Linear Additive Model Research Articles

Does Day-to-Day Variability in Stool Consistency Link to the Fecal Microbiota Composition?

IntroductionStool consistency has been associated with fecal microbial composition. Stool consistency often varies over time, in subjects with and without gastrointestinal disorders, raising the question whether variability in the microbial composition should be considered in microbiota studies. We evaluated within-subject day-to-day variability in stool consistency and the association with the fecal microbiota in irritable bowel syndrome (IBS) and healthy subjects, over seven days.MethodsTwelve IBS patients and 12 healthy subjects collected fecal samples during seven consecutive days. Stool consistency was determined by the patient-reported Bristol Stool Scale (BSS) and fecal dry weight percentage. 16S rRNA V4 gene sequencing was performed and microbial richness (alpha diversity; Chao1 index, observed number of species, effective Shannon index) and microbial community structure (beta diversity; Bray-Curtis distance, generalized UniFrac, and taxa abundance on family level) were determined.ResultsLinear mixed-effects models showed significant associations between stool consistency and microbial richness, but no time effect. This implies that between-subject but not within-subject variation in microbiota over time can partially be explained by variation in stool consistency. Redundancy analysis showed a significant association between stool consistency and microbial community structure, but additional linear mixed-effects models did not demonstrate a time effect on this.ConclusionThis study supports an association between stool consistency and fecal microbiota, but no effect of day-to-day fluctuations in stool consistency within seven days. This consolidates the importance of considering stool consistency in gut microbiota research, though confirms the validity of single fecal sampling to represent an individual’s microbiota at a given time point. NCT00775060.

Bayesian Estimation of Partially Linear Additive Spatial Autoregressive Models with P-Splines

In this paper, we aim to develop a partially linear additive spatial autoregressive model (PLASARM), which is a generalization of the partially linear additive model and spatial autoregressive model. It can be used to simultaneously evaluate the linear and nonlinear effects of the covariates on the response for spatial data. To estimate the unknown parameters and approximate nonparametric functions by Bayesian P-splines, we develop a Bayesian Markov Chain Monte Carlo approach to estimate the PLASARM and design a Gibbs sampler to explore the joint posterior distributions of unknown parameters. Furthermore, we illustrate the performance of the proposed model and estimation method by a simulation study and analysis of Chinese housing price data.

Improved Tolerance Limits by Combining Analytical and Experimental Data: An Information Integration Methodology

We propose a coherent methodology for integrating different sources of information on a response variable of interest, in order to accurately predict percentiles of its distribution. Under the assumption that one of the sources is more reliable than the other(s), the approach combines factors formed from the data into an additive linear regression model. Quantile regression, designed for quantifying the goodness of fit precisely at a desired quantile, is used as the optimality criterion in model-fitting. Asymptotic confidence interval construction methods for the percentiles are adopted to compute statistical tolerance limits for the response. The approach is demonstrated on a materials science case study that pools together information on failure load from physical tests and computer model predictions. A small simulation study assesses the precision of the inferences. The methodology gives plausible percentile estimates. Resulting tolerance limits are close to nominal coverage probability levels.

OTEH-12. Assessing Adaptive Responses to Loss of Extrachromosomal DNA Amplification

BackgroundOncogene activation through somatic gene amplification happens frequently in GBM, with over 70% of these tumors presenting amplification of at least one putative driver gene, oftentimes in small extrachromosomal circular DNA segments composed of chromatin (ecDNA). A molecularly diverse and representative panel of GBM patient-derived cancer stem-like cells (CSC) and orthotopic mouse xenografts (PDX), which retain the original genomic abnormalities and ecDNA amplifications, was employed to assess adaptive response to the absence of ecDNA amplification.MethodsWe have isolated ecDNA negative cell populations from two patient-derived models. HF3035 harbors a MET amplification and HF3253 harbors a PDGFRA constitutively active genomic rearrangement and extrachromosomal amplification. We conducted paired, whole RNA-sequencing on 20 HF3253 populations (ecDNA+/-: 6 clones from 3 biological replicate PDXs and 4 clones from 4 in vitro technical replicates) and 12 HF3035 population (ecDNA+/-: 6 clones from 3 biological replicate PDXs).ResultsNonparametric differentially expressed gene (DEG) analysis using NOISeqBio (R/Bioconductor), identified 564 differentially expressed genes (482 upregulated in ecDNA(-)) employing a stringent false discovery rate of 0.05. Genes significantly associated with PDGF stimulation, central carbon metabolism, and H3K27me3 were downregulated in ecDNA(-), while genes significantly associated with astrocytic processes, neuronal differentiation, and EGFR signaling were upregulated in ecDNA(-) (EnrichR). We employed an additive linear model with PDX serving as a blocking factor to compare ecDNA+ and ecDNA- populations in both models (R/edgeR). 2071 genes were upregulated in ecDNA+ PDX specimens and 2365 genes were downregulated. Specifically, E2F targets were highly enriched in ecDNA+ populations, in addition to mRNA pre-processing. ecDNA loss primarily targeted glycogen metabolism, NTRK signaling, and inositol phosphate catabolism.ConclusionsWe have identified PDX-specific and non-specific features to an adaptive response to the loss of ecDNA amplification. Notably, a signature adaptation is an upregulation of seemingly redundant receptor tyrosine kinases.

Building a multiple linear regression model with LEGO brick data

We present an innovative activity that uses data about LEGO sets to help students self-discover multiple linear regression. Students are guided to predict the price of a LEGO set posted on Amazon.com (Amazon price) using LEGO characteristics such as the number of pieces, the theme (i.e., product line), and the general size of the pieces. By starting with graphical displays and simple linear regression, students are able to develop additive multiple linear regression models as well as interaction models to accomplish the task. We provide examples of student responses to the activity and suggestions for teachers based on our experiences.

Estimation of varying coefficient models with measurement error

We propose a semiparametric estimator for varying coefficient models when the regressors in the nonparametric components are measured with error. Varying coefficient models are an extension of other popular semiparametric models, including partially linear and nonparametric additive models, and deliver an attractive solution to the curse-of-dimensionality. We use deconvolution kernel estimation in a two-step procedure and show that the estimator is consistent and asymptotically normally distributed. We do not assume that we know the distribution of the measurement error a priori. Instead, we suppose we have access to a repeated measurement of the noisy regressor and present results using the approach of Delaigle, Hall and Meister (2008) and, for cases when the measurement error may be asymmetric, the approach of Li and Vuong (1998) based on Kotlarski’s (1967) identity. We show that the convergence rate of the estimator is significantly reduced when the distribution of the measurement error is assumed unknown and possibly asymmetric. We study the small sample behaviour of our estimator in a simulation study and apply it to a real dataset. In particular, we consider the role of cognitive ability in augmenting the effect of risk preferences on earnings.

Ocean satellite data assimilation using the implicit equal-weights variational particle smoother

The implicit equal-weights variational particle smoother (IEWVPS) is a combination of the particle filter (PF) and weak-constraint 4-dimensional variational (4D-Var) method, that inherits the merits of both. The IEWVPS avoids the filter degeneracy of particle filters through an implicit equal-weights scheme and reduces the root mean square deviations (RMSDs) by introducing the 4D-Var method. This method has been tested using the Lorenz 96 model in a previous study, and we now implement it in the Regional Ocean Model System (ROMS), which is a realistic and complex ocean model. Two key problems, the representation of the analysis error covariance and the choice of the parameter α, were solved during this implementation. With an eddy-permitting model, satellite-based sea surface height (SSH) and sea surface temperature (SST) observations were assimilated with a set of 40 particles IEWVPS scheme. Compared with the ensemble 4D-Var method, the IEWVPS can reduce the bias introduced by perturbed atmospheric forcing, effectively improving temperature simulations in the upper 50 m while maintaining the RMSD of SSH at the same level. Therefore, the cooling effect caused by typhoons in the upper ocean is better characterized under the IEWVPS scheme than with previously used method. The ratio of RMSD to the ensemble spread indicates that the ensemble quality of the IEWVPS is much better than that of the ensemble 4D-Var. In addition, the computational cost of the IEWVPS is only slightly larger than that of the ensemble 4D-Var. One additional tangent linear model integration, one additional nonlinear model integration, and perturbation fields inputs/outputs are still needed.

Genome-wide association study and inbreeding depression on body size traits in Qira black sheep (Ovis aries).

Qira black sheep is a famous indigenous sheep breed in China. The objectives of this study are to identify candidate genes related to body size, and to estimate the level of inbreeding depression on body size based on runs of homozygosity in Qira black sheep. Here, 188 adult Qira black sheep were genotyped with a high density (630K) SNP chip and genome-wide association study for body weight and body size traits (including withers height, body slanting length, tail length, chest girth, chest width, and chest depth) were performed using an additive linear model. In consequence, 12 genome- and chromosome-wide significant SNPs and, accordingly, six candidate genes involved in muscle differentiation, metabolism and cell processes were identified. Of them, ZNF704 (zinc finger protein 704) was identified for body weight; AK2 (adenylate kinase 2) and PARK2 (parkin RBR E3 ubiquitin protein ligase) for tail length; MOCOS (molybdenum cofactor sulfurase) and ELP2 (elongator acetyltransferase complex subunit 2) for chest width; and MFAP1 (microfibril associated protein 1) for chest girth. Additionally, inbreeding depressions on body size were observed in the current herd. These results will provide insightful understandings into the genetic mechanisms of adult body size, and into the conservation and utilization of Qira black sheep.

Sistem Pendukung Keputusan Team Survei Terbaik Pada SNI (Serikat Nelayan Indonesia) Menggunakan Metode SMART (Studi Kasus: Sekretariat SNI, Medan)

Surveys are usually confused with the term observation in the everyday sense, surveys are also the main way to collect primary data if secondary data are deemed incomplete to answer a question, if the secondary data is complete enough and the hypothesis can be tested with secondary data then Direct primary data collection using the survey method is no longer necessary, a survey is looking for detailed factual information that is currently symptomatic and identifying problems or to justify conditions and ongoing activities to find out what people are doing which is the target of research in solving problems, as material for planning and making decisions in the future. SMART (Simple Multi - Attribute Rating Technique) is a multi-criteria decision-making method developed by Edward in 1977.This multi-criteria decision-making technique is based on the theory that each alternative consists of a number of criteria that have values and each criterion has a weight describes how important it is compared to other criteria. This weighting is used to assess each alternative in order to obtain the best alternative. SMART uses a linear additive model to predict the value of each alternative. SMART is a flexible decision making method.

Inference for partially linear additive higher-order spatial autoregressive model with spatial autoregressive error and unknown heteroskedasticity

This article extends spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) which is the most popular spatial econometric model to the case of an arbitrary finite number of nonparametric additive terms and spatial autoregressive models with spatial autoregressive disturbances of arbitrary finite order (SARAR(R,S)). We propose a sieve two-stage least squares (S2SLS) regression and generalized method of moments (GMM) procedure of the high-order spatial autoregressive parameters of the disturbance process. Under some sufficient conditions, we show that the proposed estimator for the finite dimensional parameter is consistent and asymptotically normally distributed. We show that each proposed estimator for the additive terms is consistent and also asymptotically normally distributed at a rate slower than Consistent estimators for the asymptotic variances of the proposed estimators are provided. In addition, using asymptotic properties to make statistical inference for the parametric and additive is also considered. Monte Carlo evidence suggests that the estimation procedure performs reasonably well in small samples and the proposed approach has some practical value. The proposed method is applied to analyzing factors which affect haze pollution.

The impact of employee remuneration inequalities on employee productivity in the South African workplace

Orientation: This article is part of an ongoing research project on various aspects of employee productivity in the South African workplace.Research purpose: The aim of this article is to determine firm-based employee productivity impacts as a result of employee remuneration inequalities (excess-remuneration and under-remuneration) in the South African workplace.Motivation for the study: The study focuses on understanding the impact and magnitude of employee remuneration inequalities on employee productivity in a unionised South African workplace.Research design: The article adopts two distinct estimation models. The aim of the additive multivariate linear estimation model is to determine the sign and the significance of the impact of both under- and excess-remuneration levels on employee productivity when employee characteristics such as levels of training, work experience and managerial involvement are considered. The second model is a fixed-effect panel data estimation where the full sample set of the relevant firm-based data is used. The aim of the panel data estimations is to estimate the robustness of the additive multivariate linear estimates. The manufacturing industry of Gauteng has been chosen as the case study, given the importance of this industry, in the gross geographical product of Gauteng province and the availability of firm-based data.Main findings: Estimation results indicate a strong and significant negative impact of under-remuneration on employee productivity levels. Excess-remuneration levels have a small positive impact on employee productivity levels.Practical/managerial implications: The estimations indicate the necessity to eliminate remuneration inequalities and opt for equalised remuneration structures for similar occupations in the market to enhance employee productivity levels.Contribution/value-added: The study contributes to our understanding of the impact of remuneration inequalities for similar occupations on employee productivity.

Crop photoperiodism model 2.0 for the flowering time of sorghum and rice that includes daily changes in sunrise and sunset times and temperature acclimation.

Daylength determines flowering dates. However, questions remain regarding flowering dates in the natural environment, such as the synchronous flowering of plants sown simultaneously at highly contrasting latitudes. The daily change in sunrise and sunset times is the cue for the flowering of trees and for the synchronization of moulting in birds at the equator. Sunrise and sunset also synchronize the cell circadian clock, which is involved in the regulation of flowering. The goal of this study was to update the photoperiodism model with knowledge acquired since its conception. A large dataset was gathered, including four 2-year series of monthly sowings of 28 sorghum varieties in Mali and two 1-year series of monthly sowings of eight rice varieties in the Philippines to compare with previously published monthly sowings in Japan and Malaysia, and data from sorghum breeders in France, Nicaragua and Colombia. An additive linear model of the duration in days to panicle initiation (PI) and flowering time using daylength and daily changes in sunrise and sunset times was implemented. Simultaneous with the phyllochron, the duration to PI of field crops acclimated to the mean temperature at seedling emergence within the usual range of mean cropping temperatures. A unique additive linear model combining daylength and daily changes in sunrise and sunset hours was accurately fitted for any type of response in the duration to PI to the sowing date without any temperature input. Once calibrated on a complete and an incomplete monthly sowing series at two tropical latitudes, the model accurately predicted the duration to PI of the concerned varieties from the equatorial to the temperate zone. Including the daily changes in sunrise and sunset times in the updated photoperiodism model largely improved its accuracy at the latitude of each experiment. More research is needed to ascertain its multi-latitudinal accuracy, especially at latitudes close to the equator.

How does trade development affect environmental performance? New assessment from partially linear additive panel analysis

Assessing the linkage between trade development and environmental quality has recently attracted increasing attention because of the rapid advancement of trade globalization along with the severe degradation of the environment. However, neglecting the possible nonlinear relationship between the variables, the existing studies mainly use linear models to explore the trade-environment linkage. To fill this gap, this paper introduces a novel partially linear additive panel model that highlights nonlinear and heterogeneous features to investigate the direct and indirect influences of trade development on environmental performance over the period 2005–2017 in China. The results show that trade has an inverse “U-shaped” direct effect on the environment. In contrast, trade development can indirectly improve environmental quality through economic growth. Nonlinear marginal analysis demonstrates that such an impact not only presents individual differences but also embodies the trait of temporal dynamics. Empirical results further reveal the importance of our model, which can automatically identify the variable nexus and thus reduce the setting error of the parametric models. Model evaluation also confirms the superiority of the used model over existing parametric and nonparametric models. The partially linear additive panel model provides a new evaluation method for environmental impact regarding trade development, and can be applied to other fields where an assessment of the effect of socio-economic factors on the environment is needed.

GMM Estimation of a Partially Linear Additive Spatial Error Model

This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric additive terms under some regular conditions. In addition, the finite sample performance for our estimates is assessed by Monte Carlo simulations. Lastly, the proposed method is illustrated by analyzing Boston housing data.

First genome-wide association study on rocuronium dose requirements shows association with SLCO1A2

BackgroundRocuronium, a common neuromuscular blocking agent, is mainly excreted unchanged in urine (10–25%) and bile (>70%). Age, sex, liver blood flow, smoking, medical conditions, and ethnic background can affect its pharmacological actions. However, reasons for the wide variation in rocuronium requirements are mostly unknown. We hypothesised that pharmacogenetic factors might explain part of the variation. MethodsOne thousand women undergoing surgery for breast cancer were studied. Anaesthesia was maintained with propofol (50–100 μg kg−1 min−1) and remifentanil (0.05–0.25 μg kg−1 min−1). Neuromuscular block was maintained with rocuronium to keep the train-of-four ratio at 0–10%. DNA was extracted from peripheral blood and genotyped with a next-generation genotyping array. The genome-wide association study (GWAS) was conducted using an additive linear regression model with PLINK software. The FINEMAP tool and data from the Genotype-Tissue Expression project v8 were utilised to study the locus further. ResultsThe final patient population comprised 918 individuals. Of the clinical variables tested, age, BMI, ASA physical status, and total dose of propofol correlated significantly (all P<0.001) with the rocuronium dose in a linear regression model. The GWAS highlighted one genome-wide significant locus in chromosome 12. The single-nucleotide polymorphisms (SNPs) with the most significant evidence of association were located in or near SLCO1A2. The two top SNPs, rs7967354 (P=5.3e−11) and rs11045995 (P=1.4e−10), and the clinical variables accounted for 41% of the variability in rocuronium dosage. ConclusionsGenetic variation in the gene SLCO1A2, encoding OATP1A2, an uptake transporter, accounted for 4% of the variability in rocuronium consumption. The underlying mechanism remains unknown.

The Effects of Scallop Dredge Fishing Practices on Physical, Behavioral, and Physiological Stress in Discarded Yellowtail Flounder, Windowpane, and Fourspot Flounder

AbstractThe Atlantic sea scallop Placopecten magellanicus dredge fishery is one of the most lucrative commercial fishing industries in the northeastern United States, and fish bycatch can comprise up to ~42% of the total catch. Benthic species, such as flatfish, are particularly susceptible to unintended capture in scallop dredge gear, and mitigating bycatch and associated mortality has been mandated a priority for fisheries management. Based on this management need, the present study evaluated the physical, physiological, and behavioral stress responses of Yellowtail Flounder Limanda ferruginea, Windowpane Scophthalmus aquosus, and Fourspot Flounder Paralichthys oblongus to capture in the scallop dredge fishery. More specifically, we used generalized additive models and linear regression models to assess the influence of various fishing practices, environmental conditions, and biological factors on injury condition, physiological parameters, and reflex indicators. Although these flatfish species appeared to be physically resilient to capture based on an observable injury assessment, dredge capture and handling factors proved stressful, with the degree of immediate mortality, physiological disturbances, and reflex impairment varying by species. While multiple factors influenced the degree of stress in these species, based on our results the reduction of tow duration and limiting air exposure/sorting duration would likely be the most effective strategies to mitigate the impact of scallop dredge fishing on these flatfish species.

Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes

AbstractDisease progression is often monitored by intermittent follow‐up “visits” in longitudinal cohort studies, resulting in interval‐censored failure time outcomes. Furthermore, the timing and frequency of visits is often found related to a person's history of disease‐related variables in practice. This article develops a semiparametric estimation approach using weighted binomial regression and a kernel smoother to analyze interval‐censored failure time data. Visit times are allowed to be subject‐specific and outcome‐dependent. We consider a collection of widely used semiparametric regression models, including additive hazards and linear transformation models. For additive hazards models, the nonparametric component has a closed‐form estimator and the estimators of regression coefficients are shown to be asymptotically multivariate normal with sandwich‐type covariance matrices. Simulations are conducted to examine the finite sample performance of the proposed estimators. A data set from the Toronto Psoriatic Arthritis (PsA) Cohort Study is used to illustrate the proposed methodology.

Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters

Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters

A penalized likelihood approach for efficiently estimating a partially linear additive transformation model with current status data

Current status data are commonly encountered in medical and epidemiological studies in which the failure time for study units is the outcome variable of interest. Data of this form are characterized by the fact that the failure time is not directly observed but rather is known relative to an observation time, i.e., the failure times are either left- or right-censored. Due to its structure, the analysis of such data can be challenging. To circumvent these challenges and to provide for a flexible modeling construct which can be used to analyze current status data, herein a partially linear additive transformation model is proposed. In the formulation of this model, constrained B-splines are employed to model the monotone transformation function and nonparametric covariate effects. To provide for more efficient estimators, a penalization technique is used to regularize the estimation of all unknown functions. An easy to implement hybrid algorithm is developed for model fitting, and a simple and consistent estimator of the large-sample variance-covariance matrix for regression parameter estimators is proposed. It is shown theoretically that the proposed estimators of the finite-dimensional regression coefficients are root-n consistent, asymptotically normal, and achieve the semiparametric information bound, while the estimators of the nonparametric components attain the optimal rate of convergence. The finite-sample performance of the proposed methodology is evaluated through extensive numerical studies and is further demonstrated through the analysis of human papillomavirus (HPV) data.

DESIGN AND IMPLEMENTATION OF A REAL TIME ATTITUDE ESTIMATION SYSTEM WITH LOW COST SENSORS / PROJETO E IMPLEMENTAÇÃO DE UM SISTEMA DE ESTIMAÇÃO DE ATITUDE EM TEMPO REAL COM SENSORES DE BAIXO CUSTO

Getting the attitude of drones, underwater vehicles or other six degree of freedom (DoF) devices is one of the most challenging tasks in the project of navigation control systems. For this reason, many projects use proprietary software or are limited to simulations. This work presents a complete system for attitude deter- mination capable of provide estimated attitude and calibrated measurements using MEMS, with a low-cost and low-power microcontroller. The accelerometer and magnetometer are calibrated online on the embedded system using least squares method without any external devices. The states estimation is computed with a fast algebraic quaternion algorithm (less than 1.8ms) using an additive linear Kalman Filter and model measurements.