The use of Shewhart control charts when conducting cavitation studies to identify and eliminate non-random causes of variability.

One of the processes that accompanies the operation of various hydraulic systems is cavitation. This phenomenon is often accompanied by cavitation erosion, that is, the progressive loss of parent material from a solid surface due to continued exposure. The problem of obtaining accurate and reliable data when conducting cavitation studies remains relevant. This article discusses the adaptation of the use of Shewhart control charts when conducting cavitation studies, in order to determine the presence of non-random "special" causes of variability. A graph of changes in the process parameters over time was constructed to carry out statistical control of the stability of the process in order to determine the boundaries of the system variability of the process in order to predict the behavior of the process. As a result of the conducted research, recommendations were developed to increase the accuracy of output data when conducting cavitation studies. It has been confirmed that the use of control charts as a tool for quality control of laboratory measurements allows us to establish that the process has achieved a statistically controlled state, which allows us to maintain a high degree of stability and quality of the research being carried out. This method makes it possible:•to determine the presence of non-random "special" causes of variability during cavitation studies.•to quickly identify and eliminate the "special" causes of variability during cavitation studies.

Refinement and Evaluation of the Critical Thinking Self-Assessment Scale.

Background and Purpose: Critical thinking (CT) skills are necessary tools for enhancing patient care. The Critical Thinking Self-Assessment Scale (CTSAS) was based on Facione et al.'s (1990) schema of 6 CT skills and 16 subskills. Although early results indicated a strong instrument, it was lengthy at 115 items. The study purpose was to statistically reduce the number of items in the instrument. Methods: Using a sample of 712 undergraduate nursing students, item analysis and confirmatory factor analysis were used to determine items to retain and delete. The scale was validated by comparing to the Need for Cognition Scale. Results: Items were reduced to 46 and spread over the 16 subskills. Conclusions: The revised CTSAS is a valid, reliable tool that is greatly reduced in length without compromising its psychometric properties. Faculty could use the measure as a reflection of students' levels on these skills and design learning activities to target problem areas.

Development and Testing of an Instrument to Measure Professionalism in Nursing.

Background and Purpose: Evaluation of professionalism in nursing has proven challenging as no objective measurement tool exists. The purpose of this study was to develop and test the Professionalism in Nursing Scale (PNS) for reliability and validity, which will facilitate evaluation of the constructs of professionalism in nursing. Participants were senior nursing students and registered nurses with at least a baccalaureate degree and a minimum of 3 years of experience working either in academia at an accredited university or in a practice setting in a Magnet hospital. Methods: Methodological research was used to design an instrument that measures professionalism in nursing. Phase 1 included item development, scaling, and evaluation of the content validity index, using 10 content experts. Phase 2 included pilot and field testing using participants meeting the inclusion criteria. Questionnaires were sent electronically to evaluate the relevance of each attribute of professionalism using a Likert scale. Phase 3 was scale evaluation, including reliability and validity of the PNS. Results: Final results of exploratory factor analysis supported a 33-item five-factor model. The factors were named Ethics and Interprofessional Collaboration, Excellence, Professional Engagement, Caring, and Self-Awareness. The overall reliability rate of the PNS was 0.97. Findings demonstrated the reliability and validity of the PNS for measuring professionalism in academic and clinician nurses and nursing students. Conclusions: Measuring professionalism in nursing can assist driving improvement of patient care, accountability, and team collaboration. A discussion of the PNS within the context of academia and clinical practice, along with implications for research, practice, education, and policy will be presented.

Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.

Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer. Women with cervical cancer diagnosed between 2013 and 2015 were included in our study using data from the Surveillance, Epidemiology, and End Results (SEER) database. Using this dataset, the study assesses the performance of Weibull, Cox proportional hazards models, and Random Survival Forests in terms of predictive accuracy and risk factor identification. The findings reveal that machine learning models, particularly Random Survival Forests (RSF), outperform traditional statistical methods in both predictive accuracy and the discernment of crucial prognostic factors, underscoring the advantages of machine learning in handling complex survival data. However, for a survival dataset with a small number of predictors, statistical models should be used first. The study finds that RSF models enhance survival analysis with more accurate predictions and insights into survival risk factors but highlights the need for larger datasets and further research on model interpretability and clinical applicability.

