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In insurance, proposing an accurate premium that is adjusted to the insured risk profile allows companies to better manage their portfolios and to be more competitive. Machine learning methods have recently been adopted for various improvements in insurance ratemaking, especially in the automobile industry. These models are specifically used to mine potential data information and to build a predictive model for a variable of interest using explanatory variables. In this paper, we aim to provide a pricing method for ratemaking individual healthcare insurance contracts using machine learning algorithms that are applied to a Tunisian healthcare insurance portfolio. We start with a simple Classification and Regression Tree, and we work toward more advanced methods that are Random Forest, Extreme gradient boosting, Support Vector Regression, and Artificial Neural network regression model. The predictive performance of these non-parametric methods is compared with the standard generalized linear model. Our results showed the applicability of machine learning in the healthcare insurance market and that the XGBoost algorithm outperforms the predictive capacity of the classical generalized linear model. [ABSTRACT FROM AUTHOR]

BACKGROUND: About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied. OBJECTIVE: This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses. METHODS: In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days). RESULTS: Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter. CONCLUSIONS: These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life.

Objective: In response to high demand and prolonged wait times for cognitive behavioural therapy (CBT) in Ontario, Canada, we developed predictive models to stratify patients into high- or low-intensity treatment, aiming to optimize limited healthcare resources. Method: Using client records (n = 953) from Ontario Shores Centre for Mental Health Sciences (January 2017–2021), we estimated four binary outcome models to assign patients into complex and standard cases based on the probability of reliable improvement in Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scores. We evaluated two choices of cut-offs for patient complexity assignment: models at an ROC (receiver operating characteristic)-derived cut-off and a 0.5 probability cut-off. Final model effectiveness was assessed by assigning treatment intensity (high-intensity or low-intensity CBT) based on the combined performance of both GAD-7 and PHQ-9 models. We compared the treatment assignment recommendations provided by the models to those assigned by clinical assessors. Results: The predictive models demonstrated higher accuracy in selecting treatment modality compared to provider-assigned treatment selection. The ROC cut-off achieved the highest prediction accuracy of case complexity (0.749). The predictive models exhibited large sensitivity and specificity trade-offs (which influence the rates of patient assignment to high-intensity CBT) despite having similar accuracy statistics (ROC cut-off = 0.749, 0.5 cut-off = 0.690), emphasizing the impact of cut-off choices when implementing predictive models. Conclusions: Overall, our findings suggest that the predictive model has the potential to improve the allocation of CBT services by shifting incoming clients with milder symptoms of depression to low-intensity CBT, with those at highest risk of not improving beginning in high-intensity CBT. We have demonstrated that models can have significant sensitivity and specificity trade-offs depending on the chosen acceptable threshold for the model to make positive predictions of case complexity. Further research could assess the use of the predictive model in real-world clinical settings. Plain Language Summary Title: Stratified Care in Cognitive Behavioural Therapy: A Comparative Evaluation of Predictive Modeling Approaches for Individualized Treatment Plain Language Summary: In response to high demand and prolonged wait times for cognitive behavioral therapy (CBT) in Ontario, Canada, we developed predictive models to stratify patients into high- or low-intensity treatment, aiming to optimize limited healthcare resources. We used client records from Ontario Shores Centre for Mental Health Sciences (January 2017–2021). The final model's effectiveness was assessed by assigning treatment intensity (high-intensity or low-intensity CBT) based on treatment success. Treatment success was determined using validated symptom scales. We compared the treatment assignment recommendations provided by the predictive model to those assigned by clinical assessors. Overall, our findings suggest that the predictive model has the potential to improve the allocation of CBT services by shifting incoming clients with milder symptoms of depression to low-intensity CBT, with those at highest risk of not improving beginning in high-intensity CBT. [ABSTRACT FROM AUTHOR]

Purpose: To identify the independent risk variables that contribute to the emergence of microalbuminuria(MAU) in type 2 diabetes mellitus(T2DM), to develop two different prediction models, and to show the order of importance of the factors in the better prediction model combined with a SHAP(Shapley Additive exPlanations) plot. Methods: Retrospective analysis of data from 981 patients with T2DM from March 2021 to March 2023. This dataset included socio-demographic characteristics, disease attributes, and clinical biochemical indicators. After preprocessing and variable screening, the dataset was randomly divided into training and testing sets at a 7:3 ratio. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the training set. Subsequently, prediction models for MAU were constructed using two algorithms: Random Forest and BP neural network. The performance of these models was evaluated using k-fold cross-validation (k = 5), and metrics such as the area under the ROC curve (AUC), accuracy, precision, recall, specificity, and F1 score were utilized for assessment. Results: The final variables selected through multifactorial logistic regression analysis were age, BMI, stroke, diabetic retinopathy(DR), diabetic peripheral vascular disease (DPVD), 25 hydroxyvitamin D (25(OH)D), LDL cholesterol, neutrophil-to-lymphocyte ratio (NLR), and glycated haemoglobin (HbA1c) were used to construct the risk prediction models of Random Forest and BP neural network, respectively, and the Random Forest model demonstrated superior overall performance (AUC = 0.87, Accuracy = 0.80, Precision = 0.79, Recall = 0.84, Specificity = 0.76, F1 Score = 0.81). The SHAP feature matrix plot revealed that HbA1c, NLR, and 25(OH)D were the three most significant factors in predicting the development of MAU in T2DM, with 25(OH)D acting as an independent protective factor. Conclusion: Effective identification of MAU in T2DM, therapeutic strategies for controllable high-risk factors, and prevention or delay of diabetic kidney disease(DKD) can all be achieved with the help of the risk prediction model developed in this study. [ABSTRACT FROM AUTHOR]

Background and Purpose: Delirium is a common mental disorder after adult cardiovascular surgery. Fifteen to 23% of patients undergoing cardiovascular surgery and cardiomyopathy experience delirium, and the efficacy of treatment interventions for delirium has been consistently unsatisfactory. Methods: A total of 729 patients who underwent cardiovascular surgery were randomly allocated into a training set and a validation set. A nomogram was developed using a logistic regression model to predict the incidence of delirium following cardiovascular surgery. The validity of the model was assessed by determining the receiver operating characteristic (ROC) curve, calculating the area under the ROC curve (AUROC), performing a calibration plot, and executing a decision curve analysis. This model was internally validated using the bootstrap method. Results: Postoperative delirium (POD) occurred in 165 cases (22.6%) among the 729 patients. Predictors included age, transient ischemic attack, length of preoperative stay, preoperative left ventricular injection fraction and N‐terminal pro‐B‐type natriuretic peptide level, and intraoperative infusion of dexmedetomidine and human fibrinogen. The nomogram showed sufficient differentiation and calibration (AUROC = 0.754, 95% confidence interval = 0.703–0.804). The calibration graphs showed that the predictive values of the nomogram were in agreement with the actual values. The analysis of the training and validation sets suggested that the model possessed specific clinical significance. Conclusions: In summary, the predictive model consists of seven factors that can roughly predict the occurrence of POD in patients who undergo cardiovascular surgery. [ABSTRACT FROM AUTHOR]

