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The physiological activities within cells are mainly regulated through protein–protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated. [ABSTRACT FROM AUTHOR]

A common approach for biological assessment is to compare current observations of biota at a site to predictions of the biota that would occur if the site were in reference condition. To estimate these reference expectations, predictive bioassessment models use only data collected at reference sites and estimate relationships between biota and environmental variables that are unaffected by human activities (i.e., immutable variables). However, in some areas where human activities are pervasive, few reference sites are available. Here, we introduce a new approach for bioassessment, in which we first estimate relationships between widely available measurements of both immutable and human-influenced landscape variables and local environmental conditions (conductivity, dissolved total P, total suspended solids, percentage of sand and fines in the substrate, and dissolved organic C), and then we estimate the relationship between these local environmental variables and total macroinvertebrate richness. We then calculate the values of the local environmental variables under reference conditions by adjusting predictors that are strongly influenced by human activities to levels that are consistent with those observed at reference sites. Reference values of local environmental variables are then combined with the model for taxon richness to calculate taxon richness expected under reference conditions. Predictions of total taxon richness in validation data using the new approach are similar in accuracy and precision as predictions calculated using a traditional approach that focuses only on reference site data. The new approach complements existing bioassessment methods by highlighting types of sites in which estimates of reference expectations are uncertain and by improving our understanding of how instream stressors affect expected total taxon richness. [ABSTRACT FROM AUTHOR]

Small area estimates are usually constructed from complex survey data. If the design is informative for the model, then procedures that ignore the sample design can suffer from important biases. Past work on small area estimation under informative sampling has focused heavily on linear models or on the prediction of means. We propose to generalize existing small area procedures for an informative sample design. We develop procedures in the context of a broad class of exponential dispersion families with random small area effects. We consider two models for the survey weights. We construct predictions of means as well as more general parameters that are nonlinear functions of the model response variable. We evaluate the procedures through simulation using a logistic mixed model. We then apply the methods to construct small area estimates of several functions of a wetlands indicator using data from the National Resources Inventory, a large scale agricultural survey. [ABSTRACT FROM AUTHOR]

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such as neural networks and fuzzy systems, this research demonstrates significant improvements in forecast precision, particularly in scenarios with limited or noisy data. Additionally, the paper discusses the integration of NSFs with advanced machine learning algorithms for climate pattern recognition and anomaly detection, enabling timely identification of climate change indicators and facilitating proactive measures for adaptation and mitigation. Through empirical validation using real-world climate datasets, this study underscores the efficacy of NSFs in enhancing the predictive capabilities of climate change models, thereby contributing to more informed decision-making in climate-related policies and strategies. [ABSTRACT FROM AUTHOR]

Objective To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. Methods A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. Results Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78–0.784) and precision-recall AUC of 0.208 (95% CI 0.195–0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706–0.723) and a negative predictive value (NPV) of 95%. Conclusions The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout. [ABSTRACT FROM AUTHOR]

Objectives: To explore socio-behavioral, clinical, and imaging findings associated with antepartum intimate partner violence (IPV) and aid in risk stratification of at-risk individuals. Methods: We analyzed electronic medical records during indexed pregnancies for 108 pregnant patients who self-reported antepartum IPV (cases) and 106 age-matched pregnant patients who did not self-report antepartum IPV (controls). Sociodemographic, clinical, and radiology data were analyzed via chi-squared and Fisher's exact tests with p < 0.05 as the threshold for significance. Stepwise logistic regression was applied to derive a risk prediction model. Results: The proportion of cases reporting emotional IPV (76% vs. 52%) and/or physical IPV (45% vs. 31%) during pregnancy significantly increased from prior to pregnancy. Cases were significantly more likely to report prepregnancy substance use (odds ratio [OR] = 2.60; 95% confidence interval [CI]: 1.13–5.98), sexually transmitted infections (OR = 3.48; 95%CI: 1.64–7.37), abortion (OR = 3.17; 95%CI: 1.79, 5.59), and preterm birth (OR = 5.97; 95%CI: 1.69–21.15). During pregnancy, cases were more likely to report unstable housing (OR = 5.26; 95%CI: 2.67–10.36), multigravidity (OR = 2.83; 95%CI: 1.44–5.58), multiparity (OR = 3.75; 95%CI: 1.72–8.20), anxiety (OR = 3.35; 95%CI: 1.85–6.08), depression (OR = 5.58; 95%CI: 3.07–10.16), substance use (OR = 2.92; 95%CI: 1.28–6.65), urinary tract infection (UTI) (OR = 3.26; 95%CI: 1.14–9.32), intrauterine growth restriction (OR = 10.71; 95%CI: 1.35–85.25), and cesarean delivery (OR = 2.25; 95%CI: 1.26–4.02). Cases had significantly more OBGYN abnormalities on imaging and canceled more radiological studies (OR = 5.31). Logistic regression found housing status, sexually transmitted infection history, preterm delivery history, abortion history, depression, and antepartum UTI predictive of antepartum IPV. The risk prediction model achieved good calibration with an area under the curve of 0.79. Conclusions: This study identifies significant disparities among patients experiencing antepartum IPV, and our proposed risk prediction model can inform risk assessment in this setting. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to assess the effectiveness of fractional exhaled nitric oxide (FeNO) combined with pulmonary function testing (PFT) for predicting the treatment outcome of patients with severe asthma receiving dupilumab. Methods: A total of 31 patients with severe asthma visiting our hospital from January 2022 to June 2023 were included in this study, with 28 patients completing a 16-week course of dupilumab treatment. Baseline clinical data, including demographic information, blood eosinophil counts, serum IgE levels, FeNO, asthma control test (ACT), asthma control questionnaire (ACQ), and other parameters, were collected. A predictive model using a generalized linear model was established. Results: Following the 16-week course of dupilumab treatment, 22 patients showed effective response based on GETE scores, while 6 patients were nonresponders. Notably, significant improvements were observed in clinical parameters such as blood eosinophil counts, serum IgE levels, FeNO, FEV1, FEV1%, ACT, and ACQ in both response groups (p < 0.05). FeNO and pulmonary function tests demonstrated AUC values of 0.530, 0.561, and 0.765, respectively, in predicting the clinical efficacy of dupilumab, which were lower than when FeNO was combined with FEV1%. The combination of FeNO and FEV1% had a sensitivity of 1.000 and specificity of 0.591 in predicting treatment response. Conclusion: The combined assessment of FeNO and FEV1% provides improved accuracy for predicting the clinical efficacy of dupilumab in managing severe asthma. However, further larger scale clinical studies with comprehensive follow-up data are needed to validate the therapeutic efficacy and applicability across diverse patient populations. [ABSTRACT FROM AUTHOR]

Variable refrigerant flow (VRF) system has been an appealing solution of air conditioning for residential and commercial buildings, due to its flexibility and cost effectiveness, while lack of ventilation capability is a major drawback. Incorporation of dedicated outdoor air system (DOAS) is a typical practice. However, good coordination between DOAS and VRF is critical for achieving desired thermal comfort is challenging due to the possible complexity of mixed sensible and latent heat exchanges. In this paper, to handle the nonlinear dynamic characteristics of VRF-DOAS system, we propose an offset-free Koopman model predictive control (MPC) strategy for thermal comfort regulation, in which the MPC design is computationally more efficient due to the convex problem formulation and the use of reduced-order Koopman models, and the offset-free MPC structure enhances the robustness to model uncertainties and unmeasured disturbances. A control-oriented model is obtained by hybridizing the first-principle and data-driven modeling approach. The proposed controls strategy is evaluated with a Modelica simulation model of a VRF-DOAS system. A Dymola-Python cosimulation platform is developed via the functional mockup interface (FMI), for which the MPC algorithms are implemented in Python. Simulation results show significantly better performance of the offset-free Koopman MPC in thermal comfort regulation. [ABSTRACT FROM AUTHOR]

