Pattern Discrimination Research Articles

Reframing family-school partnerships to disrupt disenfranchisement of Black families and promote reciprocity in collaboration

Research has long demonstrated the benefits of family-school partnerships. However, these benefits often fail to generalize to all families, especially Black families. A present and historical pattern of discrimination and exclusion has contributed to the lack of benefits yielded from Black family-school partnerships. A major contributing factor is the narrow way in which schools define family engagement. Such narrow definitions often marginalize families from non-dominant backgrounds, particularly Black families, and reinforce harmful narratives that Black parents and families are uninvolved in their children's education. The combination of continued discrimination and exclusion as well as harmful narratives has impacted Black family-school partnering. However, schools can work to repair harm and rebuild partnerships with Black families. In this article, we advance a framework for such work. After grounding the need for this framework in a historical context, we emphasize three essential components to forming equitable Black family-school partnerships: (a) grounding relationship building in social justice, (b) integrating reciprocity in family-school relationships, and (c) usage of multiple and non-dominant methods and modalities to build relationships.

P3b correlates of inspection time

Both P3b and the inspection time (IT) are related with intelligence, yet the P3b correlates of IT are not well understood. This event-related potential study addressed this question by asking participants (N = 28) to perform an IT task. There were three IT conditions with different levels of discriminative stimulus duration, i.e., 33 ms, 67 ms, and 100 ms, and a control condition with no target presentation (0 ms condition). We also measured participants' processing speed with four Elementary Cognitive Tests (ECTs), including a Simple Reaction Time task (SRT), two Choice Reaction Time tasks (CRTs), and a Pattern Discrimination task (PD). Results revealed that an increase in P3b latency with longer duration of the discriminative stimulus. Moreover, the P3b latency was negatively correlated with the accuracy of the IT task in the 33 ms condition, but not evident in the 67 and 100 ms conditions. Furthermore, the P3b latency of the 33 ms condition was positively correlated with the RT of the SRT, but not related with the RTs of CRTs or PD. A significant main effect of duration on the amplitude of P1 was also found. We conclude that the present study provides the neurophysiological correlates of the IT task, and those who are able to accurately perceive and process very briefly presented stimuli have a higher speed of information process, reflected by the P3b latency, yet this relationship is more obvious in the most difficult condition. Combined, our results suggest that P3b is related with the closure of a perceptual epoch to form the neural representation of a stimulus, in support of the “context closure” hypothesis.

"We choose this CV because we choose diversity" - What do eye movements say about the choices recruiters make?

A large body of research has established a consensus that racial discrimination in CV screening occurs and persists. Nevertheless, we still know very little about how recruiters look at the CV and how this is connected to the discriminatory patterns. This article examines the way recruiters view and select CVs and how they reason about their CV selection choices, as a first step in unpacking the patterns of hiring discrimination. Specifically, we explore how race and ethnicity signaled through the CV matter, and how recruiters reason about the choices they make. We recorded data from 40 respondents (20 pairs) who are real-life recruiters with experiences in recruitment of diverse employees in three large Swedish-based firms in the finance and retail sector in two large cities. The participating firms all value diversity, equity and inclusion in their recruitment. Their task was to individually rate 10 fictious CVs where race (signaled by face image) and ethnicity (signaled by name) were systematically manipulated, select the top three candidates, and then discuss their choices in pairs to decide on a single top candidate. We examined whether respondents' choices were associated with the parts of the CV they looked at, and how they reasoned and justified their choices through dialog. Our results show that non-White CVs were rated higher than White CVs. While we do not observe any statistically significant differences in the ratings between different racial groups, we see a statistically significant preference for Chinese over Iraqi names. There were no significant differences in time spent looking at the CV across different racial groups, but respondents looked longer at Polish names compared to Swedish names when presented next to a White face. The dialog data reveal how respondents assess different CVs by making assumptions about the candidates' job and organizational fit through limited information on the CVs, especially when the qualifications of the candidates are evaluated to be equal.

