Search

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]

Background: Adverse events of the fractured vertebra (AEFV) post-percutaneous kyphoplasty (PKP) can lead to recurrent pain and neurological damage, which considerably affect the prognosis of patients and the quality of life. This study aimed to analyze the risk factors of AEFV and develop and select the optimal risk prediction model for AEFV to provide guidance for the prevention of this condition and enhancement of clinical outcomes. Methods: This work included 383 patients with primary osteoporotic vertebral compression fracture (OVCF) who underwent PKP. The patients were grouped based on the occurrence of AEFV postsurgery, and data were collected. Group comparisons and correlation analysis were conducted to identify potential risk factors, which were then included in the five prediction models. The performance indicators served as basis for the selection of the best model. Results: Multivariate logistic regression analysis revealed the following independent risk factors for AEFV: kissing spine (odds ratio (OR) = 8.47, 95% confidence interval (CI) 1.46–49.02), high paravertebral muscle fat infiltration grade (OR = 29.19, 95% CI 4.83–176.04), vertebral body computed tomography value (OR = 0.02, 95% CI 0.003–0.13, P < 0.001), and large Cobb change (OR = 5.31, 95% CI 1.77–15.77). The support vector machine (SVM) model exhibited the best performance in the prediction of the risk of AEFV. Conclusion: Four independent risk factors were identified of AEFV, and five risk prediction models that can aid clinicians in the accurate identification of high-risk patients and prediction of the occurrence of AEFV were developed. [ABSTRACT FROM AUTHOR]

In response to the poor decision-making caused by aerodynamic coupling and model linearization in the trajectory correction model, it is proposed to use a reinforcement learning method to establish a Constrained Markov Decision Process (CMDP) model between action prediction and action constraint. Taking a classical two-dimensional trajectory correction projectile for study, the cost function within constraints of thruster violations is built, so that the sequential prediction is transformed to solve the Constrained Reinforcement Learning (CRL) problem. Accordingly, a method combined with the policy gradient algorithm based on Lagrange operator with the model prediction algorithm is proposed. The test results show that the trained optimal policy model achieves a probability of over 65% and the correction projectile falls within a radius of 5 meters from the preset target point (CEP <5m). Simulation examples have verified the decision-making of correction action sequences autonomously using the trained model, resulting in correction errors of 1.79 1.1 and 0.42m in the x,y and z directions, respectively, demonstrating high correction accuracy and autonomous decision-making capability. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) treatment guidelines recommend waiting for formal progression criteria (FPC) to be met before proceeding to the next line of therapy. As predicting progression may allow early switching to next‐line therapy while the disease burden is relatively low, we evaluated the predictive accuracy of a mathematical model to anticipate relapse 180 days before the FPC is met. A subset of 470/1143 patients from the IA16 dataset who were initially treated with VRd (Velcade (bortezomib), Revlimid (lenalidomide), and dexamethasone) in the CoMMpass study (NCT01454297) were randomly split 2:1 into training and testing sets. A model of M‐protein dynamics was developed using the training set and used to predict relapse probability in patients in the testing set given their response histories up to 12 or more months of treatment. The predictive accuracy of this model and M‐protein “velocity” were assessed via receiver operating characteristics (ROC) analysis. The final model was a two‐population tumor growth inhibition model with additive drug effect and transit delay compartments for cell killing. The ROC area under the curve value of relapse prediction 180 days ahead of observed relapse by FPC was 0.828 using at least 360 days of response data, which was superior to the M‐protein velocity ROC score of 0.706 under the same conditions. The model can predict future relapse from early M‐protein responses and can be used in a future clinical trial to test whether early switching to second‐line therapy results in better outcomes in MM. [ABSTRACT FROM AUTHOR]

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi‐evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long‐term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting. [ABSTRACT FROM AUTHOR]

Acute mountain sickness (AMS) is a well‐studied illness defined by clinical features (e.g., headache and nausea), as assessed by the Lake Louise score (LLS). Although obvious in its severe form, early stages of AMS are poorly defined and easily confused with common travel‐related conditions. Measurement of hypoxaemia, the cause of AMS, should be helpful, yet to date its utility for identifying AMS susceptibility remains unclear. This study quantified altitude‐induced hypoxaemia in individuals during an ascent to 4800 m to determine the utility of nocturnal pulse oximetry measurements for prediction of AMS. Eighteen individuals (36 ± 16 years of age) ascended to 4800 m over 12 days. Symptomology of AMS was assessed each morning via LLS criteria, with participants categorized as either AMS‐positive (LLS ≥ 3 with headache) or AMS‐negative. Overnight peripheral oxygen saturations (ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$) were recorded continuously (1 Hz) using portable oximeters. Derivatives of these recordings were compared between AMS‐positive and ‐negative subjects (Mann–Whitney U‐test). Exploratory analyses (Pearson's) were conducted to investigate relationships between overnight parameters and AMS severity. Overnight derivatives, including ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$, heart rate/ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$, variance, oxygen desaturation index, hypoxic burden and total sleep time at <80% SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$, all differed significantly between AMS‐positive and ‐negative subjects (all P < 0.01), with cumulative/relative frequency plots highlighting these differences visually. Exploratory analysis revealed that ov‐SpO2${{S}_{{\mathrm{p}}{{{\mathrm{O}}}_2}}}$ from 3850 m was correlated with peak LLS at 4800 m (r = 0.58–0.61). The findings highlight the potential for overnight oximetry to predict AMS susceptibility during ascent to high altitude. Further investigation is required to develop, evaluate and optimize predictive models to improve AMS management and prevention. [ABSTRACT FROM AUTHOR]

The Conditional Colour-Magnitude Distribution (CCMD) is a comprehensive formalism of the colour–magnitude–halo mass relation of galaxies. With joint modelling of a large sample of SDSS galaxies in fine bins of galaxy colour and luminosity, Xu et al. inferred parameters of a CCMD model that well reproduces colour- and luminosity-dependent abundance and clustering of present-day galaxies. In this work, we provide a test and investigation of the CCMD model by studying the colour and luminosity distribution of galaxies in galaxy groups. An apples-to-apples comparison of group galaxies is achieved by applying the same galaxy group finder to identify groups from the CCMD galaxy mocks and from the SDSS data, avoiding any systematic effect of group finding and mass assignment on the comparison. We find an overall nice agreement in the conditional luminosity function (CLF), the conditional colour function (CCF), and the CCMD of galaxies in galaxy groups inferred from CCMD mock and SDSS data. We also discuss the subtle differences revealed by the comparison. In addition, using two external catalogues constructed to only include central galaxies with halo mass measured through weak lensing, we find that their colour-magnitude distribution shows two distinct and orthogonal components, in line with the prediction of the CCMD model. Our results suggest that the CCMD model provides a good description of halo mass-dependent galaxy colour and luminosity distribution. The halo and CCMD mock catalogues are made publicly available to facilitate other investigations. [ABSTRACT FROM AUTHOR]

