A marginal likelihood approach for estimating penetrance from kin-cohort designs

The kin-cohort design is a promising alternative to traditional cohort or case-control designs for estimating penetrance of an identified rare autosomal mutation. In this design, a suitably selected sample of participants provides genotype and detailed family history information on the disease of interest. To estimate penetrance of the mutation, we consider a marginal likelihood approach that is computationally simple to implement, more flexible than the original analytic approach proposed by Wacholder et al. (1998, American Journal of Epidemiology 148, 623-629), and more robust than the likelihood approach considered by Gail et al. (1999, Genetic Epidemiology 16, 15-39) to presence of residual familial correlation. We study the trade-off between robustness and efficiency using simulation experiments. The method is illustrated by analysis of the data from the Washington Ashkenazi Study.     

Should we take measurements at an intermediate design point?

It is well known that, for estimating a linear treatment effect with constant variance, the optimal design divides the units equally between the two extremes of the design space. If the dose-response relation may be nonlinear, however, intermediate measurements may be useful in order to estimate the effects of partial treatments. We consider the decision of whether to gather data at an intermediate design point: do the gains from learning about nonlinearity outweigh the loss in efficiency in estimating the linear effect? Under reasonable assumptions about nonlinearity, we find that, unless sample size is very large, the design with no interior measurements is best, because with moderate total sample sizes, any nonlinearity in the dose-response will be difficult to detect. We discuss in the context of a simplified version of the problem that motivated this work-a study of pest-control treatments intended to reduce asthma symptoms in children.     

Semiparametric estimation of the covariate-specific ROC curve in presence of ignorable verification bias

Covariate-specific receiver operating characteristic (ROC) curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker, when the accuracy of the test is associated with certain covariates. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. In this article, we propose a semiparametric estimation of the covariate-specific ROC curves with a partial missing gold standard. A location-scale model is constructed for the test result to model the covariates' effect, but the residual distributions are left unspecified. Thus the baseline and link functions of the ROC curve both have flexible shapes. With the gold standard missing at random (MAR) assumption, we consider weighted estimating equations for the location-scale parameters, and weighted kernel estimating equations for the residual distributions. Three ROC curve estimators are proposed and compared, namely, imputation-based, inverse probability weighted, and doubly robust estimators. We derive the asymptotic normality of the estimated ROC curve, as well as the analytical form of the standard error estimator. The proposed method is motivated and applied to the data in an Alzheimer's disease research.     

Insight into the interactive residues between two domains of human somatic Angiotensin-converting enzyme and Angiotensin II by MM-PBSA calculation and steered molecular dynamics simulation

Angiotensin-converting enzyme (ACE), a membrane-bound zinc metallopeptidase, catalyzes the formation of Angiotensin-II (AngII) and the deactivation of bradykinin in the renin–angiotensin-aldosterone and kallikrein–kinin systems. As a hydrolysis product of ACE, AngII is regarded as an inhibitor and displays stronger competitive inhibition in the C-domain than the N-domain of ACE. However, the AngII binding differences between the two domains and the mechanisms behind AngII dissociation from the C-domain are rarely explored. In this work, molecular docking, Molecular Mechanics/Poisson–Boltzmann Surface Area calculation, and steered molecular dynamics (SMD) are applied to explore the structures and interactions in the binding or unbinding of AngII with the two domains of human somatic ACE. Calculated free energy values suggest that the C-domain–AngII complex is more stable than the N-domain–AngII complex, consistent with available experimental data. SMD simulation results imply that electrostatic interaction is dominant in the dissociation of AngII from the C-domain. Moreover, Gln106, Asp121, Glu123, and Tyr213 may be the key residues in the unbinding pathway of AngII. The simulation results in our work provide insights into the interactions between the two domains of ACE and its natural peptide inhibitor AngII at a molecular level. Moreover, the results provide theoretical clues for the design of new inhibitors.     