CONTEXT: Prognostic prediction models (PPMs) can help clinicians predict outcomes. OBJECTIVE: To critically examine peer-reviewed PPMs predicting delayed recovery among pediatric patients with concussion. DATA SOURCES: Ovid Medline, Embase, Ovid PsycInfo, Web of Science Core Collection, Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Google Scholar. STUDY SELECTION: The study had to report a PPM for pediatric patients to be used within 28 days of injury to estimate risk of delayed recovery at 28 days to 1 year postinjury. Studies had to have at least 30 participants. DATA EXTRACTION: The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist was completed. RESULTS: Six studies of 13 PPMs were included. These studies primarily reflected male patients in late childhood or early adolescence presenting to an emergency department meeting the Concussion in Sport Group concussion criteria. No study authors used the same outcome definition nor evaluated the clinical utility of a model. All studies demonstrated high risk of bias. Quality of evidence was best for the Predicting and Preventing Postconcussive Problems in Pediatrics (5P) clinical risk score. LIMITATIONS: No formal PPM Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) process exists. CONCLUSIONS: The 5P clinical risk score may be considered for clinical use. Rigorous external validations, particularly in other settings, are needed. The remaining PPMs require external validation. Lack of consensus regarding delayed recovery criteria limits these PPMs. [ABSTRACT FROM AUTHOR]

Objective: To compare the Guy’s, S.T.O.N.E, and CROES nomograms for predicting stone-free status in patients who underwent percutaneous nephrolithotomy for renal stones. Materials and Methods: The data of 1114 patients who underwent percutaneous nephrolithotomy for renal calculi between 11/2008 and 08/2018 in our clinic were retrospectively reviewed. Various parameters evaluated by preoperative computed tomography and the scoring systems of the patients and postoperative stone-free status were compared. Results: Out of 1000 patients who met the study criteria. Gender, body mass index, and stone density were not statistically different between the group with residual stones and the stone-free group. However, stone size, number of renal accesses, duration of fluoroscopy usage, duration of operation, number of stones, and complication rate were significantly higher in the group with residual stones than in the stone-free group. A statistically significant correlation was found between the postoperative stone-free rate and scoring systems. The applicability and preoperative prediction ability of all three systems were evaluated by receiver operating characteristic analysis. The area under the curve (AUC) was detected in the Guy’s, CROES, and S.T.O.N.E scoring system (AUC: 0.642, 0.665, 0.592 respectively). Conclusion: In this study, where the perioperative and postoperative results of 1000 patients were evaluated, we found that all three scoring systems could predict the stone-free rate. “We believe that the use of these scoring systems before surgery can guide surgeons.” [ABSTRACT FROM AUTHOR]

The six‐minute walking test (6MWT) is commonly used to measure functional capacity in field settings, primarily through the distance covered. This study aims to establish reference curves for the six‐minute walking distance (6MWD) and peak heart rate (PHR) and develop a predictive equation for cardiovascular capacity in Tunisian children and adolescents. A total of 1501 participants (706 boys and 795 girls), aged 10–18 years, were recruited from schools in Tunisia. The Lambda (L), Mu (M), and Sigma (S) methods (LMS method) were employed to develop smoothed percentile curves for 6MWD and PHR. Multivariate linear regression was utilized to formulate a prediction equation for 6MWD. Smoothed percentiles (3rd, 10th, 25th, 50th, 75th, 90th, and 97th) for 6MWD and PHR were presented with age. All variables showed a strong positive correlation (p < 0.001) with a six‐minute walking distance (r ranged from 0.227 to 0.558 for girls and from 0.309 to 0.610 for boys), except resting heart rate, which showed a strong negative correlation (girls: r = −0.136; boys: r = −0.201; p < 0.001). Additionally, PHR showed a weak correlation (p > 0.05). The prediction equations, based on age as the primary variable, were established for both genders. For boys: 6MWD = 66.181 + 38.142 × Age (years) (R2 = 0.372; Standard Error of Estimate (SEE) = 122.13), and for girls: 6MWD = 105.535 + 28.390 × Age (years) (R2 = 0.312; SEE = 103.66). The study provides normative values and predictive equations for 6MWD and PHR in Tunisian children and adolescents. These findings offer essential tools for identifying, monitoring, and interpreting cardiovascular functional deficits in clinical and research settings. Highlights: Established reference curves for six‐minute walking distance (6MWD) and peak heart rate (PHR) from the data of 1501 children and adolescents aged 10–18 years were recruited from multicenter schools in Tunisia.Developed predictive equations for 6MWD based on age using multivariate linear regression.Provides normative values and predictive equations for 6MWD and PHR, offering valuable tools for identifying and monitoring cardiovascular functional deficits in clinical and research settings. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic triggered a global health crisis that stimulated journalists to frame their stories around predictive models and forecasts aiming to predict the future trend of the pandemic. This article examines the audience response to predictive journalism by qualitatively analyzing readers' comments to articles covering COVID that were published in a small sample of mainstream media. Based on a thematic analysis of readers' comments, this research contributes a typology of audience response types to the models incorporated in such predictive journalism. We elaborate on each of three primary themes—reflecting affective, action-oriented, and evaluative responses—and discuss the implications of our findings and the importance of expanding research to answer questions related to the role of predictive journalism in shaping affective response, encouraging action-oriented responses and collective planning around responsibility for taking future actions, and considering the ways in which supportive and critical comments triggered by the models may be harnessed to improve communication. [ABSTRACT FROM AUTHOR]

Polysocial risk scores were recently proposed as a strategy for improving the clinical relevance of knowledge about social determinants of health. Our objective in this study was to assess whether the polysocial risk score model improves prediction of cognition and all-cause mortality in middle-aged and older adults beyond simpler models including a smaller set of key social determinants of health. We used a sample of 13 773 individuals aged ≥50 years at baseline from the 2006-2018 waves of the Health and Retirement Study, a US population-based longitudinal cohort study. Four linear mixed models were compared: 2 simple models including a priori–selected covariates and 2 polysocial risk score models which used least absolute shrinkage and selection operator (LASSO) regularization to select covariates among 9 or 21 candidate social predictors. All models included age. Predictive accuracy was assessed via R 2 and root mean-squared prediction error (RMSPE) using training/test split validation and cross-validation. For predicting cognition, the simple model including age, race, sex, and education had an R 2 value of 0.31 and an RMSPE of 0.880. Compared with this, the most complex polysocial risk score selected 12 predictors (R 2 = 0.35 and RMSPE = 0.858; 2.2% improvement). For all-cause mortality, the simple model including age, race, sex, and education had an area under the receiver operating characteristic curve (AUROC) of 0.747, while the most complex polysocial risk score did not demonstrate improved performance (AUROC = 0.745). Models built on a smaller set of key social determinants performed comparably to models built on a more complex set of social "risk factors." [ABSTRACT FROM AUTHOR]

