Automatic Processing Research Articles

SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects

The composition of the gut microbiota is a known factor in various diseases and has proven to be a strong basis for automatic classification of disease state. A need for a better understanding of microbiota data on the functional scale has since been voiced, as it would enhance these approaches’ biological interpretability. In this paper, we have developed a computational pipeline for integrating the functional annotation of the gut microbiota into an automatic classification process and facilitating downstream interpretation of its results. The process takes as input taxonomic composition data, which can be built from 16S or whole genome sequencing, and links each component to its functional annotations through interrogation of the UniProt database. A functional profile of the gut microbiota is built from this basis. Both profiles, microbial and functional, are used to train Random Forest classifiers to discern unhealthy from control samples. SPARTA ensures full reproducibility and exploration of inherent variability by extending state-of-the-art methods in three dimensions: increased number of trained random forests, selection of important variables with an iterative process, repetition of full selection process from different seeds. This process shows that the translation of the microbiota into functional profiles gives non-significantly different performances when compared to microbial profiles on 5 of 6 datasets. This approach’s main contribution however stems from its interpretability rather than its performance: through repetition, it also outputs a robust subset of discriminant variables. These selections were shown to be more consistent than those obtained by a state-of-the-art method, and their contents were validated through a manual bibliographic research. The interconnections between selected taxa and functional annotations were also analyzed and revealed that important annotations emerge from the cumulated influence of non-selected taxa.

A Similar Feature Point Matching Method for Aerial Electric Power Tower Images Based on a One-Ring Neighborhood of Vertices

Due to the susceptibility of images to various factors such as scale changes, imaging conditions, and image noise, traditional feature point matching methods are difficult to achieve ideal matching accuracy, leading to many challenges in the automatic analysis and processing of power tower images. To improve the matching accuracy of similar feature points in aerial images of electric power tower, this study proposes a method for matching similar feature points in aerial images of electric power tower based on a vertex ring neighborhood, addressing the problem of large matching errors caused by factors such as scale and imaging conditions. This method first adopts the feature point extraction technique of electric power tower image based on decomposition and filtering, and constructs the Laplacian pyramid of the original image. Subsequently, filter banks are used to decompose the pyramid image in different directions, extract local extremum points as candidate feature point sets, and merge them to obtain the final feature point set. On this basis, taking into account the spatial relationships and geometric characteristics around the feature points, a texture mapping algorithm based on vertex ring neighborhood and patch classification is applied to construct a vertex ring neighborhood structure and extract geometrically representative electric power tower features. Finally, by using a texture feature point matching method based on the principle of similarity, the similarity between the real-time image and the reference image features is calculated for matching, and combined with the Babbitt coefficient to remove mismatched feature points, accurate matching of similar feature points in aerial electric power tower images is achieved. The experimental results show that this method has a mean square error of less than 0.1Pixel in matching similar texture feature points of aerial power tower images under various working conditions, significantly improving the matching accuracy and providing an effective tool for automatic analysis and processing of power tower images.

AN INVESTIGATION INTO THE IMPACT OF EMPLOYEE STRESS MANAGEMENT ON ORGANIZATIONAL PERFORMANCE

Stress is a normal physical response to events that make you feel threatened or upset your balance in some way. When you sense danger – whether it’s real or imagined – the body's defenses kick into high gear in a rapid, automatic process known as the “fight-or- flight” reaction, or the stress response. The present study has been focused in to identify the reasons of employee stress and symptoms of the stress. It has also focused on to analyze whether the management is providing an adequate training facilities to their workers. The purpose of the study is also to identify whether the workers are able to manage their time in their job. KEY WORDS : job, management, training, stress, symptoms.

Investigating the effect of cognitive load on the intentionality bias.

According to Rosset's dual-process model of intention attribution, our judgements of intentionality can be guided either by an automatic process leading to intentional explanations of behaviour or by a higher-level and cognitively more demanding process enabling unintentional explanations of behaviour. Based on this model, under conditions of compromised cognitive capacity, individuals should judge more behaviour to be intentional rather than unintentional. This prediction was tested in one lab-based experiment and one online experiment. Specifically, we investigated whether increased working memory load would lead to higher intentionality endorsement of ambiguous action when controlling for individual differences in working memory. Results of both experiments indicated no effect of working memory load on intentionality endorsement. The implications of these results for the dual-process model of intention attribution are discussed.