The far-ultraviolet (FUV) flare activity of low-mass stars has become a focus in our understanding of the exoplanet atmospheres and how they evolve. However, direct detection of FUV flares and measurements of their energies and rates are limited by the need for space-based observations. The difficulty of obtaining such observations may push some works to use widely available optical data to calibrate multiwavelength spectral models that describe UV and optical flare emission. These models either use single temperature blackbody curves to describe this emission, or combine a blackbody curve with archival spectra. These calibrated models would then be used to predict the FUV flare rates of low-mass stars of interest. To aid these works, we used TESS optical photometry and archival HST FUV spectroscopy to test the FUV predictions of literature flare models. We tested models for partially (M0–M2) and fully convective (M4–M5) stars, 40 Myr and field age stars, and optically quiet stars. We calculated FUV energy correction factors that can be used to bring the FUV predictions of tested models in line with observations. A flare model combining optical and NUV blackbody emission with FUV emission based on HST observations provided the best estimate of FUV flare activity, where others underestimated the emission at all ages, masses, and activity levels, by up to a factor of 104 for combined FUV continuum and line emission and greater for individual emission lines. We also confirmed previous findings that showed optically quiet low-mass stars exhibit regular FUV flares. [ABSTRACT FROM AUTHOR]

Service-based systems (SBSs) are a unique category of software systems that dynamically combine various third-party services at runtime to deliver complex and adaptive functionality. This dynamic composition introduces a high level of unpredictability and uncertainty, creating potential anomalies and exerting significant pressure on system maintenance. To tackle this challenge, the conventional approach involves employing prediction-based proactive self-adaptation. Despite the prevalence of existing approaches emphasizing prediction accuracy, the critical aspect of "earliness" in predictions is often overlooked. Striking a balance between early and accurate predictions is paramount in practice. In response, we propose Proactive Self-Adaptation based on Ensemble Prediction (PSA-EP) to effectively balance the trade-off between the prediction earliness and accuracy of in SBSs. At the heart of PSA-EP lies an ensemble prediction model built upon a deep neural network and an enhanced long short-term memory (DNN-ELSTM) architecture. PSA-EP is crafted to empower SBSs to adapt to the inherent unpredictability and instability, facilitating the achievement of their adaptation goals. This adaptation mechanism not only enables effective prediction and analysis of adaptation goal violations but also addresses the reliability and performance of service level agreements (SLAs) governing quality of service (QoS). We evaluated the performance of PSA-EP in a decentralized tele-assistance system using four key metrics, and the experimental results, examined from various perspectives, underscore its exceptional performance. [ABSTRACT FROM AUTHOR]

Accurate estimation of natural gas consumption plays a crucial role in energy planning, economic stability, environmental protection, and investment decisions. Despite numerous predictive studies related to natural gas consumption, most of these studies focus on univariate prediction tasks, lacking a reasonable and efficient multivariate prediction approach. To obtain precise forecasting results, this paper constructs a new adaptive grey multivariate prediction model called AGMPM(r,N) based on the fractional-order accumulation operation and grey system theory. It is found that AGMPM(r,N) can degenerate into some grey multivariable prediction models by replacing its own hyperparameters, which reflects its uniformity. In addition, the new model is unbiased for some special time series. In particular, the grey wolf optimizer is used to facilitate the model solution process. To validate the effectiveness of AGMPM(r,N), AGMPM(r,N) and 27 competing algorithms (9 natural gas consumption prediction methods, 10 multivariate prediction models, 7 machine learning algorithms and a statistical predictive model) are used to study the natural gas consumption in China. In addition, ablation experiments are executed. The results of experimental analysis show that the MAPE and MAE of AGMPM(r,N) are 2.795% and 89.880, respectively, which are superior to all competing methods and ablation models. [ABSTRACT FROM AUTHOR]

Objective: Tuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies. Methods: We collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China. Results: The reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran's I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05). Conclusions: As a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different. [ABSTRACT FROM AUTHOR]

Time series, a type of data that measures how things change over time, remains challenging to predict. In order to improve the accuracy of time series prediction, a deep learning model CL-Informer is proposed. In the Informer model, an embedding layer based on continuous wavelet transform is added so that the model can capture the characteristics of multi-scale data, and the LSTM layer is used to capture the data dependency further and process the redundant information in continuous wavelet transform. To demonstrate the reliability of the proposed CL-Informer model, it is compared with mainstream forecasting models such as Informer, Informer+, and Reformer on five datasets. Experimental results demonstrate that the CL-Informer model achieves an average reduction of 30.64% in MSE across various univariate prediction horizons and a reduction of 10.70% in MSE across different multivariate prediction horizons, thereby improving the accuracy of Informer in long sequence prediction and enhancing the model's precision. [ABSTRACT FROM AUTHOR]

Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP's functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling. [ABSTRACT FROM AUTHOR]

Portland cement concrete (PCC) is a major contributor to human-made CO2 emissions. To address this environmental impact, fly ash geopolymer concrete (FAGC) has emerged as a promising low-carbon alternative. This study establishes a robust compressive strength prediction model for FAGC and develops an optimal mixture design method to achieve target compressive strength with minimal CO2 emissions. To develop robust prediction models, comprehensive factors, including fly ash characteristics, mixture proportions, curing parameters, and specimen types, are considered, a large dataset comprising 1136 observations is created, and polynomial regression, genetic programming, and ensemble learning are employed. The ensemble learning model shows superior accuracy and generalization ability with an RMSE value of 1.81 MPa and an R2 value of 0.93 in the experimental validation set. Then, the study integrates the developed strength model with a life cycle assessment-based CO2 emissions model, formulating an optimal FAGC mixture design program. A case study validates the effectiveness of this program, demonstrating a 16.7% reduction in CO2 emissions for FAGC with a compressive strength of 50 MPa compared to traditional trial-and-error design. Moreover, compared to PCC, the developed FAGC achieves a substantial 60.3% reduction in CO2 emissions. This work provides engineers with tools for compressive strength prediction and low carbon optimization of FAGC, enabling rapid and highly accurate design of concrete with lower CO2 emissions and greater sustainability. [ABSTRACT FROM AUTHOR]