Social determinants of health encompass the social environmental factors and lived experiences that collectively shape an individual's health. Recently, the polysocial score approach has been introduced as an innovative method for capturing the cumulative impact of a broad spectrum of social factors. This approach offers a promising opportunity to complement and enhance conventional methodologies in the advancement of research on social determinants of health. In this issue of the Journal , Jawadekar et al (Am J Epidemiol. 2024;193(9):1296‑1300) evaluate the value of the polysocial score for predicting cognitive performance and mortality among middle-aged and older adults. Models built on a smaller set of social determinants, including race/ethnicity, sex, and education, performed comparably to the polysocial score models in which a more complex set of social factors was included. In this invited commentary, I evaluate the predictive ability of the polysocial score and discuss its merits and limitations. I also summarize the practical utility of the polysocial score in predicting health outcomes and its mechanistic significance in unveiling the relationship between genetics, social environment, and lifestyles in shaping an individual's health and elucidate health disparities. Lastly, I propose several avenues for future research. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to calculate region and diagnosis‐specific minimal important changes (MICs) of the Foot and Ankle Outcome Score (FAOS) and the Foot and Ankle Ability Measure (FAAM) in patients requiring foot and ankle surgery and to assess their variability across different foot and ankle diagnoses. Methods: The study used routinely collected data from patients undergoing elective foot and ankle surgery. Patients had been invited to complete the FAOS and FAAM preoperatively and at 3–6 months after surgery, along with two anchor questions encompassing change in pain and daily function. Patients were categorised according to region of pathology and subsequent diagnoses. MICs were calculated using predictive modelling (MICPRED) and receiver operating characteristic curve (MICROC) method and evaluated according to strict credibility criteria. Results: Substantial variability of the MICs between forefoot and ankle/hindfoot region was observed, as well as among specific foot and ankle diagnoses, with MICPRED and MICROC values ranging from 7.8 to 25.5 points and 9.4 to 27.8, respectively. Despite differences between MICROC and MICPRED estimates, both calculation methods exhibited largely consistent patterns of variation across subgroups, with forefoot conditions systematically showing smaller MICs than ankle/hindfoot conditions. Most MICs demonstrated high credibility; however, the majority of the MICs for the FAOS symptoms subscale and forefoot conditions exhibited insufficient or low credibility. Conclusion: The MICs of the FAOS and FAAM vary across foot and ankle diagnoses in patients undergoing elective foot and ankle surgery and should not be used as a universal fixed value, but recognised as contextual parameters. This can help clinicians and researchers in more accurate interpretation of the FAOS and FAAM change scores. Level of Evidence: Level IV. [ABSTRACT FROM AUTHOR]

The existing machine learning-based energy efficiency and performance models for the servers have achieved high prediction accuracy with the support of abundant training data. However, the differences in data distribution among heterogeneous servers in cloud data centers make it impractical to apply a machine learning-based model trained on one server to another directly. Meanwhile, collecting sufficient data for new servers from scratch incurs expensive time costs. To address these challenges, a transfer regression-based method for cross-server energy efficiency and performance prediction, named FS-DBoost, is proposed to reduce the overhead of establishing the two prediction models by transferring feature importance and distribution knowledge from data of existing servers. Initially, to transfer feature importance information, a sparse regression model is introduced to learn a feature selection vector from the source domain. Then, we design a boosting-based transfer algorithm, DBoost, to train the models for the target server using data with selected features. DBoost adjusts the total weight of the target domain based on the relatedness between source and target domains. The experimental results demonstrate that FS-DBoost achieves an average performance improvement of over 10% on real-world datasets compared to six transfer regression methods. [ABSTRACT FROM AUTHOR]

This article develops vehicle type–based crash-prediction models for cars and heavy commercial vehicles (HCVs) as a function of the curve geometry and vehicle-based design consistency criteria under heterogeneous traffic conditions on two-lane, two-way rural highways, specifically in hilly terrains. A National Highway (NH-953) connecting Netrang and Rajpipla in India was selected. There are 38 curves in the study section, each having a different curve geometry. Speed data were collected using the radar gun for cars and HCVs. The geometric design consistency was evaluated using Criterion I (the difference between operating and design speeds). The results show that 53% of the curves for cars have good consistency, compared to 32% and 29% of the curves for HCVs, which have fair and poor consistency, respectively. The Poisson-Tweedie regression technique, which provides a unified framework to model over-dispersed, under-dispersed, zero-inflated, count-data, and multiple-response variables, was used to develop the crash prediction models. The results revealed that crashes (cars and HCVs) decrease as the curve radius, deflection angle, and length increase. Similarly, as the tangent length increases, the difference between operating and design speeds increases, making inconsistent highway alignment, resulting in increased chances of crashes. The results of the present study can help highway authorities to evaluate highway alignment consistency and develop corresponding proactive strategies to improve highway safety. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is a life-threatening disease affecting at least 64 million people worldwide. Hence, it places great stresses on patients and healthcare systems. Accordingly, providing a computerized model for HF prediction will help in enhancing diagnosis, treatment, and long-term management of HF. In this paper, we introduce a new guided attentive HF prediction approach. In this method, a sparse-guided feature ranking method is proposed. Firstly, a Gauss–Seidel strategy is applied to the preprocessed feature pool for low-rank approximation procedure with a trace-norm regularization. The resultant sparse attributes, after a Spearman ranking elimination, are employed to guide the original feature pool through linear translation-variant model. Then, a fast Newton-based method is employed for a non-negative matrix factorization for the guided feature pool. The resultant bases of the factorization process are finally utilized in the adopted deep attentive predictive model. For the final prediction stage, instead of the commonly used machine learning approaches, we introduce an attentive-based classifier. It employs sequential attention to choose the most proper salient features for efficient interpretability and learning process. For the evaluation of the proposed HF prediction model, three different datasets are employed, i.e., UCI, Faisalabad, and Framingham datasets. Compared to state-of-the-art techniques, the proposed approach outperforms their performance on all datasets with even small feature sizes. With only four feature bases, the proposed method achieves an average accuracy of 98%, while, with full feature bases, full accuracy is gained. [ABSTRACT FROM AUTHOR]

The objective of this research was to compare social, emotional, and cognitive determinants of depression and anxiety symptoms and mental wellbeing using the two-continua model of mental health and illness in two age groups. The two-continua model proposes that mental health and mental illness are not two ends of the same spectrum but are, in fact, two separate but related spectrums. This cross-sectional study used a multi-age group comparison approach to identify the relative predictive strengths of risk and protective factors for the two-continua model of mental health and mental illness. Participants (n = 458: 251 16–25 year olds, and 207 35–64 year olds) completed an online survey that measured the outcome variables of depression and anxiety symptoms and mental wellbeing. Independent variables from three conceptual areas in psychology—(1) positive psychology: optimism, pessimism, and accomplishment; (2) emotion regulation: cognitive reappraisal and expressive suppression; and (3) interpersonal theories: belonging and relationship with parent/child—were measured to ascertain the determinants of these outcome variables. The all-variables models explained 58–68% of the variance in depression symptoms, 77–80% in mental wellbeing, and 26–43% in anxiety symptoms. For both groups, the strongest predictor of mental wellbeing in these models was accomplishment. The strongest predictors in these models of mental illness symptoms differed between groups: belonging in the younger group and accomplishment in the older group. Programs targeting belonging and accomplishment could be highly effective in promoting mental wellbeing and reducing mental ill-health for these groups. Interventions require contextual investigation to locate drivers of mental wellbeing and illness for different age groups prior to implementation. [ABSTRACT FROM AUTHOR]