Rock hardness and abrasivity are among the most crucial properties that can significantly impact the interaction between rocks and mechanical tools in different parts of geoengineering projects. Accurate estimation of these properties is essential for a better understanding and optimization of geoengineering operations. Hence, the main aim of this study was to develop different machine learning (ML) models based on the geochemical measurements for predicting rock hardness and abrasivity. To do this, 159 rock samples were collected from a gold mine, and portable X-ray fluorescence spectrometry (pXRF), Leeb rebound hardness (LRH), and Cerchar abrasivity index (CAI) tests were performed on the collected rock samples. Three different ML algorithms, including random forest regressor (RFR), support vector regression, and gradient boosting regressor, were applied to develop predictive models for LRH and CAI separately. Considering the fact that the geochemical data are of the compositional type, two scenarios were followed: developing predictive models based on the original data obtained from the pXRF and the centered log-ratio (Clr) transformed data, resulting in the development of six predictive models for LRH and CAI. The performance assessment of the developed predictive models showed that RFR models outperformed the other two ML algorithms in predicting LRH and CAI. In addition, the developed models based on the original data demonstrated a better performance in both cases of LRH and CAI than the trained model based on Clr data. The result indicates that integrated pXRF measurements and RFR technique have strong potential to be used for practical and efficient rock materials characterization during exploration and extraction processes. [ABSTRACT FROM AUTHOR]

Mass cytometry enables deep profiling of biological samples at single‐cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high‐dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research. [ABSTRACT FROM AUTHOR]

In beamformed wireless cellular systems such as 5G New Radio (NR) networks, beam management (BM) is a crucial operation. In the second phase of 5G NR standardization, known as 5G-Advanced, which is being vigorously promoted, the key component is the use of artificial intelligence (AI) based on machine learning (ML) techniques. AI/ML for BM is selected as a representative use case. This article provides an overview of the AI/ML for BM in 5G-Advanced. The legacy non-AI and prime AI-enabled BM frameworks are first introduced and compared. Then, the main scope of AI/ML for BM is presented, including improving accuracy, reducing overhead and latency. Finally, the key challenges and open issues in the standardization of AI/ML for BM are discussed, especially the design of new protocols for AI-enabled BM. This article provides a guideline for the study of AI/ML-based BM standardization. [ABSTRACT FROM AUTHOR]

Modern deep learning agents usually operate in low-dimensional environments. They process pixel input, do not offer insights into their thought process, and require significant power and computational resources. These characteristics make them inapplicable for embedded devices. In this letter, we present Pythia, an edge-first framework that uses latent imagination to handle complex environments efficiently and envision future agent states. It utilizes a vector quantized variational autoencoder to reduce the high-dimensional features into a low-dimensional space, making it ideal for modern embedded devices. Moreover, Pythia offers human interpretable feedback and scales well with respect to the design space. Pythia surpassed the other state-of-the-art models in prediction accuracy on both intrinsic and extrinsic metrics. [ABSTRACT FROM AUTHOR]

Purpose: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resonance spectroscopy (WB-MRS) protocol that delves into the intricacies of tumor microenvironments, offering a comprehensive understanding of glioma progression to inform expectant surgical and adjuvant intervention. Methods: We investigated five locoregional tumor metabolites in a post-treatment population and applied machine learning (ML) techniques to analyze key relationships within seven regions of interest: contralateral normal-appearing white matter (NAWM), fluid-attenuated inversion recovery (FLAIR), contrast-enhancing tumor at time of WB-MRS (Tumor), areas of future recurrence (AFR), whole-brain healthy (WBH), non-progressive FLAIR (NPF), and progressive FLAIR (PF). Five supervised ML classification models and a neural network were developed, optimized, trained, tested, and validated. Lastly, a web application was developed to host our novel calculator, the Miami Glioma Prediction Map (MGPM), for open-source interaction. Results: Sixteen patients with histopathological confirmation of high-grade glioma prior to WB-MRS were included in this study, totaling 118,922 whole-brain voxels. ML models successfully differentiated normal-appearing white matter from tumor and future progression. Notably, the highest performing ML model predicted glioma progression within fluid-attenuated inversion recovery (FLAIR) signal in the post-treatment setting (mean AUC = 0.86), with Cho/Cr as the most important feature. Conclusions: This study marks a significant milestone as the first of its kind to unveil radiographic occult glioma progression in post-treatment gliomas within 8 months of discovery. These findings underscore the utility of ML-based WB-MRS growth predictions, presenting a promising avenue for the guidance of early treatment decision-making. This research represents a crucial advancement in predicting the timing and location of glioblastoma recurrence, which can inform treatment decisions to improve patient outcomes. [ABSTRACT FROM AUTHOR]

During the robotic grinding of vertebral plates in high-risk laminectomy procedures, programmed operations may inadvertently induce force or temperature-related damage to the bone tissue. Therefore, it is imperative to explore a control methodology aimed at minimizing such damage during the robotic grinding of vertebral plate cortical bone, contingent upon optimal grinding parameters. Initially, predictive models for both the grinding force and temperature of vertebral plate cortical bone were developed using the response surface design (RSD) methodology. Subsequently, employing the satisfaction function approach, multi-objective parameter optimization of these predictive models was conducted to ascertain the optimal combination of parameters conducive to low-damage grinding. The optimum grinding parameters identified were a speed of 6000 r/min, a depth of grind of 0.4 mm, and a feed rate of 3.8 mm/s. Moreover, a multi-layer adaptive fuzzy control strategy was devised, and a corresponding multi-layer adaptive fuzzy controller (MFLC) was then implemented to dynamically adjust the grinding feed speed. The efficacy of this control module was corroborated through Simulink simulations. Simulation results demonstrated that the magnitude of the grinding force fluctuated within the range of 2.2–2.6 N after FLC control, while the fluctuation range of the grinding force was limited to 2.2–2.48 N after MFLC control. This indicates that MFLC control brings the force closer to the target expectation value of 2.39 N compared with FLC control. Finally, the dynamic fuzzy control method predicated on optimal grinding parameters was validated through experimental porcine spine grinding conducted on a robotic vertebral plate grinding platform. [ABSTRACT FROM AUTHOR]

