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BACKGROUND: Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized. OBJECTIVE: This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks? METHODS: This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers. RESULTS: After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%). CONCLUSIONS: Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

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We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.

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BACKGROUND: Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine. RESULTS: In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods. CONCLUSIONS: GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

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BACKGROUND: Rhinoplasty enhances facial symmetry and functionality. However, the accurate and reliable quantification of nasal defects pre-surgery remains an ongoing challenge. AIM: This study introduces a novel approach for defect quantification using 2D images and artificial intelligence, providing a tool for better preoperative planning and improved surgical outcomes. MATERIALS AND METHODS: A pre-trained AI model for facial landmark detection was utilised on a dataset of 250 images of male patients aged 18 to 24 who underwent rhinoplasty for cosmetic nasal deformity correction. The analysis concentrated on 36 different distances between the facial landmarks. These distances were normalised using min-max scaling to counter image size and quality variations. Post-normalisation, statistical parameters, including mean, median, and standard deviation, were calculated to identify and quantify nasal defects. RESULTS: The methodology was tested and validated using images from different ethnicities and regions, showing promising potential as a beneficial surgical aid. The normalised data produced reliable quantifications of nasal defects (average 76.2%), aiding in preoperative planning and improving surgical outcomes and patient satisfaction. APPLICATIONS: The developed method can be extended to other facial plastic surgeries. Furthermore, it can be used to create app-based software, assist medical education, and improve patient-doctor communication. CONCLUSION: This novel method for defect quantification in rhinoplasty using AI and image processing holds significant potential in improving surgical planning, outcomes, and patient satisfaction, marking an essential step in the fusion of AI and plastic surgery.

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This study examined the relationship between early parental treatment, specifically reading to young children and later cognitive development with a Bayesian perspective. Previous research established a positive link between parental reading to infants and their cognitive development, such as receptive vocabulary, reading comprehension and motivation to read. Using data from the Millennium Cohort Study, this study analysed individuals aged 9 months to 14 years to investigate the effects of early reading to young children on nine cognitive variables. Bayesian statistical analysis controlled for pre-existing differences and covariates to establish a causal association between reading and cognitive development. The results indicated that reading to infants and toddlers positively impacted their cognitive development beyond reading skills. These findings demonstrate the usefulness of the Bayesian approach in determining scientific significance and underscore the importance of early literacy interventions in promoting cognitive development.

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PURPOSE: To assess the accuracy of intraocular lens (IOL) power calculation in different age groups using various IOL calculation formulas. METHODS: Data from 421 eyes of 421 patients ≥60 years old (ages: 60-69, n = 131; 70-74, n = 105; 75-84, n = 158 and ≥85, n = 27), who underwent uneventful cataract surgery with SN60WF IOL implantation at John A. Moran Eye Center, Salt Lake City, USA, were retrospectively obtained. The SD of the prediction error (PE), median and mean absolute PEs and the percentage of eyes within ±0.25, ±0.50, ±0.75 and ±1.00 D were calculated after constant optimizations for the following formulas: Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO) 2.0, Haigis, Hoffer Q, Hoffer QST, Holladay 1, Kane, Radial Basis Function (RBF) 3.0 and SRK/T. Results were compared between the different age groups. RESULTS: Predictability rates within 0.25D were lower for the eldest age group compared with the other groups using the EVO 2.0 (33% vs. 37%-53%, p = 0.045), Kane (26% vs. 35%-50%, p = 0.034) and SRK/T (22% vs. 31%-49%, p = 0.002). Higher median absolute refractive errors for all formulas were observed in the oldest group [range: 0.39 D (Haigis, Hoffer QSR)-0.48 D (Kane)], followed by the youngest group [range: 0.30 D (RBF 3.0)-0.39 D (Holladay 1, SRK/T)] but did not reach statistical significance. No significant differences between the groups in the distribution parameter were seen. CONCLUSION: Current IOL power calculation formulas may have variable accuracy for different age groups. This should be taken into account when planning cataract surgery to improve refractive outcomes.