Purpose: Cardiorespiratory fitness (CRF) is a criticalmarker of overall health and a key predictor of morbidity andmortality, but the existing prediction equations for CRF are primarily derived from general populations and may not be suitable for patients with obesity. Methods: Predicted CRF from different non-exercise prediction equations was compared with measured CRF of patients with obesity who underwent maximal cardiopulmonary exercise testing (CPET). Multiple linear regression was used to develop a population-specific nonexercise CRF prediction model for treadmill exercise including age, sex, weight, height, and physical activity level as determinants. Results: Six hundred sixty patients underwent CPET during the study period. Within the entire cohort, R² values had a range of 0.24 to 0.46. Predicted CRF was statistically different from measured CRF for 19 of the 21 included equations. Only 50% of patients were correctly classified into the measured CRF categories according to predicted CRF. A multiple model for CRF prediction (mL⋅min-1) was generated (R² = 0.78) and validated using two crossvalidation methods. Conclusions: Most used equations provide inaccurate estimates of CRF in patients with obesity, particularly in cases of severe obesity and low CRF. Therefore, a new prediction equation was developed and validated specifically for patients with obesity, offering a more precise tool for clinical CPET interpretation and risk stratification in this population. [ABSTRACT FROM AUTHOR]

Introduction: The skill domains of fundamental movement skills (FMS), emphasizing gross motor movements, including locomotor skills (LMS) and object control skills (OCS), along with process- and product-oriented measures of FMS, may predict cardiorespiratory fitness (CRF) and muscular fitness (MF) differently. This study investigates the longitudinal relationship from early childhood FMS, focusing on process-oriented LMS and OCS and product-oriented FMS, to CRF and MF in late childhood. Methods: The study involved 441 Finnish children (49.9% female; mean age at baseline, 5.5 yr) over a 6-yr period. FMS was evaluated using the Test of Gross Motor Development, third version, for process-oriented LMS and OCS, and the Körperkoordinationstest Für Kinder (KTK) was used to evaluate the product-oriented FMS. CRF was assessed through the total number of laps completed in the 20-m shuttle run test, whereas MF was measured via repetitions of curl-ups and push-ups. Employing a two-level cross-classified regression analysis and Cholesky decomposition, this study aimed to determine the contributions of product-oriented KTK and process-oriented LMS and OCS. Adjustments for variations in age, measurement intervals, and maturation were achieved through residualization. In addition, gender and body mass index were incorporated as covariates in the analysis. Results: The analysis revealed that process-oriented LMS (CRF: ΔR² = 0.016; MF: ΔR² = 0.014) significantly predicted later health-related fitness, whereas OCS did not. However, KTK exhibited a better ability to predict both CRF (ΔR² = 0.092) and MF (ΔR² = 0.032), overshadowing process-oriented measures. Conclusions: In conclusion, the findings suggest that KTK, which potentially encompasses a broader spectrum of fitness elements along with FMS, more effectively predicts health-related fitness components than process-oriented FMS. [ABSTRACT FROM AUTHOR]

Introduction: Environmental aesthetics is a recognized macro environmental correlate of physical activity participation. Extrapolating this effect, we hypothesize this variable to also function as a determinant of exercise participation in the microenvironment, such as the home. Specifically, we predict that the environmental aesthetics of the location of cardio and resistance training equipment will predict determinants of exercise participation within the integrated behavior change model. Methods: Adult owners of exercise equipment (N = 231) completed measures on the environmental aesthetics of their cardio and resistance training equipment, exercise time, and constructs from the integrated behavior change model across three consecutive monthly surveys. Study hypotheses were tested using multilevel structural equation modeling with participants (level 2) nested within time (level 1). Results: The model revealed predictive effects of intention, habit, and planning on exercise participation over time. The findings indicated a total effect of autonomous motivation on intention and habit. In addition, environmental aesthetics of cardio and strength training equipment predicted autonomous motivation, along with habit and intention when accounting for indirect effects. Finally, planning moderated the intention--behavior relationship, indicating that participants who scored higher on planning were more likely to translate their intentions to behavior. Conclusions: These findings provide informative notes for designing effective home-based exercise programs, which have increased in popularity over the past recent years. Specifically, experimental studies that provide exercise equipment should ensure that the location of the equipment is appealing to the participant to increase the likelihood equipment use and exercise engagement. [ABSTRACT FROM AUTHOR]

Purpose: Habitual strength and power-demanding activities of daily life may support the maintenance of adequate lower extremity functioning with aging, but this has been sparingly explored. Hence, we examined whether the characteristics of free-living sit-to-stand (STS) transitions predict a decline in lower extremity functioning over a 4-yr follow-up. Methods: A total of 340 community-dwelling older adults (60% women; age 75, 80, or 85 yr) participated in this prospective cohort study. At baseline, a thigh-worn accelerometer was used continuously (3-7 d) to monitor the number and intensity of free-living STS transitions. A decline in lower extremity functioning was defined as a drop of ≥2 points in the Short Physical Performance Battery (SPPB) from baseline to follow-up. Maximal isometric knee extension strength was measured in the laboratory. Results: Eighty-five participants (75%women) declined in SPPB over 4 yr. After adjusting for age, sex, and baseline SPPB points, higher free-living peak STS angular velocity (odds ratio (OR), 0.70; 95% confidence interval (CI), 0.52-0.92, per 20°⋅s-1 increase) protected against a future decline. When adjusting the model for maximal isometric knee extension strength, the statistical significance was attenuated (OR = 0.72; 95% CI = 0.54-0.96, per 20°⋅s-1 increase). Conclusions: Performing STS transitions at higher velocities in the free-living environment can prevent a future decline in lower extremity function. This indicates that changes in daily STS behavior may be useful in the early identification of functional loss. Free-living peak STS angular velocity may be a factor underlying the longitudinal association of lower extremity strength and performance. [ABSTRACT FROM AUTHOR]

The aims of the study were to build models using logistic regression analysis of flexibility and strength tests to prospectively predict risk factors for anterior cruciate ligament tear (ACL-tear) in female soccer (FS) players, and to determine training cut-off for risk factors of the predictive model built. A prospective cohort study of 95 female players (aged 14–33 years) was conducted. Age, anthropometric data, soccer history, lower limb range of motion (ROM) and hip maximal isometric strength (MIS) were measured. At the prospective follow-up after 12 months, 7.4% of the players had developed an ACL-tear. The model showed a significant relationship (χ2(93) = 30.531, p < 0.001) between the ACL-tear and the predictor variables (leg length, HAD-NH [hip adduction] MIS, asymmetric ROM [ankle dorsiflexion with knee extended (AD-KE) and with knee flexed (AD-KF), and HE (hip extension)], hip ROM [HIR (internal rotation) and HAB (abduction)]). The Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) for model fit were 30.24 and 51.79, respectively. The value R2 showed good model fit, 76.5% for Nagelkerke´s R2, 71.4% for McFadden´s R2 and 67.5% for Tjur´s R2. For the screening test, cut-off for leg length of ≥0.40 m, for HIR ROM of ≤44º and for asymmetry of HE ROM of ≥5° were set, which have an acceptable (AUC ≥ 0.755) discriminatory ability for the development of ACL-tear. [ABSTRACT FROM AUTHOR]