24-Hour Activity Composition is Associated with Lower Fall and Fracture Risk in Older Men.

Physical activity (PA), sedentary behavior (SB), and sleep are each individually associated with falls and fractures, but often are not examined simultaneously. Compositional data analysis examined the combined prospective associations between the proportion of time in PA, SB, and sleep relative to the remaining behaviors with recurrent falls (2+ falls in any year), any fractures, and major osteoporotic fracture (MOF) from tri-annual questionnaires, with adjudication for fractures, in 2918 older men aged 78.9 ± 5.1 years in the Osteoporotic Fractures in Men (MrOS) Study. Accelerometers were worn on the right tricep for seven consecutive 24-hour periods and measured PA (>1.5 METs), SB (≤1.5 METs), and sleep. Generalized Estimating Equations evaluated associations with recurrent falls. Cox proportional hazards regression estimated any incident fracture and MOF risk separately. Over four years of follow-up 1025 (35.2%) experienced recurrent falls; over 10 ± 4 years of follow-up, 669 (22.9%) experienced incident fractures and 370 (12.7%) experienced a MOF. Higher proportions of PA relative to SB and sleep were associated with a lower odds of recurrent falls [Odds Ratio (OR): 0.87, 95% CI: 0.76-0.99]. Higher proportions of SB relative to PA and sleep were associated with a higher odds of recurrent falls (OR: 1.38, 95% CI: 1.06-1.81) and higher risk of any fracture [Hazard Ratio (HR): 1.42, 95% CI: 1.05-1.92]. Higher proportions of sleep relative to PA and SB were associated with a lower risk of fracture (HR: 0.74, 95% CI: 0.54-0.99). No associations of activity composition with MOF were observed. When accounting for the co-dependence of daily activities, higher proportions of SB relative to the proportion of PA and sleep were associated with higher odds of recurrent falls and fracture risk. Results suggest reducing SB (and increasing PA) may lower fall and fracture risk in older men, which could inform future interventions.

Physical activity, sedentary behavior, and sleep are each individually associated with falls and fractures. However, there is only a finite amount of time for each activity in a 24-hour day and the ideal structure of the day for these activities is unknown. We evaluated the association between the combination of physical activity, sedentary behavior, and sleep together with recurrent falls and fractures in older men. Spending a higher proportion of the day in physical activity was associated with a lower risk of falls, while a higher proportion of sedentary behavior was associated with a higher risk of falls and fractures. For sleep, higher proportions of the day spent sleeping were associated with a lower risk of fractures. These results can inform future physical activity interventions aimed at lowering falls and fracture risk in older men by focusing on increasing the amount of time in physical activity by specifically lowering the amount of time in sedentary behavior.

Spatial and spatiotemporal modelling of intra-urban ultrafine particles: A comparison of linear, nonlinear, regularized, and machine learning methods.

