Recall Curve Research Articles

The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia

Abstract Background Postoperative cognitive dysfunction (POCD) frequently occurs following endovascular therapy for acute ischemic stroke (AIS). Given the complexity of predicting AIS clinically, there is a pressing need to develop a preemptive prediction model and investigate the impact of anesthesia depth on AIS. Methods A total of 333 patients diagnosed with AIS were included in the study, comprising individuals with non-POCD (n = 232) or POCD (n = 101). Univariate and multivariate logistic regression analyses were utilized to examine the independent risk factors associated with POCD. A calibration, decision curve analysis, and precision–recall curves were employed to assess the model’s goodness of fit. Results Multivariate regression analysis identified two inflammatory indicators, high-sensitivity C reactive protein (hs-CRP) and systemic immune inflammatory index (SII), and three brain injury indicators, National Institute of Health Stroke Scale (NIHSS) score, N-terminal pro-brain natriuretic peptide (NT-proBNP), and soluble protein-100 β (S100-β), which were used to construct a nomogram model. Conclusion The composite predictive model incorporating NIHSS score, hs-CRP, SII, NT-proBNP, and S100-β demonstrated efficacy in predicting POCD following AIS. Additionally, our results suggest a potential association between depth of anesthesia, cognitive impairment, and inflammatory response in AIS patients.

A Model for Defining the Boundaries of Complex Sentences and Clauses in English Translation Based on the Huffman Tree and Objective Function

To address the issues of high redundancy in sentence features and poor semantic similarity analysis in English translation, a boundary definition model for complex sentence clauses in English translation based on the Huffman tree and objective function is proposed. This model analyzes the boundary structure of complex sentences and clauses in English translation, determines the mathematical expected value of the probability distribution of boundary features, introduces the maximum entropy model to assess the key degree of different features, and utilizes conditional random fields to mine hidden features. Additionally, by employing a hierarchical clustering algorithm to analyze the distance between boundary features of complex sentences and clauses in English translation, similar images are merged based on the minimum distance between data points. Feature redundancy scores are obtained through an attention mechanism, and the weight of boundary features of complex sentences and clauses in English translation is calculated. By using the edit distance in semantic similarity to determine the boundary distance of clauses, and then using cosine similarity to calculate the similarity between the boundary features of complex clauses in English translation, a Huffman tree and objective function are introduced to construct a model for defining the boundary of complex clauses in English translation. Input the boundary feature values of complex sentences and clauses in English translation to complete the final definition. The experimental results show that the proposed method performs well in the task of defining the boundaries of complex sentences and clauses in English translation, with semantic similarity analysis results remaining above 95% and reaching up to 99%, significantly better than the comparative methods. Meanwhile, the recall curve obtained by the proposed method is closest to the ideal curve and has a small fluctuation range, stable between 90% and 98%, further verifying its accuracy and robustness in boundary delineation. In addition, when the sample data size is 1000, the confidence level of the proposed method is as high as 99.6%, which is higher than the 95.6% and 95.1% of the comparison methods. As the sample size increased to 2500, the confidence level of the proposed method remained at a high level of 99.4%, while the confidence level of the comparative method decreased to 94.2% and 94.1%, respectively. These data results fully demonstrate the effectiveness of the proposed method in reducing redundant interference and improving confidence. In summary, the proposed method has improved the performance of defining the boundaries of complex sentences and clauses in English translation.

Development and validation of a deep learning algorithm for the classification of the level of surgical difficulty in impacted mandibular third molar surgery

The aim of this study was to develop and validate a convolutional neural network (CNN) algorithm for the detection of impacted mandibular third molars in panoramic radiographs and the classification of the surgical extraction difficulty level. A dataset of 1730 panoramic radiographs was collected; 1300 images were allocated to training and 430 to testing. The performance of the model was evaluated using the confusion matrix for multiclass classification, and the actual scores were compared to those of two human experts. The area under the precision–recall curve of the YOLOv5 model ranged from 72% to 89% for the variables in the surgical difficulty index (level of occlusion, available retromolar space, impaction angle, number of roots, root curvature, and relationship to the second molar). The area under the receiver operating characteristic curve showed promising results of the YOLOv5 model for classifying third molars into three surgical difficulty levels (micro-average AUC 87%). Furthermore, the algorithm scores demonstrated good agreement with the human experts. In conclusion, the YOLOv5 model has the potential to accurately detect and classify the position of mandibular third molars, with high performance for every criterion in radiographic images. The proposed model could serve as an aid in improving clinician performance and could be integrated into a screening system.

