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Load-capacity has always been one of the performances that is paid much attention to in the development of bevel gear transmission applications. Consequently, the mathematical model of novel bevel gear with high load-capacity based on geometric elements is proposed in this paper, which could be applied to the aviation, aerospace and other fields. In parallel, the design principle and design method of the novel bevel gear are introduced in detail. Subsequently, the conditions for tooth surface continuity and non-interference are derived. Furthermore, the model of novel bevel gear is established. Finally, the load-bearing characteristics are analyzed, revealing that an increase in the number of contact points could significantly enhance the load capacity of the bevel gear pairs. When the load torque applied to bevel gear II is 100 Nm, the contact pressure endured by the bevel gear pair with five-point contact is decreased by 41.37% compared to the bevel gear pair with single-point contact. When the number of contact points is the same, increasing the distance between the contact points could also reduce the contact stress. This provides strong theoretical support for the application of the bevel gear based on the geometric elements. [ABSTRACT FROM AUTHOR]

Obtaining the load distribution and radial deformations of planetary roller screw mechanism (PRSM) for the load carrying capacity design under complex loading conditions (multi-directional loads) is beneficial to decelerate fatigue and wear of threads. However, the combined effects of axial load, radial load and turning torque (overturning moment) were ignored in previous studies due to the complicated structure of PRSM. This paper presents a multi-roller beam-bar-spring (MR-BBS) model that incorporate radial load, turning torque and thread modification. The proposed model extended the theoretic method for calculating bending behavior of screw and rollers, which can more precisely describe the locus of the threads and roller axis. The load distribution of threads and load sharing among rollers of a PRSM under different external loads and cycling process are analyzed. The results show that the load distribution is more sensitive to turning torque of screw but relatively insensitive to the radial load. Moreover, the load distribution moderately changes with the variation of modification factor. Based on thread modification, the variable lead PRSM is proposed so that the uniformity of load sharing with complex load can be optimized. A multi-roller beam-bar-spring (MR-BBS) model is developed that incorporate axial load, radial load, turning torque and thread modification. The load distribution of threads and load sharing among rollers of a PRSM under different external loads and cycling process are analyzed. The load distribution is more sensitive to turning torque of screw but relatively insensitive to the radial load. The variable lead PRSM is proposed so that the uniformity of load sharing with complex load can be optimized. [ABSTRACT FROM AUTHOR]

Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene–environment interactions (G × E). In this review, we present state‐of‐the‐art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single‐variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research. This article is categorized under:Applications of Computational Statistics > Genomics/Proteomics/GeneticsData: Types and Structure > Massive DataStatistical Models > Linear Models [ABSTRACT FROM AUTHOR]

As a key component in fields such as high-precision machine tools, aerospace and weaponry, the thermal deformations in a differential planetary roller screw (DPRS) caused by the changes in ambient temperatures can lead to uneven load distribution and increased wear, which can seriously affect its transmission accuracy, efficiency, and service life. Therefore, the temperature rise of a DPRS at different ambient temperatures was studied using the block thermal network method, and the thermal deformations were calculated. A load distribution model of DPRS considering thermal deformation was developed, and a wear prediction method combined with surface roughness was proposed. The results show that the increase in ambient temperature has an obvious negative effect on the load distribution and wear depth of DPRS. Moreover, the DPRS temperature rise, load distribution and wear depth dramatically change with the variation of parameters such as load, speed and thread pitch. A DPRS durability test device that considers changes in ambient temperature was developed. Experiments with temperature rise and DPRS wear under different ambient temperatures were conducted to validate the theoretical analysis model. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Apolipoprotein E (APOE) ε4‐carrier status or ε4 allele count are included in analyses to account for the APOE genetic effect on Alzheimer's disease (AD); however, this does not account for protective effects of APOE ε2 or heterogeneous effect of ε2, ε3, and ε4 haplotypes. METHODS: We leveraged results from an autopsy‐confirmed AD study to generate a weighted risk score for APOE (APOE‐npscore). We regressed cerebrospinal fluid (CSF) amyloid and tau biomarkers on APOE variables from the Wisconsin Registry for Alzheimer's Prevention (WRAP), Wisconsin Alzheimer's Disease Research Center (WADRC), and Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS: The APOE‐npscore explained more variance and provided a better model fit for all three CSF measures than APOE ε4‐carrier status and ε4 allele count. These findings were replicated in ADNI and observed in subsets of cognitively unimpaired (CU) participants. DISCUSSION: The APOE‐npscore reflects the genetic effect on neuropathology and provides an improved method to account for APOE in AD‐related analyses. [ABSTRACT FROM AUTHOR]

