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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]

Due to the uncertainty of geological conditions during the tunneling process, advanced prediction of TBM tunneling parameters is significant for evaluating operational safety and efficiency, especially for real-time prediction of key tunneling parameters during the steady phase of TBM operation. At present, although there are studies on constructing predictive models based on machine learning algorithms, multiparameter prediction consistent with the actual tunneling process remains challenging due to the complexity of the TBM tunneling process and the numerous tunneling parameters. Therefore, this paper proposes a real-time multiple tunneling parameters prediction method of TBM steady phase based on dual recurrent neural networks. Firstly, the irregular multidimensional time series of tunneling parameters are analyzed and processed, which are divided into an idle-push phase, a rising phase, and a steady phase; secondly, the parameters of rising phase are analyzed using a recurrent neural network, and the parameters relevant for constructing a real-time prediction model are screened; then, based on the screened parameters, the Bayesian-optimized gated recurrent unit (GRU, a kind of recursive neural network) is proposed to construct a real-time prediction model for the four key tunneling parameters during the steady phase. Finally, the effectiveness and practicality of the proposed method are demonstrated by verification on real TBM tunnel datasets and comparing it with the models constructed by six commonly used machine learning algorithms. The results of this paper show that the designed prediction method is able to achieve a good combination of performance in terms of accuracy and computational time-consumption, with an average prediction accuracy of 91.1% for the four parameters for different rock grades of geology, the multiparameter prediction time for 100 samples is only 11 ms. In addition, three current similar studies using deep learning methods were compared to demonstrate the superiority of this proposal. As a method more closer to practical application, this work provides guidance for the forward-looking prediction of TBM tunneling parameters. [ABSTRACT FROM AUTHOR]

An unsupervised change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced which is able to detect change points online under the presence of non-trivial trend patterns which must be prevented from triggering false positives. Different predictive models for the required time series forecasting (Predict) step together with different statistical tests for deciding about the proximity of predicted and actual data (Compare step) are allowed. Its performance is shown for the Predict step being carried out by either an LSTM recursive neural network or an ARIMA linear time series model together with the CUSUM rule as Compare step method. It shows to perform best in comparison to several other online CPD methods for detect times in the regime of low numbers of false positive detections. The method's good performance is based on its ability to detect structural changes in the presence complex underlying trend patterns. The use case concerns tribological wear for which change points separating the run-in, steady-state, and divergent wear phases are detected. [ABSTRACT FROM AUTHOR]

In the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this article, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event‐triggered model predictive control (APET‐MPC) algorithm to investigate the purely data‐driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high‐dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET‐MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET‐MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF‐APET‐MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%. [ABSTRACT FROM AUTHOR]

This article presents a robust security‐based model predictive control (MPC) framework for a class of discrete‐time nonlinear cyber‐physical systems (CPSs) suffering random deception attacks and persistent actuator saturation. In CPSs, the attacks can significantly degrade the control performance. Naturally, a larger degree of control is required, prompting the consideration of actuator saturation in CPS's structure. In our work, the cyberattacks on the controller‐actuator (C‐A) channel are described as a stochastic process with specific probability and bounded energy. The security‐related constraint is introduced to improve the resistance and robustness against attacks. Mean‐square quadratic boundedness is derived to estimate the robustly invariant set. By placing the saturated input into a convex hull, a robust security‐based MPC optimization problem is addressed less conservatively with linear matrix inequality technique. Furthermore, the feasibility of the proposed method is proved recursively, and the stability and security of the closed‐loop system are established in mean‐square sense. In the end, the laboratory tank and reactor‐separator process are applied to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A classic way to design a nonlinear model predictive control (NMPC) scheme with guaranteed stability is to incorporate a terminal cost and a terminal constraint into the problem formulation. While a long prediction horizon is often desirable to obtain a large domain of attraction and good closed‐loop performance, the related computational burden can hinder its real‐time deployment. In this article, we propose an NMPC scheme with prediction horizon N=1$$ N=1 $$ and no terminal constraint to drastically decrease the numerical complexity without significantly impacting closed‐loop stability and performance. This is attained by constructing a suitable terminal cost from data that estimates the cost‐to‐go of a given NMPC scheme with long prediction horizon. We demonstrate the advantages of the proposed control scheme in two benchmark control problems. [ABSTRACT FROM AUTHOR]

