Robust Bayesian variable selection for gene-environment interactions.

Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

Robust Inferential Techniques Applied to the Analysis of the Tropospheric Ozone Concentration in an Urban Area.

This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador; the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O3 analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O3 concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O3 concentration levels during the study period were not harmful to human health.

Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques.

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

Global wheat planting suitability under the 1.5°C and 2°C warming targets.

The potential distribution of crops will be impacted by climate change, but there is limited research on potential wheat distributions under specific global warming targets. This study employed the Maxent model to predict the potential distribution of wheat under the 1.5°C and 2°C warming targets based on data from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) multimodel ensemble, and the effect of global warming on wheat planting suitability was analyzed. Our results indicated global warming would significantly change wheat planting suitability. Over half of the areas experienced changes in wheat planting suitability under two warming targets, and the effect became more pronounced with increasing temperatures. Additionally, global warming might promote wheat planting in more regions. The area with an increase in wheat planting suitability was observed to be 9% higher than those experiencing a decrease on average. Moreover, global warming could exacerbate the disparity between global wheat supply and demand in countries/regions. Traditional wheat-producing countries/regions are poised to benefit from the warming effects of climate change, while less developed and wheat import-dependent countries/regions may face greater challenges in achieving wheat self-sufficiency. To address this potential challenge, the promotion and inter-regional exchange of agronomic technologies, and the development of more rational trade standards are urgently needed. Since socioeconomic factors have a significant impact on wheat cultivation, further investigation is required to determine how the wheat planting distribution may change in the future under the combined impact of climate change, supply-demand relationship, and policy.

Drawing Attention to the Measurement Uncertainty Arising from Sampling in Chemical and Physicochemical Analyses: An Overview.

Sometimes, analytical chemicals forget that the measurement process begins with the selection of the sample; thus, it must be understood that the measurement uncertainty is constituted by the association of the uncertainty arising from the sampling and the uncertainty arising from the traditional analytical process, that which is carried out in the laboratory. The analytical process is well-controlled, so its uncertainty is well defined; however, the uncertainty arising from sampling, for not having this controlled environment, is often not evident, so that there is still no culture to consider it for the calculation of measurement uncertainty. This study discusses the importance of the sampling uncertainty concerning the analytical uncertainty and details the current approaches available in the literature, such as the classical analysis of variance, the robust analysis of variance, and the range statistics. Moreover, this work highlights the recent manuscripts that are using these mentioned approaches, correlating them to the matrices, chemical and physical-chemical analytes, and analytical techniques. Finally, some case studies using the uncertainty information in compliance assessment show that the measurement uncertainty arising from sampling in chemical and physicochemical analyses cannot always be neglected.

Modelling the causes of boiler accidents: implications for economic and social sustainability at the workplace.

This study aims to examine the causes of boiler accidents in the context of the ready-made garment (RMG) industry of Bangladesh as an emerging economy. On the basis of a comprehensive review of the existing literature, previous accident reports, and technical discussion with relevant personnel in the industries and regulating authorities, a total of 14 causes of boiler accidents were identified. This study merged neutrosophic (N) theory with the analytic hierarchy process (AHP) for prioritizing the causes of boiler accidents. Finally, to examine the reliability of the results, a robustness analysis was performed. The findings reveal that the lack of standard legislation, non-standard boiler operation, use of expired, non-registered, and non-certified boilers, faulty design of boilers, and the shortage of skilled boiler operators are the top five notable causes of boiler accidents in the RMG industry. The findings provide valuable insights for industrial managers and policymakers to formulate strategies to reduce boiler accidents.

Analysis and challenges of robust E-exams performance under COVID-19.

Nowadays under COVID 2019, e-learning has become a potential prop approach of technology in education that provides contemporary learners with authentic knowledge acquisitions. As a practical contribution, electronic examination (e-exam) is a novel approach in e-learning designed to solve traditional examination issues. It is a combination of assorted questions designed by specialized software to detect an individual's performance. Despite intensive research in this area, the performance of e-exams faces challenges such as authentication of the examinee's identity and answered papers. This paper aims to present the experiences of educational organizations in e-exam and e-evaluation as an essential tool of e-learning in various countries. The paper recommends that under the global pandemic COVID 2019 evaluating students using intensive continuous evaluation, including e-exam supported by authentication methods, which may help detect and reduce or even prevent student violations. The results show that the most used LMS tools were the Moodle and proprietary solutions which were 75% both among many other LMS tools i.e., Blackboard and eFront. The least develop countries are prefer to use open source and proprietary due to the zero cost of these solutions. The internet speed, cost and authenticity were the most challenges faced e-exams centers, which were 99%, 82%, and 68%, respectively.

Traditional and Rank-Based Tests for Ordered Alternatives in a Cluster Correlated Model.

Methods for the analysis of one-factor randomized groups designs with ordered treatments are well established, but they do not apply in the case of more complex experiments. This article describes ordered treatment methods based on maximum-likelihood and robust estimation that apply to designs with clustered data, including those with a vector of covariates. The contrast coefficients proposed for the ordered treatment estimates yield higher power than those advocated by Abelson and Tukey; the proposed robust estimation method is shown (using theory and simulation) to yield both high power and robustness to outliers. Extensions for nonmonotonic alternatives are easily obtained.

Signal-to-noise ratio constrained feedback control: Robust stability analysis.

The explicit consideration of a communication channel model in a feedback control loop is known to be constrained by a fundamental limitation for stabilizability on the channel signal-to-noise ratio (SNR) when the linear time invariant (LTI) plant model is unstable. The LTI modelling approach for real, usually nonlinear, processes compromises accuracy versus complexity of the resulting model. This in turn introduces a gap between the proposed model and the real process, which is known as the model uncertainty. In this paper we then study SNR limitations by considering the continuous-time scenario and the case of an additive coloured Gaussian noise (ACGN) channel with bandwidth limitation, for which we then quantify the infimal SNR subject to the simultaneous presence of plant, channel and noise model uncertainties. We observe, for the special case of memoryless additive white Gaussian noise (AWGN) channels, that the obtained SNR limitation subject to plant model uncertainty can be redefined as a channel capacity limitation for stabilization.

Group sequential designs with robust semiparametric recurrent event models.

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures.

Intensity-modulated radiotherapy for whole pelvis irradiation in prostate cancer: A dosimetric and plan robustness study between photons and protons.

PURPOSE: To evaluate the dosimetric impact and plan robustness of using Pencil Beam Scanning (PBS) in patients that requires prophylactic pelvic lymph nodes (PLNs) irradiation for prostate cancer. MATERIAL AND METHODS: Five intermediate to high-risk prostate patients previously treated using volumetric modulated arc therapy (VMAT), were selected for this study. Comparative proton radiotherapy plans were generated, where a three-field intensity modulated proton therapy (IMPT) plan was for the phase 1 planning target volume (PTV1) with PLNs. A technique with two posterior oblique fields using single field uniform dose (SFUD) was used for phase 2 (PTV2) volume, that comprises of the prostate and proximal seminal vesicles (Pro + proxSVs). Plan evaluation was performed on PTV coverage and dose to the organs at risk (OARs) using VMAT plans as a baseline (BL). Robust analysis on clinical target volume (CTV) coverage for the PBS plans was simulated with a 3 and 5 mm setup errors and a 3.5% range uncertainty. RESULTS: For target coverage, PTV1 and PTV2 showed negligible differences with a comparable homogeneity index (HI) values for both modalities. Proton plans produced a statistically significant lower mean dose to the bladder (32.5 Gy(RBE) vs. 46.5 Gy) and rectum (33.6 Gy(RBE) vs. 42.7 Gy). Dose to the bladder and rectum was equivalent at the high dose region. For the bowel cavity, the mean dose for proton plans were 45% lower compared to VMAT plans. Similarly, proton plans were able to achieve an overall reduction in integral dose for both treatment phase. CTV coverage remained high with all the simulated setup and range errors. CONCLUSIONS: Proposed beam geometries for PTV1 and PTV2 proton plans presented good treatment accuracy with similar target coverage as the VMAT plans. Better sparing of OARs was achieved at the low-medium dose region for the proton plans.

