High-quality Samples Research Articles

Generative and contrastive graph representation learning with message passing.

Self-supervised graph representation learning (SSGRL) has emerged as a promising approach for graph embeddings because it does not rely on manual labels. SSGRL methods are generally divided into generative and contrastive approaches. Generative methods often suffer from poor graph quality, while contrastive methods, which compare augmented views, are more resistant to noise. However, the performance of contrastive methods depends heavily on well-designed data augmentation and high-quality negative samples. Pure generative or contrastive methods alone cannot balance both robustness and performance. To address these issues, we propose a self-supervised graph representation learning method that integrates generative and contrastive ideas, namely Contrastive Generative Message Passing Graph Learning (CGMP-GL). CGMP-GL incorporates the concept of contrast into the generative model and message aggregation module, enhancing the discriminability of node representations by aligning positive samples and separating negative samples. On one hand, CGMP-GL integrates multi-granularity topology and feature information through cross-view multi-level contrast while reconstructing masked node features. On the other hand, CGMP-GL optimizes node representations through self-supervised contrastive message passing, thereby enhancing model performance in various downstream tasks. Extensive experiments over multiple datasets and downstream tasks demonstrate the effectiveness and robustness of CGMP-GL.

Exploring the influence of pre-analytical variables on gene expression measurements and relative expression orderings in cancer research

Gene expression profiling is an effective method for identifying predictive and prognostic biomarkers. However, measurements are prone to uncertainty and errors due to various pre-analytical variables. Systematic evaluating effects of these variables on gene expression measurements and relative expression orderings (REOs) of gene pairs, is necessary. A total of 18 datasets were collected, comprising over 800 paired samples. These paired samples were utilized to assess the impact of pre-analytical variables on gene expression measurements and REOs, including sampling methods, tumor sample heterogeneity, fixed time delays, preservation conditions, degradation levels, library preparation kits, amplification kits, RNA quantity, measuring platforms, and laboratory sites at single and multi-variable level. Low-quality samples served as the case group, while paired high-quality samples constituted the control group. In both single and multiple variable analyses, comparing each case sample to paired control sample revealed thousands of genes exhibited a twofold change in expression values. In contrast, on average, 82% and 76% of gene pairs keep consistent REO pattern between paired samples in single-variable and multi-variable analyses, respectively. Notably, the rate steadily increased after excluding gene pairs with the closest expression levels. Statistical analyses shown a higher proportion of differentially expressed genes (DEGs) than that of reversed gene pairs between case and control groups in both single-variable and multi-variable analyses. Furthermore, the proportion of reversal gene pairs among all gene pairs involving DEGs remained below 20% in the majority of comparisons. Our research demonstrates that REOs exhibit higher robustness under the influence of pre-analytical variables. These findings indicate the potential of the REOs-based approach in transcriptomics research and its applicability for biomarker studies.

Surface defect detection on industrial drum rollers: Using enhanced YOLOv8n and structured light for accurate inspection.

Drum roller surface defect detection is of great research significance for control production quality. Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, and the accuracy is not enough, this paper designs an image acquisition system based on line fringe structured light and proposes an improved deep learning network model based on YOLOv8n to achieve efficient detection of defects on the rolling surface of a drum roller. In the aspect of image acquisition, this paper selected the line fringe structured light as the system light source, which made up for the problem that the traditional light source does not reflect the defect characteristics. In terms of algorithms, firstly, using deformable convolution instead of standard convolution to enhance the feature extraction ability of the backbone network. Then, a new feature fusion module was proposed to enable the fusion network to learn additional original information. Finally, Wise-IoU was applied to replace CIoU in the loss function, so that the network pays more attention to the high-quality samples. The experimental results show that the improved YOLOv8n algorithm has a certain improvement in detection accuracy. The main average accuracy (mAP) is 97.2%, and the detection time is 4.3ms. The system and algorithm designed in this paper can better ensure the production quality of drum rollers. While effective, the model's standard rectangular bounding boxes may limit precision for elongated defects. Future work could explore rotated bounding boxes and broader dataset diversity to enhance generalization in real-world applications.