Introduction Material and Methods Results Conclusions Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high‐income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean section (CS) rates after induction of labor (IOL) demand for improved counseling on delivery mode within this context.We aim to develop a prognostic model for predicting vaginal delivery after labor induction using computational learning. Secondary aims include elaborating a prognostic model for CS due to abnormal fetal heart rate and labor dystocia, and evaluation of these models' feature importance, using maternal clinical predictors at IOL admission. The best performing model was assessed in an independent validation data using the area under the receiver operating curve (AUROC). Internal model validation was performed using 10‐fold cross‐validation. Feature importance was calculated using SHAP (SHapley Additive exPlanation) values to interpret the importance of influential features. Our main outcome measures were mode of delivery after induction of labor, dichotomized as vaginal or cesarean delivery and CS indications, dichotomized as abnormal fetal heart rate and labor dystocia.Our sample comprised singleton term pregnant women (n = 2434) referred for IOL to a tertiary Obstetrics center between January 2018 and December 2021. Prediction of vaginal delivery obtained good discrimination in the independent validation data (AUROC = 0.794, 95% CI 0.783–0.805), showing high positive and negative predictive values (PPV and NPV) of 0.752 and 0.793, respectively, high specificity (0.910) and sensitivity (0.766). The CS model showed an AUROC of 0.590 (95% CI 0.565–0.615) and high specificity (0.893). Sensitivity, PPV and NVP values were 0.665, 0.617, and 0.7, respectively. Labor features associated with vaginal delivery were by order of importance: Bishop score, number of previous term deliveries, maternal height, interpregnancy time interval, and previous eutocic delivery.This prognostic model produced a 0.794 AUROC for predicting vaginal delivery. This, coupled with knowing the features influencing this outcome, may aid providers in assessing an individual's risk of CS after IOL and provide personalized counseling. [ABSTRACT FROM AUTHOR]

This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). The performance of these models is evaluated on time series data at different water depths and wave conditions, including both measured and simulated data with a focus on real-time prediction of ocean waves at a given location without resolving for the surrounding ocean surface, in other words, short-time single-point forecasting. The work presented includes training the models on historical wave data and testing their ability to predict phase-resolved future surface wave patterns for short-time forecasts. Additionally, this study discusses the feasibility of deploying these models for extended time intervals. It provides valuable insights into the trade-offs between accuracy and practicality in the real-time implementation of predictive models for wave elevation, which are needed in wave energy converters to optimise the control algorithm. [ABSTRACT FROM AUTHOR]

The growing energy demand and the need for climate mitigation strategies have spurred interest in the application of CO2–enhanced oil recovery (CO2–EOR) and carbon capture, utilization, and storage (CCUS). Furthermore, natural hydrogen (H2) production and underground hydrogen storage (UHS) in geological media have emerged as promising technologies for cleaner energy and achieving net–zero emissions. However, selecting a suitable geological storage medium is complex, as it depends on the physicochemical and petrophysical characteristics of the host rock. Solubility is a key factor affecting the above–mentioned processes, and it is critical to understand phase distribution and estimating trapping capacities. This paper conducts a succinct review of predictive techniques and present novel simple and non–iterative predictive models for swift and reliable prediction of solubility behaviors in CO2–brine and H2–brine systems under varying conditions of pressure, temperature, and salinity (T–P–m salts), which are crucial for many geological and energy–related applications. The proposed models predict CO2 solubility in CO2 + H2O and CO2 + brine systems containing mixed salts and various single salt systems (Na+, K+, Ca2+, Mg2+, Cl−, SO42−) under typical geological conditions (273.15–523.15 K, 0–71 MPa), as well as H2 solubility in H2 + H2O and H2 + brine systems containing NaCl (273.15–630 K, 0–101 MPa). The proposed models are validated against experimental data, with average absolute errors for CO2 solubility in pure water and brine ranging between 8.19 and 8.80% and for H2 solubility in pure water and brine between 4.03 and 9.91%, respectively. These results demonstrate that the models can accurately predict solubility over a wide range of conditions while remaining computationally efficient compared to traditional models. Importantly, the proposed models can reproduce abrupt variations in phase composition during phase transitions and account for the influence of different ions on CO2 solubility. The solubility models accurately capture the salting–out (SO) characteristics of CO2 and H2 gas in various types of salt systems which are consistent with previous studies. The simplified solubility models for CO2 and H2 presented in this study offer significant advantages over conventional approaches, including computational efficiency and accuracy across a wide range of geological conditions. The explicit, derivative–continuous nature of these models eliminates the need for iterative algorithms, making them suitable for integration into large–scale multiphase flow simulations. This work contributes to the field by offering reliable tools for modeling solubility in various subsurface energy and environmental–related applications, facilitating their application in energy transition strategies aimed at reducing carbon emissions. [ABSTRACT FROM AUTHOR]

Organic solar cells (OSCs), renowned for their lightweight, cost efficiency, and adaptability nature, stand out as a promising option for developing renewable energy. Improving the power conversion efficiency (PCE) of OSCs is essential, and researchers are delving into novel materials to achieve this. Traditional approaches are often laborious and costly, highlighting the need for predictive modeling. Machine learning (ML), especially via quantitative structure–property relationship (QSPR) models, is streamlining material development, with a goal to exceed a 20% PCE. In this review, the application of ML in OSCs is explored, and recent studies utilizing ML approaches for PCE prediction are reviewed, encompassing empirical functions, ML algorithms, self‐devised ML frameworks, and the combination with automated experimental technologies. First, the benefits of ML in predicting PCE for OSCs are addressed. Second, the development of high‐efficiency predictive models for both fullerene and nonfullerene acceptors is delved into. The impact of various ML algorithm models on PCE prediction is then assessed, taking into account the construction of predictive models. Moreover, the quality of databases and the selection of descriptors are considered. Databases and descriptors based on experimental studies are further categorized. Finally, prospects for the future development of OSCs are proposed. [ABSTRACT FROM AUTHOR]