Relating assimilation data to identification data in second language learners

Commonly cited models of bilingual phonology, e.g., the Speech Learning Model and the Perceptual Assimilation Model, project hypotheses about second language (L2) developmental patterns based on hypothetical assimilation patterns between categories in the learner’s first language (L1) and L2, with the addition of a learning component which differentiates an L2 and L1 system. This paper relates two experiments with two participant groups, one native Korean, and one native Taiwan Mandarin. Both groups identify English obstruents /p b t d f v θ ð/ in four prosodic positions (onset, coda, and intervocalic pre-stress and post-stress) using their native non-Roman graphemes in the first experiment, and using English labels in the second. The assimilation results generate predictions for discrimination performance in the second experiment, assuming only the L1 categories. The two groups show large differences in assimilation pattern due largely to the non-sibilant fricative /f/ in Mandarin, in opposition to the Korean system which has only non-sibilant /h/. Analyses reveal a strong correlation between L2 discrimination patterns and predictions based solely on the L2-L1 assimilation results, suggesting that the perceptual system of L2 learners can be surprisingly well-captured as a single perceptual system with a bilingual array of segmental categories.

Identification and Verification of Error-Related Potentials Based on Cerebellar Targets.

The error-related potential (ErrP) is a weak explicit representation of the human brain for individual wrong behaviors. Previously, ErrP-related research usually focused on the design of automatic correction and the error correction mechanisms of high-risk pipeline-type judgment systems. Mounting evidence suggests that the cerebellum plays an important role in various cognitive processes. Thus, this study introduced cerebellar information to enhance the online classification effect of error-related potentials. We introduced cerebellar regional characteristics and improved discriminative canonical pattern matching (DCPM) in terms of data training and model building. In addition, this study focused on the application value and significance of cerebellar error-related potential characterization in the selection of excellent ErrP-BCI subjects (brain-computer interface). Here, we studied a specific ErrP, the so-called feedback ErrP. Thirty participants participated in this study. The comparative experiments showed that the improved DCPM classification algorithm proposed in this paper improved the balance accuracy by approximately 5-10% compared with the original algorithm. In addition, a correlation analysis was conducted between the error-related potential indicators of each brain region and the classification effect of feedback ErrP-BCI data, and the Fisher coefficient of the cerebellar region was determined as the quantitative screening index of the subjects. The screened subjects were superior to other subjects in the performance of the classification algorithm, and the performance of the classification algorithm was improved by up to 10%.

How Discrimination Narratives Resolve Ambiguity: The Case of Islamophobia in Quebec

Abstract Discrimination frequently appears in ambiguous rather than overt forms. How do individuals manage the challenges associated with ambiguous discrimination, such as classifying incidents of negative but ambiguous treatment? Building on studies of microaggressions and perceived discrimination, this article develops an explanation rooted in a novel theory of discrimination narratives. Discrimination narratives express collective beliefs about discrimination’s patterns and features, which enable individuals to resolve ambiguity in their personal experiences and expectations. Based on a study of perceived Islamophobia in the Canadian province of Quebec, the article describes one common discrimination narrative and uncovers how Muslim Quebecers use it to 1) classify negative but ambiguous treatment by imputing missing information; (2) direct their attention to social situations they perceive to be high-risk; and (3) adjust to anticipated patterns in discrimination. Implications for research on ambiguity, microaggressions, perceived discrimination, and narratives are discussed.