European Society of Toxicologic Pathology-Pathology 2.0 Mass Spectrometry Imaging Special Interest Group: Mass Spectrometry Imaging in Diagnostic and Toxicologic Pathology for Label-Free Detection of Molecules-From Basics to Practical Applications.

Mass Spectrometry Imaging (MSI) is a powerful tool to understand molecular pathophysiology and therapeutic and toxicity mechanisms, as well as for patient stratification and precision medicine. MSI, a label-free technique offering detailed spatial information on a large number of molecules in different tissues, encompasses various techniques including Matrix-Assisted Laser Desorption Ionization (MALDI), Desorption Electrospray Ionization (DESI), and Secondary Ion Mass Spectrometry (SIMS) that can be applied in diagnostic and toxicologic pathology. Given the utmost importance of high-quality samples, pathologists play a pivotal role in providing comprehensive pathobiology and histopathology knowledge, as well as information on tissue sampling, orientation, morphology, endogenous biomarkers, and pathogenesis, which are crucial for the correct interpretation of targeted experiments. This article introduces MSI and its fundamentals, and reports on case examples, determining the best suited technology to address research questions. High-level principles and characteristics of the most used modalities for spatial metabolomics, lipidomics and proteomics, sensitivity and specific requirements for sample procurement and preparation are discussed. MSI applications for projects focused on drug metabolism, nonclinical safety assessment, and pharmacokinetics/pharmacodynamics and various diagnostic pathology cases from nonclinical and clinical settings are showcased.

Fire Severity Outperforms Remote Sensing Indices in Exploring Post-Fire Vegetation Recovery Dynamics in Complex Plateau Mountainous Regions

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies.

A novel oversampling method based on Wasserstein CGAN for imbalanced classification

Class imbalance is a crucial challenge in classification tasks, and in recent years, with the advancements in deep learning, research on oversampling techniques based on GANs has proliferated. These techniques have proven to be excellent in addressing the class imbalance issue by capturing the distributional features of minority samples during training and generating high-quality new samples. However, oversampling methods based on GANs may suffer from gradient vanishing, resulting in mode collapse, and produce noise and boundary-blurring issues when generating new samples. This paper proposes a novel oversampling method based on a conditional GAN (CGAN) incorporating Wasserstein distance. It generates an initial balanced dataset from minority class samples using the CGAN oversampling approach and then uses a noise and boundary recognition method based on K-means and k nearest neighbors algorithm to address the noise and boundary-blurring issues. The proposed method generates new samples that are highly consistent with the original sample distribution and effectively solves the problems of noise data and class boundary blurring. Experimental results on multiple public datasets show that the proposed method achieves significant improvements in evaluation metrics such as Recall, F1_score, G-mean, and AUC.

Protocol for invivo chromatin immunoprecipitation on purified chromatin isolated from mouse liver nuclei.

Chromatin immunoprecipitation (ChIP) is used to investigate genome binding bytranscription factors, but it can be problematic. We present a protocol to isolate fixed DNA-protein complexes from mouse liver prior to chromatin shearing. We describe steps for liver disaggregation and cross-linking, DNA-protein complex isolation, chromatin shearing, and quality control analysis as well as procedures for ChIP, DNA purification, and ChIP analysis. This protocol yields high-quality samples using commercial antibodies. For complete details on the use and execution of this protocol, please refer to Akl etal.1.