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Monitoring PCDD/Fs emissions from municipal solid waste incinerations (MSWIs) is of paramount importance, yet it can be time-consuming and labor-intensive. Predictive models offer an alternative approach for estimating their levels. However, robust models specific to PCDD/Fs were lacking. In this study, we collected 190 PCDD/Fs samples from 4 large-scale MSWIs in China, with the average PCDD/Fs levels and TEQ levels of 0.987 ng/m3 and 0.030 ng TEQ/m3, respectively. We developed and evaluated predictive models, including traditional statistical methods, e.g., linear regression (LR) as well as machine learning models such as back propagation-artificial neural networks (BP ANN) and random forest (RF). Correlation analysis identified 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, 2,3,4,6,7,8-HxCDF were better indicator congeners for PCDD/Fs estimation (R2 > 0.9, p < 0.001). The predictive results favored the RF model, exhibiting a high R2 value and low root mean square error (RMSE) and mean absolute error (MAE). Additionally, the RF model showed excellent prediction ability during external validation, with low absolute relative error (ARE) of 10.9 %-12.6 % for the three indicator congeners in the normal PCDD/F TEQ levels group (<0.1 ng TEQ/m3) and slightly higher ARE values (13.8 %-17.9 %) for the high PCDD/F TEQ levels group (>0.1 ng TEQ/m3). In conclusion, our findings strongly support the RF model's effectiveness in predicting PCDD/Fs TEQ emission from MSWIs.

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The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs.

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It is widely accepted that the corrosion resistance of stainless steel originates from a compact Cr2O3 layer in the native passive film that serves as a barrier to aggressive ions. However, this suggestion has been questioned by some researchers. They believe that protectiveness might be related to the film recovery. Herein, the pitting development of bare 316 L stainless steel was compared with a corrosion-resistance enhanced steel obtained by tuning the native passive film of the alloy. Statistical software was employed for tracing the size and number of pits on the alloy surface. The statistical results for 12 weeks in 1 M sodium chloride solution (80 °C) revealed that there was a crossover in the growing rates of stable pits (diameter > 9 µm) between the bare alloy and the film-enhanced one. Stable pits on bare 316 L occurred early but showed a comparatively slow increase in the following weeks, demonstrating that self-repairability of metastable pits rather than impermeability of the native passive film plays the key role in the early stage of pitting corrosion.

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In order to facilitate systematic reviews and meta-analyses by medical researchers， this paper provides a tutorial-style review of the relevant concepts， necessary steps， and some key considerations in conducting systematic reviews and meta-analyses. Specifically， it offers detailed explanations on literature search， evaluation of study bias， effect size selection， outcomes pooling， and testing and correction of publication bias. Additionally， the paper provides references to softwares and literature for implementing the methods， and concludes with a brief overview of the requirements for writing research papers.

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BACKGROUND: DNA methylation (5-mC) is being widely recognized as an alternative in the detection of sequence variants in the diagnosis of some rare neurodevelopmental and imprinting disorders. Identification of alterations in DNA methylation plays an important role in the diagnosis and understanding of the etiology of those disorders. Canonical pipelines for the detection of differentially methylated regions (DMRs) usually rely on inter-group (e.g., case versus control) comparisons. However, these tools might perform suboptimally in the context of rare diseases and multilocus imprinting disturbances due to small cohort sizes and inter-patient heterogeneity. Therefore, there is a need to provide a simple but statistically robust pipeline for scientists and clinicians to perform differential methylation analyses at the single patient level as well as to evaluate how parameter fine-tuning may affect differentially methylated region detection. RESULT: We implemented an improved statistical method to detect differentially methylated regions in correlated datasets based on the Z-score and empirical Brown aggregation methods from a single-patient perspective. To accurately assess the predictive power of our method, we generated semi-simulated data using a public control population of 521 samples and investigated how the size of the control population, methylation difference, and region size affect DMR detection. In addition, we validated the detection of methylation events in patients suffering from rare multi-locus imprinting disturbance and evaluated how this method could complement existing tools in the context of clinical diagnosis. CONCLUSION: In this study, we present a robust statistical method to perform differential methylation analysis at the single patient level and describe its optimal parameters to increase DMRs identification performance. Finally, we show its diagnostic utility when applied to rare disorders.