Background: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy. Methods: We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC). Results: The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively. Conclusions: Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients. [ABSTRACT FROM AUTHOR]

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

This study investigated whether machine learning (ML) has better predictive accuracy than logistic regression analysis (LR) for gait independence at discharge in subacute stroke patients (n = 843) who could not walk independently at admission. We developed prediction models using LR and five ML algorithms—specifically, the decision tree (DT), support vector machine, artificial neural network, ensemble learning, and k-nearest neighbor methods. Functional Independence Measure sub-items were used to evaluate the ability to walk independently. Model predictive accuracies were evaluated using areas under receiver operating characteristic curves (AUCs) as well as accuracy, precision, recall, F1 score, and specificity. The AUC for DT (0.812) was significantly lower than those for the other algorithms (p < 0.01); however, the AUC for LR (0.895) did not differ significantly from those for the other models (0.893–0.903). Other performance metrics showed no substantial differences between LR and ML algorithms. In conclusion, the DT algorithm had significantly low predictive accuracy, and LR showed no significant difference in predictive accuracy compared with the other ML algorithms. As its predictive accuracy is similar to that of ML, LR can continue to be used for predicting the prognosis of gait independence, with additional advantages of being easily understandable and manually computable. [ABSTRACT FROM AUTHOR]

The global prevalence of diabetes is escalating, with estimates indicating that over 536.6 million individuals were afflicted by 2021, accounting for approximately 10.5% of the world's population. Effective management of diabetes, particularly monitoring and prediction of blood glucose levels, remains a significant challenge due to the severe health risks associated with inaccuracies, such as hypoglycemia and hyperglycemia. This study addresses this critical issue by employing a hybrid Transformer-LSTM (Long Short-Term Memory) model designed to enhance the accuracy of future glucose level predictions based on data from Continuous Glucose Monitoring (CGM) systems. This innovative approach aims to reduce the risk of diabetic complications and improve patient outcomes. We utilized a dataset which contain more than 32000 data points comprising CGM data from eight patients collected by Suzhou Municipal Hospital in Jiangsu Province, China. This dataset includes historical glucose readings and equipment calibration values, making it highly suitable for developing predictive models due to its richness and real-time applicability. Our findings demonstrate that the hybrid Transformer-LSTM model significantly outperforms the standard LSTM model, achieving Mean Square Error (MSE) values of 1.18, 1.70, and 2.00 at forecasting intervals of 15, 30, and 45 minutes, respectively. This research underscores the potential of advanced machine learning techniques in the proactive management of diabetes, a critical step toward mitigating its impact. [ABSTRACT FROM AUTHOR]

Background: Mutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types. Results: Here, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks. Conclusions: PNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a hematological disease exhibiting aberrant clonal expansion of cancerous plasma cells in the bone marrow. The effects of treatments for MM vary between patients, highlighting the importance of developing prognostic models for informed therapeutic decision-making. Most previous models were constructed at the gene level, ignoring the fact that the dysfunction of the pathway is closely associated with disease development and progression. The present study considered two strategies that construct predictive models by taking pathway information into consideration: pathway score method and group lasso using pathway information. The former simply converted gene expression to sample-wise pathway scores for model fitting. We considered three methods for pathway score calculation (ssGSEA, GSVA, and z-scores) and 14 data sources providing pathway information. We implemented these methods in microarray data for MM (GSE136324) and obtained a candidate model with the best prediction performance in interval validation. The candidate model is further compared with the gene-based model and previously published models in two external data. We also investigated the effects of missing values on prediction. The results showed that group lasso incorporating Vax pathway information (Vax(grp)) was more competitive in prediction than the gene model in both internal and external validation. Immune information, including VAX pathways, seemed to be more predictive for MM. Vax(grp) also outperformed the previously published models. Moreover, the new model was more resistant to missing values, and the presence of missing values (<5%) would not evidently deteriorate its prediction accuracy using our missing data imputation method. In a nutshell, pathway-based models (using group lasso) were competitive alternatives to gene-based models for MM. These models were documented in an R package (https://github.com/ShuoStat/MMMs), where a missing data imputation method was also integrated to facilitate future validation. Author summary: Traditionally, prognostic models were mainly constructed at the gene level, ignoring the role of pathway functions in disease development and progression. Enlightened by this, we advocated guiding the model building with well-established prior knowledge (pathway information). We investigated several approaches that could incorporate pathway information in model building. The results showed that pathway-based models exhibit superior predictive capabilities compared to their gene-based counterparts. Furthermore, these pathway-based models were more robust to missing values. The proposed models also outperformed previously published gene-based models. Beyond their prediction performance, the pathway-based models can directly reveal the association between the pathway functions and survival outcomes, demonstrating their advantage in the model interpretability. This enhanced interpretability not only deepens our understanding of disease mechanisms but also facilitates informed decision-making in clinical and research settings. We urge for more attention to be given to developing modeling methods that incorporate prior knowledge. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has a tremendous deal of potential for accurate brain age prediction using neuroimaging data, but its performance is frequently limited by the size of the training dataset and the computer’s memory needs. Under this circumstance, a unique brain age prediction model with the following five phases of development. The input image is first pre-processed via median filtering, which keeps the edges of the raw image while removing the noise. The pre-processed images are segmented using the improved Balanced Iterative Reducing and Clustering employing Hierarchies (BIRCH) algorithm. Then, features including statistics (Mean, Median, Standard Deviation), Improved Median Robust Extended LBP (MRELBP), and sharpness score are extracted from these segmented images. The suggested coot interfaced Archimedes with Gaussian Map Estimation (CIAGME) optimization approach, which combines the Archimedes and coot optimization techniques, will select the optimal features from those extracted features. The deep ensemble approach, which includes deep classifiers like CNN, Bi-LSTM, and Deep maxout, will provide a prediction based on the optimal features that have been chosen. Lastly, the recommended CIAGME-based predictions model’s outperformance is evaluated, and the result is effectively evaluated. [ABSTRACT FROM AUTHOR]