Gas Path Fault Diagnosis Method Under Small Samples with Inter-Class Imbalance

Abstract Current data-driven methods for diagnosing gas path faults in aero-engines often rely on large, costly fault sample sets and face challenges related to class imbalances. These include disparities in the quantity of normal and fault data, differences among fault types, and variations in fault severity levels. This paper proposes a novel generative model, TL-GMVAE, which integrates Gaussian Mixture Models (GMM) with Variational Autoencoders (VAE) and incorporates a transfer learning (TL) strategy. Using a large dataset of normal operational data, the VAE learns latent feature mappings of engine behavior and establishes a joint probability distribution across sensor measurements. To account for complexity in engine operating conditions, the GMM is used as the sampling distribution of the VAE. This enhances the model's ability to represent diverse operating scenarios. The pre-trained model is then fine-tuned with a small dataset of gas path fault data, transferring knowledge to fault domains. Each fault-specific TL-GMVAE model serves as an independent generator for synthetic fault samples. The proposed approach is validated using several established classifiers. The impact of different Fault-Normal ratios and imbalances across fault categories on classification accuracy is analyzed. Additionally, the robustness of the method to individual engine variability is evaluated. Results demonstrate that the TL-GMVAE generates high-quality fault samples and significantly improves fault diagnosis accuracy. These findings highlight its potential application in aero-engine health monitoring and fault diagnosis systems.

Fast Simulation of the Zero Degree Calorimeter Responses with Generative Neural Networks

Applying machine learning methods to high-energy physics simulations has recently emerged as a rapidly developing area. A prominent example is the Zero Degree Calorimeter (ZDC) simulation in the ALICE experiment at CERN, where substituting the traditional computationally extensive Monte Carlo methods with generative models radically reduces computation time. Although numerous studies have addressed the fast ZDC simulation, there remains significant potential for innovations. Recent developments in generative neural networks have enabled the creation of models capable of producing high-quality samples indistinguishable from real data. In this paper, we apply the latest advances to the simulation of the ZDC neutron detector and achieve a significant improvement in the Wasserstein metric compared to existing methods with a low generation time of 5 ms per sample. Our focus is on exploring novel architectures and state-of-the-art generative frameworks. We compare their performance against established methods, demonstrating competitive outcomes in speed and efficiency. The source code and hyperparameters of the models can be found at https://github.com/m-wojnar/zdc.

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem of requiring annotated data by mining intrinsic properties in the given data. However, the existing contrastive learning methods are not suitable for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e.g., meta-path) in graph data while ignoring noises in node attributes and graph topologies. We develop a robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidances of node attributes and graph topologies and integrates and enhances them by a reciprocally contrastive mechanism to better model heterogeneous graphs. In this new approach, we adopt distinct but suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately. We further use both attribute similarity and topological correlation to construct high-quality contrastive samples. Extensive experiments on four large real-world heterogeneous graphs demonstrate the superiority and robustness of HGCL over several state-of-the-art methods.

Improved generative adversarial networks for denoising fabric defect images

In order to solve the problems of noise, blurring, and loss of detail in fabric defect images due to camera location and factory environments, a fabric defect image denoising method based on improved generative adversarial network (GAN) is proposed. A mathematical model is developed to simulate the noisy images of fabric blemishes in a real factory environment. GAN, as a deep learning method widely used in the field of computer vision, generates high-quality data samples through the competition between generators and discriminators. In this study, the traditional GAN is improved by introducing multiple loss terms and adopting a new adaptive weighting strategy in order to make the denoised image closer to the original clear image while better preserving the structural and detailed features of the image. In addition, the network structure is improved by introducing a multiscale discriminator, a convolutional block attention module (CBAM) and a residual network to capture feature information at different spatial scales more effectively and to promote the propagation of low-level features. The experimental results show that the method can effectively remove fabric defective image noise, and its subjective effect and objective indexes are better than similar algorithms. The research results are of great value to quality inspectors, related researchers, and image denoising technology professionals in the textile industry. These results help to improve the efficiency and accuracy of fabric defect detection and image denoising processing, thus promoting the development of related technologies.

Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning

The prediction of blast loading for complex structures using deep learning requires extensive training data from field experiments or numerical simulations. However, the destructive nature of explosions complicates the collection of adequate field data, and traditional simulations are often time-consuming. To address these challenges, a Bayesian deep learning approach is proposed that quantifies prediction uncertainty. This method utilizes an uncertain selection strategy to actively choose high-quality samples, enhancing the simulation process and iteratively expanding the training dataset. The experimental results demonstrate that this Bayesian deep active learning method achieves a mean absolute percentage error (MAPE) of 6.1% for peak overpressure predictions. Additionally, more than 73.1% of confidence intervals include true values, with prediction times under 20 ms for single-point blasts. Notably, only 60% of the training data is required to achieve the same accuracy as conventional deep learning methods. This approach facilitates rapid and reliable predictions of blast loading for complex structures while significantly reducing training costs.