Applying latent growth mixture modeling (GMM), this study delves into the examination of self-esteem trajectories in a sample of 5,597 older adults over a nine-year period. Four distinct patterns of self-esteem changes have emerged: low, decreasing, increasing, and high. Additionally, the study explores the relationships between each trajectory and various predictors encompassing demographic factors, socioeconomic status, health, and interpersonal relationships. The findings highlight the significance of these factors in predicting the likelihood of an individual following a specific self-esteem trajectory. Notably, maintaining employment, fostering satisfactory social relationships, and being free of frequent depressive feelings emerged as strong predictors for the stability and increase of high self-esteem. Intriguingly, an average or above-average income was unexpectedly associated with lower levels of self-esteem. The study emphasizes the contribution of GMM to advancing aging research. [ABSTRACT FROM AUTHOR]

Objective: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)‐based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features. Methods: In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three‐dimensional double‐echo steady‐state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS‐RM aid. Results: Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS‐RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876–0.963), a sensitivity of 84.4% (95% CI 83.9%–84.9%), and a specificity of 85.6% (95% CI 85.2%–86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333–0.614) and 0.586 (95% CI 0.429–0.743) without the assistance of JS‐RM to 0.874 (95% CI 0.847–0.901) and 0.812 (95% CI 0.742–0.881) with JS‐RM assistance, respectively (P < 0.001). Conclusion: JS‐RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence. [ABSTRACT FROM AUTHOR]

Summary: Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought‐resistant varieties. This study aimed to dissect the genetic architecture and high‐yielding wheat ideotypes under terminal drought.An automated high‐throughput phenotyping platform was used to examine 28 392 image‐based digital traits (i‐traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i‐traits examined, 17 073 were identified as drought‐related. A genome‐wide association study (GWAS) identified 5320 drought‐related significant single‐nucleotide polymorphisms (SNPs) and 27 SNP clusters.A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs.We developed an advanced prediction model for wheat yield and drought resistance using 24 i‐traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high‐yielding ideotype and an approach for the rapid breeding of drought‐resistant wheat. [ABSTRACT FROM AUTHOR]

Aim: Flower traits are critical to the mutualistic networks that underpin ecosystem function. However, the broad‐scale drivers of flower traits are unclear. Using the megadiverse islands of Malesia, we test three hypotheses: (1) Flower size, openness and colourfulness (i.e. the trend from white to colourful flowers) will decrease with increasing temperature, but not with increasing precipitation seasonality. (2) Flower size, openness and colourfulness will increase with island area, and decrease with isolation. (3) Models parameterised by climate, rather than non‐climatic island characteristics will have the greatest predictive capacity as climate affects both pollinator communities and energetic/physiological constraints upon flower traits. Location: Fourteen Malesian islands. Taxon: Angiosperms. Methods: We applied Bayesian phylogenetic models of occurrence for >2500 species in 400 m elevation bands, across islands, as a function of flower trait interactions with either climatic variables or islands characteristics. We compare predictive capacity of models based on climate versus island characteristics using Leave One Out‐Cross Validation. Results: All six flower traits examined varied with at least temperature, precipitation seasonality, island area and isolation. Flowers are larger in cold environments. The lowland tropics are mostly inhabited by white flowered species. At cold high elevations, red and pink flowered species are more frequent whereas green and purple flowered species increase in the drought‐prone seasonally dry tropics. Flower openness declined with precipitation seasonality and increased with isolation. Models parameterised by climate performed best for perianth length, red and white flowers, whereas pink flowers and flower openness were better predicted using island characteristics. Main Conclusions: There are relationships between flower traits and climatic gradients within Malesia. However, island characteristics have also left a legacy upon current flower trait distributions. These island biogeographical impacts are likely key for the eco‐evolutionary drivers of flower traits. [ABSTRACT FROM AUTHOR]

The present study investigated whether multicultural variables derived from the Multicultural Assessment‐Intervention Process model (shifting, gender roles, acculturation, perceived discrimination) could predict levels of internalized weight bias (IWB) or disordered eating behaviours in 429 Asian American adult women. A facet of shifting, White beauty conformity and mainstream acculturation were the only multicultural variables that significantly predicted all five body‐ and eating‐related outcomes (IWB, restraint, eating, shape and weight concerns). Gender roles and two aspects of perceived discrimination (social exclusion and stigmatization) also showed predictive ability for one or more of the outcome measures. Results highlight the relationship between multicultural and body‐ and eating‐related variables for Asian American women and underscore the salience of body shape and weight, the importance placed on conforming to Western culture and beauty standards and the detrimental effects of doing so for this population. Clinical implications and future research are discussed. [ABSTRACT FROM AUTHOR]

Conventional approaches to forecasting the risk of athlete injuries are constrained by their narrow scope in feature extraction, often failing to adequately account for temporal dependencies and the effects of long-term memory. This paper enhances the Long Short-Term Memory (LSTM) network, specifically tailoring it to harness temporal data pertaining to athletes. This advancement significantly boosts the accuracy and effectiveness of predicting the risk of injuries among athletes. The network structure of the LSTM model was improved, and the collected data was converted into the temporal data form of the LSTM input. Finally, historical data labeled with injury labels were used to train the improved LSTM model, and gradient descent iterative optimization was used to adjust the parameters of the improved LSTM model. The improved LSTM network model was compared with the traditional athlete injury risk prediction model in terms of performance. The incorporation of enhanced LSTM networks for the analysis of temporal athlete data holds significant research significance. This approach has the potential to substantially enhance the accuracy and effectiveness of athlete injury risk prediction, contributing to a deeper understanding of the temporal dynamics influencing injuries in sports. [ABSTRACT FROM AUTHOR]

Background: Providing care for people with dementia incorporates a level of burden which can affect quality of life for both the caregiver and the recipient of care. This study measures the level of burden experienced by Jordanian caregivers for people with dementia and explore related predictors. Methods: Through a cross‐sectional survey, participants were invited through convenience sampling to participate in a structured interview. Results: A total of 406 participants completed the survey. According to Zarit Burden Interview, the mean burden score of the sample was 26.2 (SD = 16.2). This score falls under the mild to moderate burden level. Older age of the patient, severe dementia, lower number of caregivers, if the caregiving negatively affected family relations, and if the caregiving negatively affected jobs, were significant predictors of burden. Conclusion: Caregivers in the current study reported no burden to a minimum burden. This result does not mean that these caregivers have no or minimal stress or that they do not have psychological needs; on the contrary, these results call for more attention to providing extra psychological and emotional support to caregivers of patients with dementia in order to decrease the burden level and maintain their efforts in caregiving. Future studies are required to discern the shape and context of unmet caregiver needs, assessment, and support. [ABSTRACT FROM AUTHOR]