The impact of pedigree structure on heritability estimates for pulse pressure in three studies

OBJECTIVES: Pulse pressure (PP) is a measure of large artery stiffness and has been shown to be an important predictor of cardiovascular morbidity and mortality. The aims of the present study were to investigate the heritability of PP in three studies, the Diabetes Heart Study (DHS), the Insulin Resistance Atherosclerosis Family Study (IRAS FS), and the NHLBI Family Heart Study (FHS), to estimate the residual heritability after inclusion of a common set of covariates, and to investigate the impact of pedigree structure on estimating heritability. METHODS AND RESULTS: DHS is primarily a sibling pair nuclear family study design, while both IRAS FS and FHS have large pedigrees. Heritability estimates of log-transformed PP were obtained using variance component models. After adjusting for age, gender, ethnicity/center, height, diabetes status, and mean arterial pressure (MAP), heritability estimates of PP were 0.40 +/- 0.08 , 0.22 +/- 0.05, and 0.19 +/- 0.03 in DHS, IRAS FS, and FHS, respectively. The heritability estimate from DHS was significantly different from both IRAS FS and FHS (both p values <0.05). A random re-sampling technique (modified bootstrap) was used to explore the heritability in the IRAS FS and FHS data when these pedigrees were trimmed to mimic the DHS pedigree structure. The re-sampling method (mimicking a sibling pair nuclear family design in all studies) yielded PP heritability estimates of 0.37, 0.34, and 0.27 in DHS, IRAS FS, and FHS, respectively. There was no significant difference among the heritability estimates from the three studies based on the re-sampling method. CONCLUSION: We have shown that PP has a moderately heritable component in three different studies. These data illustrate the influence of pedigree structure can have on estimating heritability. Thoughtful comparisons of heritability estimates must consider study design factors such as pedigree structure.     

A moment-based method for estimating the proportion of true null hypotheses and its application to microarray gene expression data

Due to advances in experimental technologies, it is feasible to collect measurements for a large number of variables. When these variables are simultaneously screened by a statistical test, it is necessary to consider the adjustment for multiple hypothesis testing. The false discovery rate has been proposed and widely used to address this issue. A related problem is the estimation of the proportion of true null hypotheses. The long-standing difficulty to this problem is the identifiability of the nonparametric model. In this study, we propose a moment-based method coupled with sample splitting for estimating this proportion. If the p values from the alternative hypothesis are homogeneously distributed, then the proposed method will solve the identifiability and give its optimal performances. When the p values from the alternative hypothesis are heterogeneously distributed, we propose to approximate this mixture distribution so that the identifiability can be achieved. Theoretical aspects of the approximation error are discussed. The proposed estimation method is completely nonparametric and simple with an explicit formula. Simulation studies show the favorable performances of the proposed method when it is compared to the other existing methods. Two microarray gene expression data sets are considered for applications.     

Feeding under predation hazard: testing models of adaptive behavior with stream fish

Many empirical studies support the premise that animals consider both the benefits of feeding and the cost of mortality when making behavioral decisions, and many theoretical studies predict animal behavior in the presence of a feeding-mortality trade-off. However, empirical work is lacking in studies that quantitatively assess alternative models. Using data from two sets of behavioral experiments examining stream minnows (bluehead chubs) foraging in the presence of sunfish predators (green sunfish), we assess, via statistical model fitting, the utility of four basic optimization models of foraging behavior. Our analysis of feeding and mortality of the minnows indicates that mortality is incurred so as to feed above maintenance requirements, that feeding rate is suppressed in response to the presence of predators, and that the balance of feeding against mortality can be estimated using a life-history parameter theta, interpreted theoretically as the marginal rate of substitution of mortality rate for growth rate. Our results indicate that both body size and age are probably involved in determining the value of theta, and we suggest that future studies should focus on estimating and understanding this parameter.     

Engineering cytosolic acetyl-coenzyme A supply in Saccharomyces cerevisiae: Pathway stoichiometry,free-energy conservation and redox-cofactor balancing

Saccharomyces cerevisiae is an important industrial cell factory and an attractive experimental model for evaluating novel metabolic engineering strategies. Many current and potential products of this yeast require acetyl coenzyme A (acetyl-CoA) as a precursor and pathways towards these products are generally expressed in its cytosol. The native S. cerevisiae pathway for production of cytosolic acetyl-CoA consumes 2 ATP equivalents in the acetyl-CoA synthetase reaction. Catabolism of additional sugar substrate, which may be required to generate this ATP, negatively affects product yields. Here, we review alternative pathways that can be engineered into yeast to optimize supply of cytosolic acetyl-CoA as a precursor for product formation. Particular attention is paid to reaction stoichiometry, free-energy conservation and redox-cofactor balancing of alternative pathways for acetyl-CoA synthesis from glucose. A theoretical analysis of maximally attainable yields on glucose of four compounds (n-butanol, citric acid, palmitic acid and farnesene) showed a strong product dependency of the optimal pathway configuration for acetyl-CoA synthesis. Moreover, this analysis showed that combination of different acetyl-CoA production pathways may be required to achieve optimal product yields. This review underlines that an integral analysis of energy coupling and redox-cofactor balancing in precursor-supply and product-formation pathways is crucial for the design of efficient cell factories.     