BACKGROUND: Machine learning methods are proposed to improve the predictions of ambient air pollution, yet few studies have compared ultrafine particles (UFP) models across a broad range of statistical and machine learning approaches, and only one compared spatiotemporal models. Most reported marginal differences between methods. This limits our ability to draw conclusions about the best methods to model ambient UFPs. OBJECTIVE: To compare the performance and predictions of statistical and machine learning methods used to model spatial and spatiotemporal ambient UFPs. METHODS: Daily and annual models were developed from UFP measurements from a year-long mobile monitoring campaign in Quebec City, Canada, combined with 262 geospatial and six meteorological predictors. Various road segment lengths were considered (100/300/500 m) for UFP data aggregation. Four statistical methods included linear, non-linear, and regularized regressions, whereas eight machine learning regressions utilized tree-based, neural networks, support vector, and kernel ridge algorithms. Nested cross-validation was used for model training, hyperparameter tuning and performance evaluation. RESULTS: Mean annual UFP concentrations was 13,335 particles/cm3. Machine learning outperformed statistical methods in predicting UFPs. Tree-based methods performed best across temporal scales and segment lengths, with XGBoost producing the overall best performing models (annual R2 = 0.78-0.86, RMSE = 2163-2169 particles/cm3; daily R2 = 0.47-0.48, RMSE = 8651-11,422 particles/cm3). With 100 m segments, other annual models performed similarly well, but their prediction surfaces of annual mean UFP concentrations showed signs of overfitting. Spatial aggregation of monitoring data significantly impacted model performance. Longer segments yielded lower RMSE in all daily models and for annual statistical models, but not for annual machine learning models. CONCLUSIONS: The use of tree-based methods significantly improved spatiotemporal predictions of UFP concentrations, and to a lesser extent annual concentrations. Segment length and hyperparameter tuning had notable impacts on model performance and should be considered in future studies.

Addressing immortal time bias in precision medicine: Practical guidance and methods development.

OBJECTIVE: To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study. STUDY SETTING AND DESIGN: Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes. DATA SOURCES AND ANALYTIC SAMPLE: Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015. PRINCIPAL FINDINGS: While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations. CONCLUSIONS: Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

An overview of statistical methods for biomarkers relevant to early clinical development of cancer immunotherapies.

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

Combining mutation and recombination statistics to infer clonal families in antibody repertoires.

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B-cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution, and dynamics. We present HILARy (high-precision inference of lineages in antibody repertoires), an efficient, fast, and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and dN/dS ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.

A comparative study of home healthcare quality in urban and rural home health agencies throughout the USA (2010-22).

Urban-rural disparities in medical care, including in home healthcare, persist globally. With aging populations and medical advancements, demand for home health services rises, warranting investigation into home healthcare disparities. Our study aimed to (i) investigate the impact of rurality on home healthcare quality, and (ii) assess the temporal disparities and the changes in disparities in home healthcare quality between urban and rural home health agencies (HHAs), incorporating an analysis of geospatial distribution to visualize the underlying patterns. This study analyzed data from HHAs listed on the Centers for Medicare and Medicaid Services website, covering the period from 2010 to 2022. Data were classified into urban and rural categories for each HHA. We employed panel data analysis to examine the impact of rurality on home healthcare quality, specifically focusing on hospital admission and emergency room (ER) visit rates. Disparities between urban and rural HHAs were assessed using the Wilcoxon test, with results visualized through line and dot plots and heat maps to illustrate trends and differences comprehensively. Rurality is demonstrated as the most significant variable in hospital admission and ER visit rates in the panel data analysis. Urban HHAs consistently exhibit significantly lower hospital admission rates and ER visit rates compared to rural HHAs from 2010 to 2022. Longitudinally, the gap in hospital admission rates between urban and rural HHAs is shrinking, while there is an increasing gap in ER visit rates. In 2022, HHAs in Mountain areas, which are characterized by a higher proportion of rural regions, exhibited higher hospital admission and ER visit rates than other areas. This study underscores the persistent urban-rural disparities in home healthcare quality. The analysis emphasizes the ongoing need for targeted interventions to address disparities in home healthcare delivery and ensure equitable access to quality care across urban and rural regions. Our findings have the potential to inform policy and practice, promoting equity and efficiency in the long-term care system, for better health outcomes throughout the USA.

Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling.

Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area.

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Development and Psychometric Evaluation of the End-of-Life Nursing Competency Scale for Clinical Nurses.