Background: The incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases. Methods: This retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis. Results: Fully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889–0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making. Conclusions: Combined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making. [ABSTRACT FROM AUTHOR]

Ischaemic stroke is the leading cause of death worldwide, and early neurological deterioration(END) occurs in 20%-40% of patients, which is the main cause of severe neurological deficits and disability, and even increased mortality. The occurrence of END is closely related to the poor prognosis of the patients, so it is important to identify the risk factors for the occurrence of END in patients with AIS and target intervention at an early stage factors and targeted intervention is of great significance. Up to December 20, 2023, a comprehensive search was conducted across PubMed, Embase, Web of Science, MedLine, and The Cochrane Library for studies focusing on predictive models for END in acute stroke patients. Included studies either developed or validated predictive models. The Prediction Model Risk of Bias Assessment tool was utilized to assess bias in these prediction models. Pooled area under the curve values were calculated using DerSimonian and Laird random-effects model. Nineteen studies, each presenting an original model, were identified. Predominantly constructed through logistic multiple regression, these models demonstrated robust predictive performance (area under the curve ≥0.80). Key predictors of END in acute ischemic stroke patients included blood glucose levels, baseline National Institute of Health Stroke Scale scores, extent of cerebral infarction, and stenosis in the carotid and middle cerebral arteries. Clinical practitioners should closely monitor high-frequency predictors of END in patients. However, the varying quality of current models necessitates the selection of models that balance performance with operational simplicity in clinical practice. [ABSTRACT FROM AUTHOR]

As the technology node in semiconductor manufacturing continuously shrinks, the etch-induced etch bias introduced during the etching process cannot be ignored and necessitates correction. The prevailing approach to addressing this issue is model-based etch bias correction. This method involves simulating the etching process by training an etch model that predicts the bias between the After Development Inspection (ADI) contour and the After Etch Inspection (AEI) contour. However, the reliability of the etch data for model training is compromised due to pattern shrinkage during Scanning Electron Microscope (SEM) imaging, which impairs the model’s prediction accuracy. To mitigate these issues, the contour averaging method is frequently employed, although it lacks thorough theoretical explanation and experimental verification. In this study, we validate the effectiveness of contour averaging theoretically and empirically. A relationship is derived between the prediction error of the etch model and the number of averaged contours, showing that contour averaging minimizes measurement errors of etch data. We also demonstrate the improved prediction accuracy of etch model using contour averaging, with both real and generated etch data. [ABSTRACT FROM AUTHOR]

Computational simulation has been used in the semiconductor industry since the 1950s to provide engineers and managers with a faster, more cost-effective method of designing semiconductors. With increased pressure in the semiconductor industry to move towards greener and more sustainable manufacturing, it is crucial to understand the impact of computational simulation and artificial intelligence on environmental sustainability, specifically reducing greenhouse gas (GHG) emissions. This paper quantifies the degree to which various types of simulation used for hardware, process, and device optimization can be adopted for different applications in wafer fabrication equipment research and development, along with the potential reduction in physical experimentation, saving silicon, gases, chemicals, and wafers. With this understanding and an estimation of the equivalent carbon cost impact of the computation itself, analyzed projects demonstrated a significant (>80%) decrease in emissions, primarily driven by the ability to use fewer patterned and blanket wafers whose carbon footprint appears to be orders of magnitude larger than that of used modeling resources. The paper concludes with an attempt to quantify the environmental savings from virtualization across our entire research organization and to illustrate the potential future impact of described activities. [ABSTRACT FROM AUTHOR]

Restoration outcomes are notoriously unpredictable and this challenges the capacity to reliably meet goals. To harness ecological restoration's full potential, significant advances to predictive capacity must be made in restoration ecology. We outline a process for predicting restoration outcomes, based on the model of iterative forecasting. We then describe six challenges that impede predictive capabilities in restoration and, for each, an agenda for overcoming the challenge. Key challenges include the lack of clear goals, insufficient knowledge of why restoration outcomes vary, difficulty quantifying known drivers of variation prior to initiation of restoration projects, model uncertainty, the need to scale up local understanding to guide large‐scale restoration efforts, and temporally variable conditions that hinder long‐term forecast accuracy. Meeting these challenges will require research to resolve key drivers of variation in restoration outcomes; however, there is also a critical need to begin forecasting efforts in restoration ecology immediately. Although early efforts may be of limited practical utility, iterating between model development and evaluation will resolve data needs, minimize uncertainty, and lead to predictions that practitioners can confidently embrace. In turn, a robust predictive capacity will help to reliably meet goals, enhance cost‐effectiveness, and guide policy decisions to help see out the promise of the Decade on Ecosystem Restoration. [ABSTRACT FROM AUTHOR]

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The objective of this study is to evaluate and optimize the costs of unit operations in artisanal underground mining through the application of predictive models based on machine learning. Four models were trained and validated: Multiple Linear Regression (MLR), Random Forest (RF), Decision Tree (DT), and Artificial Neural Networks (ANN), using a dataset that includes operational, geological, and economic variables collected over a three-month period. Among the evaluated models, Decision Trees demonstrated the best performance, achieving a coefficient of determination R² of 0.90, which enabled the identification of optimal cost parameters for critical activities such as drilling and blasting (41.32 US$/tn), shoveling (17.03 US$/tn), hauling (4.13 US$/tn), loading (16.08 US$/tn), ventilation (8.81 US$/tn), and ground support (1.51 US$/tn). Exceeding these costs results in cost overruns in unit operations, negatively impacting the profitability of the company. The results of this study provide an innovative approach to cost optimization in artisanal underground mining, enhancing profitability and contributing to more informed decision-making. The implications of these findings suggest immediate applications and open new opportunities for future research in other mining contexts. [ABSTRACT FROM AUTHOR]