Comment on Cowling's "Spatial methods for line transect surveys"

We consider the problem of estimating the parameters of a two-dimensional Neyman-Scott process, from data collected through a line transect survey. Cowling (1998, Biometrics 54, 828-839) suggested an estimation method based on a one-dimensional K-function along the transect line. However, her expression for the theoretical K-function is wrong. In this article, we correct her K-function.     

Non-invasive methods for genotyping of stoats (

Stoats (Mustela erminea) are a significant pest in New Zealand. A critical aspect of their management is the ability to identify individuals in order to estimate abundance or to determine the origin of residual animals after control, particularly as the trap-shy nature of stoats reduces the utility of trapping to gain this information. We investigated non-invasive ?capture? methods as an alternative to live-trapping or removal methods for estimating stoat abundance. First we determined whether sufficient variability exists at six microsatellite DNA loci to reliably identify individuals in the potentially bottlenecked, introduced stoat populations of New Zealand. In December 2001 we conducted a 7-night pilot field experiment using a modified hair-tube design, where we obtained a total of 64 hair samples. Sufficient DNA was extracted from 3?6 hair follicles to genotype a total of 51 samples. DNA quality declined if samples were left in the field for several nights before being collected, and daily checks proved best for maximising the quality of DNA obtained, while minimising the risk of multiple ?captures? of stoats. Conclusions were that non-invasive molecular sampling is likely to be a viable technique for estimating population density of stoats in New Zealand beech forest but that additional variable loci are required.     

Design and Analysis of Line Transect Surveys for Primates

Line transect surveys are widely used for estimating abundance of primate populations. The method relies on a small number of key assumptions, and if these are not met, substantial bias may occur. For a variety of reasons, primate surveys often do not follow what is generally considered to be best practice, either in survey design or in analysis. The design often comprises too few lines (sometimes just 1), subjectively placed or placed along trails, so lacks both randomization and adequate replication. Analysis often involves flawed or inefficient models, and often uses biased estimates of the locations of primate groups relative to the line. We outline the standard method, emphasizing the assumptions underlying the approach. We then consider options for when it is difficult or impossible to meet key assumptions. We explore the performance of these options by simulation, focusing particularly on the analysis of primate group sizes, where many of the variations in survey methods have been developed. We also discuss design issues, field methods, analysis, and potential alternative methodologies for when standard line transect sampling cannot deliver reliable abundance estimates.     

Design of a binary biomarker study from the results of a pilot study

Biomarkers are increasingly used in clinical and epidemiologic studies. Prior to these studies, small pilot studies are often conducted to assess the reproducibility of the biomarker. This article discusses how the results of a pilot study can be used to design subsequent studies when the biomarker is a binary assessment. We consider situations in which the pilot study has two factors (e.g., laboratory and individual) that are either crossed or nested. We discuss how binary random-effects models can be used for estimating the sources of variation and how parameter estimates from these models can be used to appropriately design future studies. We also show that fitting a linear variance components model that ignores the binary nature of the data is a simple alternative method that results in nearly unbiased and moderately efficient estimators of important design parameters. We illustrate the methodology with data from a study assessing the reproducibility of p53 immunohistochemistry in bladder tumors.     

Visual and buying sequence features-based product image recommendation using optimization based deep residual network

A recommendation system is an imaginative resolution for managing the restrictions in e-commerce services with item details and user details. Also, it is used to determine the user preferences to recommend the items they expected to buy. Several conventional collaborative filtering techniques are devised in the recommender model, but it has some complexities. Hence, an innovative optimization-driven deep residual network is devised in this paper for a product recommendation system. Here, the product of images is used for extracting features where the Convolutional neural network (CNN) features are computed, and then it is given as input to the deep residual network aimed at product recommendation. The deep residual network is trained using developed Elephant Herding Feedback Artificial Optimization (EHFAO), which is obtained by integrating Elephant Herding optimization (EHO) into the Feedback Artificial Tree (FAT). Here, the item grouping is carried out on input data based on K-means clustering. After item grouping, Cosine similarity is used to perform matching of groups, where the best group is acquired among all the available groups. Extraction of list of visitors is done from the best group. Then, the list of items is obtained from the sequence of best visitor. Next, the corresponding binary sequence is obtained for the applicable sequence of visitor. From this sequence of best visitor, the recommended product is acquired. Then, the recommended product is subjected to the sentiment analysis for which the score is determined. Here, the sentiment analysis helps to decide whether the product is recommended or not recommended. If the score is positive, then the same product is recommended; otherwise, the new product is recommended. The proposed EHFAO-based deep residual network attained better performance in comparison to the other techniques with a maximal F-measure at 84.061%, 84.061% precision, 87.845% recall along with minimal Mean Squared Error (MSE) of 0.216.     