Tight sandstone reservoirs have extremely low porosity and permeability. Bedding-parallel fractures (BPFs) contribute prominently to the storage and seepage capability. However, the distribution of BPFs is remarkably heterogeneous, impeding the prediction and modeling of sweet spots. BPFs are controlled fundamentally by laminations, which are widely distributed in lacustrine tight reservoirs and provide most weakness planes. Based on core and thin section data, BPFs of the upper Triassic Chang 7 tight oil reservoir are characterized microscopically. The lamination combination unit, which is defined by distinctive lamination assemblage and relatively stable lamination thickness and space, is utilized as a homogeneous unit to measure the density of lamination and related BPFs. The influence of laminations on BPFs is discussed further. Results show that most bedding-parallel fractures are unfilled, with apertures generally <40 μιτι, mainly <10 μιτι. Larger apertures correlate with low filling degrees. The distribution of BPFs is intricately controlled by lamination type, density, and thickness. (1) BPFs tend to develop along different types by a priority sequence which reflects their mechanical strength. The development degree of BPFs also depends on the mechanical contrast with adjacent laminations; (2) When controlled by a single type of lamination, the density of BPFs increases with lamination density under a turning point and then decreases; (3) BPFs prefer to develop along the thinner lamination and are usually inside it, while controlled by thick lamination, BPFs tend to extend along the edge. The change in the thickness of laminations leads to a change in the development position of BPFs, indicating that the position of the weak plane controls the development position of BPFs; (4) When multiple types of lamination coexist, the type and thickness of laminations jointly influence the development of BPFs. Plastic thin laminations are conducive to the development of BPFs, while brittle thick laminations are not conducive. When the thickness of the plastic lamination is close to or less than that of the brittle, the influence of lamination type dominates BPFs, while the thickness of the plastic laminations is much larger than the brittle, the influence of lamination thickness will dominate. [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]

AbstractBased on the sound speed and physical property measurements of seafloor sediments obtained in the central part of the South Yellow Sea, a multi-parameter prediction model for the sound speed of seafloor sediments based on seven parameters, including density, porosity, water content, liquid limit, plasticity index, compression coefficient, and median grain size, was developed by using the BP-AdaBoost fusion algorithm. The results show that the model, configured with 7 input layer neurons, 10 hidden layer neurons, 1 output node, a learning rate of 0.1, and 300 training iterations, achieved a correlation coefficient of 0.962. The mean absolute error (MAE) of the predicted sound speed was 10.19 m/s, and the mean relative error (MAPE) was 0.66%. These results are better than those of single-parameter and dual-parameter prediction equations and other machine learning models. The BP-AdaBoost model prediction method of sound speed of seafloor sediments established in this paper is better than the single-parameter and dual-parameter prediction equations and other prediction models, and it can provide a new way for the prediction of sound speed of seafloor sediments in the study area. [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]

Summary The Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s. [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]

ABSTRACT From early in the coronavirus disease 2019 (COVID‐19) pandemic, there was interest in using machine learning methods to predict COVID‐19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing‐RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS‐CoV‐2 infection status and extensive study participant meta‐data. This allowed us to rigorously assess state‐of‐the‐art machine learning techniques to predict SARS‐CoV‐2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks. [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]

The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation. [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]

Objectives To assess whether there are identifiable subgroups of disease activity trajectory in a population of JDM patients—followed throughout childhood and into adulthood—and determine factors that predict those trajectory groupings. Methods This is a retrospective, longitudinal inception cohort of patients with idiopathic inflammatory myopathies, largely JDM. We sought to identify baseline factors that predict membership into different groups (latent classes) of disease activity trajectory. Results A total of 172 patients (64% females), with median age at diagnosis of 7.7 years, were analysed. We studied 4725 visits (1471 patient-years). We identified three latent classes of longitudinal disease activity, as measured by the modified DAS (DASm), with distinct class trajectories predicted by DASm at baseline, and by the changes of DASm from either baseline to 3 months or baseline to 6 months (early response to therapy). In the analysis in which DASm at baseline and the changes of DASm from baseline to 6 months are included as predictors, Class 1 (10%) has persistently high disease activity, Class 2 (34%) is characterized by moderate disease activity and Class 3 (56%) is characterized by individuals with a high early disease activity but an apparently good response to treatment and long-term low disease activity. Conclusion High early disease activity, and treatment resistance in the first few months, predict a more chronic longitudinal course of JDM. [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]

Research on clinical versus statistical prediction has demonstrated that algorithms make more accurate predictions than humans in many domains. Geopolitical forecasting is an algorithm-unfriendly domain, with hard-to-quantify data and elusive reference classes that make predictive model-building difficult. Furthermore, the stakes can be high, with missed forecasts leading to mass-casualty consequences. For these reasons, geopolitical forecasting is typically done by humans, even though algorithms play important roles. They are essential as aggregators of crowd wisdom, as frameworks to partition human forecasting variance, and as inputs to hybrid forecasting models. Algorithms are extremely important in this domain. We doubt that humans will relinquish control to algorithms anytime soon—nor do we think they should. However, the accuracy of forecasts will greatly improve if humans are aided by algorithms. [ABSTRACT FROM AUTHOR]