Mango fruit is a rich source of bioactive compounds such as carotenoids, phenolics, and ascorbic acid. This research aimed at predicting the content of these bioactive compounds in 'Tommy Atkins' mangoes using optical properties, i.e., the absorption coefficients related to chlorophylls (µa630, µa650, µa670, µa690) and carotenoids (µa540), and the scattering parameters (Mie's A and b), measured during the shelf-life period at 20 °C by time-resolved reflectance spectroscopy. The µa540 and Mie's b increased during shelf-life, while µa670 and Mie's A decreased. Ascorbic acid (AA) and the total antioxidant capacity decreased during shelf-life, while the total carotenoids increased, and the total phenols (TPC) did not significantly change. The major constituent of the nonsaponified extracts, (all-E)-β-carotene, increased during the shelf-life period. A similar trend was observed for the total (all-E)-violaxanthin esters, the total (9Z)-violaxanthin esters and the total neoxanthin esters. Carotenoids are responsible for the yellow-orange color of mangoes: (all-E)-β-carotene was mainly related to a* and h° pulp color while the total (all-E)-violaxanthin esters were mainly linked to b*, C*, and the yellowness index. Using multiple regression analysis, good prediction models were achieved for the total carotenoids (R2adj = 83.1%), the total xanthophylls (R2adj = 78%), (all-E)-β-carotene (R2adj = 77%) and the total (all-E)-violaxanthin esters (R2adj = 74%), while less satisfactory predictions were obtained for AA and TPC. [ABSTRACT FROM AUTHOR]

Background: New-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML). Methods: The data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions. Results: Among 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873–0.888) in validation and 0.769 (0.756–0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use. Conclusion: We developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population. Methods: Data from the prospective Fasa Cohort Study, initiated in rural Fars province, Iran (March 2014), were employed for this purpose. The required data were collected using blood tests, questionnaires, liver ultrasonography, and physical examinations. A two-step approach identified key predictors from over 100 variables: (1) statistical selection using mean decrease Gini in random forest and (2) incorporation of clinical expertise for alignment with known MASLD risk factors. The hold-out validation approach (with a 70/30 train/validation split) was utilized, along with 5-fold cross-validation on the validation set. Logistic regression, Naïve Bayes, support vector machine, and light gradient-boosting machine (LightGBM) algorithms were compared for model construction with the same input variables based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Results: A total of 6,180 adults (52.7% female) were included in the study, categorized into 4816 non-MASLD and 1364 MASLD cases with a mean age (± standard deviation [SD]) of 48.12 (± 9.61) and 49.47 (± 9.15) years, respectively. Logistic regression outperformed other ML algorithms, achieving an accuracy of 0.88 (95% confidence interval [CI]: 0.86-0.89) and an AUC of 0.92 (95% CI: 0.90-0.93). Among more than 100 variables, the key predictors included waist circumference, body mass index (BMI), hip circumference, wrist circumference, alanine aminotransferase levels, cholesterol, glucose, high-density lipoprotein, and blood pressure. Conclusion: Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) recurrence following surgical resection remains a significant clinical challenge, necessitating reliable predictive models to guide personalised interventions. In this study, we sought to harness the power of artificial intelligence (AI) to develop a robust predictive model for HCC recurrence using comprehensive clinical datasets. Methods: Leveraging data from 958 patients across multiple centres in Australia and Hong Kong, we employed a multilayer perceptron (MLP) as the optimal classifier for model generation. Results: Through rigorous internal cross‐validation, including a cohort from the Chinese University of Hong Kong (CUHK), our AI model successfully identified specific pre‐surgical risk factors associated with HCC recurrence. These factors encompassed hepatic synthetic function, liver disease aetiology, ethnicity and modifiable metabolic risk factors, collectively contributing to the predictive synergy of our model. Notably, our model exhibited high accuracy during cross‐validation (.857 ±.023) and testing on the CUHK cohort (.835), with a notable degree of confidence in predicting HCC recurrence within accurately classified patient cohorts. To facilitate clinical application, we developed an online AI digital tool capable of real‐time prediction of HCC recurrence risk, demonstrating acceptable accuracy at the individual patient level. Conclusion: Our findings underscore the potential of AI‐driven predictive models in facilitating personalised risk stratification and targeted interventions to mitigate HCC recurrence by identifying modifiable risk factors unique to each patient. This model aims to aid clinicians in devising strategies to disrupt the underlying carcinogenic network driving recurrence. [ABSTRACT FROM AUTHOR]

Cucumbers (Cucumis sativus) are known for their high water content and nutritional value, making them a valuable addition to a balanced diet. The objective of this study was to evaluate the physicochemical quality (color, moisture, water activity, and texture), and antioxidant/antiradical properties of freshly packed greenhouse-cultivated cucumbers over a 28-day storage period, utilizing a combination of high-throughput analytical techniques, including attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy, instrumental analysis, image analysis, and chemometrics. The key findings indicated that the cucumbers underwent a significant loss of moisture during the storage period, with a concomitant decrease in firmness. Color analysis revealed an increase in the yellowness of both the peel and flesh, while image analysis demonstrated increasing dissimilarity and textural disorder, particularly evident by the 13th day of storage. The spectrophotometric and spectroscopic findings demonstrated that the quality of the cucumbers was maintained during the storage period. Meanwhile, regression and discriminant analyses were employed to interpret the collected data. Predictive models were developed with high accuracy to estimate the actual storage days and flesh quality attributes based on textural and color features derived from image analysis. Overall, this study highlights the importance of controlled storage conditions in maintaining cucumber quality and provides a comprehensive approach to monitoring post-harvest changes. [ABSTRACT FROM AUTHOR]

Building systems for predicting human socio-emotional states has promising applications; however, if trained on biased data, such systems could inadvertently yield biased decisions. Bias mitigation remains an open problem, which tackles the correction of a model's disparate performance over different groups defined by particular sensitive attributes (e.g., gender, age, and race). In this work, we design a novel fairness loss function named Multi-Group Parity (MGP) to provide a generalised approach for bias mitigation in personality computing. In contrast to existing works in the literature, MGP is generalised as it features four ‘multiple’ properties (4Mul): multiple tasks, multiple modalities, multiple sensitive attributes, and multi-valued attributes. Moreover, we explore how to incrementally mitigate the biases when more sensitive attributes are taken into consideration sequentially. Towards this problem, we introduce a novel algorithm that utilises an incremental learning framework to mitigate bias against one attribute data at a time without compromising past fairness. Extensive experiments on two large-scale multi-modal personality recognition datasets validate the effectiveness of our approach in achieving superior bias mitigation under the proposed four properties and incremental debiasing settings. [ABSTRACT FROM AUTHOR]

In creativity support and computational co-creativity contexts, the task of discovering appropriate prompts for use with text-to-image generative models remains difficult. In many cases the creator wishes to evoke a certain impression with the image, but the task of conferring that succinctly in a text prompt poses a challenge: affective language is nuanced, complex, and very much influenced by the training trajectory of each specific AI model. In this work we introduce a method for generating images conditioned on desired affect, quantified using a psychometrically validated three-component approach, that can be combined with conditioning on text descriptions. We first train a neural network for estimating the affect content of text and images from semantic embeddings, and then demonstrate how this can be used to exert control over a variety of generative models. We show examples of how affect modifies the outputs, provide quantitative and qualitative analysis of its capabilities, and discuss possible extensions and use cases. We also show the capacity of our affect-guided generation to output images which re-frame or extend ideas in original ways that may not have been immediately apparent to the human prompt-writer. [ABSTRACT FROM AUTHOR]

As one of the world's most prevalent mental illnesses, depression is not easy to detect since it affects different people in different ways. Recently, linguistic features extracted from transcribed texts have been widely explored in depression detection because they contain a variety of cues about psychological activities. However, the detection performance is limited due to the following two reasons: 1) the dialogue structure is ignored, which causes the Inconsistent Context problem; and 2) Imbalanced Regression occurs due to the long-tailed distribution of depression datasets. To this end, in this paper we investigate the relationship between the local topic and global context in interview transcripts, and bridge the gap between depression symptoms and depression severity. In particular, we propose a model called Conditional Variational Topic-enriched Auto-Encoder (CVTAE), which can capture the spatial features from local topics via variational inference, and the temporal features from the global context with attention mechanism. Besides, we apply the re-weighting strategies to assigning weights to the depression labels with different values. Extensive experiments on the DAIC-WOZ dataset in English and a self-constructed database NCUDID in Chinese demonstrate the effectiveness and robustness of CVTAE, while the comprehensive ablation study and case study show its interpretability. [ABSTRACT FROM AUTHOR]

Objectives: This study investigated the impact of social activities on cognitive functioning and psychopathological symptoms. Methods: Participants aged 55 or older were enrolled through communities. Initial measures assessed demographic data, neuropsychological functioning, psychopathological state, and happiness. Social activities were evaluated using a modified 12-item tool, with 3–4 activities as the cutoff. Follow-up after 6–9 months included Mini-Mental State Examination (MMSE), Beck Depression Inventory – II (BDI-II), Beck Anxiety Inventory (BAI), Health Assessment Questionnaire (HAQ), and Patient Health Questionnaire-15 (PHQ-15) measurements. Predictive models for psychiatric and cognitive statuses were built using multiple linear regression, adjusting for baseline conditions. Results: Initially, 516 older individuals enrolled, with 403 undergoing follow-up. During follow-up, the low participation group reported lower MMSE scores, higher BAI scores, and increased PHQ-15 risk. Negative correlations between social activity numbers and PHQ-15 results were found. Engagement in social clubs correlated positively with higher MMSE scores, while regular interactions with one's adult child(ren) were linked to decreased BAI scores. Conclusions: The quantity of social activities was associated with lower somatic distress. Social club engagement positively influenced cognition, and regular interactions with one's adult child(ren) mitigated anxiety among older individuals. Clinical implications: Enough types of social activities, participating in social clubs, and adequate interactions with children protected against psychopathologies. [ABSTRACT FROM AUTHOR]

In Malaysia, fast growth in e-commerce speeds a business need to understand and predict consumer online behavior in order to be more competitive. While the whole world is embracing big data analytics, many businesses in Malaysia, particularly those in the ecommerce sector, find it hard to harness these technologies to their benefit. The absence of specific predictive models and the complexity of socio-cultural diversity further complicate the efforts toward understanding consumer preferences. Therefore, this research tries to fill in some of the gaps by applying decision tree and rule-based algorithms to classify online purchasing behavior amongst Malaysian consumers. The study looks into the data from an online survey comprising 560 respondents with a view to demographic, factors influences, and purchasing behaviour. The performance of six machine learning models comprising J48, Random Tree, REPTree representing decision trees and JRip, PART, and OneR as rule-based algorithms was assessed. Feature selection, pre-processing, and SMOTE were applied in order to balance class inequalities of the dataset. The result indicated that the highest accuracy of 89.34% was achieved by the Random Tree algorithm, while the rule-based algorithm PART reached an accuracy of 87.56%. Results of these models open up the possibility of providing very important insights from a business perspective into consumer behaviour and thus offer actionable data which allows them to complete their job of finetuning marketing strategies and engaging customers. The current study contributes to the literature by highlighting decision tree and rule-based classification models as very useful in the Malaysian e-commerce context. These developed predictive models can serve as building blocks where businesses might know more about consumer behavior, personalize marketing, and reach operationally efficient levels. Future research may involve integrating other influencing variables and applying them across industries.

Abstract Loss to follow-up (LTFU) in tuberculosis (TB) management increases morbidity and mortality, challenging effective control strategies. This study aims to develop and evaluate machine learning models to predict loss to follow-up in TB patients, improving treatment adherence and outcomes. Retrospective data encompassing tuberculosis patients who underwent treatment or registration at the National Center for Clinical Medical Research on Infectious Diseases from January 2017 to December 2021 were compiled. Employing machine learning techniques, namely SVM, RF, XGBoost, and logistic regression, the study aimed to prognosticate LTFU. A comprehensive cohort of 24,265 tuberculosis patients underwent scrutiny, revealing a LTFU prevalence of 12.51% (n = 3036). Education level, history of hospitalization, alcohol consumption, outpatient admission, and prior tuberculosis history emerged as precursors for pre-treatment LTFU. Employment status, outpatient admission, presence of chronic hepatitis/cirrhosis, drug adverse reactions, alternative contact availability, and health insurance coverage exerted substantial influence on treatment-phase LTFU. XGBoost consistently surpassed alternative models, boasting superior discriminative ability with an average AUC of 0.921 for pre-treatment LTFU and 0.825 for in-treatment LTFU. Our study demonstrates that the XGBoost model provides superior predictive performance in identifying LTFU risk among tuberculosis patients. The identification of key risk factors highlights the importance of targeted interventions, which could lead to significant improvements in treatment adherence and patient outcomes.

With the advancement of technology, remote work and virtual classes have become increasingly common, leading to prolonged periods in front of computers and, consequently, to discomfort and even lower back pain. This study compares machine learning algorithms to identify and prevent low back pain, a common health problem. A predictive model for early diagnosis and prevention of these injuries was developed using datasets from open data repositories. Six machine learning models were used to train the data. Results showed that logistic regression was the most effective model, with performance curves of 70%, 90%, and 99%. Performance metrics indicated 86% accuracy, 85% recall, and 86% F1-score. Accuracy of 70%, recall of 71%, and F1-score of 63% reflect the robust ability of the model to address the problem. In addition, an intuitive interface was implemented using Gradio Software to improve data visualization. [ABSTRACT FROM AUTHOR]

Calcium aluminate cement (CAC) is an important hydraulic cementitious material. It is widely used in construction, metallurgy, chemical industry and other fields due to its high early strength. The factors affecting its strength are also very complex. The research focus of this paper is to establish a prediction model for the compressive strength of CAC paste, so as to assist scientific research and practical engineering to quickly predict the strength of CAC paste at different ages under different mix ratios and curing conditions. In this paper, 273 sets of data are trained and tested based on support vector regression (SVR), random forest regression (RFR), gradient boosting (GB) and extreme gradient boosting (XGB) algorithms. It is found that the prediction accuracy of GB model can reach 89%. Meanwhile, based on the GB model, the feature importance analysis, global interpretation and dependence analysis are carried out. It is found that the main factors affecting the strength of CAC are relative humidity, silica fume content and curing temperature. To obtain high-strength CAC paste, the recommended mix ratio and curing conditions are as follows: Al2O3 content is 67%, CaO content is 32%, silica fume replacement rate is 10%, water–cement ratio is 0.1, relative humidity is 90%, curing temperature is 5 °C and low-temperature treatment time is greater than 60 days. Finally, a graphical user interface is established to facilitate direct prediction of CAC paste under new mix ratio and curing conditions. [ABSTRACT FROM AUTHOR]

[Objective] The influence of different storage temperatures on the quality and total number of microbial colonies of Premna microphylla Turez jelly was studied, and the shelf-life prediction model based on quality indexes and the total number of colonies were established. [Methods] P. microphylla Turez jelly were stored at temperatures of 1, 4, 10, 15, 20 and 25 ℃, and the hardness, adhesiveness, springiness, chewiness, water holding capacity and total number of colonies were regularly measured. The influence of different temperatures on the quality indexes and the total number of colonies were discussed, and the fitting equations of each monitoring index were determined. According to the quality indexes and total number of colonies of P. microphylla Turez jelly, P. microphylla Turez jelly's shelf-life prediction model was established by combining the Arrhenius model and Belehradck model. [Results] The results show that the different storage temperature has no significant change in springiness, while the storage temperature has a significant effect on the hardness, adhesiveness, chewiness, water-holding capacity, and total number of colonies. The zero-level response equation was the best fit for the change in water holding capacity of P. microphylla Turez jelly, with an average fit of 0.978. On the influence of hardness, viscosity, and chewability, the first-order reaction had the best fitting, and the average fitting degree was 0.967, 0.904, and 0.977, respectively. The Logistics equation had the best fitting effect on the total number of colonies of P. microphylla Turez jelly, and the average fitting was 0.992. Five shelf-life prediction models were validated under the storage temperature of 3 ℃. The relative errors between the measured and predicted values were 7.50%, 8.13%, 4.38%, 5.00%, and 3.75%, respectively, with good prediction results. [Conclusion] The models can effectively predict the shelf life of P. microphylla Turez jelly under storage conditions from 1 ℃ to 25 ℃.

Abstract With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the “rms” package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.

Quality control of programs for detection of significant prostate cancer (sPCa) could be defined by the correlation between observed and reference 95% confidence intervals (CIs) for Prostate Imaging-Reporting and Data System (PI-RADS) categories. We used the area under the receiver operating characteristic curve (AUC) for the Barcelona magnetic resonance imaging (MRI) predictive model to screen the quality of ten participant centers in the sPCa opportunistic early detection program in Catalonia. We set an AUC of 3.0 ng/ml and/or a suspicious digital rectal examination who underwent multiparametric MRI and two- to four-core targeted biopsies of PI-RADS ≥3 lesions and/or 12-core systematic biopsy, AUC values ranged from 0.527 to 0.914 and were

Introduction: This study aimed to establish efficient, cost-effective, and early predictive models for adverse pregnancy outcomes based on the combinations of a minimum number of miRNA biomarkers, whose altered expression was observed in specific pregnancy-related complications and selected maternal clinical characteristics. Methods: This retrospective study included singleton pregnancies with gestational hypertension (GH, n = 83), preeclampsia (PE, n = 66), HELLP syndrome (n = 14), fetal growth restriction (FGR, n = 82), small for gestational age (SGA, n = 37), gestational diabetes mellitus (GDM, n = 121), preterm birth in the absence of other complications (n = 106), late miscarriage (n = 34), stillbirth (n = 24), and 80 normal term pregnancies. MiRNA gene expression profiling was performed on the whole peripheral venous blood samples collected between 10 and 13 weeks of gestation using real-time reverse transcription polymerase chain reaction (RT-PCR). Results: Most pregnancies with adverse outcomes were identified using the proposed approach (the combinations of selected miRNAs and appropriate maternal clinical characteristics) (GH, 69.88%; PE, 83.33%; HELLP, 92.86%; FGR, 73.17%; SGA, 81.08%; GDM on therapy, 89.47%; and late miscarriage, 84.85%). In the case of stillbirth, no addition of maternal clinical characteristics to the predictive model was necessary because a high detection rate was achieved by a combination of miRNA biomarkers only [91.67% cases at 10.0% false positive rate (FPR)]. Conclusion: The proposed models based on the combinations of selected cardiovascular disease-associated miRNAs and maternal clinical variables have a high predictive potential for identifying women at increased risk of adverse pregnancy outcomes; this can be incorporated into routine first-trimester screening programs. Preventive programs can be initiated based on these models to lower cardiovascular risk and prevent the development of metabolic/cardiovascular/cerebrovascular diseases because timely implementation of beneficial lifestyle strategies may reverse the dysregulation of miRNAs maintaining and controlling the cardiovascular system. [ABSTRACT FROM AUTHOR]