This study aimed to develop and establish psychometric properties of the End-of-Life Nursing Competency Scale for Clinical Nurses. The initial items were derived from an in-depth literature review and field interviews. The content validation of these items was assessed over three rounds by experts in end-of-life nursing care. The study included 437 clinical nurses from four hospitals in S, E, and D cities in South Korea. The final exploratory factor analysis resulted in a scale consisting of 21 items with the following five factors that explained 68.44% of the total variance: Physical care-imminent end-of-life, legal and administrative processes, psychological care-patient and family, psychological care-nurses' self, and ethical nursing. The final model with these five subscales was validated through confirmatory factor analysis. Both item convergent-discriminant validity and known-group validity, which compared two groups based on clinical experience (p < 0.008) and working department (p < 0.008), were satisfactory. The internal consistency, as measured by Cronbach's α, ranged from 0.62 to 0.89 for the subscales and was 0.91 for the total scale. This scale has been validated as a reliable and effective instrument for clinical nurses to self-assess their end-of-life nursing competencies in a clinical setting.

Identification of Amino Acids and Polyphenolic Metabolites in Human Plasma by UHPLC-ESI-QTOF-MS/MS, after the Chronic Intake of a Functional Meal in an Elderly Population.

Novel foods especially formulated and targeted for the elderly population should provide sufficient nutrients and bioactive ingredients to counteract the natural age-related deterioration of various organs and tissues. Dietary protein and phenolic compounds achieve this goal; however, older adults have alterations in their gastrointestinal system that may impact their bioavailability and few studies have been aimed at this population. Since phenolic compounds are the subject of multiple biotransformations by host and microbiome enzymes during the digestion process, identification of their bioavailable forms in human plasma or tissues represents a considerable analytical challenge. In this study, UHPLC-ESI-QTOF/MS-MS, chemometrics, and multivariate statistical methods were used to identify the amino acids and phenolic compounds that were increased in the plasma of elderly adults after a 30-day intervention in which they had consumed an especially formulated muffin and beverage containing Brosimum alicastrum Sw. seed flour. A large interindividual variation was observed regarding the amino acids and phenolic metabolites identified in the plasma samples, before and after the intervention. Three phenolic metabolites were significantly increased in the population after the intervention: protocatechuic acid, 5-(methoxy-4'-hydroxyphenyl) valerolactone, and phloretic acid. These metabolites, as well as others that were not significantly increased (although they did increase in several individuals), are probably the product of the microbiota metabolism of the major phenolic compounds present in the B. alicastrum Sw. seed flour and other food ingredients. A significant decrease in 4-ethyl-phenol, a biomarker of stress, was observed in the samples. Results showed that the incorporation of foods rich in phenolic compounds into the regular diet of older adults contributes to the increase in bioactive compounds in plasma, that could substantially benefit their mental, cardiovascular, and digestive health.

Re-evaluating Methods for Assessing Differences in Response in Ileal vs. Colonic Crohn's Disease: A Post-hoc Analysis of the FITZROY Trial.

BACKGROUND AND AIMS: The ileum is the most commonly affected segment of the gastrointestinal tract in Crohn's disease (CD). We aimed to determine whether disease location affects response to filgotinib, a Janus kinase (JAK) inhibitor, in patients with moderate-to-severely active Crohn's disease (CD) and applying appropriate methods to account for differences in measuring disease activity in the ileum compared to the colon. METHODS: This post-hoc analysis of data from the FITZROY phase 2 trial (NCT02048618) compared changes in the Crohn's Disease Activity Index (CDAI) and Simple Endoscopic Score for Crohn's Disease (SES-CD) amongst patients with ileal-dominant and isolated colonic CD treated with 10 weeks of filgotinib 200 mg daily or placebo. A mixed effects model for repeated measures was used to test whether ileal disease responded differently than colonic disease, by evaluating for effect modification using the interaction term of treatment assignment-by-disease location. RESULTS: Numerically greater proportions of patients with isolated colonic disease compared to ileal-dominant CD achieved clinical remission (CDAI <150, 75.9% vs. 41.6%) and endoscopic response (SES-CD reduction by 50%, 52.5% vs. 15.5%) at Week 10. However, after adjusting for baseline disease activity by disease location and within-patient clustering effects, there was no significant difference in treatment response by disease location (mean difference in ΔCDAI between ileal-dominant vs. isolated colonic disease +9.24 [95% CI: -87.19, +105.67], p=0.85; mean difference in ΔSES-CD -1.93 [95% CI: -7.03, +3.44], p=0.48). CONCLUSIONS: Filgotinib demonstrated similar efficacy in ileal-dominant and isolated colonic CD when controlling for baseline disease activity and clustering effects.