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits. Author summary: Complex human traits are influenced by both genetic and environmental factors. Recent evidence suggests that genetic effects on human traits can sometimes be mediated by the environment. One example is that parental genomes could influence parental behavior and family environment, which, in turn, shape the phenotypes of their children. Since parent and offspring genotypes are correlated, these environmentally mediated effects will be captured by conventional genome-wide association studies and may potentially bias our interpretation of genotype-phenotype associations. In this work, we propose a principled statistical framework named PARSEC to partition the genetic components of single traits and the shared genetic components between traits into direct and indirect effect paths. It marks an important methodological innovation by resolving a limitation in almost all existing genetic correlation estimation approaches and may have broad and impactful applications in future GWAS analysis for complex traits that are affected by parental genetics and family environment. [ABSTRACT FROM AUTHOR]

Previous lubrication and wear analysis for the planetary roller screw mechanism (PRSM) has mainly concentrated on the isothermal conditions without considering temperature rise. However, the PRSMs applied in aviation and aerospace always operate in intensely varying thermal conditions with intricate thermal convection and thermal radiation environments. For this purpose, a modified Archard wear model is established by incorporating the entraining speed of threads and the load distribution, considering axial thermal errors. The variation of bulk temperature is characterized by the thermal network model (TNM). The modified Weierstrass–Mandelbrot (W-M) function is used to evaluate the lubrication conditions of the machined surfaces. Meanwhile, the contact flash temperature, wear depth, and precision loss rate are investigated in dry contact and lubricated phrase for mixed thermal elastohydrodynamic lubrication (TEHL) point contact. The results reveal that the temperature rise has a more pronounced influence on the precision maintenance capability compared to the uneven load sharing. The wear results are also compared to previous experimental simulation obtained using a rolling-sliding tribometer that utilize a rolling wheel on a rotating disk with similar patterns. It indicates that the wear life span of PRSMs can be improved by reasonable machining trajectory, thermal management, and lubrication parameters. [ABSTRACT FROM AUTHOR]

Epigenetic clocks have been widely used to predict disease risk in multiple tissues or cells. Their success as a measure of biological ageing has prompted research on the connection between epigenetic pathways of ageing and the socioeconomic gradient in health and mortality. However, studies examining social correlates of epigenetic ageing have yielded inconsistent results. We conducted a comprehensive, comparative analysis of associations between various dimensions of socioeconomic status (SES) (education, income, wealth, occupation, neighbourhood environment, and childhood SES) and eight epigenetic clocks in two well-powered US ageing studies: The Multi-Ethnic Study of Atherosclerosis (MESA) (n = 1,211) and the Health and Retirement Study (HRS) (n = 4,018). In both studies, we found robust associations between SES measures in adulthood and the GrimAge and DunedinPoAm clocks (Bonferroni-corrected p-value < 0.01). In the HRS, significant associations with the Levine and Yang clocks were also evident. These associations were only partially mediated by smoking, alcohol consumption, and obesity, which suggests that differences in health behaviours alone cannot explain the SES gradient in epigenetic ageing in older adults. Further analyses revealed concurrent associations between polygenic risk for accelerated intrinsic epigenetic ageing, SES, and the Levine clock, indicating that genetic risk and social disadvantage may contribute additively to faster biological aging. [ABSTRACT FROM AUTHOR]