Existing concrete creep coefficient prediction models have the limitation of not considering the structural characteristics of CFT. For this reason, these models tend to overestimate the creep deformation of CFT. Therefore, in order to overcome the limitations of existing CFT creep experiments, this study proposes a creep-experiment method involving the use of CFT that passively changes the load applied to a single concrete specimen by calculating the stress redistribution between the concrete and a steel tube in CFT based on a step-by-step method. Furthermore, by actually applying the proposed experimental method, a creep experiment of CFT lasting for approximately 163 days was performed and a superficial creep coefficient model of CFT was proposed based on long-term strain data from the experiment. In order to verify the proposed superficial creep coefficient model, it was compared with two design criteria (CEB-FIP and ACI) based on the experimental results of this study and references. As a result, compared to the existing design criteria, the value predicted by the proposed superficial creep coefficient model showed good agreement with the experimental results of this study and the references, proving that the proposed creep-experiment method of CFT and superficial creep coefficient model are reasonable. [ABSTRACT FROM AUTHOR]

Background: Chidamide is subtype-selective histone deacetylase (HDAC) inhibitor that showed promising result in clinical trials to improve prognosis of angioimmunoblastic T-cell lymphoma (AITL) patients. However, in real world settings, contradictory reports existed as to whether chidamide improve overall survival (OS). Therefore, we aimed to develop an interpretable machine learning (Machine learning)-based model to predict the 2-year overall survival of AITL patients based on chidamide usage and baseline features. Methods: A total of 183 patients with AITL were randomly divided into training set and testing set. We used 5 ML algorithms to build predictive models. Recursive feature elimination (RFE) method was used to filter for the most important features. The ML models were interpreted and the relevance of the selected features was determined using the Shapley additive explanations (SHAP) method and the local interpretable model-agnostic explanationalgorithm. Results: A total of 183 patients with newly diagnosed AITL from 2012 to 2022 from 3 centers in China were enrolled in our study. Seventy-one patients were dead within 2 years after diagnosis. Five ML algorithms were built based on chidamide usage and 16 baseline features to predict 2-year OS. Catboost model presented to be the best predictive model. After RFE screening, 12 variables demonstrated the best performance (AUC = 0.8651). Using chidamide ranked third among all the variables that correlated with 2-year OS. Conclusion: This study demonstrated that the Catboost model with 12 variables could effectively predict the 2-year OS of AITL patients. Combining chidamide in the treatment therapy was positively correlated with longer OS of AITL patients. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a deep learning model to predict skeletal malocclusions with an acceptable level of accuracy using airway and cephalometric landmark values obtained from analyzing different CBCT images. Background: In orthodontics, multitudinous studies have reported the correlation between orthodontic treatment and changes in the anatomy as well as the functioning of the airway. Typically, the values obtained from various measurements of cephalometric landmarks are used to determine skeletal class based on the interpretation an orthodontist experiences, which sometimes may not be accurate. Methods: Samples of skeletal anatomical data were retrospectively obtained and recorded in Digital Imaging and Communications in Medicine (DICOM) file format. The DICOM files were used to reconstruct 3D models using 3DSlicer (slicer.org) by thresholding airway regions to build up 3D polygon models of airway regions for each sample. The 3D models were measured for different landmarks that included measurements across the nasopharynx, the oropharynx, and the hypopharynx. Male and female subjects were combined as one data set to develop supervised learning models. These measurements were utilized to build 7 artificial intelligence-based supervised learning models. Results: The supervised learning model with the best accuracy was Random Forest, with a value of 0.74. All the other models were lower in terms of their accuracy. The recall scores for Class I, II, and III malocclusions were 0.71, 0.69, and 0.77, respectively, which represented the total number of actual positive cases predicted correctly, making the sensitivity of the model high. Conclusion: In this study, it is observed that the Random Forest model was the most accurate model for predicting the skeletal malocclusion based on various airway and cephalometric landmarks. [ABSTRACT FROM AUTHOR]

Prediction of water-conducting fractured zone (WCFZ) of mine overburden is the premise for reducing or eliminating water inrush hazards in undersea mining. To obtain a more robust and precise prediction of WCFZ in undersea mining, a WCFZ prediction dataset with 122 cases of fractured zones was constructed. Five machine learning algorithms (linear regression, XGBRegressor, RandomForestRegressor, LineareSVR, and KNeighborsRegressor) were employed to develop five corresponding predictive models, taking multiple factors into account.The optimal parameters for each model are obtained through ten-fold cross-validation (10CV). The model's predictive performance was validated and assessed using two metrics, namely the coefficient of determination (R2) and mean squared error (MSE). A comparison was made with the regression performance of commonly used empirical formulas. The results indicate that the constructed model outperforms reliance solely on theoretical criteria, showing a high R2 value of up to 0.925 and a low MSE value of 3.61. The proposed model was validated in a recently established mining area on Sanshan Island, China. It shows low absolute and relative errors of 0.71 m and 2.01%, respectively, between the predicted value from the model and observation result from the field, demonstrating a high level of consistency with on-site conditions. This paves a path to leveraging machine learning algorithms for predicting the height of WCFZ. [ABSTRACT FROM AUTHOR]

A dislocation-density based crystalline plasticity (DCP) and nonlinear finite element (FE) analysis were used to predict, and fundamentally understand how and why fracture nucleation and propagation are related to the interrelated microstructural mechanisms of dislocation-density pileups, GB structure, orientation, and total and partial dislocation density interactions within and adjacent for a random low angle grain boundary (LAGB) and a random high angle GB (HAGB). The GB orientations and structures were obtained from micropillar experiments, such that LAGBs and the HAGBs can be accurately represented and used for the modeling predictions. The normal stress, density of pileups, and dislocation-density accumulation along and within the GB were higher for the low angle GB bicrystal. These interrelated phenomena delineate how fracture for high angle GBs nucleate and propagate at lower nominal strains than the lower angle GB bicrystal case. These predictions underscore how fundamental mechanisms can be identified and used to understand how failure nucleates and propagates for different GB structures and orientations. [ABSTRACT FROM AUTHOR]