Automated Detection of Rheumatic Heart Disease via Unsegmented Heart Sound Analysis

Rheumatic Heart Disease (RHD) remains a significant cause of morbidity and mortality in many parts of the world, particularly in low-resource settings. Early detection and intervention are critical to mitigating its progression and reducing associated complications. This paper presents an automated approach for the detection of RHD utilizing unsegmented heart sound analysis through deep learning techniques. By leveraging Spectro-temporal representations of raw heart sound signals, our proposed method aims to capture subtle yet discriminative patterns indicative of RHD pathology. The deep learning model is trained on a dataset comprising a diverse range of heart sounds, including those from both RHD-positive and RHD-negative individuals. Through rigorous evaluation of unseen data, our approach demonstrates promising performance in accurately distinguishing between RHD and healthy heart sounds. Furthermore, the proposed methodology offers the potential for scalable and cost-effective screening in resource-limited settings, thereby facilitating early identification and management of RHD cases. This work contributes to the advancement of computer-aided diagnostic tools for cardiovascular diseases, with implications for improving healthcare outcomes and reducing the burden of RHD globally. Key Words: Rheumatic Heart Disease, Heart Sound Analysis, Deep Learning, Automated Detection, Unsegmented Signals, Spectro-Temporal Representation, Cardiovascular Disease.

Civil Society, Volunteerism, and Youth Citizenship in Fiji

Fiji’s multi-ethnic society is historically characterised by low levels of inter-ethnic trust and a segregated civil society, typified by low participation of youth, the poor, ethnic minorities, and less literate members of society. How does this actually existing civil society shape the social transactions, value subjectivities, norms and habits of citizenship bred through volunteering and other forms of civic engagement in these contexts? Drawing on data from a mixed method study on youth volunteering in Fiji, this paper interrogates prevailing normative assumptions on volunteerism’s role in retooling civic renewal and citizenship. Being socially situated, the outcomes of youth volunteering vary. Specifically, youth volunteering in organisations that value inclusion has midwifed progressive citizenship values; while, participation in bonding type civil society reproduces exclusionary citizenship, social disparities and patterns of discrimination and privilege. The implication is that for volunteerism to produce desired progressive citizenship values and attitudes, civic organisations transmitting such values need to be specifically focussed on progressive goals.

Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.

A deep learning approach for mental health quality prediction using functional network connectivity and assessment data.

While one can characterize mental health using questionnaires, such tools do not provide direct insight into the underlying biology. By linking approaches that visualize brain activity to questionnaires in the context of individualized prediction, we can gain new insights into the biology and behavioral aspects of brain health. Resting-state fMRI (rs-fMRI) can be used to identify biomarkers of these conditions and study patterns of abnormal connectivity. In this work, we estimate mental health quality for individual participants using static functional network connectivity (sFNC) data from rs-fMRI. The deep learning model uses the sFNC data as input to predict four categories of mental health quality and visualize the neural patterns indicative of each group. We used guided gradient class activation maps (guided Grad-CAM) to identify the most discriminative sFNC patterns. The effectiveness of this model was validated using the UK Biobank dataset, in which we showed that our approach outperformed four alternative models by 4-18% accuracy. The proposed model's performance evaluation yielded a classification accuracy of 76%, 78%, 88%, and 98% for the excellent, good, fair, and poor mental health categories, with poor mental health accuracy being the highest. The findings show distinct sFNC patterns across each group. The patterns associated with excellent mental health consist of the cerebellar-subcortical regions, whereas the most prominent areas in the poor mental health category are in the sensorimotor and visual domains. Thus the combination of rs-fMRI and deep learning opens a promising path for developing a comprehensive framework to evaluate and measure mental health. Moreover, this approach had the potential to guide the development of personalized interventions and enable the monitoring of treatment response. Overall this highlights the crucial role of advanced imaging modalities and deep learning algorithms in advancing our understanding and management of mental health.

