predicted to be sensitive to variation in environmental conditions such that risk and uncertainty are internalized by cognitive, affective, and psychobiological mechanisms. In turn, internalizing of environmental uncertainty is expected to be associated with attitudes toward risk behaviors and investments in education. Native American youth aged 10–19 years (n = 89) from reservation communities participated in a study examining this pathway. Measures included family environmental risk and uncertainty, present and future time perspective, adolescent attachment, attitudes toward risk, investments in education, and salivary cortisol. Results support the idea that environmental risk and uncertainty are internalized during development. In addition, internalizing mechanisms significantly predicted attitudes toward risk and education: (1) lower scores on future time perspective and higher cortisol predicted higher scores on risk attitudes, and (2) higher scores on future time perspective and lower scores on problems with attachment predicted higher self-reported school performance. Gender differences were seen, with males anticipating a shorter lifespan than females, which predicted higher scores on risk attitudes and lower school performance. Implications for research on adolescent problem behavior and academic achievement are discussed.     

Development and Validation of Computed Tomography Derived Anthropometric Regression Equations for Estimating Abdominal Adipose Tissue Distribution

The purpose of this study was twofold: (1) to develop multiple regression equations for predicting computed tomography (CT) derived intra-abdominal (IAF), subcutaneous (SCF), and total (TOTF= IAF+SCF) abdominal adipose tissue areas from anthropometric measures in adult white males with a large range of age (18–71 years) and percent body fat (2.0–40.6); and (2) to validate the new and existing equations that used similar Hounsfield Units (HU) for determining IAF for estimating these fat depots. One hundred fifty-one white male subjects had IAF, SCF, and TOTF determined by a single CT scan, skinfold and circumference measures taken and body density determined. Linear intra-correlations and factor analysis procedures were used to identify variables for inclusion in stepwise multiple regression solutions. IAF was estimated from age, waist circumference, the sum of mid-thigh and lower thigh circumferences, and vertical abdominal skinfold. SCF was estimated from age, umbilicus circumference, chest and suprailiac skinfolds. TOTF was estimated from age, body mass index (BMI), chest skinfold, and umbilicus circumference. R² for IAF, SCF, and TOTF was .73, .77, and .86 respectively. The existing and the new equations were validated on an independent sub-sample of 51 subjects. The only existing equation that met validation criteria had a validation R² = .67 for IAF. All three new equations met validation criteria with R ² validations of .75, .79, and .85 for IAF, SCF, and TOTF respectively. It is concluded that the new equations might be used as an inexpensive estimation of IAF, SCF, and TOTF in adult white males varying greatly in age and percent body fat.     

Regression Model for Time to Flowering of Chickpea Landraces

Russian Journal of Genetics - Regression models for time to flowering had been developed for VIR chickpea landraces collected in Turkey and Ethiopia. Predicted flowering time coincides closely with...     

介绍一种计算国家财富的新方法

介绍一种计算国家财富的新方法曹志平（北京农业大学生物学院,１０００９４）ＩｎｔｒｏｄｕｃｔｉｏｎｔｏａＮｅｗＡｐｐｒｏａｃｈｏｆＥｓｔｉｍａｔｉｎｇｔｈｅＮａｔｉｏｎａｌＷｅａｌｔｈ．ＣａｏＺｈｉｐｉｎｇ（ＣｏｌｌｅｇｅｏｆＢｉｏｌｏｇｙ,Ｂｅｉｊ...     

A Simple Numerical Procedure for Estimating the Prior Probability in the Biomathematical Proof of Paternity

This note gives a simple procedure for finding the maximum likelihood estimate of the prior probabilities in paternity cases. The procedure is based on a fixed point principle.     

A First-Passage Time Approach to Diffusion in Liquids and Superionic Conductors

Abstract

As a new tool to investigate single-particle motion in condensed matter, a first-passage time (FPT) approach to diffusion is developed and applied to the molecular dynamics simulations of simple liquids and superionic conductor CaF₂. It is shown that a continuous diffusion model reproduces the observed FPT distribution quite well for both liquids and CaF₂, which enables us to evaluate diffusion constants with good accuracy by our method. On a length scale as small as a lattice constant, however, the effect of hopping appears in the FPT distribution of F^? ions, which can not be described by a continuous diffusion model. A simple hopping diffusion model is proposed and examined from the FPT viewpoint.     

No Longer Confidential: Estimating the Confidence of Individual Regression Predictions

Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is–a fact that often reduces a user’s trust into a computational method. In analogy to the concept of an experimental error, we sketch how estimators for individual prediction errors can be used to provide confidence intervals for individual predictions. Two novel statistical methods, named CONFINE and CONFIVE, can estimate the reliability of an individual prediction based on the local properties of nearby training data. The methods can be applied equally to linear and non-linear regression methods with very little computational overhead. We compare our confidence estimators with other existing confidence and applicability domain estimators on two biologically relevant problems (MHC–peptide binding prediction and quantitative structure-activity relationship (QSAR)). Our results suggest that the proposed confidence estimators perform comparable to or better than previously proposed estimation methods. Given a sufficient amount of training data, the estimators exhibit error estimates of high quality. In addition, we observed that the quality of estimated confidence intervals is predictable. We discuss how confidence estimation is influenced by noise, the number of features, and the dataset size. Estimating the confidence in individual prediction in terms of error intervals represents an important step from plain, non-informative predictions towards transparent and interpretable predictions that will help to improve the acceptance of computational methods in the biological community.     

A Rapid and Efficient Estimation Procedure for the Negative Binomial Distribution

This paper presents a new noniterative procedure for estimating the parameters of a negative binomial distribution. The procedure uses the first moment equation and an equation based on the weighted sample mean, with weights ω_x∝ α^z. The selection of a value for α is examined. A simulation study has been carried out and also the method has been applied to the 35 data sets analysed by Martin and Katti (1965, Biometrics) in order to compare it with the method of moments and with the method of maximum likelihood (ML). We conclude that the new procedure has greater relative efficiency than the method of moments; it gives estimates which are consistently close to ML and are easy to calculate.     

In Vitro Induction of Antibody-Dependent Cytotoxic Macrophages by the Local Anesthetic Lidocaine

Normal macrophages were activated to antibody-dependent cytotoxic effector cells by in vitro treatment with the local anesthetic lidocaine. Experiments on the dose-response and time course of the effect of lidocaine showed that incubation of normal macrophages with 10 mM lidocaine for 10 min at 28 C was enough for induction of antibody-dependent cellular cytotoxicity. The activation by lidocaine was accompanied by enhanced phagocytosis of sheep red blood cells (SRBC) sensitized with anti-SRBC antiserum, but not enhanced ingestion of polystyrene latex particles (PLP). These findings suggest that lidocaine, which has various effects on cell membranes, induces some perturbation of macrophage membranes, resulting in activation of Fc receptor functions in antibody-dependent cytotoxicity and phagocytosis.     

A Regression Modeling Approach for Describing Patterns of HIV Genetic Variation

We introduce a novel approach for describing patterns of HIV genetic variation using regression modeling techniques. Parameters are defined for describing genetic variation within and between viral populations by generalizing Simpson's index of diversity. Regression models are specified for these variation parameters and the generalized estimating equation framework is used for estimating both the regression parameters and their corresponding variances. Conditions are described under which the usual asymptotic approximations to the distribution of the estimators are met. This approach provides a formal statistical framework for testing hypotheses regarding the changing patterns of HIV genetic variation over time within an infected patient. The application of these methods for testing biologically relevant hypotheses concerning HIV genetic variation is demonstrated in an example using sequence data from a subset of patients from the Multicenter AIDS Cohort Study.     

Quantifying the Impact of Gestational Diabetes Mellitus,Maternal Weight and Race on Birthweight via Quantile Regression

Background

Quantile regression, a robust semi-parametric approach, was used to examine the impact of gestational diabetes mellitus (GDM) across birthweight quantiles with a focus on maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG).

Methods

Using linked birth certificate, inpatient hospital and prenatal claims data we examined live singleton births to non-Hispanic white (NHW, 135,119) and non-Hispanic black (NHB, 76,675) women in South Carolina who delivered 28–44 weeks gestation in 2004–2008.

Results

At a maternal BMI of 30 kg/m² at the 90^th quantile of birthweight, exposure to GDM was associated with birthweights 84 grams (95% CI 57, 112) higher in NHW and 132 grams (95% CI: 104, 161) higher in NHB. Results at the 50^th quantile were 34 grams (95% CI: 17, 51) and 78 grams (95% CI: 56, 100), respectively. At a maternal GWG of 13.5 kg at the 90^th quantile of birthweight, exposure to GDM was associated with birthweights 83 grams (95% CI: 57, 109) higher in NHW and 135 grams (95% CI: 103, 167) higher in NHB. Results at the 50^th quantile were 55 grams (95% CI: 40, 71) and 69 grams (95% CI: 46, 92), respectively.

Summary

Our findings indicate that GDM, maternal prepregnancy BMI and GWG increase birthweight more in NHW and NHB infants who are already at the greatest risk of macrosomia or being large for gestational age (LGA), that is those at the 90^th rather than the median of the birthweight distribution.