Intradialytic hypotension (IDH) is common in hemodialysis patients and can lead to several complications. Risk factors for IDH include demographic characteristics, comorbidities, dialysis procedure factors, and so on. Clinical studies on predictive models for dialysis-induced hypotension have shown inconsistent results. This systematic review aims to evaluate published prediction models for IDH, analyzing their characteristics, predictors, efficacy, and the methodological quality and applicability. The protocol has been prepared using the Preferred Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. The systematic review protocol for IDH prediction in hemodialysis patients has been registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY2023110081, DOI: 10.37766/inplasy2023.11.0081). A comprehensive search across five major databases (PubMed, Web of Science, Cochrane Library, CNKI, and Wanfang) will be conducted for studies on prediction models of IDH among hemodialysis patients. Two researchers will independently screen literature, extract data, and evaluate the bias risk and applicability of included studies using prediction modelling study tools. This systematic review will provide critical insights into the efficacy and quality of reporting of the IDH model in hemodialysis patients. This will guide clinical staff in selecting the most appropriate IDH prediction model and inform future research endeavors in IDH prediction. [ABSTRACT FROM AUTHOR]

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 acquisition. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory 80% concentration < 1 μg/mL). For continuous outcomes, the CP approach performs nearly as well as the PC approach when the individual-bnAb prediction algorithms have strong performance, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials. [ABSTRACT FROM AUTHOR]

The research aims to lift the accuracy of table tennis trajectory prediction through advanced computer vision and deep learning techniques to achieve real-time and accurate table tennis ball position and motion trajectory tracking. The study concentrates on the innovative application of a micro-miniature fourth-generation real-time target detection algorithm with a gated loop unit to table tennis ball motion analysis by combining physical models and deep learning methods. The results show that in the comparison experiments, the improved micro-miniature fourth-generation real-time target detection algorithm outperforms the traditional target detection algorithm, with the loss value decreasing to 1.54. Its average accuracy in multi-target recognition is dramatically increased to 86.74%, which is 22.36% higher than the original model, and the ping-pong ball recognition experiments show that it has an excellent accuracy in various lighting conditions, especially in low light, with an average accuracy of 89.12%. Meanwhile, the improved model achieves a processing efficiency of 85 frames/s. In addition, compared with the traditional trajectory prediction model, the constructed model performs the best in table tennis ball trajectory prediction, with errors of 4.5 mm, 25.3 mm, and 35.58 mm. The results show that the research trajectory prediction model achieves significant results in accurately tracking table tennis ball positions and trajectories. It not only has practical application value for table tennis training and competition strategies, but also provides a useful reference for the similar techniques application in other sports. [ABSTRACT FROM AUTHOR]

Microbes have been demonstrated to be closely linked to diseases that pose a major threat to human health. Computing technologies can help researchers find potential microbe-drug associations more quickly and precisely. In this study, we introduced a novel computational prediction model called GLNNMDA based on global and local features of microbes and drugs to infer possible microbe-drug correlations. In GLNNMDA, we first constructed a heterogeneous network based on known microbe-drug relationships by integrating multiple similarity metrics of drugs and microbes. Subsequently, low-dimensional features will be extracted for nodes in the heterogeneous network by adopting the graph attention encoder. Next, based on combining these low-dimensional features with multiple properties of microbes and drugs to form a new comprehensive feature matrix, we would utilize the GLF module to extract the global and local features for microbes and drugs respectively, and then, we would further fuse these global and local features to come up with predictions of possible microbe-drug associations. Moreover, in order to evaluate the prediction performance of GLNNMDA, under the framework of fivefold cross-validation, intensive comparative experiments and case studies were done on different well-known public databases. The results showed that GLNNMDA obtained the highest AUC values as well as AUPR values of 0.9802 ± 0.0011, 0.9773 ± 0.0021 and 0.8586 ± 0.0004, 0.8008 ± 0.0031 in the two databases, MDAD and aBiofilm, respectively, compared to the state-of-the-art competing prediction methods. In addition, case studies of well-known microorganisms and drugs have demonstrated the effectiveness of GLNNMDA in inferring potential microbial drug associations, which implies that GLNNMDA may be a useful tool for microbe-drug association prediction in the future. The source code is available at: "https://github.com/KuangHaiYue/GLNNMDA.git". [ABSTRACT FROM AUTHOR]

In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients. [ABSTRACT FROM AUTHOR]

Background: Haemoglobin variation (ΔHb) induced by fluid transfer through the intestitium has been proposed as a useful tool for detecting hydrostatic pulmonary oedema (HPO). However, its use in the emergency department (ED) setting still needs to be determined. Methods: In this observational retrospective monocentric study, ED patients admitted for acute dyspnoea were enrolled. Hb values were recorded both at ED presentation (T0) and after 4 to 8 h (T1). ΔHb between T1 and T0 (ΔHbT1-T0) was calculated as absolute and relative value. Two investigators, unaware of Hb values, defined the cause of dyspnoea as HPO and non-HPO. ΔHbT1-T0 ability to detect HPO was evaluated. A machine learning approach was used to develop a predictive tool for HPO, by considering the ability of ΔHb as covariate, together with baseline patient characteristics. Results: Seven-hundred-and-six dyspnoeic patients (203 HPO and 503 non-HPO) were enrolled over 19 months. Hb levels were significantly different between HPO and non-HPO patients both at T0 and T1 (p < 0.001). ΔHbT1-T0 were more pronounced in HPO than non-HPO patients, both as relative (-8.2 [-11.2 to -5.6] vs. 0.6 [-2.1 to 3.3] %) and absolute (-1.0 [-1.4 to -0.8] vs. 0.1 [-0.3 to 0.4] g/dL) values (p < 0.001). A relative ΔHbT1-T0 of -5% detected HPO with an area under the receiver operating characteristic curve (AUROC) of 0.901 [0.896–0.906]. Among the considered models, Gradient Boosting Machine showed excellent predictive ability in identifying HPO patients and was used to create a web-based application. ΔHbT1-T0 was confirmed as the most important covariate for HPO prediction. Conclusions: ΔHbT1-T0 in patients admitted for acute dyspnoea reliably identifies HPO in the ED setting. The machine learning predictive tool may represent a performing and clinically handy tool for confirming HPO. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer is well known as the commonest cancer in men and the second leading cause of cancer-related death. CT, MRI and bone scintigraphy are considered the commonly widely used imaging diagnostic tools for detection, staging and follow-up of prostate cancer. Prostate-specific membrane antigen (PSMA) is a membrane glycoprotein, that can be concentrated in prostate cancer cells up to 100 times higher than in normal cells. PSMA-targeted imaging modalities have now proven their efficacy in diagnosis, staging and follow-up of prostate cancer. The use of 68Ga PSMA PET-CT has efficiently improved the detection of loco-regional and metastatic disease. 68Ga PSMA PET-CT also has an effective role in the primary diagnosis, staging, and detecting biochemical recurrence after curative treatment and in metastasis-targeted therapy. This work aims to review the role of 68Ga PSMA PET-CT in anatomical staging of prostate cancer in correlation with histopathological staging. Results: Zonal correlation between 68Ga PSMA findings and biopsy results showed sensitivity ranging between 76.9 and 90.6% and specificity ranging from 85.7 to 100%. There was high significant correlation between the SUVmax uptake and the biopsy results, between the SUVmax uptake and the local staging as well as between the Gleason score and 68Ga PSMA PET/CT findings. Conclusions: 68Ga PSMA PET/CT is a highly promising imaging modality with an effective role in detection of prostate cancer showing high sensitivity and specificity in prediction of zonal histopathological results and loco-regional Gleason score staging with significant positive correlation between the SUV uptake results, Gleason score and the PSA levels. [ABSTRACT FROM AUTHOR]