Developing a Health Support System to Promote Care for the Elderly.

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis. This equipment can minimize the risk of serious accidents or severe health hazards. The objective of the present research project is to design an armband with an accurate location tracking system. This is of particular importance for individuals with dementia and Alzheimer's disease, who frequently leave their homes and are unable to find their way back. The proposed armband also includes a fingerprint identification system that allows only authorized personnel to use it. Furthermore, in hospitals and healthcare facilities the biometric identification system can be used to trace periodic medical or nursing visits. This process improves the reliability and transparency of healthcare. The test results indicate that the armband functions in accordance with the desired design specifications, with performance evaluation of the main features including fall detection, where a hit rate of 100% was obtained, a fingerprint recognition test demonstrating accuracy from 88% to 100% on high-quality samples, and a GPS tracking test determining position with a difference of between 1.8 and 2.1 m. The proposed solution may be of benefit to healthcare professionals, supported housing providers, elderly people as target users, or their family members.

Prediction of transcript isoforms and identification of tissue-specific genes in cucumber

BackgroundIdentification of global transcriptional events is crucial for genome annotation, as accurate annotation enhances the efficiency and comparability of genomic information across species. However, the annotation of transcripts in the cucumber genome remains to be improved, and many transcriptional events have not been well studied.ResultsWe collected 1,904 high-quality public cucumber transcriptome samples from the National Center for Biotechnology Information (NCBI) to identify and annotate transcript isoforms in the cucumber genome. Over 44.26 billion Q30 clean reads were mapped to the cucumber genome with an average mapping rate of 92.75%. Transcriptome assembly identified 151,453 transcripts spanning 20,442 loci. Among these, 12.7% of transcripts exactly matched annotated genes in the cucumber reference genome. More than 80% of the transcripts were classified as novel isoforms. Approximately 96.6% of these isoforms originated from known gene loci, while around 3.3% were derived from novel gene loci. Coding potential prediction identified 4,543 long non-coding RNAs (lncRNAs) across 3,376 loci. Building on these results, we identified tissue-specific transcripts in 10 tissues. Among that, 1,655 annotated genes and 4,214 predicted transcripts were considered as tissue-specific. The root exhibited the highest number of tissue-specific transcripts, followed by shoot apex. Subsequent selective pressure analysis revealed that tissue-specific regions experienced stronger directional selection compared to non-specific regions.ConclusionsBy analyzing thousands of published transcriptome data, we identified abundant transcriptional events and tissue-specific transcripts in cucumbers. This study presented here adds the great value to the public data and offers insights for further exploration of a more comprehensive tissue regulatory network in cucumber.

Can clinical findings at admission allow withholding of antibiotics in patients hospitalized for community acquired pneumonia when a test for a respiratory virus is positive?

BackgroundCurrent guidelines recommend empiric antibiotic therapy for patients who require hospitalization for community-acquired pneumonia (CAP). We sought to determine whether clinical, imaging or laboratory features in patients hospitalized for CAP in whom PCR is positive for a respiratory virus enable exclusion of bacterial coinfection so that antibiotics can be withheld.MethodsFor this prospective study, we selected patients in whom an etiologic diagnosis was likely to be reached, namely those who provided a high-quality sputum sample at or shortly after admission, and in whom PCR was done to test for a respiratory virus. We performed quantitative bacteriologic studies on sputum to determine the presence of bacterial infection or coinfection and reviewed all clinical, imaging and laboratory studies.ResultsOf 122 CAP patients studied, 77 (63.1%) had bacterial infection, 16 (13.1%) viral infection, and 29 (23.8%) bacterial/viral coinfection. Underlying pulmonary disease and a history of smoking were more common in bacterial pneumonia. Upper respiratory symptoms were more common, and mean white blood cell (WBC) counts were lower viral pneumonia. Nevertheless, no clinical, laboratory or imaging findings allowed exclusion of bacterial coinfection in patients who tested positive for a respiratory virus. In fact, patients with bacterial/viral coinfection were sicker than those with bacterial or viral pneumonia; 30% were admitted required transfer to the ICU during their hospital course, compared to 17% and 19% of patients with bacterial or viral infection, respectively (p < .05). In this study, 64.4% of patients who tested positive for a respiratory virus had a bacterial coinfection.ConclusionsIf a test for a respiratory virus test is positive in a patient hospitalized for CAP, no sufficiently differentiating features exclude bacterial coinfection, thereby supporting the recommendation that empiric antibiotics be administered to all patients who are sufficiently ill to require hospitalization for CAP.

