Laboratory Tests on the Possibility of Using Flax Fibers as a Plant-Origin Reinforcement Component in Composite Friction Materials for Vehicle Braking Systems.

Braking systems are extremely important in any vehicle. They convert the kinetic energy of motion into thermal energy that is dissipated into the atmosphere. Different vehicle groups have different nominal and maximum speeds and masses, so the amount of thermal energy that needs to be absorbed by the friction pads and then dissipated can vary significantly. Conventional friction materials are composite materials capable of withstanding high temperatures (in the order of 500-600 °C) and high mechanical loads resulting from braking intensity and vehicle weight. In small vehicles traveling at low speeds, where both the amount of thermal energy and its density are limited, the use of slightly weaker friction materials with better ecological properties can be considered. This work proposes a prototype composite friction material using flax fibers as reinforcement instead of the commonly used aramid. A number of samples were prepared and subjected to laboratory tests. The samples were prepared using components of plant origin, specifically flax fibers. This component acted as reinforcement in the composite friction material, replacing aramid commonly used for this purpose. The main tribological characteristics were determined, such as the values of the coefficients of friction and the coefficients of abrasive wear rate. For this purpose, an authorial method using ball-cratering contact was used. The results were analyzed using statistical methods. It was found that the composite material using flax fibers does not differ significantly in its tribological properties from conventional solutions; so, it can be assumed that it can be used in the vehicle's braking system.

Statistical Analysis Methods and Reporting of Patient-Reported Outcomes in Randomized Controlled Trials for Cancer Conducted in Japan: A Systematic Review.

The Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints Data (SISAQOL) initiative was established in 2016 to assess the quality and standardization of patient-reported outcomes (PRO) data analysis in randomized controlled trials (RCTs) on advanced breast cancer. The initiative identified deficiencies in PRO data reporting, including nonstandardized methods for handling missing data. This study evaluated the reporting of health-related quality of life (HRQOL) in Japanese cancer RCTs to provide insights into the state of PRO reporting in Japan. The study reviewed PubMed articles published from 2010 to 2018. Eligible studies included Japanese cancer RCTs with ≥50 adult patients (≥50% were Japanese) with solid tumors receiving anticancer treatments. The evaluation criteria included clarity of the HRQOL hypotheses, multiplicity testing, primary analysis methods, and reporting of clinically meaningful differences. Twenty-seven HRQOL trials were identified. Only 15% provided a clear HRQOL hypothesis, and 63% examined multiple HRQOL domains without adjusting for multiplicity. Model-based methods were the most common statistical methods for the primary HRQOL analysis. Only 22% of the trials explicitly reported clinically meaningful differences in HRQOL. Baseline assessments were reported in most trials, but only 26% reported comparisons between the treatment groups. HRQOL analysis was based on the intention-to-treat population in 19% of the trials, and 74% reported compliance at follow-up; however, 41% did not specify how missing values were handled. Although the rates of reporting clinical hypotheses and clinically meaningful differences were relatively low, the current state of HRQOL evaluation in the Japanese cancer RCT appears comparable to that of previous studies.

Prenatal air pollution and children's autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods.

Background: Maternal nutrient intake may moderate associations between environmental exposures and children's neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores. Methods: We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009-2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 µm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children's autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months). Results: Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores. Conclusion: In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

Greedy Ensemble Hyperspectral Anomaly Detection.

Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport-Beach-Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

Internal validation of Automated Visual Evaluation (AVE) on smartphone images for cervical cancer screening in a prospective study in Zambia.