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Rock is used as a foundation and building material in many engineering projects and it is important to determine/predict its engineering properties before project construction. Petrographic and textural characteristics are useful parameters for predicting engineering properties of rocks in such applications. In this research, fifteen rock samples were taken and their engineering characteristics, namely dry and saturated unit weights, porosity, water absorption, slake durability index (SDI), Schmidt rebound hardness (SRH), ultrasonic P-wave velocity (UPV), and uniaxial compressive strength (UCS), were measured in the laboratory. Petrographic and textural characteristics of the rocks, determined from thin section and X-ray diffraction investigations, led to the evaluation of the texture coefficient (TC). Based on simple regression analysis (SRA), the TC values have direct relationships with density, SDI, SRH, UPV, and UCS, and inverse relationships with porosity and water absorption. Experimental models were developed using multiple regression analysis (MRA) and artificial neural network (ANN) to predict Id2, SRH, UPV, and UCS of the tested rocks from the values of TC. Some statistical parameters including Pearson regression coefficient (R), coefficient values account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE), and performance index (PI) were calculated to assess the performances of the MRA and ANN models. The correlations between experimental and calculated values of Id2, SRH, UPV, and UCS indicated that predicted values of the ANN models are more valid than the MRA. Additionally, the residual error of the ANN models varies less than the MRA. Finally, it has been concluded that the SRA, MRA, and ANN methods can successfully predict the rock engineering properties from the TC.

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The increasing availability of multivariate data within biomedical research calls for appropriate statistical methods that can describe and model complex relationships between variables. The extended ANOVA simultaneous component analysis (ASCA+) framework combines general linear models and principal component analysis (PCA) to decompose and visualize the separate effects of experimental factors. It has recently been demonstrated how linear mixed models can be included in the framework to analyze data from longitudinal experimental designs with repeated measurements (RM-ASCA+). The ALASCA package for R makes the ASCA+ framework accessible for general use and includes multiple methods for validation and visualization. The package is especially useful for longitudinal data and the ability to easily adjust for covariates is an important strength. This paper demonstrates how the ALASCA package can be applied to gain insights into multivariate data from interventional as well as observational designs. Publicly available data sets from four studies are used to demonstrate the methods available (proteomics, metabolomics, and transcriptomics).

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Power calculation is a necessary step when planning genome-wide association studies (GWAS) to ensure meaningful findings. Statistical power of GWAS depends on the genetic architecture of phenotype, sample size, and study design. While several computer programs have been developed to perform power calculation for single SNP association testing, it might be more appropriate for GWAS power calculation to address the probability of detecting any number of associated SNPs. In this paper, we derive the statistical power distribution across causal SNPs under the assumption of a point-normal effect size distribution. We demonstrate how key outcome indices of GWAS are related to the genetic architecture (heritability and polygenicity) of the phenotype through the power distribution. We also provide a fast, flexible and interactive power calculation tool which generates predictions for key GWAS outcomes including the number of independent significant SNPs, the phenotypic variance explained by these SNPs, and the predictive accuracy of resulting polygenic scores. These results could also be used to explore the future behaviour of GWAS as sample sizes increase further. Moreover, we present results from simulation studies to validate our derivation and evaluate the agreement between our predictions and reported GWAS results.

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BACKGROUND: An important component of asthma care is understanding potential causes of high asthma admissions (HAADs) or readmissions (HARDs) with potential of risk mitigation. Crucial to this research is accurately distinguishing these events from background seasonal changes and time trends. To date, classification methods have been based on ad hoc and untested definitions which may hamper understanding causes of HAADs and HARDs due to misclassification. The aim of this article is to introduce an easily applied robust statistical approach, with high classification accuracy in other settings-the Seasonal Hybrid Extreme Studentized Deviate (S-H-ESD) method. METHODS: We demonstrate S-H-ESD on a time series between 1996 and 2009 of all daily paediatric asthma hospital admissions in Victoria, Australia. RESULTS: S-H-ESD clearly identified HAADs and HARDs without applying ad hoc classification definitions, while appropriately accounting for seasonality and time trend. Importantly, it was done with statistical testing, providing evidence in support of their identification. CONCLUSION: S-H-ESD is useful and statistically appropriate for accurate classification of HAADs and HARDS. It obviates ad hoc approaches and presents as a means of systemizing their accurate classification and detection. This will strengthen synthesis and efficacy of research toward understanding causes of HAADs and HARDs for their risk mitigation.

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Ergosterol as a primary metabolite and precursor of vitamin D2, is the most plentiful mycosterols in fungal cell membrane. Process optimization to increase the yield and productivity of biological products is a topic of interest. Ultrasonic waves have many applications in biotechnology, like cell disruption, and enhancement of primary and secondary metabolites production. This study disclosed an optimal condition for ultrasound-assisted production (UAP) of ergosterol from Penicillium brevicompactum MUCL 19,011 using L9 Taguchi statistical method. The intensity (IS), time of sonication (TS), treatment frequency (TF), and number of days of treatment (DT) were allocated to study the effects of ultrasound on ergosterol production. The results were analyzed using Minitab version 19. The maximum ergosterol, 11 mg/g cell dry weight (CDW), was produced on the tenth day while all factors were at a low level. The days of treatment with a contribution of 45.48% was the most significant factor for ergosterol production. For the first time, this study revealed the positive effect of ultrasound on the production of ergosterol. Ergosterol production increased 73% (4.63 mg/g CDW) after process optimization. Finally, a mathematical model of ultrasound factors with a regression coefficient of R2 = 0.978 was obtained for the ergosterol production during ultrasound treatment.