Psychological artificial intelligence (AI) applies insights from psychology to design computer algorithms. Its core domain is decision-making under uncertainty, that is, ill-defined situations that can change in unexpected ways rather than well-defined, stable problems, such as chess and Go. Psychological theories about heuristic processes under uncertainty can provide possible insights. I provide two illustrations. The first shows how recency—the human tendency to rely on the most recent information and ignore base rates—can be built into a simple algorithm that predicts the flu substantially better than did Google Flu Trends's big-data algorithms. The second uses a result from memory research—the paradoxical effect that making numbers less precise increases recall—in the design of algorithms that predict recidivism. These case studies provide an existence proof that psychological AI can help design efficient and transparent algorithms. [ABSTRACT FROM AUTHOR]

Postural stabilization is essential to effectively interact with our environment. Humans preemptively adjust their posture to counteract impending disturbances, such as those encountered during interactions with moving objects, a phenomenon known as anticipatory postural adjustments (APAs). APAs are thought to be influenced by predictive models that incorporate object motion via retinal motion and extraretinal signals. Building on our previous work that examined APAs in relation to the perceived momentum of moving objects, here we explored the impact of object motion within different visual field sectors on the human capacity to anticipate motion and prepare APAs for contact between virtual moving objects and the limb. Participants interacted with objects moving toward them under different gaze conditions. In one condition, participants fixated on either a central point (central fixation) or left-right of the moving object (peripheral fixation), whereas in another, they followed the moving object with smooth pursuit eye movements (SPEMs). We found that APAs had the smallest magnitude in the central fixation condition and that no notable differences in APAs were apparent between the SPEM and peripheral fixation conditions. This suggests that the visual system can accurately perceive motion of objects in peripheral vision for posture stabilization. Using Bayesian model averaging, we also evaluated the contribution of different gaze variables, such as eye velocity and gain (ratio of eye and object velocity) and showed that both eye velocity and gain signals were significant predictors of APAs. Taken together, our study underscores the roles of oculomotor signals in the modulation of APAs. NEW & NOTEWORTHY: We show that the human visuomotor system can detect motion in peripheral vision and make anticipatory adjustments to posture before contact with moving objects, just as effectively as when the eye movement system tracks those objects with smooth pursuit eye movements. These findings pave the way for research into how age-induced changes in spatial vision, eye movements, and motion perception could affect the control of limb movements and postural stability during motion-mediated interactions with objects. [ABSTRACT FROM AUTHOR]

Background: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). Methods: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer–Lemeshow (HL), AUC, sensitivity and specificity. Results: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. Conclusions: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting. [ABSTRACT FROM AUTHOR]

Background: Chromosomal 1q gains and amplifications (+1q21) are frequently observed in patients with newly diagnosed multiple myeloma (NDMM). However, the interpretation of the high‐risk (HR) prognostic implications stemming from 1q21 abnormalities remain challenging to implement effectively. Methods: In a comprehensive analysis of 367 consecutive patients with symptomatic MM, we assessed the prognostic significance of +1q21 using FISH with a threshold of 7.4%. The patient cohort was randomly divided into a training set (66.5%, n = 244) and a validation set (33.5%, n = 133). A multivariate Cox regression analysis was conducted to identify significant prognostic factors associated with PFS. Weight scores were assigned to each risk factor based on the β‐value of the corresponding regression coefficient. A predictive risk‐scoring model involving +1q21 was then developed, utilizing the total score derived from these weight scores. The model's discriminative ability was evaluated using the AUC in both the training and validation sets. Finally, we compared the performance of the +1q21‐involved risk with the established R‐ISS and R2‐ISS models. Results: Upon initial diagnosis, 159 patients (43.32%) exhibited +1q21, with 94 (59.11%) having three copies, referred to as Gain(1q21), and 65 (40.89%) possessing four or more copies, referred to as Amp (1q21). Both were significantly linked to a reduced PFS in myeloma (p < 0.05), which could be effectively mitigated by ASCT. The +1q21‐involved risk model, with an AUC of 0.697 in the training set and 0.725 in the validation set, was constructed including Gain(1q21), Amp(1q21), no‐ASCT, and TP53 deletion. This model, termed the ultra‐high‐risk (UHR) model, demonstrated superior performance in predicting shorter PFS compared to the R‐ISS stage 3 and R2‐ISS stage 4. Conclusion: The UHR model, which integrates the presence of +1q21 with no‐ASCT and TP53 deletion, is designed to identify the early relapse subgroup among patients with +1q21 in NDMM. [ABSTRACT FROM AUTHOR]