Background: Labor pain is the nastiest conceivable pain women are involved in during labor and delivery. In this way, the end of labor pain is frequently outlined by myths and equivocalness. Hence, giving a compelling absence of pain in labor remained a challenge specifically in developing countries including Ethiopia. Objectives: This systematic review and meta-analysis aimed to explore the pooled prevalence of labor analgesia and associated factors to pregnant women in Ethiopia. Design: A systematic review and meta-analysis were utilized in agreement with the Preferred Reporting Items for Systematic Reviews. Data Sources: PubMed/Medline, SCOPUS, EMBASE, Web of Science, Google Scholars, and the Cochrane Library and supplemented it with manual were deliberately looked at until January 1–30, 2024. Methods: Two authors independently extricated all principal information utilizing standardized data extraction designs, and the analysis was done utilizing STATA version 17. Heterogeneity over the studies was evaluated utilizing I 2 measurement. The funnel plot and Egger's weighted regression tests were utilized to assess subjective and objective publication biases respectively. Also, the pooled effect of labor pain management and the associations were evaluated utilizing a random-effects model. Results: The general pooled prevalence of labor analgesia in the present study was 23.3% with a (95% confidence interval (CI): 13.5, 33.1). Maternal age (odds ratio (OR): 1.91; 95% CI: 1.11, 2.77), parity of the mother (OR: 0.28; 95% CI: 0.06, 0.63), history of pregnancy misfortune (OR: 0.12; 95% CI: 0.11, 0.36), length of labor (OR: 2.09; 95% CI: 1.06, 3.13), and awareness about labor analgesia (OR: 1.91; 95% CI: 0.34, 3.49) were significantly related with labor analgesia among pregnant women in Ethiopia. Conclusions: The generally pooled prevalence of labor analgesia among pregnant women in Ethiopia was low. Maternal age, parity of the mother, history of pregnancy loss, length of labor, and awareness of labor analgesia were factors influencing labor analgesia among pregnant women in Ethiopia. This finding proposes exceptional consideration to make laboring mothers free of pain by scaling up the strategies and utilizing labor pain administration in a way that universally recognized standards are met. Registration number: PROSPERO CRD: 42024525636 [ABSTRACT FROM AUTHOR]

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting highquality and high-yield crops. [ABSTRACT FROM AUTHOR]

Transferring and replicating predictive algorithms across healthcare systems constitutes a unique yet crucial challenge that needs to be addressed to enable the widespread adoption of machine learning in healthcare. In this study, we explored the impact of important differences across healthcare systems and the associated Electronic Health Records (EHRs) on machine-learning algorithms to predict mental health crises, up to 28 days in advance. We evaluated both the transferability and replicability of such machine learning models, and for this purpose, we trained six models using features and methods developed on EHR data from the Birmingham and Solihull Mental Health NHS Foundation Trust in the UK. These machine learning models were then used to predict the mental health crises of 2907 patients seen at the Rush University System for Health in the US between 2018 and 2020. The best one was trained on a combination of US-specific structured features and frequency features from anonymized patient notes and achieved an AUROC of 0.837. A model with comparable performance, originally trained using UK structured data, was transferred and then tuned using US data, achieving an AUROC of 0.826. Our findings establish the feasibility of transferring and replicating machine learning models to predict mental health crises across diverse hospital systems. [ABSTRACT FROM AUTHOR]

Background: Lacunes, a characteristic feature of cerebral small vessel disease (CSVD), are critical public health concerns, especially in the aging population. Traditional neuroimaging techniques often fall short in early lacune detection, prompting the need for more precise predictive models. Methods: In this retrospective study, 587 patients from the Neurology Department of the Affiliated Hospital of Hebei University who underwent cranial MRI were assessed. A nomogram for predicting lacune incidence was developed using LASSO regression and binary logistic regression analysis for variable selection. The nomogram's performance was quantitatively assessed using AUC-ROC, calibration plots, and decision curve analysis (DCA) in both training (n = 412) and testing (n = 175) cohorts. Results: Independent predictors identified included age, gender, history of stroke, carotid atherosclerosis, hypertension, creatinine, and homocysteine levels. The nomogram showed an AUC-ROC of 0.814 (95% CI: 0.791-0.870) for the training set and 0.805 (95% CI: 0.782-0.843) for the testing set. Calibration and DCA corroborated the model's clinical value. Conclusion: This study introduces a clinically useful nomogram, derived from binary logistic regression, that significantly enhances the prediction of lacunes in patients undergoing brain MRI for various indications, potentially advancing early diagnosis and intervention. While promising, its retrospective design and single-center context are limitations that warrant further research, including multi-center validation. [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]

