Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.

BACKGROUND: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators. METHODS: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software. RESULTS: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD (P<0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models. CONCLUSION: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning.

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies.

BACKGROUND: A Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs. METHODS: We compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed. RESULTS: For most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate. CONCLUSIONS: We recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Establishment and application of perceived age prediction model for the periocular aging research of Chinese Han women.

BACKGROUND: The assessment of skin aging through skin measurements faces limitations, making perceived age evaluation a more valuable and direct tool for assessing skin aging. Given that the aging process markedly affects the appearance of the eye contour, characterizing the eye region could be beneficial for perceived age assessment. This study aimed to analyze age-correlated changes in the eye contour within the Chinese Han female population and to develop, validate, and apply a multiple linear regression model for predicting perceived age. MATERIALS AND METHODS: A naïve panel of 107 Chinese women assessed the perceived ages of 212 Chinese Han women. Instrumental analysis evaluated periorbital parameters, including palpebral fissure width (PFW), palpebral fissure height (PFH), acclivity of palpebral fissure (AX), angle of inner canthal (AEN), and angle of outer canthal (AEX). These parameters were used to construct a multiple linear regression model for predicting the perceived ages of Chinese Han women. A combined treatment using Fotona 4D and an anti-aging eye cream, formulated with plant extracts, peptides, and antioxidants, was conducted to verify the cream's anti-aging efficacy and safety. This eye cream was then tested in a large-scale clinical trial involving 101 participants. The prediction model was employed in this trial to assess the perceived ages of the women after an 8-week application of the eye cream. RESULTS: All parameters were observed to decrease with age. An intergroup comparison indicated that eyelid aging in Chinese Han women accelerates beyond the age of 50. Consequently, a linear regression model was constructed and validated, with the perceived age being calculated as 183.159 - 1.078 * AEN - 4.487 * PFW + 6.061 * PFH - 1.003 * AX - 0.328 * AEX. The anti-aging efficacy and safety of the eye cream were confirmed through combined treatment with Fotona 4D, showing improvements in wrinkles, elasticity, and dark circles under the eyes. In a large-scale clinical evaluation using this eye cream, a perceived age prediction model was applied, suggesting that 8 weeks of use made participants appear 2.25 years younger. CONCLUSION: Our study developed and validated a multiple linear regression model to predict the perceived age of Chinese Han women. This model was successfully utilized in a large-scale clinical evaluation of anti-aging eye cream, revealing that 8 weeks of usage made participants appear 2.25 years younger. This method effectively bridges the gap between clinical research and consumer perceptions, explores the complex factors influencing perceived age, and aims to improve anti-aging formulations.

A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance-covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4-way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance-covariance matrices. Voxel-wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance-covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance-covariance structure using LME modeling.

Factors related to the low-risk perception of driving after cannabis use.

INTRODUCTION: Modifying risk perceptions related to driving after cannabis use (DACU) could deter individuals from enacting this behavior, as low-risk perception is associated with DACU engagement. This study identified sociodemographic characteristics, substance use, other driving behaviors, peer norms, and psychological characteristics that are associated with lower risk perception regarding DACU. METHODS: Canadian drivers aged 17-35 who have used cannabis in the past year (n = 1,467) completed an online questionnaire. A multivariate linear regression model allowed for identifying variables associated with the low-risk perception of DACU (i.e. believing it to be safe as one's driving ability is not impaired by cannabis or by being high). RESULTS: Lower risk perception of DACU was associated with identifying as male, weekly to daily cannabis use, engagement in DACU, general risky driving behaviors, being a passenger of a driver who engages in DACU, number of friends who engage in DACU, and peer approval of DACU. Having driven under the influence of alcohol, living in urban areas, having received traffic tickets in the past three years, and declaring past-week irritability and cognitive problems were associated with holding a higher risk perception related to DACU. DISCUSSION: Road education and prevention programs should target attitudes and perceptions regarding risks shaped by sociocultural norms and past risky driving experiences. They need to reach out more specifically to drivers with the identified characteristics associated with the low-risk perception of DACU. These interventions can potentially help reduce the rate of individuals who engage in this behavior.