In this work, we present a study about the application of bi-temporal, large interval aerial laser scans for constructing of tree growth models and estimating site index quality based on the measured increments from the laser scans. We compared two LiDAR scans with 14 years of difference in the national park area, where most areas are unmanaged. We derived the increment curve based on the Chapman-Richard growth formula. We used site index estimates from forest management plans from the national scale as the ground truth (both absolute and relative). We constructed three predictive models for site index estimates from bi-temporal scans, in modalities with and without stand age. Including the stand age improved all models, but even without the age, the models performed relatively well for differentiation between better and worse sites. At this moment, it is not directly possible to estimate age from remotely sensed data, but consistent monitoring, with laser scanning or photogrammetry, undoubtedly detects the harvest or dieback, so in the future, age can be considered as a variable easily estimated from remotely sensed data and so remote sensed material are viable source for understanding of forest growth and production. [ABSTRACT FROM AUTHOR]

Background: Secure forensic hospital treatments are resource-intensive, aiming to rehabilitate offenders and enhance public safety. While these treatments consume significant portions of mental health budgets and show efficacy in some countries, their effectiveness in Czechia remains underexplored. Previous research has highlighted various factors influencing the likelihood of discharge from these institutions. Notably, the role of sociodemographic variables and the length of stay (LoS) in the context of forensic treatments has presented inconsistent findings across studies. Methods: The study, part of the 'Deinstitutionalization project' in Czechia, collected data from all inpatient forensic care hospitals. A total of 793 patients (711 male, 79 female and 3 unknown) were included. Data collection spanned 6 months, with tools like HoNOS, HoNOS-Secure, MOAS, HCR-20V3 and AQoL-8D employed to assess various aspects of patient health, behaviour, risk and quality of life. Results: The study revealed several determinants influencing patient discharge from forensic hospitals. Key assessment tools, such as HoNOS secure scores and the HCR-20 clinical subscale, showed that higher scores equated to lower chances of release. Furthermore, specific diagnoses like substance use disorder increased discharge odds, while a mental retardation diagnosis significantly reduced it. The type of index offense showed no influence on discharge decisions. Conclusion: Factors like reduced risk behaviours, absence of mental retardation diagnosis, social support and secure post-release housing plans played significant roles. The results underscored the importance of using standardized assessment tools over clinical judgement. A standout insight was the unique challenges faced by patients diagnosed with mental retardation, emphasizing a need for specialized care units or tailored programmes. [ABSTRACT FROM AUTHOR]

Psychological distress in women represents a critical aspect; however, few studies have comprehensively addressed how health management strategies, consideration of future consequences (CFC), cognitive reappropriation, and self-efficacy may influence this phenomenon, especially in contexts that require a comprehensive approach to women's mental health. This study aimed to evaluate the effects of future consequence consideration, cognitive reappraisal, and self-efficacy in health management on psychological distress in American women, as well as to demonstrate their equivalence according to age. Data from 2788 women responding to the 2019 Health Information National Trends Survey 5, cycle 3 were used. A predictive model was considered using structural equation modeling (SEM). Cognitive reappraisal and self-efficacy in health management (SEHM) have a significant mediating effect between CFC and psychological distress. In addition, the invariance of the SEM model was presented as a function of age. The findings underscore the importance of incorporating cognitive components into mental health interventions for women, focusing on modifying thoughts and perceptions about the efficacy of health care to mitigate negative emotions. [ABSTRACT FROM AUTHOR]

Contextual risk factors, such as adverse childhood experiences (ACEs), have a significant impact on the mental health of Black youth. Surprisingly, few studies focus efforts specifically on Black youth. The present study investigates the influence of ACEs on psychological distress among Black youth. Additionally, guided by social cognitive theory, this study highlights emotional self-control as a protective mechanism against the negative consequences of ACEs. Our findings show that ACEs (T1) predicted psychological distress among Black youth a year later (T2). Emotional self-control emerged as a significant buffer of ACEs on the association with psychological distress. Thus, to prevent negative mental health outcomes for Black youth, it is imperative to focus prevention efforts on the crucial risk factors that affect healthy development. By working to increase emotional self-control among Black youth who suffered adverse childhood experiences, negative mental health outcomes over time can be reduced. [ABSTRACT FROM AUTHOR]