The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

PurposeThe availability of population datasets and machine learning techniques heralded a new era of sophisticated prediction models involving a large number of routinely collected variables. However, severe class imbalance in clinical datasets is a major challenge. The aim of this study is to investigate the impact of commonly-used resampling techniques in combination with commonly-used machine learning algorithms in a clinical dataset, to determine whether combination(s) of these approaches improve upon the original multivariable logistic regression with no resampling.MethodsWe previously developed and internally validated a multivariable logistic regression 30-day mortality prediction model in 30,619 patients using preoperative and intraoperative features.Using the same dataset, we systematically evaluated and compared model performances after application of resampling techniques [random under-sampling, near miss under-sampling, random oversampling, and synthetic minority oversampling (SMOTE)] in combination with machine learning algorithms (logistic regression, elastic net, decision trees, random forest, and extreme gradient boosting).ResultsWe found that in the setting of severe class imbalance, the impact of resampling techniques on model performance varied by the machine learning algorithm and the evaluation metric. Existing resampling techniques did not meaningfully improve area under receiving operating curve (AUROC). The area under the precision recall curve (AUPRC) was only increased by random under-sampling and SMOTE for decision trees, and oversampling and SMOTE for extreme gradient boosting. Importantly, some combinations of algorithm and resampling technique decreased AUROC and AUPRC compared to no resampling.ConclusionExisting resampling techniques had a variable impact on models, depending on the algorithms and the evaluation metrics. Future research is needed to improve predictive performances in the setting of severe class imbalance.

Generative modeling of the Circle of Willis using 3D-StyleGAN

The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatment of cerebrovascular disease. In medical imaging, the performance of deep learning models is limited by the diversity and size of training datasets. To address medical data scarcity, generative AI models have been applied to generate synthetic vessel neuroimaging data. However, the proposed methods produce synthetic data with limited anatomical fidelity or downstream utility in tasks concerning vessel characteristics.We adapted the StyleGANv2 architecture to 3D to synthesize Time-of-Flight Magnetic Resonance Angiography (TOF MRA) volumes of the CoW. For generative modeling, we used 1782 individual TOF MRA scans from 6 open source datasets. To train the adapted 3D StyleGAN model with limited data we employed differentiable data augmentations, used mixed precision and a cropped region of interest of size 32 × 128 × 128 to tackle computational constraints. The performance was evaluated quantitatively using the Fréchet Inception Distance (FID), MedicalNet distance (MD) and Area Under the Curve of the Precision and Recall Curve for Distributions (AUC-PRD). Qualitative analysis was performed via a visual Turing test. We demonstrated the utility of generated data in a downstream task of multiclass semantic segmentation of CoW arteries. Vessel segmentation performance was assessed quantitatively using the Dice coefficient and the Hausdorff distance.The best-performing 3D StyleGANv2 architecture generated high-quality and diverse synthetic TOF MRA volumes (FID: 12.17, MD: 0.00078, AUC-PRD: 0.9610). Multiclass vessel segmentation models trained on synthetic data alone achieved comparable performance to models trained using real data in most arteries. The addition of synthetic data to a baseline training set improved segmentation performance in underrepresented artery segments, similar to the addition of real data.In conclusion, generative modeling of the Circle of Willis via synthesis of 3D TOF MRA data paves the way for generalizable deep learning applications in cerebrovascular disease. In the future, the extensions of the provided methodology to other medical imaging problems or modalities with the inclusion of pathological datasets has the potential to advance the development of more robust AI models for clinical applications.

Basketball robot object detection and distance measurement based on ROS and IBN-YOLOv5s algorithms.