OBJECTIVES: Visual inspection with acetic acid (VIA) is a low-cost approach for cervical cancer screening used in most low- and middle-income countries (LMICs) but, similar to other visual tests, is subjective and requires sustained training and quality assurance. We developed, trained, and validated an artificial-intelligence-based "Automated Visual Evaluation" (AVE) tool that can be adapted to run on smartphones to assess smartphone-captured images of the cervix and identify precancerous lesions, helping augment VIA performance. DESIGN: Prospective study. SETTING: Eight public health facilities in Zambia. PARTICIPANTS: A total of 8204 women aged 25-55. INTERVENTIONS: Cervical images captured on commonly used low-cost smartphone models were matched with key clinical information including human immunodeficiency virus (HIV) and human papillomavirus (HPV) status, plus histopathology analysis (where applicable), to develop and train an AVE algorithm and evaluate its performance for use as a primary screen and triage test for women who are HPV positive. MAIN OUTCOME MEASURES: Area under the receiver operating curve (AUC); sensitivity; specificity. RESULTS: As a general population screening tool for cervical precancerous lesions, AVE identified cases of cervical precancerous and cancerous (CIN2+) lesions with high performance (AUC = 0.91, 95% confidence interval [CI] = 0.89-0.93), which translates to a sensitivity of 85% (95% CI = 81%-90%) and specificity of 86% (95% CI = 84%-88%) based on maximizing the Youden's index. This represents a considerable improvement over naked eye VIA, which as per a meta-analysis by the World Health Organization (WHO) has a sensitivity of 66% and specificity of 87%. For women living with HIV, the AUC of AVE was 0.91 (95% CI = 0.88-0.93), and among those testing positive for high-risk HPV types, the AUC was 0.87 (95% CI = 0.83-0.91). CONCLUSIONS: These results demonstrate the feasibility of utilizing AVE on images captured using a commonly available smartphone by nurses in a screening program, and support our ongoing efforts for moving to more broadly evaluate AVE for its clinical sensitivity, specificity, feasibility, and acceptability across a wider range of settings. Limitations of this study include potential inflation of performance estimates due to verification bias (as biopsies were only obtained from participants with visible aceto-white cervical lesions) and due to this being an internal validation (the test data, while independent from that used to develop the algorithm was drawn from the same study).

Addressing missing outcome data in randomised controlled trials: A methodological scoping review.

BACKGROUND: Missing outcome data is common in trials, and robust methods to address this are needed. Most trial reports currently use methods applicable under a missing completely at random assumption (MCAR), although this strong assumption can often be inappropriate. OBJECTIVE: To identify and summarise current literature on the analytical methods for handling missing outcome data in randomised controlled trials (RCTs), emphasising methods appropriate for data missing at random (MAR) or missing not at random (MNAR). STUDY DESIGN AND SETTING: We conducted a methodological scoping review and identified papers through searching four databases (MEDLINE, Embase, CENTRAL, and CINAHL) from January 2015 to March 2023. We also performed forward and backward citation searching. Eligible papers discussed methods or frameworks for handling missing outcome data in RCTs or simulation studies with an RCT design. RESULTS: From 1878 records screened, our search identified 101 eligible papers. 90 (89%) papers described specific methods for addressing missing outcome data and 11 (11%) described frameworks for overall methodological approach. Of the 90 methods papers, 30 (33%) described methods under the MAR assumption, 48 (53%) explored methods under the MNAR assumption and 11 (12%) discussed methods under a hybrid of MAR and MNAR assumptions. Control-based methods under the MNAR assumption were the most common method explored, followed by multiple imputation under the MAR assumption. CONCLUSION: This review provides guidance on available analytic approaches for handling missing outcome data, particularly under the MNAR assumption. These findings may support trialists in using appropriate methods to address missing outcome data.

Comparative Effectiveness and Safety Outcomes Between Denosumab and Bisphosphonate in South Korea.

Both bisphosphonates and denosumab are the mainstays of treatment for osteoporosis to prevent fractures. However, there are still few trials directly comparing the prevention of fractures and the safety of two drugs in the treatment of osteoporosis. We aimed to compare the efficacy and safety between denosumab and bisphosphonates using a nationwide claims database. The database was covered with ten million, 20% of the whole Korean population sampled by age and sex stratification of the Health Insurance Review and Assessment Service in South Korea. Among 228,367 subjects who were over 50 years of age and taking denosumab or bisphosphonate from Jan 2018 to April 2022, the analysis was performed on 91,460 subjects after 1: 1 propensity score matching. The primary outcome was treatment effectiveness; total fracture, major osteoporotic fracture, femur fracture, pelvic fracture, vertebral fracture, adverse drug reactions; acute kidney injury, chronic kidney disease, and atypical femoral fracture. Total fracture and osteoporotic major fracture, as the main outcomes of efficacy, were comparable in the denosumab and bisphosphonate group (HR 1.06, 95% CI 0.98-1.15, p=0.14; HR 1.13, 95% CI 0.97-1.32, p=0.12, respectively). Safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture also did not show any differences between the two groups. In subgroup analysis according to ages, the denosumab group under 70 years of age had a significantly lower risk for occurrences of acute kidney injury compared to the bisphosphonate group under 70 years of age (HR 0.53, 95% CI 0.29-0.93, p=0.03). In real-world data reflecting clinical practice, denosumab, and bisphosphonate showed comparable effectiveness for total fracture and osteoporosis major fracture and safety for acute kidney injury, chronic kidney disease, and atypical femoral fracture.