Objective: This study aims to develop a predictive model for estimating the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE) and to elucidate the relationship between various factors and ACD Methods: Individuals diagnosed with SLE for at least one year were enrolled and categorized into two groups: those with ACD and those without anemia symptoms. Patients were randomly assigned to training and test sets at an 8:2 ratio. The least absolute shrinkage and selection operator (LASSO) method was used to select predictors, followed by logistic regression for modeling. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) for both training and test sets. Results: The study included a total of 216 patients, with 172 in the training set and 44 in the test set. LASSO identified 6 variables for constructing the predictive model, resulting in an area under the curve (AUC) of 0.833 (95% CI, 0.773-0.892) in the training set and 0.861 (95% CI, 0.750-0.972) in the test set. Calibration curves indicated consistency between expected and observed probabilities. DCA indicated that the model yielded a net benefit with threshold probabilities ranging from 20% to 90% in the training set and from 10% to 80% in the test set. Conclusion: This study presents a predictive model for assessing the risk of ACD in SLE patients. The model effectively captures the underlying mechanism of ACD in SLE and empowers clinicians to make well-informed treatment adjustments. Key Points • Development of a New Predictive Model: This study introduces a new predictive model to evaluate the likelihood of anemia of chronic disease (ACD) in patients with systemic lupus erythematosus (SLE). The model utilizes routine laboratory parameters to identify high-risk individuals, addressing a significant gap in current clinical practice. • Reflection of Potential Mechanisms for ACD Development: By incorporating the factors needed to construct the predictive model, this study also sheds light on the potential mechanisms of ACD development in SLE patients. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly. [ABSTRACT FROM AUTHOR]

The chiller energy consumption prediction plays an important role in reducing the building energy consumption. Continuously improving the performance of the prediction model is the key to ensuring the accuracy of the chiller energy consumption prediction. As a result, a new improved ensemble model of the chiller energy consumption prediction is proposed, based on the idea of algorithmic ensemble aided by an attention mechanism (AM). To guarantee that the proposed model can see information from many spatial and structural viewpoints, the improved ensemble model incorporates the benefits of several base prediction methods. The suggested approach chooses crucial features to simplify the model input by utilizing AM, thereby reducing the complexity of the samples. The improved ensemble model was experimentally verified in an actual multi-chiller system, and the results showed that the established model could obtain good prediction results. Finally, the proposed model was compared with existing, well-known prediction models. To evaluate the accuracy of these models, the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), variance accounted for (VAF), and coefficient of determination (R 2) were used as the evaluation indices. With an MAE of 2.858, an MAPE of 1.2755 %, an MSE of 20.1878, a VAF of 93.6058, and an R 2 of 0.9923, the findings demonstrate that the improved ensemble model has a greater prediction accuracy than the other well-known prediction methods. The suggested model enhances the variety of empirical model algorithm libraries. [ABSTRACT FROM AUTHOR]

Aims: To develop and evaluate prediction models for medium‐term weight loss response in behavioural weight management programmes. Materials and Methods: We conducted three longitudinal analyses using the Action for HEalth in Diabetes (LookAHEAD) trial, Weight loss Referrals for Adults in Primary care (WRAP) trial, and routine data from the National Health Service Greater Glasgow and Clyde Weight Management Service (NHS‐GGCWMS). We investigated predictors of medium‐term weight loss (>5% body weight) over 3 years in NHS‐GGCWMS and, separately, predictors of weight loss response in LookAHEAD over 4 years. We validated predictors in both studies using WRAP over 5 years. Predictors of interest included demographic and clinical variables, early weight change in‐programme (first 4 weeks) and overall in‐programme weight change. Results: In LookAHEAD and WRAP the only baseline variables consistently associated with weight loss response were female sex and older age. Of 1152 participants in NHS‐GGCWMS (mean age 57.8 years, 60% female, type 2 diabetes diagnosed for a median of 5.3 years), 139 lost weight over 3 years (12%). The strongest predictor of weight loss response was early weight change (odds ratio 2.22, 95% confidence interval 1.92–2.56) per 1% weight loss. Losing 0.5% weight in the first 4 weeks predicted medium‐term weight loss (sensitivity 89.9%, specificity 49.5%, negative predictive value 97.3%). Overall in‐programme weight change was also associated with weight loss response over 3 years in NHS‐GGCWMS and over 5 years in WRAP. Conclusions: Not attaining a weight loss threshold of 0.5% early in weight management programmes may identify participants who would benefit from alternative interventions. [ABSTRACT FROM AUTHOR]

Human factors during aircraft maintenance have a significant impact on aviation safety. Human factors analysis, therefore, is an essential aspect in aviation. The researchers have analyzed human factors in detail. yet, the existing studies are mostly reactive in nature. In the present study, incidents during aircraft maintenance related to human factors were investigated using the HFACS-ME classification system. Critical human factors and their latent conditions were identified and mapped. Identification of active and latent human factors enables proactive mitigation of weaknesses in the system thus ensuring maintenance safety. Interventions to mitigate human factors and improve the maintenance setup at the shop floor, management, and organization levels were formulated. The proposed model validated that past incidents could be effectively utilized as predictors to identify and mitigate active and latent human factors during aircraft maintenance by implementing proactive interventions. KEY POINTS: A proactive approach to identify, prioritize, and mitigate active and latent human factors in maintenance tasks in aviation. Active and latent human factors in aircraft maintenance incidents were analyzed and mapped using HFACS-ME. Dependency of factors was established. interventions at all levels in the organization were proposed to mitigate human factors. The proposed model could be effectively utilized to mitigate human factors by implementing proactive interventions during aircraft maintenance. [ABSTRACT FROM AUTHOR]

Serum creatinine in neonates follows complex dynamics due to maturation processes, most pronounced in the first few weeks of life. The development of a mechanism‐based model describing complex dynamics requires high expertise in pharmacometric (PMX) modeling and substantial model development time. A recently published machine learning (ML) approach of low‐dimensional neural ordinary differential equations (NODEs) is capable of modeling such data from newborns automatically. However, this efficient data‐driven approach in itself does not result in a clinically interpretable model. In this work, an approach to deriving an interpretable model with reasonable PMX‐type functions is presented. This "translation" was applied to derive a PMX model for serum creatinine in neonates considering maturation processes and covariates. The developed model was compared to a previously published mechanism‐based PMX model whereas both models had similar mechanistic structures. The developed model was then utilized to simulate serum creatinine concentrations in the first few weeks of life considering different covariate values for gestational age and birth weight. The reference serum creatinine values derived from these simulations are consistent with observed serum creatinine values and previously published reference values. Thus, the presented NODE‐based ML approach to model complex serum creatinine dynamics in newborns and derive interpretable, mathematical‐statistical components similar to those in a conventional PMX model demonstrates a novel, viable approach to facilitate the modeling of complex dynamics in clinical settings and pediatric drug development. [ABSTRACT FROM AUTHOR]

The rapid growth in the use of pediatric physiologically based pharmacokinetic (PBPK) models, particularly for regulatory applications, has focused emphasis on model verification and ensuring system parameters are robust, including how these change with age. Uncertainty remains regarding the ontogeny of some enzymes and transporters, in this study 2 published ontogeny profiles for hepatic CYP3A4 were compared. Clinical pharmacokinetic data on 4 intravenously administered CYP3A4 substrates (alfentanil, fentanyl, midazolam, and sildenafil) used across the pediatric age range was collected from the literature. The PBPK models were verified in the adult population and then used to compare the Salem and a modified Upreti ontogeny profiles for CYP3A4 in terms of parent drug clearance and area under the curve from birth onward. Overall, the modified Upreti ontogeny profile resulted in 15 out of 17 age‐related predictions within 2‐fold and 12 out of 17 predictions within 1.5‐fold ranges of observed values, for the Salem ontogeny these values were 12 out of 17 and 8 out of 17, respectively. The Upreti ontogeny profile performed better than Salem, average fold error and absolute average fold error were 1.14 and 1.35 compared to 1.56 and 1.90, respectively. Identifying the optimal CYP3A4 ontogeny is important for regulatory use of PBPK especially given the number of drugs cleared by this enzyme. This study broadens the evidence from previous studies that Upreti is more favorable than Salem, but further work is needed especially in the neonatal and early infant age range. [ABSTRACT FROM AUTHOR]