With the combination of artificial intelligence and robotics technology, more and more professional robots are entering the public eye. Basketball robot competition, as a very good target system for autonomous robot research, is very suitable for conducting research on robot autonomous perception system object detection. However, traditional basketball robots have problems such as recognition difficulties, which seriously affect the recognition of robot targets and distance measurement based on recognition. To improve the performance of basketball robots in competitions, research was conducted to improve the object detection system. Firstly, a basketball robot object detection system based on robot operating system was designed. In the software layer of the object detection system, an algorithm that combines YOLOv5s and laser detection was used, and an appropriate instance batch normalization network module was introduced in the YOLOv5s algorithm to improve the model's generalization ability. The experiment outcomes indicated that the improved algorithm had intersection over union (IoU), structural information loss, ambiguity and signal-to-noise ratio of 0.96, 0.03, 0.13, and 0.98, respectively, and performed the best in the other comparison models. The recall curve area and F1 value of the improved algorithm were 0.95 and 0.9789, respectively. In the detection of basketball, volleyball, and calibration columns, the average classification accuracy of the improved model was 95.87%, and the average calibration box accuracy was 97.05%. From this, the algorithm proposed in the study has robust performance and can efficiently achieve object detection and recognition of basketball robots. The improved algorithm proposed in the study provides more reliable and rich information for the perception ability of basketball robots, as well as for their subsequent decision-making and action planning, thereby improving the overall technical level of the robots.

Fake News Detection in Hausa Language Using Transfer Learning Method

Fake news poses a significant threat to societies worldwide, including in Hausa-speaking regions, where misinformation is rapidly disseminated via social media. The lack of NLP resources tailored to this language exacerbated the problem of fake news in the Hausa language. While extensive research has been conducted on counterfeit news detection in languages such as English, little attention has been paid to languages like Hausa, leaving a significant portion of the global population vulnerable to misinformation. Traditional machine-learning approaches often fail to perform well in low-resource settings due to insufficient training data and linguistic resources. This study aims to develop a robust model for detecting fake news in the Hausa language by leveraging transfer learning techniques with adaptive fine-tuning. A dataset of over 6,600 news articles, including both fake and truthful articles, was collected from various sources between January 2022 and December 2023. Cross-lingual transfer Learning (XLT) was employed to adapt pre- trained models for the low-resource Hausa language. The model was fine-tuned and evaluated using performance metrics such as accuracy, precision, recall, F-score, AUC-ROC, and PR curves. Results demonstrated a high accuracy rate in identifying fake news, with significant improvements in detecting misinformation within political and world news categories. This study addresses the gap in Hausa- language natural language processing (NLP) and contributes to the fight against misinformation in Nigeria. The findings are relevant for developing AI- driven tools to curb fake news dissemination in African languages.

Performance of Risk Models for Antimicrobial Resistance in Adult Patients With Sepsis

The results of prediction models that stratify patients with sepsis and risk of resistant gram-negative bacilli (GNB) infections inform treatment guidelines. However, these models do not extrapolate well across hospitals. To assess whether patient case mix and local prevalence rates of resistance contributed to the variable performance of a general risk stratification GNB sepsis model for community-onset and hospital-onset sepsis across hospitals. This was a retrospective cohort study conducted from January 2016 and October 2021. Adult patients with sepsis at 10 acute-care hospitals in rural and urban areas across Missouri and Illinois were included. Inclusion criteria were blood cultures indicating sepsis, having received 4 days of antibiotic treatment, and having organ dysfunction (vasopressor use, mechanical ventilation, increased creatinine or bilirubin levels, and thrombocytopenia). Analyses were completed in April 2024. The model included demographic characteristics, comorbidities, vital signs, laboratory values, procedures, and medications administered. Culture results were stratified for ceftriaxone-susceptible GNB (SS), ceftriaxone-resistant but cefepime-susceptible GNB (RS), and ceftriaxone- and cefepime-resistant GNB (RR). Negative cultures and other pathogens were labeled SS. Deep learning models were developed separately for community-onset (patient presented with sepsis) and hospital-onset (sepsis developed ≥48 hours after admission) sepsis. The models were tested across hospitals and patient subgroups. Models were assessed using area under the receiver operating characteristic curve (AUROC) and area under precision recall curve (AUPRC). A total of 39 893 patients with 85 238 sepsis episodes (43 207 [50.7%] community onset; 42 031 [48.3%] hospital onset) were included. Median (IQR) age was 65 (54-74) years, 21 241 patients (53.2%) were male, and 18 830 (47.2%) had a previous episode of sepsis. RS contributed to 3.9% (1667 episodes) and 5.7% (2389 episodes) of community-onset and hospital-onset sepsis episodes, respectively, and RR contributed to 1.8% (796 episodes) and 3.9% (1626 episodes), respectively. Previous infections and exposure to antibiotics were associated with the risk of resistant GNB. For example, in community-onset sepsis, 375 RR episodes (47.1%), 420 RS episodes (25.2%) and 3483 of 40 744 (8.5%) SS episodes were among patients with resistance to antimicrobial drugs (P < .001). The AUROC and AUPRC results varied across hospitals and patient subgroups for both community-onset and hospital-onset sepsis. AUPRC values correlated with the prevalence rates of resistant GNB (R = 0.79; P = .001). In this cohort study of 39 893 patients with sepsis, variable model performance was associated with prevalence rates of antimicrobial resistance rather than patient case mix. This variability suggests caution is needed when using generalized models for predicting resistant GNB etiologies in sepsis.