Quality of life and its related-influencing factors in patients with cervical cancer based on the scale QLICP-CE(V2.0).

BACKGROUND: Quality of life research can guide clinical workers to adopt more targeted treatment and intervention measures, so as to achieve the purpose of improving patients' quality of life. The objective of this study was to evaluate health-related quality of life in Chinese patients with cervical cancer and to explore its influencing factors. METHODS: A total of 186 patients with cervical cancer were investigated by using the QLICP-CE (V2.0) scale (Quality of Life Instruments for Cancer Patients-Cervical Cancer) developed by our group in China. The data were analyzed by t-test, one-way ANOVA, univariate analysis, and multivariate linear regression. RESULTS: The total score of quality of life scale for cervical cancer patients was (62.58 ± 12.69), Univariate analysis of objective clinical indexes showed that creatinine concentration was a negative influence factor in the psychological domain, potassium ion concentration was a negative influence factor in the common symptoms and side effect domain, erythrocyte content was a positive influence factor physical domain and common general domain. Multiple linear regression results suggested that clinical staging was the influencing factor of common symptom and side effect domain, common general module and total score of scale. Marital status has different degrees of influence on the psychological, social, and common general domains. The level of education also influenced scores in the social domain. CONCLUSION: The total score of quality of life in patients with cervical cancer who received active treatment was acceptable. Marital status, clinical staging, and educational level are the factors that affect the quality of life of patients with cervical cancer. At the same time, potassium ion concentration, red blood cell count and creatinine concentration also have important effects on quality of life in patients with cervical cancer. Therefore, it is very important to give personalized treatment and nursing to patients based on various factors.

Codetermination of antimicrobial agents in rabbit tear fluid using LC-MS/MS assay: Insights into ocular pharmacokinetic study.

Managing ocular microbial infections typically requires pharmacotherapy using antibiotic eye drops, such as moxifloxacin hydrochloride (MFX), combined with an antifungal agent like amphotericin B (AB). We carried out and validated an LC-MS/MS assay to quantify these compounds in rabbit tear fluid in order to look into the pharmacokinetics of these two drugs. We employed a protein precipitation technique for the extraction of drugs under examination. A Waters Symmetry C18 column was used to separate the analytes and internal standard. The composition of the mobile phase was like (A) 0.1% v/v formic acid in water and (B) methanol. The detection of MFX and AB was accomplished through the utilization of positive ion electrospray ionization under multiple reaction monitoring mode. The linearity curves for both analytes exhibited an acceptable trendline across a concentration range of 2.34-300 ng/mL for MFX and 7.81-1000 ng/mL for AB in surrogate rabbit tear fluid. The lower limit of quantitation for MFX was 2.34 ng/mL, while for AB, it was 7.81 ng/mL. The approach was strictly validated, encompassing tests of selectivity, linearity (with r2 > 0.99), precision, accuracy, matrix effects, and stability. Consequently, we employed this method to evaluate the pharmacokinetics profiles of MFX and AB in rabbit tear fluid following single topical doses.

The Michaelis-Menten Reaction at Low Substrate Concentrations: Pseudo-First-Order Kinetics and Conditions for Timescale Separation.

We demonstrate that the Michaelis-Menten reaction mechanism can be accurately approximated by a linear system when the initial substrate concentration is low. This leads to pseudo-first-order kinetics, simplifying mathematical calculations and experimental analysis. Our proof utilizes a monotonicity property of the system and Kamke's comparison theorem. This linear approximation yields a closed-form solution, enabling accurate modeling and estimation of reaction rate constants even without timescale separation. Building on prior work, we establish that the sufficient condition for the validity of this approximation is s 0 ≪ K , where K = k 2 / k 1 is the Van Slyke-Cullen constant. This condition is independent of the initial enzyme concentration. Further, we investigate timescale separation within the linear system, identifying necessary and sufficient conditions and deriving the corresponding reduced one-dimensional equations.