The parametrization of the oblique corrections through S, T, and U – later extended by V, W and X – is a convenient way of comparing the predictions for various electroweak observables at the one-loop level between the Standard Model and its extensions. That parametrization assumes that the extensions under consideration have S U (2) × U (1) gauge symmetry and the tree-level relation m W = m Z cos θ W between the Weinberg angle and the gauge-boson masses. In models where that relation does not hold at the Lagrangian level, the parameter T is not ultraviolet-finite, making the parametrization inadequate. We present expressions that parametrize the difference of the various predictions of two models with m W ≠ m Z cos θ W in terms of oblique parameters. The parameter T does not play a role in those expressions. Conveniently, they may be reached from the ones that were derived for models with tree-level m W = m Z cos θ W , by performing a simple substitution for T. We also discuss the difficulties in using oblique parameters when comparing a model with m W ≠ m Z cos θ W to the Standard Model. Finally, we compute the relevant five oblique parameters S, U, V, W and X in the SM extended by both, hypercharge Y = 0 and Y = 1 , triplet scalars. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current status and progress of ultrasound-based AI models within hepatopancreatobiliary surgery have not been evaluated in literature. Therefore, this review aimed to provide an overview of ultrasound-based AI models used for hepatopancreatobiliary surgery, evaluating current advancements, validation, and predictive accuracies. Method: Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using AI models on ultrasound for patients undergoing hepatopancreatobiliary surgery. To be eligible for inclusion, studies needed to apply AI methods on ultrasound imaging for patients undergoing hepatopancreatobiliary surgery. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. Results: AI models have been primarily used within hepatopancreatobiliary surgery, to predict tumor recurrence, differentiate between tumoral tissues, and identify lesions during ultrasound imaging. Most studies have combined radiomics with convolutional neural networks, with AUCs up to 0.98. Conclusion: Ultrasound-based AI models have demonstrated promising accuracies in predicting early tumoral recurrence and even differentiating between tumoral tissue types during and after hepatopancreatobiliary surgery. However, prospective studies are required to evaluate if these results will remain consistent and externally valid. [ABSTRACT FROM AUTHOR]

Background: Laparoscopic pancreatoduodenectomy (LPD) is one of the most challenging operations and has a long learning curve. Artificial intelligence (AI) automated surgical phase recognition in intraoperative videos has many potential applications in surgical education, helping shorten the learning curve, but no study has made this breakthrough in LPD. Herein, we aimed to build AI models to recognize the surgical phase in LPD and explore the performance characteristics of AI models. Methods: Among 69 LPD videos from a single surgical team, we used 42 in the building group to establish the models and used the remaining 27 videos in the analysis group to assess the models' performance characteristics. We annotated 13 surgical phases of LPD, including 4 key phases and 9 necessary phases. Two minimal invasive pancreatic surgeons annotated all the videos. We built two AI models for the key phase and necessary phase recognition, based on convolutional neural networks. The overall performance of the AI models was determined mainly by mean average precision (mAP). Results: Overall mAPs of the AI models in the test set of the building group were 89.7% and 84.7% for key phases and necessary phases, respectively. In the 27-video analysis group, overall mAPs were 86.8% and 71.2%, with maximum mAPs of 98.1% and 93.9%. We found commonalities between the error of model recognition and the differences of surgeon annotation, and the AI model exhibited bad performance in cases with anatomic variation or lesion involvement with adjacent organs. Conclusions: AI automated surgical phase recognition can be achieved in LPD, with outstanding performance in selective cases. This breakthrough may be the first step toward AI- and video-based surgical education in more complex surgeries. [ABSTRACT FROM AUTHOR]

One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) is an option to address service unavailability by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration is a common LVM approach used in cloud systems, but it faces challenges due to the high rate of frequently updated memory pages known as dirty pages. Transferring these dirty pages during pre-copy migration prolongs the overall migration time. If there are large numbers of remaining memory pages after a predefined iteration of page transfer, the stop-and-copy phase is initiated, which significantly increases downtime and negatively impacts service availability. To mitigate this issue, we introduce a prediction-based approach that optimizes the migration process by dynamically halting the iteration phase when the predicted downtime falls below a predefined threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QEMU technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and default migration approach reveals a remarkable improvement, with an average 64.91% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. These findings underscore the practical advantages of our method in reducing service disruptions during live virtual machine migration in cloud systems. [ABSTRACT FROM AUTHOR]

The present study aims to develop an effective risk-prediction score (RPS) to improve screening efficiency and contribute to secondary prevention of colorectal cancer (CRC). Screening for colorectal lesions. 14,398 high-risk individuals aged 50–65 years were included. The baseline characteristics of participants with and without colorectal lesions (CL) were compared using a Chi-squared test. The overall population was randomly split into a training set and a test set in the ratio of 80% and 20%. One-factor and multifactor logistic regression analyses were performed in the training set to construct the RPS (scores of 0–9.62). Area under curve (AUC) was calculated as an estimate of predictive performance using the receiver-operating characteristic (ROC) curve in the test set. In the study population, being male, advanced age, current or previous smoking, weekly alcohol consumption, high body mass index (BMI ≥24 kg/m2), and previously detected colonic polyp were associated with higher risk of CL. Compared to the low-risk group (0–2.31 points), the ORs and 95% confidence intervals (CIs) for the moderate-risk group (2.31–3.85 points) and high-risk group (3.85–8.42 points) were 1.58 (1.44, 1.73) and 2.52 (2.30, 2.76), respectively. For every 1-point increase in score, participants had a 27% increased risk of CL (OR:1.27, 95% CI: 1.24, 1.30). For participants with CL predicted by RPS, the area under the working characteristic curve was 0.61 (P < 0.001). Our RPS can quickly and efficiently identify multiple lesions of the colorectum. Combining RPS with existing screening strategies facilitates the identification of very high-risk individuals and may help to prevent CRC. [ABSTRACT FROM AUTHOR]

This paper introduces and analyzes three innovative Linear Prediction (LP) models, consisting of two Fractional Order (FO) LP models and an integer LP model. The closed-form expressions for the proposed methods-namely, Two Parameter FO Linear Prediction (TP-FLP), FO Convex Linear Prediction (FCLP), and integer Convex Linear Prediction (CLP)-are derived for derivative order 0 ≤ α ≤ 2 . These derivations contribute to a more profound understanding of underlying mathematical principles of the proposed predictor. The foundation of our proposed methodologies is based on the presentation of the current signal sample as a linear combination of the FO derivative of two past samples, coupled with the utilization of the convex combination technique. An extensive set of experiments is conducted to compare the effectiveness of the proposed models with baseline methods involving two traditional LP models: First- and Second-order LP, along with the One Parameter FO Linear Prediction (OP-FLP) model. The performance evaluation encompasses a diverse range of signal types, including sinusoidal waves, damped sine waves, electroencephalogram (EEG), and speech signals. The simulation results demonstrate significant advancements achieved by the proposed models. Notably, the proposed FCLP exhibits a remarkable prediction gain of 24.74 dB, outperforming baseline methods such as OP-FLP, First- and Second-order LP models, that achieve prediction gains of 23.17, 23.15, and 10.14 dB, respectively. [ABSTRACT FROM AUTHOR]