This study compared the effectiveness and safety of denosumab and bisphosphonates, two primary treatments for osteoporosis, using a large South Korean nationwide claims database. Analysis of data from 91,460 individuals over 50 years old showed no significant difference in preventing fractures or in safety outcomes such as kidney injury and atypical femoral fractures between the two drugs. However, among patients under 70, denosumab was associated with a lower risk of acute kidney injury. Overall, both medications demonstrated similar effectiveness and safety in the real-world treatment of osteoporosis.

Comparative effectiveness of Denosumab vs alendronate among postmenopausal women with osteoporosis.

Although clinical trials have shown that denosumab significantly increases bone mineral density at key skeletal sites more than oral bisphosphonates, evidence is lacking from head-to-head randomized trials evaluating fracture outcomes. This retrospective cohort study uses administrative claims data from Medicare fee-for service beneficiaries to evaluate the comparative effectiveness of denosumab versus alendronate in reducing fracture risk among women with postmenopausal osteoporosis (PMO) in the US. Women with PMO ≥ 66 years of age with no prior history of osteoporosis treatment, who initiated denosumab (n = 89 115) or alendronate (n = 389 536) from 2012 to 2018, were followed from treatment initiation until the first of a specific fracture outcome, treatment discontinuation or switch, end of study (December 31, 2019), or other censoring criteria. A doubly robust inverse-probability of treatment and censoring weighted function was used to estimate the risk ratio associated with the use of denosumab compared with alendronate for hip, nonvertebral (NV; includes hip, humerus, pelvis, radius/ulna, other femur), non-hip nonvertebral (NHNV), hospitalized vertebral (HV), and major osteoporotic (MOP; consisting of NV and HV) fractures. Overall, denosumab reduced the risk of MOP by 39%, hip by 36%, NV by 43%, NHNV by 50%, and HV fractures by 30% compared with alendronate. Denosumab reduced the risk of MOP fractures by 9% at year 1, 12% at year 2, 18% at year 3, and 31% at year 5. An increase in the magnitude of fracture risk reduction with increasing duration of exposure was also observed for other NV fracture outcomes. In this cohort of almost half-a-million treatment-naive women with PMO, we observed clinically significant reductions in the risk of MOP, hip, NV, NHNV, and HV fractures for patients on denosumab compared with alendronate. Patients who remained on denosumab for longer periods of time experienced greater reductions in fracture risk.

Osteoporosis-related fractures can have a significant impact on the health and quality of life of women with postmenopausal osteoporosis (PMO), as well as pose a significant burden to society. Although clinical trials have shown that denosumab is more effective at increasing bone mineral density compared to alendronate, there is a lack of evidence evaluating the fracture risk between these two commonly used osteoporosis therapies. In this study using Medicare claims data for almost 500 000 women with PMO with no prior history of osteoporosis medication use, we compared the risk of fracture, an important outcome to patients and health care providers, between denosumab and alendronate. Advanced analytic methods were implemented to ensure the study results were valid and were not unduly influenced by biases common in observational studies. We observed clinically meaningful reductions (from 30% up to 50%) in the risk of hip, nonvertebral, non-hip nonvertebral, hospitalized vertebral, and major osteoporotic fractures for patients treated with denosumab compared with alendronate. Patients who remained on denosumab for longer periods of time experienced greater reductions in fracture risk than those who remained on alendronate.

Estimating winning percentage of the fourth quarter in close NBA games using Bayesian logistic modeling.

This study examined the fourth quarters in the close games in the regular NBA games in the last decade, ranging from the 2013-14 season to the 2022-2023 season. A close game is categorically defined by a scenario where the point differential is confined within a 10-point margin at the onset of the fourth quarter and narrows further to a 5-point disparity by the end of the game. In total, 2,295 close games were identified in this study. Advanced game statistics, including offensive rate, defensive rate, assistance ratio, pace of game, and true shooting percentage, etc., are obtained from the NBA box scores using a python script. Understanding key factors that determine the outcome of the basketball games is critical, as such can be used to develop predictive models for coaches to design game strategies. This study developed a Bayesian Logistic Modeling approach to estimate the winning probability of a basketball team in the fourth quarter, using the pace of the last quarter and a team's shooting percentage. The accuracy of the model is used to evaluate if the model can correctly classify game outcome based on the identified game statistics in the fourth quarter of a game. The binary outcome of the close game is modeled as a Bernoulli distribution. Results reveal that the True Positive Rate and False Positive Rate is 0.93 and 0.07, respectively. Insights from this study can be used to help design coaching strategies in basketball games, illuminating potential tactical pivots that could tilt the game in their favor.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures.