Factors influencing the posterior cruciate ligament buckling phenomenon-a multiple linear regression analysis of bony and soft tissue structures of the knee joint.

PURPOSE: To determine whether posterior cruciate ligament (PCL) buckling (angular change) is associated with anterior cruciate ligament (ACL) status (intact or ruptured), meniscal bone angle (MBA), anterior tibial translation (ATT), body weight, femoral-tibial rotation (FTR), posterior tibial slope (PTS), PCL length and femoral-tibial distance (FTD) and to identify the factors that have the greatest influence. METHODS: All enrolled participants were scanned with a 3.0 T, 8-channel coil MRI system (Magnetom Verio; Siemens). Bone and soft tissue parameters were measured by MIMICS software for each subject and each measured parameter was correlated with PCL buckling phenomena. The correlated and statistically significant parameters were then analyzed by multiple linear regression to determine the magnitude of the effect of the different parameters on the PCL buckling phenomenon. RESULTS: A total of 116 subjects (50 ACL ruptured and 66 age, weight and height matched volunteers with uninjured knees) were enrolled. Among all measured parameters, there were 8 parameters that correlated with PCL angle (PCLA), of which ACL status had the strongest correlation with PCLA (r = - 0.67, p = < 0.001); and 7 parameters that correlated with PCL-posterior femoral cortex angle (PCL-PCA), of which ATT had the strongest correlation with PCL-PCA (r = 0.69, p = < 0.001). PCLIA was not significantly correlated with any of the measured parameters. Multiple linear regression analyses revealed four parameters can explain PCLA, of which ACL status had the strongest effect on PCLA (absolute value of standardized coefficient Beta was 0.508). Three parameters can explain PCL-PCA, of which ATT had the strongest effect on PCLIA (r = 0.69, p = < 0.001), ATT has the greatest effect on PCL-PCA (absolute value of normalized coefficient Beta is 0.523). CONCLUSIONS: PCLA may be a simple and easily reproducible and important supplement for the diagnosis of ACL injury; PCL-PCA is a simple and easily reproducible and important complementary tool for the detection of ATT. The use of PCLA is more recommended to aid in the diagnosis of ACL injury.

Sarcopenia prevalence using handgrip strength or chair stand performance in adults living with type 2 diabetes mellitus.

BACKGROUND: The updated European Working Group on Sarcopenia in Older People (EWGSOP2) recommends handgrip strength (HGS) and the chair stand test (CST) to assess muscle strength, with the CST being a convenient proxy for lower limb strength. However, adiposity may differentially influence these strength criteria and produce discrepant sarcopenia prevalence. OBJECTIVE: To determine the prevalence of sarcopenia using HGS or the CST, and to investigate the associations between these strength criteria and adiposity in adults with type 2 diabetes mellitus. METHODS: The EWGSOP2 definition was used to assess the prevalence of probable (low muscle strength), confirmed (plus low muscle mass) and severe (plus poor physical performance) sarcopenia. Linear regression models were used to study the association between different measures of muscle strength and adiposity. RESULTS: We used data from 732 adults with type 2 diabetes mellitus (35.7% female, aged 64 ± 8 years, body mass index 30.7 ± 5.0 kg/m2). Using the CST compared with HGS produced a higher prevalence of probable (31.7% vs. 7.1%), confirmed (5.6% vs. 1.6%) and severe (1.0% vs. 0.3%) sarcopenia, with poor agreement between strength criteria to identify probable sarcopenia. CST performance, but not HGS, was significantly associated with all measures of adiposity in unadjusted and adjusted models. CONCLUSIONS: Higher levels of adiposity may impact CST performance, but not HGS, resulting in a higher prevalence of sarcopenia in adults with type 2 diabetes mellitus. Consideration should be paid to the most appropriate measure of muscle function in this population.