NCBI RefSeq: reference sequence standards through 25years of curation and annotation.

Reference sequences and annotations serve as the foundation for many lines of research today, from organism and sequence identification to providing a core description of the genes, transcripts and proteins found in an organism's genome. Interpretation of data including transcriptomics, proteomics, sequence variation and comparative analyses based on reference gene annotations informs our understanding of gene function and possible disease mechanisms, leading to new biomedical discoveries. The Reference Sequence (RefSeq) resource created at the National Center for Biotechnology Information (NCBI) leverages both automatic processes and expert curation to create a robust set of reference sequences of genomic, transcript and protein data spanning the tree of life. RefSeq continues to refine its annotation and quality control processes and utilize better quality genomes resulting from advances in sequencing technologies as well as RNA-Seq data to produce high-quality annotated genomes, ortholog predictions across more organisms and other products that are easily accessible through multiple NCBI resources. This report summarizes the current status of the eukaryotic, prokaryotic and viral RefSeq resources, with a focus on eukaryotic annotation, the increase in taxonomic representation and the effect it will have on comparative genomics. The RefSeq resource is publicly accessible at https://www.ncbi.nlm.nih.gov/refseq.

Exploring the Application of Large Language Models Based AI Agents in Leakage Detection of Natural Gas Valve Chambers

Leakage problems occur from time to time due to the large number of equipment and complex processes during oil and gas production and transportation. The traditional detection methods highly depend on manpower with large workload and are prone to missed and false alarms, which seriously affects the efficiency and safety of oil and gas production and transportation. With the continuous improvement of information technology and the rapid advancement of artificial intelligence (AI), the research on leakage detection technology based on AI methods has attracted more and more attention. This paper discusses the application scenarios of an AI agent based on the recently emerged large language model (LLM) technology in oil and gas production leakage detection: (1) Compared with the traditional leakage detection methods, this paper innovatively employs a combination of AI-based diagnostics and infrared temperature measurement technologies to develop a specialized small model for oil and gas leakage detection, which has been proven to significantly improve the accuracy of detecting industrial venting events in natural gas valve chambers; (2) By employing retrieval-augmented generation (RAG) technology, a knowledge vector library is constructed, utilizing a series of leakage-related documents, assisting the LLM to carry out knowledge questioning and inference. Compared with the traditional SimBERT, the accuracy can be improved by about 15% in the Q&A search ability test. The correct rate is about 70% higher than the SimBERT in the Chinese complex reasoning quiz. Also, it can still remain stable under high load conditions, with the interruption rate of retrieval closing to zero. (3) By combining the specialized small model and the knowledge Q&A tool, the natural gas valve chambers’ leakage detection AI agent based on the open-source LLM model was designed and developed, which preliminarily achieved the leakage detection based on the specialized small model, and the automatic processing of the retrieval reasoning process based on the knowledge Q&A tool and the intelligent generation of corresponding leakage disposal scheme. The effectiveness of the method has been verified by actual project data. This article conducts preliminary explorations into the in-depth applications of AI agents based on LLMs in the oil and gas energy industry, demonstrating certain positive outcomes.

Towards theoretically understanding how long-term memory semantics can support working memory performance.

Working memory is the system that supports the temporary storage and processing of information. It is generally agreed that working memory is a mental workspace, with a combination of resources operating together to maintain information in mind for potential use in thought and action. Theories typically acknowledge the contributions of long-term memory to this system. One particular aspect of long-term memory, namely semantic long-term memory, can effectively supplement or "boost" working memory performance. This may be a relatively automatic process via the semantic properties of the stimuli or more active via strategy development and implementation. However, the precise mechanisms require greater theoretical understanding. In this review of the literature, we critically discuss theoretical models of working memory and their proposed links with long-term memory. We also explore empirical research that contributes to our understanding of the ways in which semantics can support performance of both verbal and visuospatial working memory tasks, with a view to potential intervention development. This includes the possibility of training people with lower performance (e.g., older adults) to use semantics during working memory tasks. We conclude that semantics may offer an opportunity to maximise working memory performance. However, to realise this potential, more research is needed, particularly in the visuospatial domain.

Understanding the Process of Acceptance Within the Nurse–Patient Therapeutic Relationship in Mental Health Care: A Grounded Theory

Background/Objective: Deepening knowledge about the meaning of acceptance in the context of the nurse–patient relationship will help us to understand the importance and value that people with mental health challenges attach to this attribute in the process of the therapeutic relationship. The objective of this study was to understand the process of acceptance as part of the therapeutic relationship between nurses and patients in the field of mental health. Methods: This study employed a qualitative research design using Strauss and Corbin’s grounded theory approach. Using theoretical sampling, data were collected through unstructured interviews with mental health nurses and with people with mental health challenges. Results: Acceptance is a complex and dynamic process that takes place when both patients and nurses feel and make the other feel like an individual human being. Acceptance is not an automatic process; rather, it requires awareness on behalf of both parties involved in the therapeutic relationship. The creation of a non-hierarchical caring environment is fundamental for fostering mutual acceptance and engagement in the therapeutic process. Conclusions: Nurses must develop self-awareness and reflect on their attitudes and biases to provide person-centered care. Nurse training and personal development are essential requirements for achieving an effective therapeutic relationship and empowering patients in their recovery process.

Limited Practical Effect of APEC ODR: A Comparative Analysis of Platform ODR and APEC ODR

Abstract: The actual demand and effect of the ODR (Online Dispute Resolution) mechanism in the field of cross-border e-commerce dispute resolution are relatively limited, and the protection of the ODR mechanism within small and medium-sized cross-border e-commerce platforms is insufficient. The reasons why the ODR mechanism cannot be fully applied to such platform disputes include the complexity of cross-border, diverse types of disputes, cost of time and money, and limited scope of application of the ODR mechanism. In addition, the lack of neutrality and standardized operating rules of the ODR mechanism within the e-commerce platform directly affects the fairness of dispute resolution. Although APEC (Asia-Pacific Economic Cooperation) ODR mechanism has shown significant advantages in improving efficiency and ensuring information security, it fails to effectively balance efficiency and fairness in terms of application scope and practical effect. Based on this, there are insufficient fairness problems in the automatic process mechanism, evidence collection procedure, and process deadline setting in the APEC ODR process. In the future, the APEC ODR and other international dispute resolution mechanisms will continue to be more internationalized and standardized. In addition, we have to be alert to the anti-globalization risks reflected in the APEC ODR, avoid geopolitical interferences as much as possible, and prevent the emergence of trade protectionism.

MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data

Background: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. Methods: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in determining the lipid subclass of ion features. We designed a new descriptor, MCH (mode of carbon and hydrogen), to increase the specificity of lipid subclass prediction in nominal mass resolution MS data. Results: The model, trained with 6760 and 6862 manually curated MS/MS spectra for the positive and negative ion modes, respectively, classified queries into one or several of 97 lipid subclasses, achieving an accuracy of 97.4% in the test set. The program was further validated using various datasets from different instruments and curators, with the average accuracy exceeding 87.2%. Using an integrated approach with molecular spectral networking, we demonstrated the utility of MS2Lipid by annotating microbiota-derived esterified bile acids, whose abundance was significantly increased in fecal samples of obese patients in a human cohort study. This suggests that the machine learning model provides an independent criterion for lipid subclass classification, enhancing the annotation of lipid metabolites within known lipid classes. Conclusions: MS2Lipid is a highly accurate machine learning model that enhances lipid subclass annotation from MS/MS data and provides an independent criterion.

Improving CNN Fish Detection and Classification with Tracking

The regular and consistent monitoring of marine ecosystems and fish communities is becoming more and more crucial due to increasing human pressures. To this end, underwater camera technology has become a major tool to collect an important amount of marine data. As the size of the data collected outgrew the ability to process it, new means of automatic processing have been explored. Convolutional neural networks (CNNs) have been the most popular method for automatic underwater video analysis for the last few years. However, such algorithms are rather image-based and do not exploit the potential of video data. In this paper, we propose a method of coupling video tracking and CNN image analysis to perform a robust and accurate fish classification on deep sea videos and improve automatic classification accuracy. Our method fused CNNs and tracking methods, allowing us to detect 12% more individuals compared to CNN alone.

The PRIDE database at 20 years: 2025 update.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's leading mass spectrometry (MS)-based proteomics data repository and one of the founding members of the ProteomeXchange consortium. This manuscript summarizes the developments in PRIDE resources and related tools for the last three years. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 534 datasets per month. This has been possible thanks to continuous improvements in infrastructure such as a new file transfer protocol for very large datasets (Globus), a new data resubmission pipeline and an automatic dataset validation process. Additionally, we will highlight novel activities such as the availability of the PRIDE chatbot (based on the use of open-source Large Language Models), and our work to improve support for MS crosslinking datasets. Furthermore, we will describe how we have increased our efforts to reuse, reanalyze and disseminate high-quality proteomics data into added-value resources such as UniProt, Ensembl and Expression Atlas.

NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit

As the body of academic literature continues to grow, researchers face increasing difficulties in effectively searching for relevant resources. Existing databases and search engines often fall short of providing a comprehensive and contextually relevant collection of academic literature. To address this issue, we propose a novel framework that leverages natural language processing (NLP) techniques. This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain. To demonstrate the effectiveness of our approach, we introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature. CyLit empowers researchers by providing access to context-specific resources and enabling the tracking of trends in the dynamic and rapidly evolving field of cyber risk. Through the automatic processing of large volumes of data, our NLP-powered solution significantly enhances the efficiency and specificity of academic literature searches. We compare the literature categorization results of CyLit with those presented in survey papers or generated by ChatGPT, highlighting the distinctive insights this tool provides in the cyber risk research literature. Using NLP techniques, we aim to revolutionize the way researchers discover, analyze, and utilize academic resources, ultimately fostering advancements in various domains of knowledge.

Automatic detection of landslide impact areas using Google Earth Engine

This research introduces an effective framework for automatically detecting landslide impact areas using Google Earth Engine (GEE). The Asia–Pacific region frequently experiences earthquakes and heavy rainfall, leading to frequent landslides that cause loss of life and property. Focusing on landslide catalogues from Taiwan and Japan, the study proposes an automatic landslide detection process using a new method termed multi-bitemporal images (MBTIs), which involves the collection of accumulated changes over time. First, set the event date and collect all images before and after the event. Second, analyse the change pixels in bi-temporal images. Third, review all change pixels to determine the total amount of accumulated changes. This method includes all bi-temporal image sets in the analysis, unlike traditional methods that only use single pairs of bi-temporal images. Clouds are filtered using a pixel-based approach and machine learning techniques. The landslide areas are analysed statistically, and appropriate thresholds for automatic landslide detection are suggested. Using reproducibility, which indicates the percentage of bi-temporal images that detect vegetation loss in mountainous areas, the proposed method achieves a 99% reduction in false positives with a reproducibility requirement of 24.21%, while maintaining true positives at 66.89%. This study analyzed 28–720 bi-temporal image sets from various regions using Sentinel-2 data, revealing that subsequent landslides can be 7–293 times larger than co-seismic landslides. In comparison, subsequent landslides were found to be 3–12 times larger than rainfall-induced landslides. Additionally, the impact of earthquake event on subsequent landslides is 2.3–24.4 times greater than that of rainfall-induced event. By using GEE, the accumulation of hundreds of satellite images can be completed within 15 min, depending on the processing requirements.

A study of automatic output power control methods for stand-alone photovoltaic power generation systems

Abstract The conventional power generation system’s output power automatic control structure is generally unidirectional, and the control coverage is small, leading to the increase in the random error obtained in the final test. Therefore, the design and research of this article are proposed. According to the current control demand, the generation system shall be stabilized first, and the power compensation shall be calculated. The multi-step mode shall be adopted to improve the control coverage and design the multi-step automatic control structure. According to this, it is constructed, and the automatic control processing is realized using jump balance. The test results show that compared with the traditional DFIG-DRWT system power output automatic control method and the traditional three-phase asynchronous generation system power output automatic control method, the random error finally obtained by the designed independent photovoltaic power station output power automatic control method is relatively effective. This indicates that the application effect and coverage of the designed power automatic control method are high, that controllability has been significantly improved, and that control flexibility has also been strengthened.

“I will raise my hand and say ‘I over-trust Autopilot’. I use it too liberally” – Drivers’ reflections on their use of partial driving automation, trust, and perceived safety

Introduction: Partially automated cars are on the road. Trust in automation and perceived safety are critical factors determining use of automation. Background: Drivers misuse partially automated driving systems. Misuse is associated with mis-calibrated trust in the automation. Research gap: Little is known about the factors impacting the perceived safety when using partial driving automation. Research objective: The main objective of the present study is to provide a comprehensive driver perspective on the psychological aspects of automation use pertaining to trust in automation, perceived safety, and its relationship with use of automation. Method: Semi-structured interviews (n = 103) were conducted with users of partially automated driving systems. Supplemented with content analysis, natural language processing (NLP) techniques were applied to perform automatic text processing. Guided seed-term analysis was conducted to identify the number of occurrences of the subcategories in the dataset. Main results: We identified human operator-related, automation-related, and environmental factors of trust and perceived safety. The identified factors were more strongly associated with perceived safety than with trust. Participants with physical and visual impairments reported to feel safer using the automation compared to driving manually. Neurotic behavior during manual driving contributed to lower trust and perceived safety using the automation. A correct mental model of the capabilities and limitations of the automation did not guarantee proper automation use. A novel conceptual, process-oriented model, titled PTS-a (predicting trust in and perceived safety of automation use), synthesizes the results of the data analysis. Informed by the cognition-leads-to-emotions approach, the model posits that trust as cognition precedes perceived safety as affective construct. Trust and perceived safety determine how human operators (mis-, dis-)use the automation. Future research: We recommend future research to perform experimental studies to identify cognitive-related thoughts and beliefs pertaining to trust in automation and perceived safety to contribute to the operationalization of these constructs, and unravel the nature of their relationship.

Neurophysiological measures of covert semantic processing in neurotypical adolescents actively ignoring spoken sentence inputs: A high-density event-related potential (ERP) study

Language comprehension requires semantic processing of individual words and their context within a sentence. Well-characterized event-related potential (ERP) components (the N400 and late positivity component (LPC/P600)) provide neuromarkers of semantic processing, and are robustly evoked when semantic errors are introduced into sentences. These measures are useful for evaluating semantic processing in clinical populations, but it is not known whether they can be evoked in more severe neurodevelopmental disorders where explicit attention to the sentence inputs cannot be objectively assessed (i.e., when sentences are passively listened to). We evaluated whether N400 and LPC/P600 could be detected in adolescents who were explicitly ignoring sentence inputs. Specifically, it was asked whether explicit attention to spoken inputs was required for semantic processing, or if a degree of automatic processing occurs when the focus of attention is directed elsewhere? High-density ERPs were acquired from twenty-two adolescents (12–17 years), under two experimental conditions: 1. individuals actively determined whether the final word in a sentence was congruent or incongruent with sentence context, or 2. passively listened to background sentences while watching a video. When sentences were ignored, N400 and LPC/P600 were robustly evoked to semantic errors, albeit with reduced amplitudes and protracted/delayed latencies. Statistically distinct topographic distributions during passive versus active paradigms pointed to distinct generator configurations for semantic processing as a function of attention. Covert semantic processing continues in neurotypical adolescents when explicit attention is withdrawn from sentence inputs. As such, this approach could be used to objectively investigate semantic processing in populations with communication deficits.

Automated deep learning approach for identifying etiologies of hypertrophic phenocopies by echocardiography

Abstract Background Left ventricular hypertrophy (LVH) is an indicator of poor prognosis across various cardiovascular diseases and is developed by various etiologies. Transthoracic echocardiography (TTE) is the most available cardiac imaging tool to detect LVH. However, it is still challenging to differentiate three non-ischemic cardiomyopathies which frequently presenting LVH: Fabry cardiomyopathy (FC), hypertrophic cardiomyopathy (HCM), and cardiac amyloidosis (CA) from morphology or cardiac function evaluated by TTE. Purpose We aimed to develop stepwise deep learning models to differentiate FC, HCM, and CA using radiomics extracted from TTE. Methods We conducted a retrospective cohort study between 2011 and 2022 in Taiwan. Patients with left ventricular (LV) mass index≥95g/m² in women or ≥115g/m² in men, or the maximal LV wall thickness≥13mm on echocardiography were enrolled. The TTE automatic processing models included four steps: image quality assessment, view classification, auto-segmentation of interventricular septum (IVS), and dynamic radiomics analysis. One echocardiographic study was scanned per patient, and each study contains multiple cardiac cycles. One sample was extracted from each cycle and all samples were evaluated by image quality assessment and view classification model that based on VGG16. Samples with apical four (A4C) and apical five chamber (A5C) views were retained for following steps. Manual contouring of the septal region served as the ground truth for auto-segmentation based on U-Net. The last step was the extraction of dynamic radiomic features from IVS and the dynamic radiomic features were analyzed using an LSTM model to differentiate three hypertrophic cardiomyopathies. Results There were 103 patients with FC, 96 patients with HCM, and 49 patients with CA in this study. Patients with CA were older and had lower LV ejection fraction (EF) than those with FC or HCM (age: 65.8±9.7 vs. 59.3±11.1 vs. 58.6±14.2 years, P=0.002; LVEF: 56.6±10.8 vs. 61.9±8.8 vs. 64.5±8.3%, P<0.001). In addition, patient with FC had larger LV mass index than those with HCM or CA. (Fig. 2A) The details of the performances of four stepwise models were provided in Figure 2 (B-F). The image quality assessment and view classification models achieved accuracies of 91.7% and 95.2%, respectively. The auto-segmentation had the median Dice score as 0.88 for contouring IVS. The results indicated that deep learning algorithms had great ability to recognize different views with proper image quality, and could contour the IVS adequately. The accuracy of dynamic radiomics-based LSTM model to differentiate three hypertrophic phenocopies was 74.5%. Conclusion We developed an automatic process with four stepwise models for analyzing the radiomics of IVS on TTE to differentiate FC, HCM and CA with the modest performance by LSTM model. The developed automatic models may provide potential implications to identify various etiologies LVH in the future.Figure 1-Workflow of this studyFigure 2-Performances of stepwise models

A Novel Dictionary Learning Algorithm Based on Prior Knowledge for fMRI Data Analysis

ABSTRACTTask‐based functional magnetic resonance imaging (fMRI) has been widely utilized for brain activation detection and functional network analysis. In recent years, the K‐singular value decomposition (K‐SVD) algorithm has gained increasing attention in the research of fMRI data analysis methods. In this study, we propose a novel temporal feature region‐growing constrained K‐SVD algorithm that incorporates task‐based fMRI temporal prior knowledge and utilizes a region‐growing algorithm to infer potential activation locations. The algorithm incorporates temporal and spatial constraints to enhance the detection of brain activation. Specifically, this paper improves the three stages of the traditional K‐SVD algorithm. First, in the dictionary initialization stage, the automatic target generation process with an independent component analysis algorithm is utilized in conjunction with prior knowledge to enhance the accuracy of initialization. Second, in the sparse coding stage, the region‐growing algorithm is employed to infer potential activation locations based on temporal prior knowledge, thereby imposing spatial constraints to limit the extent of activation regions. Finally, in the dictionary learning stage, soft constraints and low correlation constraints are applied to reinforce the consistency with prior knowledge and enhance the robustness of learning for task‐related atoms. The proposed method was validated on simulated and real fMRI data, showing superior performance in detecting brain activation compared with traditional methods. The results indicate that the algorithm accurately identifies activated brain regions, providing an effective approach for studying brain function in clinical applications.

Unmasking the Aversive Stigmatizer: Integrating Deliberate and Automatic Cognition in the Study of Mental Health Stigma and Perceived Dangerousness

Scholars posit that mental health stigma may be the product of both deliberate (i.e., intentional and conscious) and automatic (i.e., effortless and nonconscious) cognitive processes. Yet, few sociological studies have empirically tested this theoretical assumption with valid measures of automatic cognition. This study integrates explicit and implicit stigma measures to investigate the presence of a potentially unique actor in the stigma process, the aversive stigmatizer: individuals who deliberately reject negative cultural stereotypes but still hold implicit mental illness-related biases that may inadvertently influence discriminatory behaviors. Findings suggest that a substantial portion of the sample are aversive stigmatizers and that these actors are disproportionately present among certain social groups. Thus, this study sheds light on a previously hidden population that likely contributes to prejudice and discrimination, as well as advances our theoretical understanding of both public stigma and self-stigma.