MIPPIS: protein–protein interaction site prediction network with multi-information fusion

BackgroundThe prediction of protein–protein interaction sites plays a crucial role in biochemical processes. Investigating the interaction between viruses and receptor proteins through biological techniques aids in understanding disease mechanisms and guides the development of corresponding drugs. While various methods have been proposed in the past, they often suffer from drawbacks such as long processing times, high costs, and low accuracy.ResultsAddressing these challenges, we propose a novel protein–protein interaction site prediction network based on multi-information fusion. In our approach, the initial amino acid features are depicted by the position-specific scoring matrix, hidden Markov model, dictionary of protein secondary structure, and one-hot encoding. Simultaneously, we adopt a multi-channel approach to extract deep-level amino acids features from different perspectives. The graph convolutional network channel effectively extracts spatial structural information. The bidirectional long short-term memory channel treats the amino acid sequence as natural language, capturing the protein’s primary structure information. The ProtT5 protein large language model channel outputs a more comprehensive amino acid embedding representation, providing a robust complement to the two aforementioned channels. Finally, the obtained amino acid features are fed into the prediction layer for the final prediction.ConclusionCompared with six protein structure-based methods and six protein sequence-based methods, our model achieves optimal performance across evaluation metrics, including accuracy, precision, F1, Matthews correlation coefficient, and area under the precision recall curve, which demonstrates the superiority of our model.

Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning

Background and Aim: Zoonotic diseases caused by various blood parasites are important public health concerns that impact animals and humans worldwide. The traditional method of microscopic examination for parasite diagnosis is labor-intensive, time-consuming, and prone to variability among observers, necessitating highly skilled and experienced personnel. Therefore, an innovative approach is required to enhance the conventional method. This study aimed to develop a self-supervised learning (SSL) approach to identify zoonotic blood parasites from microscopic images, with an initial focus on parasite species classification. Materials and Methods: We acquired a public dataset featuring microscopic images of Giemsa-stained thin blood films of trypanosomes and other blood parasites, including Babesia, Leishmania, Plasmodium, Toxoplasma, and Trichomonad, as well as images of both white and red blood cells. The input data were subjected to SSL model training using the Bootstrap Your Own Latent (BYOL) algorithm with Residual Network 50 (ResNet50), ResNet101, and ResNet152 as the backbones. The performance of the proposed SSL model was then compared to that of baseline models. Results: The proposed BYOL SSL model outperformed supervised learning models across all classes. Among the SSL models, ResNet50 consistently achieved high accuracy, reaching 0.992 in most classes, which aligns well with the patterns observed in the pre-trained uniform manifold approximation and projection representations. Fine-tuned SSL models exhibit high performance, achieving 95% accuracy and a 0.960 area under the curve of the receiver operating characteristics (ROC) curve even when fine-tuned with 1% of the data in the downstream process. Furthermore, 20% of the data for training with SSL models yielded ≥95% in all other statistical metrics, including accuracy, recall, precision, specification, F1 score, and ROC curve. As a result, multi-class classification prediction demonstrated that model performance exceeded 91% for the F1 score, except for the early stage of Trypanosoma evansi, which showed an F1 score of 87%. This may be due to the model being exposed to high levels of variation during the developmental stage. Conclusion: This approach can significantly enhance active surveillance efforts to improve disease control and prevent outbreaks, particularly in resource-limited settings. In addition, SSL addresses significant challenges, such as data variability and the requirement for extensive class labeling, which are common in biology and medical fields. Keywords: bootstrap your own latent, fractioned data, microscopic image, pre-trained, self-supervised learning, zoonotic disease.

High-Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision (mAP@0.5) of 98.5%. Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as a thick line and star crack, the model maintained high performance, with accuracies of 94% and 96%, respectively. The Recall–Confidence and Precision–Recall curves further demonstrate the model’s robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production. Although the model demonstrates high accuracy across most defect types, certain subtle defects, such as thick lines and star cracks, remain challenging, indicating potential areas for further optimization in future work.

Targeting COVID-19 and Human Resources for Health News Information Extraction: Algorithm Development and Validation.

Global pandemics like COVID-19 put a high amount of strain on health care systems and health workers worldwide. These crises generate a vast amount of news information published online across the globe. This extensive corpus of articles has the potential to provide valuable insights into the nature of ongoing events and guide interventions and policies. However, the sheer volume of information is beyond the capacity of human experts to process and analyze effectively. The aim of this study was to explore how natural language processing (NLP) can be leveraged to build a system that allows for quick analysis of a high volume of news articles. Along with this, the objective was to create a workflow comprising human-computer symbiosis to derive valuable insights to support health workforce strategic policy dialogue, advocacy, and decision-making. We conducted a review of open-source news coverage from January 2020 to June 2022 on COVID-19 and its impacts on the health workforce from the World Health Organization (WHO) Epidemic Intelligence from Open Sources (EIOS) by synergizing NLP models, including classification and extractive summarization, and human-generated analyses. Our DeepCovid system was trained on 2.8 million news articles in English from more than 3000 internet sources across hundreds of jurisdictions. Rules-based classification with hand-designed rules narrowed the data set to 8508 articles with high relevancy confirmed in the human-led evaluation. DeepCovid's automated information targeting component reached a very strong binary classification performance of 98.98 for the area under the receiver operating characteristic curve (ROC-AUC) and 47.21 for the area under the precision recall curve (PR-AUC). Its information extraction component attained good performance in automatic extractive summarization with a mean Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score of 47.76. DeepCovid's final summaries were used by human experts to write reports on the COVID-19 pandemic. It is feasible to synergize high-performing NLP models and human-generated analyses to benefit open-source health workforce intelligence. The DeepCovid approach can contribute to an agile and timely global view, providing complementary information to scientific literature.

Machine learning approach for detection of valvular heart disease through digital cardiac auscultation: results from multi-center prospective cross-sectional study

Abstract Background Cardiac auscultation, an essential bedside diagnostic tool, often identifies significant valvular heart disease through murmurs. However, its accuracy is contingent upon the clinician's expertise, leading to a growing dependence on expensive imaging techniques. This study aimed to develop and validate a machine learning model for detecting valvular heart disease from cardiac auscultation. Methods The digital cardiac auscultation and associated echocardiograms were prospectively collected from three sites between May 1, 2022, and Dec 31, 2022. We trained, validated, and externally tested a machine learning based model to classify aortic stenosis, mitral stenosis, aortic regurgitation, mitral regurgitation, tricuspid regurgitation, oveall valvular heart disease from cardiac auscultation, age, sex, and comorbidity. We condcted training, and internal testing using datasets for eXtreme Gradient Boosting (XGBoost) machine learning model from hospital one and hospital two. The dataset from hospital three was employed for external testing. We evaluated the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity. Result We selected the cardiac auscultation and echocardiograms of a total of 4,321 patients, excluding those without valvular heart disease information, congenital heart disease, a history of valvular operation, and cardiac auscultation could not be interpreted. The diagnostic performance of heart murmurs for valvular heart disease across the three datasets generally demonstrated high sensitivity and PPV. Specifically, the sensitivity for aortic stenosis was 89.1%, mitral stenosis 88.8%, aortic regurgitation 88.5%, mitral regurgitation 89.8%, tricuspid regurgitation 90.1%, and for any valvular heart disease, it was 95.0%. However, the NPV was consistently lower, with values of 16.4%, 14.2%, 12.2%, 23.1%, 26.2%, and 65.3% for the respective conditions. In XGBoost machine learning model, the AUROC values for internal testing (N=1,403), along with their 95% confidence intervals, were as follows: aortic stenosis (0.93, 0.88–0.97), mitral stenosis (0.91, 0.81–0.97), aortic regurgitation (0.84, 0.79–0.90), mitral regurgitation (0.94, 0.90–0.98), tricuspid regurgitation (0.81, 0.75–0.87), and overall valvular heart disease (0.86, 0.82–0.89). For external testing (N=814), the AUROC values were: aortic stenosis (0.90, 0.83–0.96), mitral stenosis (0.88, 0.79–0.95), aortic regurgitation (0.94, 0.93–0.96), mitral regurgitation (0.93, 0.92–0.95), tricuspid regurgitation (0.89, 0.87–0.91), and overall valvular heart disease (0.92, 0.90–0.94). Conclusion The machine learning based model can accurately classify valvular heart diseases using information from digital cardiac auscultation.Figure 1.Study FlowFigure 2.AUC curve

An Investigation into the Susceptibility to Landslides Using Integrated Learning and Bayesian Optimization: A Case Study of Xichang City

In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy.

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

Construction and validation of a histone-related gene signature for the diagnosis of endometriosis.

Endometriosis is a common chronic disease in childbearing women and a major cause of infertility. Our study aimed to identify and validate a novel gene signature for diagnosing endometriosis based on histone-related genes (HRGs), and to investigate their biological functions in endometriosis. RNA sequence data were downloaded from the Gene Expression Omnibus database, and HRGs were retrieved from the GeneCards database. We identified differentially expressed genes using the limma package, and constructed a diagnostic model using the rms package. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed for visualization, annotation, and integrated discovery. Subsequently, we validated the model using the recall and decision curve analysis (DCA). Additionally, we analyzed the immune microenvironment features using CIBERSORT. A total of 18 differentially expressed HRGs were identified in patients with endometriosis compared with controls. GO and KEGG enrichment was mainly in spindle organization, positive regulation of the cell cycle process, progesterone-mediated oocyte maturation, and cellular senescence and cell cycle. We obtained a signature of four HRGs (JUNB, FRY, LMNB1, and SPAG1). DCA revealed that the diagnostic model benefits patients with endometriosis, regardless of the incidence. CIBERSORT analysis showed that the number of plasma cells increased significantly in endometriosis samples from all four datasets. Our findings provide novel insights into the function of HRGs in the development of endometriosis and identify a new signature of four HRGs that may serve as valuable diagnostic markers and therapeutic targets for this disease.

Risk for Suicide Attempts Assessed Using the Patient Health Questionnaire–9 Modified for Teens