The Association of Oncology Provider Density With Black-White Disparities in Cancer Mortality in US Counties.

BACKGROUND: Black-White racial disparities in cancer mortality are well-documented in the US. Given the estimated shortage of oncologists over the next decade, understanding how access to oncology care might influence cancer disparities is of considerable importance. We aim to examine the association between oncology provider density in a county and Black-White cancer mortality disparities. METHODS: An ecological study of 1048 US counties was performed. Oncology provider density was estimated using the 2013 National Plan and Provider Enumeration System data. Black:White cancer mortality ratio was calculated using 2014-2018 age-standardized cancer mortality rates from State Cancer Profiles. Linear regression with covariate adjustment was constructed to assess the association of provider density with (1) Black:White cancer mortality ratio, and (2) cancer mortality rates overall, and separately among Black and White persons. RESULTS: The mean Black:White cancer mortality ratio was 1.12, indicating that cancer mortality rate among Black persons was on average 12% higher than that among White persons. Oncology provider density was significantly associated with greater cancer mortality disparities: every 5 additional oncology providers per 100 000 in a county was associated with a .02 increase in the Black:White cancer mortality ratio (95% CI: .007 to .03); however, the unexpected finding may be explained by further analysis showing that the relationship between oncology provider density and cancer mortality was different by race group. Every 5 additional oncologists per 100 000 was associated with a 1.6 decrease per 100 000 in cancer mortality rates among White persons (95% CI: -3.0 to -.2), whereas oncology provider density was not associated with cancer mortality among Black persons. CONCLUSION: Greater oncology provider density was associated with significantly lower cancer mortality among White persons, but not among Black persons. Higher oncology provider density alone may not resolve cancer mortality disparities, thus attention to ensuring equitable care is critical.

Our study provides timely information to address the growing concern about the need to increase oncology supply and the impact it might have on racial disparities in cancer outcomes. This analysis of counties across the US is the first study to estimate the association of oncology provider density with Black-White racial disparities in cancer mortality. We show that having more oncology providers in a county is associated with significantly lower cancer mortality among the White population, but is not associated with cancer mortality among the Black population, thereby leading to a disparity. Our findings suggest that having more oncology providers alone may be insufficient to overcome existing disadvantages for Black patients to access and use high-quality cancer care. These findings have important implications for addressing racial disparities in cancer outcomes that are persistent and well-documented in the US.

A cross-sectional study on personal values of medical students: the differences from their instructors, their associations with mental wellbeing, and the influences of gender.

BACKGROUND: Personal values of Thai medical students have been observed to be diverging from those of their seniors, but the differences remain uncharacterized. Despite its potential association with mental wellbeing, the issue remain unexplored in the population. This study aimed to explore (1) the difference in personal values between medical students and instructors and (2) the association between student's value adherence to mental well-being and the interaction by gender. METHODS: An online survey was performed in 2022. Participants rated their adherence to five groups of values, namely, Self-Direction, Hedonism, Achievement & Power, Universalism & Benevolence, and Tradition. Participants also rated their mental wellbeing. Comparisons were made between the personal values of students and instructors. The association between the personal values of students and their mental wellbeing and the interaction between values and gender were analyzed in linear regression. RESULTS: Compared to instructors, students rated higher on Universalism & Benevolence, marginally higher on Hedonism, and lower on Tradition. Students' ratings on Self-Direction, Universalism & Benevolence, and Tradition predicted better mental wellbeing. Their rating on Hedonism predicted poorer mental wellbeing, the effect of which was marginally stronger in males. Ratings on Achievement & Power marginally predicted poorer mental wellbeing in females. CONCLUSION: Difference in personal values between medical students and instructors have been observed. Some of these values hold potentials over student's mental wellbeing. Curricular and medical school environmental accommodation for the changes in the characters of learners may be necessary to mitigate the adverse effects on their mental wellbeing and foster development of desirable professional characteristics.