Elastic properties, Young's modulus determination and structural stability of the tropocollagen molecule: a computational study by steered molecular dynamics

The aim of this report is to investigate at microscopic level the elastic properties of a tropocollagen-like molecule submitted to linear traction along its longitudinal axis. For this purpose, we performed steered molecular dynamics (SMD) simulations for a wide range of spring constants in order to test the molecular response based on a two-spring model connected in series. An elastic behavior was observed in an elongation range of 2.5-4% of the molecular length, estimating an "effective molecular elastic constant" of 1.02+/-0.20 kcal/mol A2 in this region. Accordingly, a Young's modulus for the tropocollagen molecule of Y=4.8+/-1.0 GPa was calculated. The complex hydrogen bond network was traced along molecular dynamics (MD) and SMD simulations revealing a rearrangement of these interactions preserving the integrity of the molecular structure when submitted to traction. No evidence of the significant role attributed to water bridges for structural stability was detected, on the contrary facts pointed out that the hydrogen bond network might be the responsible.     

Sampling designs, field techniques and analytical methods for systematic plant population surveys

Summary Intense pressures on the use and management of land underscore the need for reliable and up-to-date information on the status of native species. The outcomes of the most recent plant population surveys commissioned by agencies are generally limited by faults or omissions in survey design. There is little guidance on how to design and implement field surveys of plant populations in ways that address the most pertinent gaps in our current knowledge and provide answers of known reliability. In this paper, I used the International Union for the Conservation of Nature (IUCN) Red List criteria as a framework to define the data required from surveys to assess the conservation status of potentially threatened species. The criteria address the location and geographical range of extant populations, aspects of species' life history, the size and structure of extant populations and rates of change in abundance and range. I have described survey designs and sampling techniques for estimating these parameters. Choices of appropriate methods that consider trade-offs between desired levels of precision and rigour and sampling effort are illustrated using surveys of 13 Tasmanian Epacris species as examples. Key elements of the approach are: (i) systematic approaches to field searches and recording both positive and negative search outcomes; (ii) construction and testing of intuitive or quantitative distribution models in an explicit experimental framework; (iii) rigorous cost-effective sampling designs, systematic field methodologies and simple analytical techniques to estimate both the magnitude and uncertainty of distribution and abundance; (iv) assessment of the merits and limitations of alternative sampling options; and (v) inference of changes in distribution and abundance by judicious use of historical data and field evidence of recent population processes.     

Proposal of a method for allocation in building-related environmental LCA based on economic parameters

Application and development of the LCA methodology to the context of the building sector makes several building specific considerations necessary, as some key characteristics of products in the building sector differ considerably from those of other industrial sectors. The largest difference is that the service life of a building can stretch over centuries, rather than decades or years as seen for consumer products. The result of the long service life is that it is difficult to obtain accurate data and to make relevant assumptions about future conditions regarding, for example, recycling. These problems have implications on the issue of allocation in the building sector, in the way that several allocation procedures ascribe environmental loads to users of recycled or reused products and materials in the future which are unknown today. The long service life for buildings, building materials and building components, is associated with the introduced concept of a virtual parallel time perspective proposed here, which basically substitutes historical and future processes and values with current data. Further, the production and refining of raw material as a parallel to upgrading of recycled material, normally contains several intermediate products. A suggestion is given for how to determine the comparability of intermediate materials. The suggested method for allocation presented is based on three basic assumptions: (1) If environmental loads are to be allocated to a succeeding product life cycle, the studied actual life cycle has to take responsibility for upgrading of the residual material into secondary resources. (2) Material characteristics and design of products are important factors to estimate the recyclable amount of the material. Therefore, a design factor is suggested using information for inherent material properties combined with information of the product context at the building level. (3) The quality reduction between the materials in two following product life cycles is indicated as the ratio between the market value for the material in the products. The presented method can be a good alternative for handling the problem of open-loop recycling allocation in the context of the building sector if a consensus for the use of the fictive parallel time perspective and the use of the design factor can be established. This as the use of the time perspective and design factor is crucial to be able to deal with the problem of long service lives for buildings and building materials and the specific characteristics of the same building materials and components built into different building contexts.     