Bitter peptides are small molecular peptides produced by the hydrolysis of proteins under acidic, alkaline, or enzymatic conditions. These peptides can enhance food flavor and offer various health benefits, with attributes such as antihypertensive, antidiabetic, antioxidant, antibacterial, and immune-regulating properties. They show significant potential in the development of functional foods and the prevention and treatment of diseases. This review introduces the diverse sources of bitter peptides and discusses the mechanisms of bitterness generation and their physiological functions in the taste system. Additionally, it emphasizes the application of bioinformatics in bitter peptide research, including the establishment and improvement of bitter peptide databases, the use of quantitative structure–activity relationship (QSAR) models to predict bitterness thresholds, and the latest advancements in classification prediction models built using machine learning and deep learning algorithms for bitter peptide identification. Future research directions include enhancing databases, diversifying models, and applying generative models to advance bitter peptide research towards deepening and discovering more practical applications. [ABSTRACT FROM AUTHOR]

Backgrounds: Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative therapy for hematological malignancies, it can be associated with relevant post-transplant complications. Several reports have shown that polymorphisms in immune system genes are correlated with the development of post-transplant complications. Within this context, this work focuses on identifying novel polymorphisms in cytokine genes and developing predictive models to anticipate the risk of developing graft-versus-host disease (GVHD), transplantation-related mortality (TRM), relapse and overall survival (OS). Methods: Our group developed a 132-cytokine gene panel which was tested in 90 patients who underwent an HLA-identical sibling-donor allo-HSCT. Bayesian logistic regression (BLR) models were used to select the most relevant variables. Based on the cut-off points selected for each model, patients were classified as being at high or low-risk for each of the post-transplant complications (aGVHD II-IV, aGVHD III-IV, cGVHD, mod-sev cGVHD, TRM, relapse and OS). Results: A total of 737 polymorphisms were selected from the custom panel genes. Of these, 41 polymorphisms were included in the predictive models in 30 cytokine genes were selected (17 interleukins and 13 chemokines). Of these polymorphisms, 5 (12.2%) were located in coding regions, and 36 (87.8%) in noncoding regions. All models had a statistical significance of p<0.0001. Conclusion: Overall, genomic polymorphisms in cytokine genes make it possible to anticipate the development all complications studied following allo-HSCT and, consequently, to optimize the clinical management of patients. [ABSTRACT FROM AUTHOR]

Background: Bipolar disorder (BD) involves significant mood and energy shifts reflected in speech patterns. Detecting these patterns is crucial for diagnosis and monitoring, currently assessed subjectively. Advances in natural language processing offer opportunities to objectively analyze them. Aims: To (i) correlate speech features with manic-depressive symptom severity in BD, (ii) develop predictive models for diagnostic and treatment outcomes, and (iii) determine the most relevant speech features and tasks for these analyses. Methods: This naturalistic, observational study involved longitudinal audio recordings of BD patients at euthymia, during acute manic/depressive phases, and after-response. Patients participated in clinical evaluations, cognitive tasks, standard text readings, and storytelling. After automatic diarization and transcription, speech features, including acoustics, content, formal aspects, and emotionality, will be extracted. Statistical analyses will (i) correlate speech features with clinical scales, (ii) use lasso logistic regression to develop predictive models, and (iii) identify relevant speech features. Results: Audio recordings from 76 patients (24 manic, 21 depressed, 31 euthymic) were collected. The mean age was 46.0 ± 14.4 years, with 63.2% female. The mean YMRS score for manic patients was 22.9 ± 7.1, reducing to 5.3 ± 5.3 post-response. Depressed patients had a mean HDRS-17 score of 17.1 ± 4.4, decreasing to 3.3 ± 2.8 post-response. Euthymic patients had mean YMRS and HDRS-17 scores of 0.97 ± 1.4 and 3.9 ± 2.9, respectively. Following data pre-processing, including noise reduction and feature extraction, comprehensive statistical analyses will be conducted to explore correlations and develop predictive models. Conclusions: Automated speech analysis in BD could provide objective markers for psychopathological alterations, improving diagnosis, monitoring, and response prediction. This technology could identify subtle alterations, signaling early signs of relapse. Establishing standardized protocols is crucial for creating a global speech cohort, fostering collaboration, and advancing BD understanding. [ABSTRACT FROM AUTHOR]

Computational modeling is a new approach to optimize Young’s modulus of scaffolds by performing a minimal number of experiments. However, presenting a modeling algorithm to predict Young’s modulus and characterize the governing parameters is a challenging task. Here, a novel modeling approach has been proposed to estimate Young’s modulus of scaffolds, considering particle agglomeration and interphase interactions. Employing the characteristic parameters of these two phenomena, we modified the Maxwell model using a simple three-step algorithm to determine the optimal value of these parameters and predict Young’s modulus. Interestingly, the model provides a precision of more than 95% for all the studied cases and presents a remarkably better performance compared to the two other models. For instance, the proposed model has reduced the average absolute relative error of Young’s modulus of poly (3-hydroxybutyrate)-keratin/hydroxyapatite nanorods from 25.1% to 0.08%, demonstrating the high efficiency of this model in predicting Young’s modulus of scaffolds. The results of this study could lighten the way of fabricating nanobiocomposites with optimal mechanical properties, spending lower cost and energy. [ABSTRACT FROM AUTHOR]

Building energy consumption prediction models are powerful tools for optimizing energy management. Among various methods, artificial neural networks (ANNs) have become increasingly popular. This paper reviews studies since 2015 on using ANNs to predict building energy use and demand, focusing on the characteristics of different ANN structures and their applications across building phases—design, operation, and retrofitting. It also provides guidance on selecting the most appropriate ANN structures for each phase. Finally, this paper explores future developments in ANN-based predictions, including improving data processing techniques for greater accuracy, refining parameterization to better capture building features, optimizing algorithms for faster computation, and integrating ANNs with other machine learning methods, such as ensemble learning and hybrid models, to enhance predictive performance. [ABSTRACT FROM AUTHOR]

Investigating the transition between the secondary and the tertiary levels is a main theme in mathematics and science education. More so, this paper considers the transition that intersects with the after-effects of COVID-19, or the transition together with an educational context dominated by sociocultural differences and educational disadvantages. With this knowledge in mind, we investigated the effects of predictive mathematical models (multiple regression, logistic regression, and decision trees) to predict at-risk students at three time intervals (weeks one, three, and seven) in the semester. The idea was implemented with a first-year life science class of 130 students. Variables from an academic readiness questionnaire along with early assessment grades were used to build these models. Through a Monte Carlo cross validation method, the performance of the executed predictive models was assessed, and limitations were reported. We argue that the results obtained from predictive models can support both lecturers and students in the transition phase. The idea can be expanded to other courses in STEM fields and other educational contexts. [ABSTRACT FROM AUTHOR]