Suicide is a leading cause of death in US youths. To assess whether screening with supplemental items 10 to 13 on the Patient Health Questionnaire-9 modified for teens (PHQ-9M) improves prediction of youth suicide attempts beyond the information provided by the first 9 items alone (the PHQ-9). This retrospective cohort study used a retrospective cohort of adolescents aged 12 to 17 years who were screened for depression in outpatient facilities within a pediatric health care system between January 1, 2016, and December 31, 2022, with up to 1 year of follow-up to assess the occurrence of suicidal behavior. Follow-up was completed on December 31, 2023. Screening with the PHQ-9M. This study developed and compared prediction using 3 Cox proportional hazards regression models (CR-9, CR-13, and CR-3) of subsequent suicide attempts, determined by the hospital's electronic health records up to 1 year following the last PHQ-9M screening. The CR-9 model used the PHQ-9 and the CR-13 model used all 13 items of PHQ-9M. The CR-3 model used the 3 most impactful variables selected from the 13 PHQ-9M items and PHQ-9 total score. All models were evaluated across 4 prediction horizons (30, 90, 180, and 365 days) following PHQ-9M screenings. Evaluation metrics were the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC). Of 130 028 outpatients (65 520 [50.4%] male) with 272 402 PHQ-9M screenings, 549 (0.4%) had subsequent suicide attempts within 1 year following the PHQ-9M screening. The AUROC of the CR-9 model in the 365-day horizon was 0.77 (95% CI, 0.75-0.79); of the CR-13 model, 0.80 (95% CI, 0.78-0.82); and of the CR-3 model, 0.79 (95% CI, 0.76-0.81); the AUPRC of the CR-9 model was 0.02 (95% CI, 0.02-0.03); of the CR-13 model, 0.03 (95% CI, 0.02-0.03); and of the CR-3 model, 0.02 (95% CI, 0.02-0.03). The 3 most impactful items using adjusted hazard ratios were supplemental item 13 (lifetime suicide attempts; 3.06 [95% CI, 2.47-3.80]), supplemental item 10 (depressed mood severity in the past year; 2.99 [95% CI, 2.32-3.86]), and supplemental item 12 (serious suicidal ideation in the past month; 1.63 [95% CI, 1.25-2.12]). All of the models achieved higher AUROCs as prediction horizons shortened. In this cohort study of adolescent PHQ-9M screenings, the supplemental items on PHQ-9M screening improved prediction of youth suicide attempts compared with screening using the PHQ-9 across all prediction horizons, suggesting that PHQ-9M screening should be considered during outpatient visits to improve prediction of suicide attempts.

Aberration Modulation Correlation Method for Dim and Small Space Target Detection

The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration modulation correlation method (AMCM) for dim and small space target detection. By meticulously manipulating the light path using a wavefront corrector via a modulation signal, the target brightness will fluctuate periodically, while the background brightness remains essentially constant. Benefited by the strong correlation between targets’ characteristic changes and the modulation signal, dim and small targets can be effectively detected. Rigorous simulations and practical experiments have validated the remarkable efficacy of AMCM. Compared to conventional algorithms, AMCM boasts a substantial enhancement in the signal-to-noise ratio (SNR) detection limit from 5 to approximately 2, with an area under the precision–recall curve of 0.9396, underscoring its ability to accurately identify targets while minimizing false positives. In essence, AMCM offers an effective method for detecting dim and small space targets and is also conveniently integrated into other passive target detection systems.

Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator

The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision–recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety.

Enhancing Suicide Attempt Risk Prediction Models with Temporal Clinical Note Features.

The objective of this study was to investigate the impact of enhancing a structured-data-based suicide attempt risk prediction model with temporal Concept Unique Identifiers (CUIs) derived from clinical notes. We aimed to examine how different temporal schemes, model types, and prediction ranges influenced the model's predictive performance. This research sought to improve our understanding of how the integration of temporal information and clinical variable transformation could enhance model predictions. We identified modeling targets using diagnostic codes for suicide attempts within 30, 90, or 365 days following a temporally grouped visit cluster. Structured data included medications, diagnoses, procedures, and demographics, whereas unstructured data consisted of terms extracted with regular expressions from clinical notes. We compared models trained only on structured data (controls) to hybrid models trained on both structured and unstructured data. We used two temporalization schemes for clinical notes: fixed 90-day windows and flexible epochs. We trained and assessed random forests and hybrid long short-term memory (LSTM) neural networks using area under the precision recall curve (AUPRC) and area under the receiver operating characteristic, with additional evaluation of sensitivity and positive predictive value at 95% specificity. The training set included 2,364,183 visit clusters with 2,009 30-day suicide attempts, and the testing set contained 471,936 visit clusters with 480 suicide attempts. Models trained with temporal CUIs outperformed those trained with only structured data. The window-temporalized LSTM model achieved the highest AUPRC (0.056 ± 0.013) for the 30-day prediction range. Hybrid models generally showed better performance compared with controls across most metrics. This study demonstrated that incorporating electronic health record-derived clinical note features enhanced suicide attempt risk prediction models, particularly with window-temporalized LSTM models. Our results underscored the critical value of unstructured data in suicidality prediction, aligning with previous findings. Future research should focus on integrating more sophisticated methods to continue improving prediction accuracy, which will enhance the effectiveness of future intervention.