In recent years, the recycling and reutilization of shield tunneling residues (STR) have received extensive attention in underground engineering. To optimize the method of resource utilization of STR, this paper introduces a replacement method of STR slurry. The STR are incorporated into concrete and prepared as STR concrete (STRC) using a coarse and fine aggregate shell production process. By optimizing the mixing method of STR slurry, we obtained the best mixing process, and studied the influence of STR dosage and water–binder ratio (w/b) on the compressive strength of STRC. The results revealed that the air-dried STR preliminarily crushed to about 15 mm can be used to prepare STRC by extending the mixing time of STR slurry. The compressive strength of the concrete significantly depends on the dosage of STR slurry. The strength decreases significantly with the increase of STR dosage, and the recommended dosage is about 10%. The influence of the w/b on compressive strength is similar to that of ordinary concrete. A predictive model for the compressive strength of STRC was established by analyzing the relationship between the dosage of STR, w/b , age, and the strength of STRC. [ABSTRACT FROM AUTHOR]

It is important to understand the flow behaviour inside wedge-shaped hoppers and accurately predict the discharge rate for the processing and handling of granular materials, regardless of whether the wedge hopper is symmetric or asymmetric. In this paper, the discrete element method (DEM) was used to reveal the flow behaviour of pellet feed in an asymmetric wedge-shaped hopper from the discharge rate, flow pattern, velocity distribution, normal contact force between particles and free-fall arch. The results showed that, with the hopper angle decreasing, the area of the active region of the particles increased, the stagnation zone decreased, and the free-fall arch became unstable. When the unilateral hopper angle was less than 45°, the discharge rate of the wedge-shaped hopper increased, and the average discharge rate reached the maximum value of 1.070 kg s−1 when the left and right hopper angles were equal to 15°. In addition, using the discharging mass proportion coefficient to represent the size of the region in which the unilateral hopper angle affected the flow of particles in the hopper, the hopper angle term in the Brown and Sellers model was corrected. The predictive errors of the corrected discharge rate model were less than 7.6% and 3.3% in the simulated and actual discharging tests respectively, which was better than that of the Brown and Sellers model. The results of the study could provide a theoretical basis for the intelligent upgrading of feed accurate handling equipment, and provide a reference basis for the design of asymmetric wedge-shaped hopper. • Simplified modeling of pellet feeds applied. • Discharge characteristics of an asymmetric wedge-shaped hopper revealed. • Discharge rate prediction model for an asymmetric wedge-shaped hopper developed. [ABSTRACT FROM AUTHOR]

Fingerling counting is a basic operation in fish farming and provides an important guideline for many aspects of aquaculture. However, most of the current counting methods are inefficient or computationally cumbersome. This study proposed a high-speed, non-intrusive fingerling counting method based on multiple detectors and a motion prediction model, which achieved high-accuracy counting under the condition of low-frame rate. Firstly, to effectively detect and locate the adherent fingerlings, the detector was accomplished by combining the mixture of Gaussian-based (MOG) segmentation algorithm and the local extremum-based blob detection algorithm. Secondly, three different functions were used to construct a motion prediction model to predict the approximate probability of the fingerlings at each position in the previous frame. Thirdly, the cost matrix was constructed with probability as the feature to associate the fingerlings in the consecutive frames, and the newly appeared fingerlings were counted in real-time, realising the continuous fingerling counting with high precision. Through testing and analysis on 52 collected datasets under low-frame-rate (10 fps) acquisition conditions using largemouth bass (Micropterus salmoides , 3–5 cm) and crucian carp (Carassius auratus , 2–6 cm), results indicated that the best motion prediction model with segmentation function reached over 99% average counting accuracy for both species, with a standard deviation of accuracy less than 0.7%. This method provides a low-cost, high-speed, and stable application solution for computer vision-based fingerling counting. • Combined Mixture of Gaussian segmentation and blob detection for fingerling counting. • Integration of a motion prediction model achieved over 99% average counting accuracy. • Open access to a dataset containing annotation files for fingerlings counting. [ABSTRACT FROM AUTHOR]

Accurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders. Cox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots. Research findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86–0.98), single (HR = 0.11, 95% CI: 0.02–0.68), having children (one child, HR = 0.07, 95%CI: 0.01–0.87; more than one child, HR = 0.33, 95%CI: 0.04–2.48), living in dormitory (HR = 0.25, 95%CI: 0.08–0.77), non-student (employee, HR = 0.24, 95% CI: 0.07–0.88; non-employee, HR = 0.09, 95% CI: 0.02–0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82–0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23–22.48), living alone (HR = 4.37, 95% CI: 1.60–11.94), having suicide history (HR = 2.51, 95% CI: 1.06–5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00–1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00–1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit. A novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures. [ABSTRACT FROM AUTHOR]

It is widely accepted that loneliness is associated with health problems, but less is known about the predictors of loneliness. In this study, we constructed a model to predict individual risk of loneliness during adulthood. Data were from the prospective population-based FinHealth cohort study with 3444 participants (mean age 55.5 years, 53.4% women) who responded to a 81-item self-administered questionnaire and reported not to be lonely at baseline in 2017. The outcome was self-reported loneliness at follow-up in 2020. Predictive models were constructed using bootstrap enhanced LASSO regression (bolasso). The C-index from the final model including 11 predictors from the best bolasso -models varied between 0.65 (95% CI 0.61 to 0.70) and 0.71 (95% CI 0.67 to 0.75) the pooled C -index being 0.68 (95% CI 0.61 to 0.75). Although survey-based individualised prediction models for loneliness achieved a reasonable C-index, their predictive value was limited. High detection rates were associated with high false positive rates, while lower false positive rates were associated with low detection rates. These findings suggest that incident loneliness during adulthood. may be difficult to predict with standard survey data. • We constructed a survey -based model predicting individual level risk of loneliness. • The overall predictive performance of the model was reasonable. • However, high detection rate was accompanied with high false positive rate. • The model included variables related to social relations, but also to poor sleep, female sex and depressive symptoms. [ABSTRACT FROM AUTHOR]

To further improve the operating efficiency of linear induction motor, this paper proposes a loss minimization model predictive current control method based on governable loss online calculation. First, the method derives the equivalent circuit of linear induction motor containing independent iron loss branches, and establishes the loss model containing iron loss. Second, the expressions of stator d‐axis current with governable losses and secondary flux amplitude are derived from theoretical analysis, and then a given value of stator d‐axis current is derived when the governable losses are minimal. Then, a chain observer is designed to observe the secondary flux and calculate the governable loss and stator d‐axis current under the current operation. Finally, a method is proposed to introduce the stator d‐axis current observation results into the model predictive current control, which ensures the loss suppression effect and improves the dynamic performance at the same time. In addition, this article also compares the loss minimization controllers with or without iron loss. The simulation results are consistent with the theory, and the optimal control of the secondary flux of the linear induction motor can be successfully realized, thus effectively reducing the energy loss of the motor in the dynamic operation process. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]