Estimating genetic parameters of digital behavior traits and their relationship with production traits in purebred pigs.

BACKGROUND: With the introduction of digital phenotyping and high-throughput data, traits that were previously difficult or impossible to measure directly have become easily accessible, offering the opportunity to enhance the efficiency and rate of genetic gain in animal production. It is of interest to assess how behavioral traits are indirectly related to the production traits during the performance testing period. The aim of this study was to assess the quality of behavior data extracted from day-wise video recordings and estimate the genetic parameters of behavior traits and their phenotypic and genetic correlations with production traits in pigs. Behavior was recorded for 70 days after on-test at about 10 weeks of age and ended at off-test for 2008 female purebred pigs, totaling 119,812 day-wise records. Behavior traits included time spent eating, drinking, laterally lying, sternally lying, sitting, standing, and meters of distance traveled. A quality control procedure was created for algorithm training and adjustment, standardizing recording hours, removing culled animals, and filtering unrealistic records. RESULTS: Production traits included average daily gain (ADG), back fat thickness (BF), and loin depth (LD). Single-trait linear models were used to estimate heritabilities of the behavior traits and two-trait linear models were used to estimate genetic correlations between behavior and production traits. The results indicated that all behavior traits are heritable, with heritability estimates ranging from 0.19 to 0.57, and showed low-to-moderate phenotypic and genetic correlations with production traits. Two-trait linear models were also used to compare traits at different intervals of the recording period. To analyze the redundancies in behavior data during the recording period, the averages of various recording time intervals for the behavior and production traits were compared. Overall, the average of the 55- to 68-day recording interval had the strongest phenotypic and genetic correlation estimates with the production traits. CONCLUSIONS: Digital phenotyping is a new and low-cost method to record behavior phenotypes, but thorough data cleaning procedures are needed. Evaluating behavioral traits at different time intervals offers a deeper insight into their changes throughout the growth periods and their relationship with production traits, which may be recorded at a less frequent basis.

Penalized weighted smoothed quantile regression for high-dimensional longitudinal data.

Quantile regression, known as a robust alternative to linear regression, has been widely used in statistical modeling and inference. In this paper, we propose a penalized weighted convolution-type smoothed method for variable selection and robust parameter estimation of the quantile regression with high dimensional longitudinal data. The proposed method utilizes a twice-differentiable and smoothed loss function instead of the check function in quantile regression without penalty, and can select the important covariates consistently using the efficient gradient-based iterative algorithms when the dimension of covariates is larger than the sample size. Moreover, the proposed method can circumvent the influence of outliers in the response variable and/or the covariates. To incorporate the correlation within each subject and enhance the accuracy of the parameter estimation, a two-step weighted estimation method is also established. Furthermore, we prove the oracle properties of the proposed method under some regularity conditions. Finally, the performance of the proposed method is demonstrated by simulation studies and two real examples.

Evaluation of surface roughness, wettability and adhesion of multispecies biofilm on 3D-printed resins for the base and teeth of complete dentures.