Robust Analysis of Time Series in Virome Metagenomics.

Metagenomics is a powerful tool for assessing the functional and taxonomic contents in biological samples as it makes feasible to study, simultaneously, the whole living community related to a host organism or medium: all the microbes, including virus, bacteria, archaea, fungi, and protists. New DNA and RNA sequencing technologies are dramatically decreasing the cost per sequenced base, so metagenomic sequencing is becoming more and more widespread in biomedical and environmental research. This is opening the possibility of complete longitudinal metagenomic studies, which could unravel the dynamics of microbial communities including intra-microbiome and host-microbiome interactions through in-depth analysis of time series. For viruses, this is particularly interesting because it allows broad interaction studies of viruses and hosts in different time scales, as in bacteria-phages coevolution studies.This chapter presents computational methods for an automatic and robust analysis of metagenomic time series in virome metagenomics (RATSVM). The same theoretical frame and computational protocol is also suitable for longitudinal studies of spatial series to uncover the dynamics of a microbial community with viruses along a selected dimension in the space. In order to conveniently illustrate the procedure, real data from a published virome study is used. The computational protocol presented here requires only basic computational knowledge. Several scripts have been prepared to ease and automate the most complicate steps, they are available in the RATSVM public repository. For some of the methods a mid-range computing server is advisable, and for some others, it is required. A fat-node with large memory and fast I/O would be the best choice for optimum results.

T-type Corrected-Loss Estimation for Error-in-Variable Model.

In this paper, we consider a linear model in which the covariates are measured with errors. We propose a t-type corrected-loss estimation of the covariate effect, when the measurement error follows the Laplace distribution. The proposed estimator is asymptotically normal. In practical studies, some outliers that diminish the robustness of the estimation occur. Simulation studies show that the estimators are resistent to vertical outliers and an application of Six-Minute Walk test is presented to show that the proposed method performs well.

H∞ state estimation for memristive neural networks with time-varying delays: The discrete-time case.

This paper investigates the H∞ state estimation problem for a class of discrete-time memristive neural networks (DMNNs) with time-varying delays. For the sake of coping with the switched weight matrices, the DMNNs are recast into a tractable model by defining a series of state-dependent switched signals. Based on the tractable model, the robust analysis method and Lyapunov stability theory are developed to devise a sufficient condition which ensures the global asymptotical stability of the estimation error system with a prescribed H∞ performance. The desired state estimator gain matrix and optimal performance index can be accomplished via solving a convex optimization problem subject to several linear matrix inequalities (LMIs). Finally, one numerical example is presented to check the effectiveness of the theoretical results.

Comparison of traditional, trimmed traditional and robust Youden charts.

BACKGROUND: Traditional Youden chart can be distorted by non-normally distributed data. Here, an optimized Youden chart was developed and compared with the traditional and trimmed traditional Youden charts. METHODS: The urea concentrations were determined by 28 laboratories to provide data for the construction of Youden charts. Normality of these data was tested. Outliers were excluded prior to construction of the trimmed traditional Youden chart. Non-robust and robust estimators were computed to construct the traditional and robust Youden charts, respectively. Robust between-laboratory z-score (ZBi) and within-laboratory z-score (ZWi) were obtained to assess whether or not these charts can reasonably present the urea results. Expected outcomes were the points related to acceptable (|ZBi| and |ZWi| ≤ 2), questionable (2 < |ZBi| < 3 and/or 2 < |ZWi| < 3), and unacceptable (|ZBi| and/or |ZWi| ≥ 3) results fall inside, on/near, and outside the ellipse, respectively. RESULTS: Only the data from lot 201111 are non-normally distributed. Five- and 2-pair outliers are excluded from the data of lots 201111 and 201112, respectively. The concordance rates of the traditional, trimmed traditional and robust Youden charts are 87.1%, 92.9%, and 94.3%, respectively. CONCLUSIONS: Among the three charts, the robust Youden chart presents the urea results best.