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Automotive paint is one of the most important evidence in solving vehicle-related criminal cases. It contains the critical information about the suspected vehicle, providing essential clues for the investigation. In this study, a novel approach based on optical coherence tomography combined with multivariate statistical methods was proposed to facilitate rapid, accurate and nondestructive identification of different brands of automotive paints. 164 automotive paint samples from 8 different manufacturers were analyzed by a spectral-domain optical coherence tomography system (SD-OCT). Two-dimensional cross-sectional OCT images and three-dimensional OCT reconstruction of vehicle paints of different paints were obtained to show the internal structural differences. Visual discrimination of A-scan data after registration and averaging processing was first used to distinguish different samples. An scanning electron microscope was utilized to obtain the cross-sectional image of the sample to evaluate the effectiveness of OCT technique. Then the original A-scan data, first derivative data and second derivative data of 136 paints with four layers from 7 different manufacturers were collected. Multivariate statistical methods, including principal component analysis (PCA), multi-layer perceptron (MLP), k-nearest neighbor (KNN) algorithm and Bayes discriminant analysis (BDA), were used to analyze different datasets. The results show the hybrid PCA and BDA model based on the first derivative OCT data achieved the best result of 100% accuracy on the testing dataset for identifying automotive paints. It is demonstrated that the OCT technique combined with multivariate statistics could be a promising method for identifying the automotive paints rapidly and accurately.

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PURPOSE: To assess the accuracy of the Kane formula for intraocular lens (IOL) power calculation in the pediatric population. METHODS: The charts of pediatric patients who underwent cataract surgery with in-the-bag IOL implantation with one of two IOL models (SA60AT or MA60AC) between 2012 and 2018 in The Hospital for Sick Children, Toronto, Ontario, CanFada, were retrospectively reviewed. The accuracy of IOL power calculation with the Kane formula was evaluated in comparison with the Barrett Universal II (BUII), Haigis, Hoffer Q, Holladay 1, and Sanders-Retzlaff-Kraff Theoretical (SRK/T) formulas. RESULTS: Sixty-two eyes of 62 patients aged 6.2 (IQR 3.2-9.2) years were included. The SD values of the prediction error obtained by Kane (1.38) were comparable with those by BUII (1.34), Hoffer Q (1.37), SRK/T (1.40), Holaday 1 (1.41), and Haigis (1.50), all p > 0.05. A significant difference was observed between the Hoffer Q and Haigis formulas (p = 0.039). No differences in the median and mean absolute errors were found between the Kane formula (0.54 D and 0.91 ± 1.04 D) and BUII (0.50 D and 0.88 ± 1.00 D), Hoffer Q (0.48 D and 0.88 ± 1.05 D), SRK/T (0.72 D and 0.97 ± 1.00 D), Holladay 1 (0.63 D and 0.94 ± 1.05 D), and Haigis (0.57 D and 0.98 ± 1.13 D), p = 0.099. CONCLUSION: This is the first study to investigate the Kane formula in pediatric cataract surgery. Our results place the Kane among the noteworthy IOL power calculation formulas in this age group, offering an additional means for improving IOL calculation in pediatric cataract surgery. The heteroscedastic statistical method was first implemented to evaluate formulas' predictability in children.

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High-throughput sequencing for B cell receptor (BCR) repertoire provides useful insights for the adaptive immune system. With the continuous development of the BCR-seq technology, many efforts have been made to develop methods for analyzing the ever-increasing BCR repertoire data. In this review, we comprehensively outline different BCR repertoire library preparation protocols and summarize three major steps of BCR-seq data analysis, i. e., V(D)J sequence annotation, clonal phylogenetic inference, and BCR repertoire profiling and mining. Different from other reviews in this field, we emphasize background intuition and the statistical principle of each method to help biologists better understand it. Finally, we discuss data mining problems for BCR-seq data and with a highlight on recently emerging multiple-sample analysis.

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