Ventilator-Associated Pneumonia After Cardiac Arrest and Prevention Strategies: A Narrative Review.

Background and Objectives: Ventilator-associated pneumonia (VAP) poses a significant threat to the clinical outcomes and hospital stays of mechanically ventilated patients, particularly those recovering from cardiac arrest. Given the already elevated mortality rates in cardiac arrest cases, the addition of VAP further diminishes the chances of survival. Consequently, a paramount focus on VAP prevention becomes imperative. This review endeavors to comprehensively delve into the nuances of VAP, specifically in patients requiring mechanical ventilation in post-cardiac arrest care. The overarching objectives encompass (I) exploring the etiology, risk factors, and pathophysiology of VAP, (II) delving into available diagnostic modalities, and (III) providing insights into the management options and recent treatment guidelines. Methods: A literature search was conducted using PubMed, MEDLINE, and Google Scholar databases for articles about VAP and Cardiac arrest. We used the MeSH terms "VAP", "Cardiac arrest", "postcardiac arrest syndrome", and "postcardiac arrest syndrome". The clinical presentation, diagnostic, and management strategies of VAP were summarized, and all authors reviewed the selection and decided which studies to include. Key Content and Findings: The incidence and mortality rates of VAP exhibit significant variability, yet a recurring pattern emerges, marked by prolonged hospitalization and exacerbated clinical outcomes. This pattern is attributed to the elevated incidence of drug-resistant infections and the delayed initiation of antimicrobial treatment. This review focuses on VAP, aiming to offer valuable insights into the efficient identification and management of this fatal complication in post-cardiac arrest patients. Conclusion: The prognosis for survival after cardiac arrest is already challenging, and the outlook becomes even more daunting when complicated by VAP. The timely diagnosis of VAP and initiation of antibiotics pose considerable challenges, primarily due to the invasive nature of obtaining high-quality samples and the time required for speciation and identification of antimicrobial sensitivity. The controversy surrounding prophylactic antibiotics persists, but promising new strategies have been proposed; however, they are still awaiting well-designed clinical trials.

Ultrasonic characterization of 3D-printed polymer objects.

3D printing technology, also known as Additive Manufacturing (AM), has revolutionized object prototyping, offering a simple, cost-effective, and efficient approach to creating structures with diverse spatial features. However, the mechanical properties of 3D-printed structures are highly dependent on the material type and manufacturing technique employed. In this study, ultrasonic testing methods were used to comprehensively characterize standard samples produced using two popular printing techniques: material extrusion and vat photopolymerization. The investigation focuses on seven commonly used 3D printing polymer materials, namely nylon, PET-G, flexible polymer, polycarbonate, acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), and photopolymer resin. Through ultrasonic testing, the mechanical parameters of objects made of different polymer materials were found. Some of these parameters are Young's modulus, shear modulus, acoustic impedance, and absorption. A comparative analysis of these parameters in different objects provides insights about their respective performance and behavior. This information may be useful to enhance the design and performance of ultrasonic lenses and lab-on-a-chip devices. Findings indicate that the vat photopolymerization printing process yields high-quality samples that exhibit minimal deviations in thickness, diameter, and surface parallelism. Moreover, microscopic analysis of the vat photopolymerization samples revealed low levels of porosity, which suggests that the material can be considered homogeneous. In contrast, the material extrusion samples showed significant porosity in the form of gaps between the deposited filaments, which had a direct impact on their mechanical and acoustic properties.

Evidence of Dirac Quantum Spin Liquid in YbZn_{2}GaO_{5}.

The emergence of a quantum spin liquid (QSL), a state of matter that can result when electron spins are highly correlated but do not become ordered, has been the subject of a considerable body of research in condensed matter physics [1,2]. Spin liquid states have been proposed as hosts for high-temperature superconductivity [3] and can host topological properties with potential applications in quantum information science [4]. The excitations of most quantum spin liquids are not conventional spin waves but rather quasiparticles known as spinons, whose existence is well established experimentally only in one-dimensional systems; the unambiguous experimental realization of QSL behavior in higher dimensions remains challenging. Here, we investigate the novel compound YbZn_{2}GaO_{5}, which hosts an ideal triangular lattice of effective spin-1/2 moments with no detectable inherent chemical disorder. Thermodynamic and inelastic neutron scattering measurements performed on high-quality single crystal samples of YbZn_{2}GaO_{5} exclude the possibility of long-range magnetic ordering down to 0.06K, demonstrate a quadratic power law for the specific heat and reveal a continuum of magnetic excitations in parts of the Brillouin zone. Both low-temperature thermodynamics and inelastic neutron scattering spectra suggest that YbZn_{2}GaO_{5} is a U(1) Dirac QSL with spinon excitations concentrated at certain points in the Brillouin zone. We advanced these results by performing additional specific heat measurements under finite fields, further confirming the theoretical expectations for a Dirac QSL on the triangular lattice of YbZn_{2}GaO_{5}.

Optimal tissue acquisition method for pancreatic mass.

Pancreatic masses pose a diagnostic difficulty due to the technical complexities related to tissue acquisition. Endoscopic ultrasound (EUS)-guided tissue acquisition has transformed the field by allowing access to pancreatic lesions through fine-needle and biopsy. However, diagnostic accuracy differs based on tumor characteristics and procedural factors. This narrative review explores the nuances of tissue acquisition methods for pancreatic tumors, including factors such as tumor location, size, histological characteristics, and needle selection. It assesses the efficacy of different needle designs and maneuvers, including suction techniques and needle passes. Moreover, the diverse tissue preparation methods, including cytological smear, cell block, and direct histology, are discussed, highlighting the importance of tailored approaches based on tumor characteristics. Additionally, the roles of macroscopic on-site evaluation and rapid on-site evaluation in optimizing specimen adequacy are investigated. Furthermore, percutaneous ultrasound-guided biopsy is considered an alternative approach, particularly in settings where EUS is impractical. Additionally, the review emphasizes the emerging trend of using tissue for genetic testing and molecular analysis, requiring high-quality sample acquisition. Future directions in tissue acquisition techniques and their integration into clinical practice are discussed, providing promising avenues for pancreatic disease diagnosis and treatment.

Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning

Conventional personality assessment methods depend on subjective input, while game-based AI predictive methods offer a dynamic and objective framework. However, training these models requires large and labeled datasets, which are challenging to obtain from real players with diverse personality traits. In this paper, we propose a multi-agent system using Deep Reinforcement Learning in a game environment to generate the necessary labeled data. Each agent is trained with custom reward functions based on the HiDAC system that encourages trait-aligned behaviors to emulate specific personality traits based on the OCEAN personality trait model. The Multi-Agent Posthumous Credit Assignment (MA-POCA) algorithm facilitates continuous learning, allowing agents to emulate behaviors through self-play. The resulting gameplay data provide diverse, high-quality samples. This approach allows for robust individual and team assessments, as agent interactions reveal the impact of personality traits on team dynamics and performance. Ultimately, this methodology provides a scalable, unbiased methodology for human personality evaluation in various settings, establishing new standards for data-driven assessment methods.