An analytical review on the use of artificial intelligence and machine learning in diagnosis, prediction, and risk factor analysis of multiple sclerosis.

Medical research offers potential for disease prediction, like Multiple Sclerosis (MS). This neurological disorder damages nerve cell sheaths, with treatments focusing on symptom relief. Manual MS detection is time-consuming and error prone. Though MS lesion detection has been studied, limited attention has been paid to clinical analysis and computational risk factor prediction. Artificial intelligence (AI) techniques and Machine Learning (ML) methods offer accurate and effective alternatives to mapping MS progression. However, there are challenges in accessing clinical data and interdisciplinary collaboration. By analyzing 103 papers, we recognize the trends, strengths and weaknesses of AI, ML, and statistical methods applied to MS diagnosis. AI/ML-based approaches are suggested to identify MS risk factors, select significant MS features, and improve the diagnostic accuracy, such as Rule-based Fuzzy Logic (RBFL), Adaptive Fuzzy Inference System (ANFIS), Artificial Neural Network methods (ANN), Support Vector Machine (SVM), and Bayesian Networks (BNs). Meanwhile, applications of the Expanded Disability Status Scale (EDSS) and Magnetic Resonance Imaging (MRI) can enhance MS diagnostic accuracy. By examining established risk factors like obesity, smoking, and education, some research tackled the issue of disease progression. The performance metrics varied across different aspects of MS studies: Diagnosis: Sensitivity ranged from 60 % to 98 %, specificity from 60 % to 98 %, and accuracy from 61 % to 97 %. Prediction: Sensitivity ranged from 76 % to 98 %, specificity from 65 % to 98 %, and accuracy from 62 % to 99 %. Segmentation: Accuracy ranged up to 96.7 %. Classification: Sensitivity ranged from 78 % to 97.34 %, specificity from 65 % to 99.32 %, and accuracy from 71 % to 97.94 %. Furthermore, the literature shows that combining techniques can improve efficiency, exploiting their strengths for better overall performance.

Analytic Methods for Understanding the Temporal Patterning of Dietary and 24-H Movement Behaviors: A Scoping Review.

Dietary and movement behaviors [physical activity (PA), sedentary behavior (SED), and sleep] occur throughout a 24-h day and involve multiple contexts. Understanding the temporal patterning of these 24-h behaviors and their contextual determinants is key to determining their combined effect on health. A scoping review was conducted to identify novel analytic methods for determining temporal behavior patterns and their contextual correlates. We searched Embase, ProQuest, and EBSCOhost databases in July 2022 to identify studies published between 1997 and 2022 on temporal patterns and their contextual correlates (e.g., locational, social, environmental, personal). We included 14 studies after title and abstract (n = 33,292) and full-text (n = 135) screening, of which 11 were published after 2018. Most studies (n = 4 in adults; n = 5 in children and adolescents), examined waking behavior patterns (i.e., both PA and SED) of which 3 also included sleep and 6 included contextual correlates. PA and diet were examined together in only 1 study of adults. Contextual correlates of dietary, PA, and sleep temporal behavior patterns were also examined. Machine learning with various clustering algorithms and model-based clustering techniques were most used to determine 24-h temporal behavior patterns. Although the included studies used a diverse range of methods, behavioral variables, and assessment periods, results showed that temporal patterns characterized by high SED and low PA were linked to poorer health outcomes, than those with low SED and high PA. This review identified temporal behavior patterns, and their contextual correlates, which were associated with adiposity and cardiometabolic disease risk, suggesting these methods hold promise for the discovery of holistic lifestyle exposures important to health. Standardized reporting of methods and patterns and multidisciplinary collaboration among nutrition, PA, and sleep researchers; statisticians; and computer scientists were identified as key pathways to advance future research on temporal behavior patterns in relation to health.

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.