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Statistical techniques to assess publication integrity in groups of randomized trials: a narrative review.

OBJECTIVES: To describe statistical tools available for assessing publication integrity of groups of randomized controlled trials (RCTs). STUDY DESIGN AND SETTING: Narrative review. RESULTS: Freely available statistical tools have been developed that compare the observed distributions of baseline variables with the expected distributions that would occur if successful randomization occurred. For continuous variables, the tools assess baseline means, baseline P values, and the occurrence of identical means and/or standard deviation. For categorical variables, they assess baseline P values, frequency counts for individual or all variables, numbers of trial participants randomized or withdrawing, and compare reported with independently calculated P values. The tools have been used to identify publication integrity concerns in RCTs from individual groups, and performed at an acceptable level in discriminating intentionally fabricated baseline summary data from genuine RCTs. The tools can be used when concerns have been raised about RCT(s) from an individual/group and when the whole body of their work is being examined, when conducting systematic reviews, and could be adapted to aid screening of RCTs at journal submission. CONCLUSION: Statistical tools are useful for the assessment of publication integrity of groups of RCTs.

Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies.

Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

Can face recognition be selectively preserved in some cases of amnesia? A cautionary tale.

Evidence suggests that some patients with isolated hippocampal damage appear to present with selective preservation of unfamiliar face recognition relative to other kinds of visual test stimuli (e.g., words). Bird and Burgess (2008) formulated a review and secondary analysis of a group of 10 cases all tested on a clinical assessment of word and face recognition memory (RMT, Warrington, 1984), which confirmed the key memory dissociation at the group level. The current work provides an updated secondary analysis of such cases with a larger published sample (N = 52). In addition to group-level analyses, we also re-evaluate evidence using a single case statistical approach (Crawford & Garthwaite, 2005), enabling us to determine how many would make criteria for a 'classical dissociation' (Crawford, Garthwaite, & Gray, 2003). Overall, group-level analyses indicated the key pattern of significant differences confined to words was limited to small control sample comparisons. When using the large control sample provided by Bird and Burgess (2008), hippocampal cases as a group were significantly poorer for both classes of items. Furthermore, our single-case approach indicated few had a performance pattern of a relative difference across face > word categories that would meet statistical significance; namely within individual differences across categories that would warrant a significant 'classical dissociation'. Moreover, these analyses also found several cases with a 'classical dissociation' in the reverse direction: namely preserved recognition of words. Such analyses serve to demonstrate the need for a more conservative statistical approach to be undertaken when reporting selective 'preservation' of a category in recognition memory. Whilst material specificity has important implications for understanding the role of the hippocampus in memory, our results highlight the need for statistical methods to be unquestionably rigorous before any claims are made. Lastly, we highlight other methodological issues critical to group analyses and make suggestions for future work.

Should we ignore SARS-CoV-2 disease?

Current World Health Organization (WHO) reports claim a decline in COVID-19 testing and reporting of new infections. To discuss the consequences of ignoring severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, the endemic characteristics of the disease in 2023 with the ones estimated before using 2022 data sets are compared. The accumulated numbers of cases and deaths reported to the WHO by the 10 most infected countries and global figures were used to calculate the average daily numbers of cases DCC and deaths DDC per capita and case fatality rates (CFRs = DDC/DCC) for two periods in 2023. In some countries, the DDC values can be higher than the upper 2022 limit and exceed the seasonal influenza mortality. The increase in CFR in 2023 shows that SARS-CoV-2 infection is still dangerous. The numbers of COVID-19 cases and deaths per capita in 2022 and 2023 do not demonstrate downward trends with the increase in the percentages of fully vaccinated people and boosters. The reasons may be both rapid mutations of the coronavirus, which reduced the effectiveness of vaccines and led to a large number of re-infections, and inappropriate management.

Computational frameworks integrating deep learning and statistical models in mining multimodal omics data.