Probing carbohydrate product expulsion from a processive cellulase with multiple absolute binding free energy methods

Understanding the enzymatic mechanism that cellulases employ to degrade cellulose is critical to efforts to efficiently utilize plant biomass as a sustainable energy resource. A key component of cellulase action on cellulose is product inhibition from monosaccharide and disaccharides in the product site of cellulase tunnel. The absolute binding free energy of cellobiose and glucose to the product site of the catalytic tunnel of the Family 7 cellobiohydrolase (Cel7A) of Trichoderma reesei (Hypocrea jecorina) was calculated using two different approaches: steered molecular dynamics (SMD) simulations and alchemical free energy perturbation molecular dynamics (FEP/MD) simulations. For the SMD approach, three methods based on Jarzynski's equality were used to construct the potential of mean force from multiple pulling trajectories. The calculated binding free energies, -14.4 kcal/mol using SMD and -11.2 kcal/mol using FEP/MD, are in good qualitative agreement. Analysis of the SMD pulling trajectories suggests that several protein residues (Arg-251, Asp-259, Asp-262, Trp-376, and Tyr-381) play key roles in cellobiose and glucose binding to the catalytic tunnel. Five mutations (R251A, D259A, D262A, W376A, and Y381A) were made computationally to measure the changes in free energy during the product expulsion process. The absolute binding free energies of cellobiose to the catalytic tunnel of these five mutants are -13.1, -6.0, -11.5, -7.5, and -8.8 kcal/mol, respectively. The results demonstrated that all of the mutants tested can lower the binding free energy of cellobiose, which provides potential applications in engineering the enzyme to accelerate the product expulsion process and improve the efficiency of biomass conversion.     

Single Molecule Detection Technologies in Miniaturized High Throughput Screening: Fluorescence Correlation Spectroscopy

Fluorescence assay technologies used for miniaturized high throughput screening are broadly divided into two classes. Macroscopic fluorescence techniques (encompassing conventional fluorescence intensity, anisotropy [also often referred to as fluorescence polarization] and energy transfer) monitor the assay volume- and time-averaged fluorescence output from the ensemble of emitting fluorophores. In contrast, single-molecule detection (SMD) techniques and related approaches, such as fluorescence correlation spectroscopy (FCS), stochastically sample the fluorescence properties of individual constituent molecules and only then average many such detection events to define the properties of the assay system as a whole. Analysis of single molecular events is accomplished using confocal optics with an illumination/detection volume of approximately 1 fl (10(-15) L) such that the signal is insensitive to miniaturization of HTS assays to 1 μl or below. In this report we demonstrate the general applicability of one SMD technique (FCS) to assay configuration for target classes typically encountered in HTS and confirm the equivalence of the rate/equilibrium constants determined by FCS and by macroscopic techniques. Advantages and limitations of the current FCS technology, as applied here, and potential solutions, particularly involving alternative SMD detection techniques, are also discussed.     

When Comparing Alternative Fuel‐Vehicle Systems,Life Cycle Assessment Studies Should Consider Trends in Oil Production

Petroleum from unconventional reserves is making an increasingly important contribution to the transportation fuel supply, but is generally more expensive and has greater environmental burdens than petroleum from conventional sources. Life cycle assessments (LCAs) of alternative fuel‐vehicle technologies typically consider conventional internal combustion engine vehicles fueled by gasoline produced from the average petroleum slate used in refineries as a baseline. Large‐scale deployment of alternative fuel‐vehicle technologies will decrease petroleum demand and lead to decreased production at the economic margin (unconventional oil), but this is not considered in most current LCAs. If marginal petroleum resources have larger impacts than average petroleum resources, the environmental benefits of petroleum demand reduction are underestimated by the current modeling approaches. Often, models include some consequential‐based impacts (such as indirect land‐use change for biofuels), but exclude others (such as avoided unconventional oil production). This approach is inconsistent and does not provide a robust basis for public policy and private investment strategy decisions. We provide an example to illustrate the potential scale of these impacts, but further research is needed to establish and quantify these marginal effects and incorporate them into LCAs of both conventional and alternative fuel‐vehicle technologies.     

Right-hand-side updating for fast computing of genomic breeding values

Background

Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.

Methods

In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed “improved residual updating”. The second alternative algorithm, here termed “right-hand-side updating” (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model.

Results

Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm.

Conclusions

The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models.