Background: The apolipoprotein E gene (APOE) is the predominant genetic risk factor for late‐onset Alzheimer disease (AD), with three alleles contributing to disease risk and strongly associated with many AD endophenotypes: ε2 (reduced risk), ε3 (reference), and ε4 (increased risk). Researchers often include APOE ε4 carrier status (ε4+/‐) in analyses to account for the genetic effect; however, this does not adequately account for the protective effects of APOE ε2 or the heterogeneous effect of different APOE genotypes (ε2ε2, ε2ε3, ε2ε4, ε3ε3, ε3ε4, and ε4ε4). We hypothesized that accounting for the effect of the full APOE genotype would better recapitulate the influence of APOE in statistical analyses than APOE ε4 status. Method: To generate a weighted risk score for APOE genotype (APOE‐npscore), we log‐transformed the odds ratios from a recent study reporting APOE genetic risk for neuropathology‐confirmed AD. We regressed cerebrospinal fluid (CSF) AD endophenotypes on the APOE‐npscore using data from the Wisconsin Registry for Alzheimer's Prevention (WRAP: 233 cognitively unimpaired (CU), 3 mild cognitive impairment (MCI‐AD)) and Wisconsin Alzheimer's Disease Research Center (WADRC: 287 CU, 42 MCI‐AD, 48 dementia‐AD). In preliminary analyses, APOE‐npscores were tested against CSF amyloid‐beta1‐42 (Aβ42) and phosphorylated tau 181 (pTau181) measured using Elecsys® immunoassays (Roche Diagnostics International Ltd, Rotkreuz, Switzerland). Result: After adjusting for sex and age at lumbar puncture, the APOE‐npscore was associated with CSF Aβ42 (β = ‐115.9, p = 5.7e‐17) and pTau181 (β = 2.05, p = 4.7e‐8). APOE ε4 carrier status also associated with CSF Aβ42 (β = ‐238.0, p = 1.9e‐13) and pTau181 (β = 4.25, p = 1.1e‐6); however, models using APOE‐npscore explained 2.4% more variation in CSF Aβ42 and 0.8% more variation in CSF pTau181 than APOE ε4 status. These findings were similar in the subset of cognitively unimpaired participants. Conclusion: Our preliminary results demonstrate the benefit of using a pseudo‐continuous variable like the neuropathology‐based APOE risk score instead of binary APOE ε4 status. The APOE‐npscore allows researchers to use a continuous measure to account for APOE genetic risk for AD that does not collapse important genotype categories. This increases statistical power, avoids modeling issues that result from small cell sizes, and allows for a more nuanced interpretation of the genetic effect in analyses. [ABSTRACT FROM AUTHOR]

This paper proposes an optimized two-layer Adaboost.M2 model, which resolves a multi-class identification issue for Chinese herbal medicine and aims to enhance the accuracy and reliability of classification. Various base classifiers with probabilistic outputs are integrated in first layer and then transferred to Adaboost.M2 iteration process. Classical fusion rules are verified for optimal combination of classifiers. Identification capacities of base classifiers are investigated using diversity measurement and supply some instructions for optimization of classifier sets. Experimental results show that optimal Adaboost.M2 model integrated with SVM, PNN and LDA achieves the best accuracy of 91.75%, compared to 87.62% from the best single classifier SVM. Corresponding fusion rules are validated with error sensitivity and mean rule is selected while the least error of 8.25% is arrived. The contribution of the paper is that the optimized two-layer Adaboost.M2 with multiple classifiers is a flexible tool to make valid probabilistic and precise prediction for E-nose application in Chinese herbal medicine. This approach also proposes an idea for various ensemble system application, supplies a feasible solution for online classification. [ABSTRACT FROM AUTHOR]

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies., Competing Interests: Competing interests statement:The authors declare no competing interest.

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Introduction: Apolipoprotein E (APOE) ε4-carrier status or ε4 allele count are included in analyses to account for the APOE genetic effect on Alzheimer's disease (AD); however, this does not account for protective effects of APOE ε2 or heterogeneous effect of ε2, ε3, and ε4 haplotypes., Methods: We leveraged results from an autopsy-confirmed AD study to generate a weighted risk score for APOE (APOE-npscore). We regressed cerebrospinal fluid (CSF) amyloid and tau biomarkers on APOE variables from the Wisconsin Registry for Alzheimer's Prevention (WRAP), Wisconsin Alzheimer's Disease Research Center (WADRC), and Alzheimer's Disease Neuroimaging Initiative (ADNI)., Results: The APOE-npscore explained more variance and provided a better model fit for all three CSF measures than APOE ε4-carrier status and ε4 allele count. These findings were replicated in ADNI and observed in subsets of cognitively unimpaired (CU) participants., Discussion: The APOE-npscore reflects the genetic effect on neuropathology and provides an improved method to account for APOE in AD-related analyses., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank ( n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity., Competing Interests: The authors declare no conflict of interest.

In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used to discriminate nine kinds of ginsengs of different species or production places. A flexible machine learning framework, Venn machine (VM) was introduced to make probabilistic predictions for each prediction. Three Venn predictors were developed based on three classical probabilistic prediction methods (Platt's method, Softmax regression and Naive Bayes). Three Venn predictors and three classical probabilistic prediction methods were compared in aspect of classification rate and especially the validity of estimated probability. A best classification rate of 88.57% was achieved with Platt's method in offline mode, and the classification rate of VM-SVM (Venn machine based on Support Vector Machine) was 86.35%, just 2.22% lower. The validity of Venn predictors performed better than that of corresponding classical probabilistic prediction methods. The validity of VM-SVM was superior to the other methods. The results demonstrated that Venn machine is a flexible tool to make precise and valid probabilistic prediction in the application of E-nose, and VM-SVM achieved the best performance for the probabilistic prediction of ginseng samples.

The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.