BACKGROUND: In health research, multimodal omics data analysis is widely used to address important clinical and biological questions. Traditional statistical methods rely on the strong assumptions of distribution. Statistical methods such as testing and differential expression are commonly used in omics analysis. Deep learning, on the other hand, is an advanced computer science technique that is powerful in mining high-dimensional omics data for prediction tasks. Recently, integrative frameworks or methods have been developed for omics studies that combine statistical models and deep learning algorithms. METHODS AND RESULTS: The aim of these integrative frameworks is to combine the strengths of both statistical methods and deep learning algorithms to improve prediction accuracy while also providing interpretability and explainability. This review report discusses the current state-of-the-art integrative frameworks, their limitations, and potential future directions in survival and time-to-event longitudinal analysis, dimension reduction and clustering, regression and classification, feature selection, and causal and transfer learning.

Reliability and Validity of the Perceived Racism Scale-Racism on the Job Subscale-in a Sample of Black Nurses.

Background and Purpose: We utilized the Perceived Racism Scale-Racism on the Job subscale-to assess how frequently Black nurses experienced racism on the job in the past year (ROTJ-Y) and throughout their lifetime (ROTJ-L). We aimed to assess the reliability and assess construct validity of each subscale in a sample of 53 nurses. Methods: Reliability was evaluated using coefficient alphas, item correlations, and interitem correlations. Construct validity was examined using exploratory factor analysis. Results: Results demonstrated that the subscales are reliable and valid. Coefficient alphas for the ROTJ-Y and ROTJ-L were .93 and .91, respectively. Exploratory factor analysis revealed a unidimensional factor for both subscales. Conclusion: This study demonstrated that the Racism on the Job subscales are psychometrically sound measures of workplace racism among Black nurses.

A protocol for the prospective study of urinary cadmium with risk of fracture, bone loss, and muscle loss.

Cadmium (Cd) is a heavy metal and natural element found in soil and crops with increasing concentrations linked to phosphate fertilizers and sewage sludge applied to crop lands. A large fraction of older US men and woman have documented Cd exposure. Cd exposure has proven health concerns such as risk of lung cancer from inhalation and impaired renal function; however, growing evidence suggests it also influences bone and muscle health. Given that low levels of Cd could affect bone and muscle, we have designed prospective studies using the two largest and most detailed US studies of bone health in older men and women: the Osteoporotic Fractures in Men Study and the Study of Osteoporotic Fractures. We are investigating the association of urinary cadmium (U-Cd), as a surrogate for long-term Cd exposure, with bone and muscle health. Building off suggestive evidence from mechanistic and cross-sectional studies, this will be the first well-powered prospective study of incident fracture outcomes, bone loss, and muscle loss in relation to U-Cd, an established biomarker of long-term Cd exposure. The following is a proposed protocol for the intended study; if successful, the proposed studies could be influential in directing future US policy to decrease Cd exposure in the US population similar to recent policies adopted by the European Union to limit Cd in fertilizers.

Transdiagnostic psychopathology in the light of robust single-trial event-related potentials.

Recent evidence indicates that event-related potentials (ERPs) as measured on the electroencephalogram (EEG) are more closely related to transdiagnostic, dimensional measures of psychopathology (TDP) than to diagnostic categories. A comprehensive examination of correlations between well-studied ERPs and measures of TDP is called for. In this study, we recruited 50 patients with emotional disorders undergoing 14 weeks of transdiagnostic group psychotherapy as well as 37 healthy comparison subjects (HC) matched in age and sex. HCs were assessed once and patients three times throughout treatment (N = 172 data sets) with a battery of well-studied ERPs and psychopathology measures consistent with the TDP framework The Hierarchical Taxonomy of Psychopathology (HiTOP). ERPs were quantified using robust single-trial analysis (RSTA) methods and TDP correlations with linear regression models as implemented in the EEGLAB toolbox LIMO EEG. We found correlations at several levels of the HiTOP hierarchy. Among these, a reduced P3b was associated with the general p-factor. A reduced error-related negativity correlated strongly with worse symptomatology across the Internalizing spectrum. Increases in the correct-related negativity correlated with symptoms loading unto the Distress subfactor in the HiTOP. The Flanker N2 was related to specific symptoms of Intrusive Cognitions and Traumatic Re-experiencing and the mismatch negativity to maladaptive personality traits at the lowest levels of the HiTOP hierarchy. Our study highlights the advantages of RSTA methods and of using validated TDP constructs within a consistent framework. Future studies could utilize machine learning methods to predict TDP from a set of ERP features at the subject level.