AIEgen-deep: Deep learning of single AIEgen-imaging pattern for cancer cell discrimination and preclinical diagnosis

This study introduces AIEgen-Deep, an innovative classification program combining AIEgen fluorescent dyes, deep learning algorithms, and the Segment Anything Model (SAM) for accurate cancer cell identification. Our approach significantly reduces manual annotation efforts by 80%–90%. AIEgen-Deep demonstrates remarkable accuracy in recognizing cancer cell morphology, achieving a 75.9% accuracy rate across 26,693 images of eight different cell types. In binary classifications of healthy versus cancerous cells, it shows enhanced performance with an accuracy of 88.3% and a recall rate of 79.9%. The model effectively distinguishes between healthy cells (fibroblast and WBC) and various cancer cells (breast, bladder, and mesothelial), with accuracies of 89.0%, 88.6%, and 83.1%, respectively. Our method's broad applicability across different cancer types is anticipated to significantly contribute to early cancer detection and improve patient survival rates.

Emotion Regulation Moderates Associations between Discrimination and Cannabis Use Patterns among Sexual Minority Young Adult Women.

Sexual minority young adults (SMYAs) experience discrimination and have high cannabis use prevalence. Discrimination may be associated with cannabis use, including hazardous use and co-use with tobacco, depending on emotion regulation and gender. Fall 2020 survey data assessed discrimination, use frequency of emotion regulation strategies (i.e., cognitive reappraisal, expressive suppression), current cannabis use, hazardous use, and cannabis-tobacco dual use among SMYAs (age 18-34) in 6 United States metropolitan areas (women: n=450, M age =24.1, SD=4.7, 69.6% bisexual, 18.2% lesbian/gay, 12.2% other; men: n=254, M age=24.7, SD=4.5, 33.5% bisexual, 54.3% gay, 12.2% other). Multivariable logistic regression examined the moderating roles of cognitive reappraisal and expressive suppression on associations of discrimination with cannabis use outcomes, stratified by gender and adjusted for age, race and ethnicity, and employment. Among SMYA women, 89.5% experienced any discrimination; 53.1% reported current cannabis use, of whom 49.4% and 47.7% reported hazardous use and cannabis-tobacco dual use, respectively. Adjusting for sociodemographics, experiencing greater discrimination was associated with greater odds of hazardous cannabis use (aOR=1.08, 95% CI [1.02, 1.15]) and cannabis-tobacco dual use (aOR=1.04, 95% CI [1.01, 1.08]) among SMYA women with greater use of expressive suppression. Among SMYA men, 83.9% experienced any discrimination; 49.2% reported current cannabis use, of whom 55.2% and 44.0% reported hazardous use and cannabis-tobacco dual use. Discrimination and emotion regulation were unrelated to cannabis use outcomes among men. Conclusions: Given high rates of discrimination experiences among SMYAs, emotion regulation skills training may empower SMYAs, particularly women, to cope with discrimination without using cannabis.

Development of non-destructive methods for the assessment of authenticity of sports whey protein supplements

In the category of sports supplements, whey protein powder is one of the popular supplements for muscle building applications. Therefore, verification of the sport supplements as authentic products has become a universal concern. This work aimed to propose vibrational spectroscopy including near infrared (NIR) and infrared (IR) as rapid and non-destructive testing tools for the detection and quantification of maltodextrin, milk powder and milk whey powder in whey protein supplements. Initially, principal component analysis was applied to data for pattern recognition and the results displayed a fine pattern of discrimination. Partial least square discrimination analysis (PLS-DA) and K-nearest neighbours (KNN) were exploited as supervised method modelling classification. This process was done in order to respond to two vital questions whether the sample is adulterated or not and what is the kind of adulteration. PLS-DA showed better classification results rather than KNN according to the figure of merits of the model. Partial least square regression (PLSR) was employed on pre-treated spectra to quantify the amount of adulteration in sport whey supplements. Eventually, it seems vibrational spectroscopy could be implemented as a simple, and low-cost analysis method for the detection and quantification of mentioned adulterants in whey protein supplements.

Characterizing the discriminability of visual categorical information in strongly connected voxels

Functional brain responses are strongly influenced by connectivity. Recently, we demonstrated a major example of this: category discriminability within occipitotemporal cortex (OTC) is enhanced for voxel sets that share strong functional connectivity to distal brain areas, relative to those that share lesser connectivity. That is, within OTC regions, sets of ‘most-connected’ voxels show improved multivoxel pattern discriminability for tool-, face-, and place stimuli relative to voxels with weaker connectivity to the wider brain. However, understanding whether these effects generalize to other domains (e.g. body perception network), and across different levels of the visual processing streams (e.g. dorsal as well as ventral stream areas) is an important extension of this work. Here, we show that this so-called connectivity-guided decoding (CGD) effect broadly generalizes across a wide range of categories (tools, faces, bodies, hands, places). This effect is robust across dorsal stream areas, but less consistent in earlier ventral stream areas. In the latter regions, category discriminability is generally very high, suggesting that extraction of category-relevant visual properties is less reliant on connectivity to downstream areas. Further, CGD effects are primarily expressed in a category-specific manner: For example, within the network of tool regions, discriminability of tool information is greater than non-tool information. The connectivity-guided decoding approach shown here provides a novel demonstration of the crucial relationship between wider brain connectivity and complex local-level functional responses at different levels of the visual processing streams. Further, this approach generates testable new hypotheses about the relationships between connectivity and local selectivity.

Family-based and external discrimination experienced by multiracial individuals: Links to internalizing symptoms and familial support.

Multiracial individuals are exposed to many forms of interpersonal racial discrimination, including general discrimination against their monoracial groups and discrimination against being multiracial. Because their families include members of different racial groups, multiracial people may also be exposed to various forms of discrimination from within the family. In the present study, we leverage recent advances in latent profile analysis to identify distinct patterns of family-based and external (i.e., from outside the family unit) discrimination experienced by multiracial college students, the differential impacts of these discrimination patterns on depressive and anxiety symptoms, and whether parental support of participants' multiracial experiences and identity impacts their exposure to different forms of discrimination. In a sample of 635 diverse multiracial college students (Mage = 21.2, SD = 5.3, range = 18-57, 74.0% female) from three U.S. universities, we identified three distinct discrimination profiles: High External and Familial Discrimination (43.2%), Average External Low Familial Discrimination (32.1%), and Low External and Familial Discrimination (24.7%). Profiles differed in depressive and anxiety symptomatology, with those in the High External and Familial Discrimination profile displaying the worst outcomes. Parental support of multiracial experiences was associated with lower levels of family-based discrimination. The complex relations between parental support, family-based discrimination, and multiracial participants' internalizing symptomology are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Shear-interference assisted deep-learning for enhancing spatially multiplexing capacity of free-space communication

The information capacity is a critical measure in free-space optical (FSO) communication. Traditional mode-multiplexing techniques employed in optical communication utilizing Laguerre-Gaussian beams (LGBs) necessitate proper topological charge interval to prevent mode-crosstalk. In this paper, we propose a novel approach that integrates deep learning (DL) into the LGB-based optical communication model, incorporating shearing interference techniques. This combination enables the FSO system to achieve the theoretical limit of capacity, resulting in a remarkable 300 % increase in information capacity compared to traditional techniques. Experimental results demonstrate that the constructed neural network attains a flawless pattern recognition accuracy of 100 %. Additionally, we evaluate various partially coherent light scenarios under the influence of beam decoherence. The results reveal that accurate discrimination of multiplexing patterns is maintained when the fringe contrast exceeds 0.43. Even at a degraded contrast level of 0.18, the recognition accuracy remains impressively high at 91.16 %. To demonstrate practicality, we successfully encode and transmit a cyan-magenta-yellow-black (CMYK) color image using the proposed system, achieving a low bit error rate of 7.5 × 10-5. These findings highlight the potential of DL-assisted mode-division multiplexing for future optical communication applications.

Portable Mid-Infrared Spectroscopy Combined with Chemometrics to Diagnose Fibromyalgia and Other Rheumatologic Syndromes Using Rapid Volumetric Absorptive Microsampling.

The diagnostic criteria for fibromyalgia (FM) have relied heavily on subjective reports of experienced symptoms coupled with examination-based evidence of diffuse tenderness due to the lack of reliable biomarkers. Rheumatic disorders that are common causes of chronic pain such as rheumatoid arthritis, systemic lupus erythematosus, osteoarthritis, and chronic low back pain are frequently found to be comorbid with FM. As a result, this can make the diagnosis of FM more challenging. We aim to develop a reliable classification algorithm using unique spectral profiles of portable FT-MIR that can be used as a real-time point-of-care device for the screening of FM. A novel volumetric absorptive microsampling (VAMS) technique ensured sample volume accuracies and minimized the variation introduced due to hematocrit-based bias. Blood samples from 337 subjects with different disorders (179 FM, 158 non-FM) collected with VAMS were analyzed. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. The OPLS-DA algorithm enabled the classification of the spectra into their corresponding classes with 84% accuracy, 83% sensitivity, and 85% specificity. The OPLS-DA regression plot indicated that spectral regions associated with amide bands and amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases.

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics.

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

PseAAC2Vec protein encoding for TCR protein sequence classification

The classification and prediction of T-cell receptors (TCRs) protein sequences are of significant interest in understanding the immune system and developing personalized immunotherapies. In this study, we propose a novel approach using Pseudo Amino Acid Composition (PseAAC) protein encoding for accurate TCR protein sequence classification. The PseAAC2Vec encoding method captures the physicochemical properties of amino acids and their local sequence information, enabling the representation of protein sequences as fixed-length feature vectors. By incorporating physicochemical properties such as hydrophobicity, polarity, charge, molecular weight, and solvent accessibility, PseAAC2Vec provides a comprehensive and informative characterization of TCR protein sequences. To evaluate the effectiveness of the proposed PseAAC2Vec encoding approach, we assembled a large dataset of TCR protein sequences with annotated classes. We applied the PseAAC2Vec encoding scheme to each sequence and generated feature vectors based on a specified window size. Subsequently, we employed state-of-the-art machine learning algorithms, such as support vector machines (SVM) and random forests (RF), to classify the TCR protein sequences. Experimental results on the benchmark dataset demonstrated the superior performance of the PseAAC2Vec-based approach compared to existing methods. The PseAAC2Vec encoding effectively captures the discriminative patterns in TCR protein sequences, leading to improved classification accuracy and robustness. Furthermore, the encoding scheme showed promising results across different window sizes, indicating its adaptability to varying sequence contexts.

Influence of sex, age, ethnicity/race, and body mass index on the cerumen volatilome using two data analysis approaches: binary and semiquantitative.

Human cerumen analysis is an innovative and non-invasive trend in diagnosing diseases. Recently, new cerumen volatile-based methods using binary (volatile presence/absence) and semiquantitative (volatile intensity) data approaches have shown great potential in detecting biomarkers for cancer, chronic and rare diseases, and xenobiotic exposures. However, to date, the impacts of demographic factors such as body mass index (BMI), sex, age, and ethnicity/race in cerumen data have not been widely described, which can hamper interpretation in biomarker discovery investigations. This study examined the effects of such factors in cerumen, defining the baseline volatile organic metabolites (VOMs) across different physiological groups. Cerumen samples from seventy volunteers were analyzed using headspace/gas chromatography-mass spectrometry (HS/GC-MS) and multivariate statistical analysis using binary and semiquantitative data approaches. In the binary data approach, several VOMs exhibited patterns of high occurrence in some specific demographic groups. However, no pattern of discrimination that could be attributed to demographic factors was observed. In the semiquantitative approach, the relative abundance of cerumen VOMs was more impacted by sex and BMI than age and ethnicity/race. In summary, we describe how cerumen VOM occurrence and abundance are affected by patient phenotype, which can pave the way for more personalized medicine in future cerumen volatile-based methods.