Objectives: Temporomandibular disorders (TMDs) have a relatively high prevalence among university students. This study aimed to identify independent risk factors for TMD in university students and develop an effective risk prediction model. Methods: This study included 1,122 university students from four universities in Changchun City, Jilin Province, as subjects. Predictive factors were screened by using the least absolute shrinkage and selection operator (LASSO) regression and the machine learning Boruta algorithm in the training cohort. A multifactorial logistic regression analysis was used to construct a TMD risk prediction model. Internal validation of the model was conducted via bootstrap resampling, and an external validation cohort comprised 205 university students undergoing oral examinations at the Stomatological Hospital of Jilin University. Results: The prevalence of TMD among university students was 44.30%. Ten predictive factors were included in the model, comprising gender, facial cold stimulation, unilateral chewing, biting hard or resilient foods, clenching teeth, grinding teeth, excessive mouth opening, malocclusion, stress, and anxiety. The model demonstrated good predictive ability with area under the receiver operating characteristic curve (AUC) values of 0.853, 0.838, and 0.821 in the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curves demonstrated that the predicted results were consistent with the actual results, and the decision curve analysis (DCA) indicated the model's high clinical utility. Conclusions: An online nomogram of TMD in university students with good predictive performance was constructed, which can effectively predict the risk of TMD in university students. The model provides a useful tool for the early identification and treatment of TMDs in university students, helping clinicians to predict the probability of TMDs in each patient, thus providing more personalized and accurate treatment decisions for patients. [ABSTRACT FROM AUTHOR]

While genome-wide association studies are valuable in identifying CRC survival predictors, the benefit of adding blood DNA methylation (blood-DNAm) to clinical features, including the TNM system, remains unclear. In a multi-site population-based patient cohort study of 2116 CRC patients with baseline blood-DNAm, we analyzed survival predictions using eXtreme Gradient Boosting with a 5-fold nested leave-sites-out cross-validation across four groups: traditional and comprehensive clinical features, blood-DNAm, and their combination. Model performance was assessed using time-dependent ROC curves and calibrations. During a median follow-up of 10.3 years, 1166 patients died. Although blood-DNAm-based predictive signatures achieved moderate performances, predictive signatures based on clinical features outperformed blood-DNAm signatures. The inclusion of blood-DNAm did not improve survival prediction over clinical features. M1 stage, age at blood collection, and N2 stage were the top contributors. Despite some prognostic value, incorporating blood DNA methylation did not enhance survival prediction of CRC patients beyond clinical features. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data's utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection. [ABSTRACT FROM AUTHOR]

CRISPR is a precise and effective genome editing technology; but despite several advancements during the last decade, our ability to computationally design gRNAs remains limited. Most predictive models have relatively low predictive power and utilize only the sequence of the target site as input. Here we suggest a new category of features, which incorporate the target site genomic position and the presence of genes close to it. We calculate four features based on gene expression and codon usage bias indices. We show, on CRISPR datasets taken from 3 different cell types, that such features perform comparably with 425 state-of-the-art predictive features, ranking in the top 2–12% of features. We trained new predictive models, showing that adding expression features to them significantly improves their r2 by up to 0.04 (relative increase of 39%), achieving average correlations of up to 0.38 on their validation sets; and that these features are deemed important by different feature importance metrics. We believe that incorporating the target site's position, in addition to its sequence, in features such as we have generated here will improve our ability to predict, design and understand CRISPR experiments going forward. [ABSTRACT FROM AUTHOR]

Background: While home is frequently expressed as the favorite place of death (PoD) among terminally ill cancer patients, various factors affect the fulfillment of this wish. The determinants of the PoD of cancer patients in countries without healthcare system-integrated palliative and supportive care have not been studied before. This study aimed at identifying the predictors of the PoD of patients who suffer from advanced cancer by developing a reliable predictive model among who received home-based palliative care in Iran as a representative of the countries with isolated provision of palliative care services. Methods: In a cross-sectional study, electronic records of 4083 advanced cancer patients enrolled in the Iranian Cancer Control Center (MACSA) palliative homecare program, who died between February 2018 and February 2020 were retrieved. Multivariable binary logistic regression analysis as well as subgroup analyses (location, sex, marital status, and tumor topography) was performed to identify the predictors of PoD. Results: Of the 2398 cases included (mean age (SD) = 64.17 (14.45) year, 1269 (%52.9) male), 1216 (50.7%) patients died at home. Older age, presence and intensity of medical homecare in the last two weeks and registration in the Tehran site of the program were associated with dying at home (P < 0.05). Gynecological or hematological cancers, presence and intensity of the calls received from the remote palliative care unit in the last two weeks were predictors of death at the hospital (p < 0.05). The model was internally and externally validated (AUC = 0.723 (95% CI = 0.702–0.745; P < 0.001) and AUC = 0.697 (95% CI = 0.631–0.763; P < 0.001) respectively). Conclusion: Our model highlights the demographic, illness-related and environmental determinants of the PoD in communities with patchy provision of palliative care. It also urges policymakers and service providers to identify and take the local determinant of the place of death into account to match the goals of palliative and supportive services with the patient preferences. [ABSTRACT FROM AUTHOR]

Background: Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods: In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell's C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results: Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions: Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure. [ABSTRACT FROM AUTHOR]