OBJECTIVE: This study evaluated the surface roughness, wettability and adhesion of multispecies biofilms (Candida albicans, Staphylococcus aureus and Streptococcus mutans) on 3D-printed resins for complete denture bases and teeth compared to conventional resins (heat-polymerized acrylic resin; artificial pre-fabricated teeth). METHODOLOGY: Circular specimens (n=39; 6.0 mm Ø × 2.0 mm) of each group were subjected to roughness (n=30), wettability (n=30) and biofilm adhesion (n=9) tests. Three roughness measurements were taken by laser confocal microscopy and a mean value was calculated. Wettability was evaluated by the contact angle of sessile drop method, considering the mean of the three evaluations per specimen. In parallel, microorganism adhesion to resin surfaces was evaluated using a multispecies biofilm model. Microbial load was evaluated by determining the number of Colony Forming Units (CFU/mL) and by scanning electron microscopy (SEM). Data were subjected to the Wald test in a generalized linear model with multiple comparisons and Bonferroni adjustment, as well as two-way ANOVA (α=5%). RESULTS: The roughness of the conventional base resin (0.01±0.04) was lower than that of the conventional tooth (0.14±0.04) (p=0.023) and 3D-printed base (0.18±0.08) (p<0.001). For wettability, conventional resin (84.20±5.57) showed a higher contact angle than the 3D-printed resin (60.58±6.18) (p<0.001). Higher microbial loads of S. mutans (p=0.023) and S. aureus (p=0.010) were observed on the surface of the conventional resin (S. mutans: 5.48±1.55; S. aureus: 7.01±0.57) compared to the 3D-printed resin (S. mutans: 4.11±1.96; S. aureus: 6.42±0.78). The adhesion of C. albicans was not affected by surface characteristics. The conventional base resin showed less roughness than the conventional dental resin and the printed base resin. CONCLUSION: The 3D-printed resins for base and tooth showed less hydrophobicity and less adhesion of S. mutans and S. aureus than conventional resins.

Examination of factors associated with the temporal stability assessment of crash severity by using generalised linear model-A case study.

Road crashes are a major public safety concern in Pakistan. Prior studies in Pakistan investigated the impact of different factors on road crashes but did not consider the temporal stability of crash data. This means that the recommendations based on these studies are not fully effective, as the impact of certain factors may change over time. To address this gap in the literature, this study aims to identify the factors contributing to crash severity in road crashes and examine how their impact varies over time. In this comprehensive study, we utilized Generalised Linear Model (GLM) on the crash data between the years 2013 to 2017, encompassing a total sample of 802 road crashes occurred on the N-5 road section in Pakistan, a 429-kilometer stretch connecting two big cities of Pakistan, i.e., Peshawar and Lahore. The purpose of the GLM was to quantify the temporal stability of the factors contributing crash severity in each year from 2013 to 2017. Within this dataset, 60% (n = 471) were fatal crashes, while the remaining 40% (n = 321) were non-fatal. The results revealed that the factors including the day of the week, the location of the crashes, weather conditions, causes of the crashes, and the types of vehicles involved, exhibited the temporal instability over time. In summary, our study provides in-depth insights aimed at reducing crash severity and potentially aiding in the development of effective crash mitigation policies in Pakistan and other nations having similar road safety problems. This research holds great promise in exploring the dynamic safety implications of emerging transportation technologies, particularly in the context of the widespread adoption of connected and autonomous vehicles.

The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model.

Popular statistical software provides the Bayesian information criterion (BIC) for multi-level models or linear mixed models. However, it has been observed that the combination of statistical literature and software documentation has led to discrepancies in the formulas of the BIC and uncertainties as to the proper use of the BIC in selecting a multi-level model with respect to level-specific fixed and random effects. These discrepancies and uncertainties result from different specifications of sample size in the BIC's penalty term for multi-level models. In this study, we derive the BIC's penalty term for level-specific fixed- and random-effect selection in a two-level nested design. In this new version of BIC, called BIC E 1 , this penalty term is decomposed into two parts if the random-effect variance-covariance matrix has full rank: (a) a term with the log of average sample size per cluster and (b) the total number of parameters times the log of the total number of clusters. Furthermore, we derive the new version of BIC, called BIC E 2 , in the presence of redundant random effects. We show that the derived formulae, BIC E 1 and BIC E 2 , adhere to empirical values via numerical demonstration and that BIC E ( E indicating either E 1 or E 2 ) is the best global selection criterion, as it performs at least as well as BIC with the total sample size and BIC with the number of clusters across various multi-level conditions through a simulation study. In addition, the use of BIC E 1 is illustrated with a textbook example dataset.

Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human.

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.

The entropy rate of Linear Additive Markov Processes.

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy.