Biology subjects

Hayes, R. A.

Publications and source records attributed to Hayes, R. A..

4 recordsLinked to original sources

bioRxiv2026

Assessing the potential of bee-collected pollen sequence data to train machine learning models for geolocation of sample origin

Pollen is a robust and widespread substance that captures a historical snapshot of a specific time and place, and it can be used to track movements through space by examining the pollen deposited on various objects. Palynology, the study of pollen, is used across fields such as conservation, natural history, and forensics, where it is particularly useful for tracing the origin and movement of objects. However, pollen has remained underutilized due to the difficulty of distinguishing many pollen taxa beyond the family level and limited pollen reference material to support location predictions. With recent developments in pollen DNA metabarcoding these issues have been rectified, but much of the available pollen data are primarily from wind-pollinated species, which are widespread and less informative of specific sample locations. Bee-collected pollen presents an untapped resource in training predictive models to geolocate sample origin. Here we compiled bee-collected pollen DNA sequence relative abundance data from three projects in the western U.S. and assessed the accuracy of supervised machine learning models to predict the location of sample origin based solely on pollen assemblage, without the need of incorporating additional data. Random Forest and k-Nearest Neighbors models yielded high accuracy across all projects. We also found that models trained on taxonomically clustered pollen assigned sequence variants (ASVs) performed slightly better than those trained on raw sequence data, but the difference was minor, indicating that models trained on raw sequence data can reliably predict location and avoid the time-consuming taxonomic assignment process. Our results demonstrate the utility of repurposing bee-collected pollen for geolocation and provide a framework for employing supervised machine learning in future geolocation efforts. HighlightsO_LIBee-collected pollen metabarcoding data was used to accurately predict sample origin C_LIO_LIRandom Forest and k-Nearest Neighbors algorithms were most accurate with lowest error C_LIO_LITaxonomically-classified and raw DNA sequence data training sets performed comparably C_LI

bioinformatics↗

bioRxiv2023

Normative modeling of brain morphometry in Clinical High-Risk for Psychosis

ImportanceThe lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in the majority of individuals at psychosis risk may be nested within the range observed in healthy individuals. ObjectiveTo quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high-risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design, Setting, and ParticipantsClinical, IQ and FreeSurfer-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1,340 CHR-P individuals [47.09% female; mean age: 20.75 (4.74) years] and 1,237 healthy individuals [44.70% female; mean age: 22.32 (4.95) years] from 29 international sites participating in the ENIGMA Clinical High Risk for Psychosis Working Group. Main Outcomes and MeasuresFor each regional morphometric measure, z-scores were computed that index the degree of deviation from the normative means of that measure in a healthy reference population (N=37,407). Average deviation scores (ADS) for CT, SA, SV, and globally across all measures (G) were generated by averaging the respective regional z-scores. Regression analyses were used to quantify the association of deviation scores with clinical severity and cognition and two-proportion z-tests to identify case-control differences in the proportion of individuals with infranormal (z<-1.96) or supranormal (z>1.96) scores. ResultsCHR-P and healthy individuals overlapped in the distributions of the observed values, regional z-scores, and all ADS vales. The proportion of CHR-P individuals with infranormal or supranormal values in any metric was low (<12%) and similar to that of healthy individuals. CHR-P individuals who converted to psychosis compared to those who did not convert had a higher percentage of infranormal values in temporal regions (5-7% vs 0.9-1.4%). In the CHR-P group, only the ADSSA showed significant but weak associations (|{beta}|<0.09; PFDR<0.05) with positive symptoms and IQ. Conclusions and RelevanceThe study findings challenge the usefulness of macroscale neuromorphometric measures as diagnostic biomarkers of psychosis risk and suggest that such measures do not provide an adequate explanation for psychosis risk. Key pointsO_ST_ABSQuestionC_ST_ABSIs the risk of psychosis associated with brain morphometric changes that deviate significantly from healthy variation? FindingsIn this study of 1340 individuals high-risk for psychosis (CHR-P) and 1237 healthy participants, individual-level variation in macroscale neuromorphometric measures of the CHR-P group was largely nested within healthy variation and was not associated with the severity of positive psychotic symptoms or conversion to a psychotic disorder. MeaningThe findings suggest the macroscale neuromorphometric measures have limited utility as diagnostic biomarkers of psychosis risk.

neuroscience↗

bioRxiv2021

Associations between brain structure and sleep patterns across adolescent development

ImportanceStructural brain maturation and sleep are complex processes that exhibit significant changes over adolescence and are linked to healthy physical and mental development. The precise timing and magnitude of these changes influence function throughout the lifespan. However, the relationships between gray matter structure and sleep patterns during adolescence are not fully understood. A detailed characterization of brain-sleep associations during this sensitive period is crucial for understanding factors contributing to optimal neurodevelopmental trajectories in adolescence. ObjectiveTo investigate whether sleep-gray matter relationships are developmentally-invariant (i.e., stable across age) or developmentally-specific (i.e., only present during discrete time windows) from late childhood through young adulthood. SettingThe Neuroimaging and Pediatric Sleep Databank was constructed from 8 research studies conducted at the University of Pittsburgh between 2009 and 2020. ParticipantsThe final sample consisted of 240 participants without current psychiatric diagnoses (9-25 years), and with good quality sleep tracking and structural MRI (sMRI) data. DesignParticipants completed a sMRI scan and 5-7 days of wrist actigraphy to assess naturalistic sleep. We examined cross-sectional associations between sMRI measures and sleep patterns, as well as the effects of age, sex, and their interaction with sMRI measures on sleep. Main Outcome(s) and Measure(s)Using Freesurfer software, we extracted cortical thickness and subcortical volumes from T1-weighted MRI. Sleep patterns (duration, timing, continuity, regularity) were estimated from wrist actigraphy. ResultsShorter sleep duration, later sleep timing, and poorer sleep continuity were associated with a stable pattern of thinner cortex and altered subcortical volumes in diverse brain regions across adolescence. In a discrete subset of regions (e.g., posterior cingulate), thinner cortex was associated with these sleep patterns from late childhood through early-to-mid adolescence, but not in late adolescence and young adulthood. Conclusions and RelevanceIn childhood and adolescence, developmentally-invariant and developmentally-specific associations exist between sleep patterns and gray matter structure, in a wide array of brain regions linked to many sensory, cognitive, and emotional processes. Sleep intervention during specific developmental periods could potentially promote healthier neurodevelopmental outcomes. KEY POINTSO_ST_ABSQuestionC_ST_ABSDoes age modulate associations between gray matter structure and actigraphic sleep patterns across adolescent development? FindingsThis cross-sectional study reports stable associations between regional gray matter structure and shorter duration, later timing, and poorer continuity of sleep from ages 9 to 25 years-old, as well as developmentally-specific associations that are present only from late childhood to early-to-mid adolescence. MeaningStronger coupling of gray matter and sleep patterns from late childhood to early-to-mid adolescence potentially implicates this discrete developmental window as a period of vulnerability to adverse sleep-brain interactions. Sleep intervention during this developmental stage may support healthier neurodevelopmental trajectories.

neuroscience↗

bioRxiv2020

Pollinator niche partitioning and asymmetric facilitation contribute to the maintenance of diversity

AbstractMechanisms that favor rare species are key to the maintenance of diversity. One of the most critical tasks for biodiversity conservation is understanding how plant-pollinator mutualisms contribute to the persistence of rare species, yet this remains poorly understood. Using a process-based model that integrates plant-pollinator and interspecific pollen transfer networks with floral functional traits, we show that niche partitioning in pollinator use and asymmetric facilitation confer fitness advantage of rare species in a biodiversity hotspot. While co-flowering species filtered pollinators via floral traits, rare species showed greater pollinator specialization leading to higher pollination-mediated male and female fitness than abundant species. When plants shared pollinator resources, asymmetric facilitation via pollen transport dynamics benefited the rare species at the cost of the abundant ones, serving as an alternative diversity-promoting mechanism. Our results emphasize the importance of community-wide plant-pollinator interactions that affect reproduction for biodiversity maintenance.

ecology↗