Background: Severe trauma is the leading cause of disability and mortality in the patients under 35 years of age. Surgical site infections (SSI) represent a significant complication in this patient population. However, they are often inadequately investigated, potentially impacting the quality of patient outcomes. The aim of this study was to investigate the epidemiology of SSI and risk factors in severe trauma patients. Methods: We conducted a multicenter retrospective cohort study screening the severe trauma patients (STP) admitted to two intensive care units of an academic institution in Marseille between years2018 and 2019. Those who underwent orthopedic or spinal surgery within 5 days after admission were included and classified into two groups according to the occurrence of SSI (defined by the Centers for Disease Control (CDC) international diagnostic criteria) or not. Our secondary goal was to evaluate STP survival at 48 months, risk factors for SSI and microbiological features of SSI. Results: Forty-seven (23%) out of 207 STP developed an SSI. Mortality at 48-months did not differ between SSI and non-SSI patients (12.7% vs. 10.0%; p = 0.59). The fractures of 22 (47%) severe trauma patients with SSI were classified as Cauchoix 3 grade and 18 (38%) SSI were associated with the need for external fixators. Thirty (64%) severe trauma patients with SSI had polymicrobial infection, including 34 (72%) due to Gram-positive cocci. Empirical antibiotic therapy was effective in 31 (66%) cases. Multivariate analysis revealed that risk factors such as low hemoglobin, arterial oxygenation levels, hyperlactatemia, high serum creatinine and glycemia, and Cauchoix 3 grade on the day of surgery were associated with SSI in severe trauma patients. The generated predictive model showed a good prognosis performance with an AUC of 0.80 [0.73–0.88] and a high NPV of 95.9 [88.6–98.5] %. Conclusions: Our study found a high rate of SSI in severe trauma patients, although SSI was not associated with 48-month mortality. Several modifiable risk factors for SSI may be effectively managed through enhanced perioperative monitoring and the implementation of a patient blood management strategy. [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) is extensively employed for forecasting the outcome of various illnesses. The objective of the study was to develop ML based classifiers using a stacking ensemble strategy to predict the Japanese Orthopedic Association (JOA) recovery rate for patients with degenerative cervical myelopathy (DCM). Methods: A total of 672 patients with DCM were included in the study and labeled with JOA recovery rate by 1-year follow-up. All data were collected during 2012–2023 and were randomly divided into training and testing (8:2) sub-datasets. A total of 91 initial ML classifiers were developed, and the top 3 initial classifiers with the best performance were further stacked into an ensemble classifier with a supported vector machine (SVM) classifier. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicted outcome was the JOA recovery rate. Results: By applying an ensemble learning strategy (e.g., stacking), the accuracy of the ML classifier improved following combining three widely used ML models (e.g., RFE-SVM, EmbeddingLR-LR, and RFE-AdaBoost). Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top 3 initial classifiers varied a lot in predicting JOA recovery rate in DCM patients. Conclusions: The ensemble classifiers successfully predict the JOA recovery rate in DCM patients, which showed a high potential for assisting physicians in managing DCM patients and making full use of medical resources. [ABSTRACT FROM AUTHOR]

This study focuses on predicting the prognosis of acute ischemic stroke patients with focal neurologic symptoms using a combination of diffusion-weighted magnetic resonance imaging (DWI) and clinical information. The primary outcome is a poor functional outcome defined by a modified Rankin Scale (mRS) score of 3–6 after 3 months of stroke. Employing nnUnet for DWI lesion segmentation, the study utilizes both multi-task and multi-modality methodologies, integrating DWI and clinical data for prognosis prediction. Integrating the two modalities was shown to improve performance by 0.04 compared to using DWI only. The model achieves notable performance metrics, with a dice score of 0.7375 for lesion segmentation and an area under the curve of 0.8080 for mRS prediction. These results surpass existing scoring systems, showing a 0.16 improvement over the Totaled Health Risks in Vascular Events score. The study further employs grad-class activation maps to identify critical brain regions influencing mRS scores. Analysis of the feature map reveals the efficacy of the multi-tasking nnUnet in predicting poor outcomes, providing insights into the interplay between DWI and clinical data. In conclusion, the integrated approach demonstrates significant advancements in prognosis prediction for cerebral infarction patients, offering a superior alternative to current scoring systems. [ABSTRACT FROM AUTHOR]

Shear strength (SS) parameters are essential for understanding the mechanical behavior of materials, particularly in geotechnical engineering and rock mechanics. This study proposes a novel hierarchical ensemble model (HEM) to predict SS parameters: cohesion (C ) and angle of internal friction (φ ). The HEM addresses the limitations of traditional machine learning models. Its performance was validated using leave-one-out cross-validation (LOOCV) and out-of-bag (OOB) evaluation methods. The model's accuracy was assessed with R-squared correlation (R2), absolute average relative error percentage (AAREP), Taylor diagrams, and quantile–quantile plots. The computational results demonstrated that the proposed HEM outperforms previous studies using the same database. The model predicted φ and C with R2 values of 0.93 and 0.979, respectively. The AAREP values were 1.96% for φ and 4.7% for C . These results indicate that the HEM significantly improves the prediction quality of φ and C , and exhibits strong generalization capability. Sensitivity analysis revealed that σ_3maxσ3max (maximum principal stress) had the greatest impact on modeling both φ and C . According to uncertainty analysis, the LOOCV and OOB had the widest uncertainty bands for the φ and C parameters, respectively. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to compare hematological parameters preand early post-chemotherapy, and evaluate their values for predicting febrile neutropenia (FN). Methods: Patients diagnosed with malignant solid tumors receiving chemotherapy were included. Blood cell counts peri-chemotherapy and clinical information were retrieved from the hospital information system. We used the least absolute shrinkage and selection operator (LASSO) method for variable selection and fitted selected variables to a logistic model. We assessed the performance of the prediction model by the area under the ROC curve. Results: The study population consisted of 4,130 patients with common solid tumors receiving a three-week chemotherapy regimen in Sichuan Cancer Hospital from February 2019 to March 2022. In the FN group, change percentage of neutrophil count decreased less (-0.02, CI: -0.88 to 3.48 vs. -0.04, CI: -0.83 to 2.24). Among hematological parameters, lower postchemotherapy lymphocyte count (OR 0.942, CI: 0.934-0.949), change percentage of platelet (OR 0.965, CI: 0.955-0.975) and higher change percentage of post-chemotherapy neutrophil count (OR 1.015, CI: 1.011- 1.018), and pre-chemotherapy NLR (OR 1.002, CI: 1.002-1.002) predicted an increased risk of FN. These factors improved the predicting model based on clinical factors alone. The AUC of the combination model was 0.8275. Conclusion: Peri-chemotherapy hematological markers improve the prediction of FN. [ABSTRACT FROM AUTHOR]

Objectives: This study modelled diabetes risk for population groups in Canada defined by socioeconomic and lifestyle characteristics and investigated inequities in diabetes risk using a validated population risk prediction algorithm. Methods: We defined population groups, informed by determinants of health frameworks. We applied the Diabetes Population Risk Tool (DPoRT) to 2017/2018 Canadian Community Health Survey data to predict 10-year diabetes risk and cases across population groups. We modelled a preventive intervention scenario to estimate reductions in diabetes for population groups and impacts on the inequity in diabetes risk across income and education. Results: The population group with at least one lifestyle and at least one socioeconomic/ structural risk factor had the highest estimated 10-year diabetes risk and number of new cases. When an intervention with a 5% relative risk reduction was modelled for this population group, diabetes risk decreased by 0.5% (females) and 0.7% (males) and the inequity in diabetes risk across income and education levels was reduced. Conclusion: Preventative interventions that address socioeconomic and structural risk factors have potential to reduce inequities in diabetes risk and overall diabetes burden. [ABSTRACT FROM AUTHOR]