Biology subjects

Schwartz, J.

Publications and source records attributed to Schwartz, J..

At least 19 recordsLinked to original sources

bioRxiv2026

ARTIFICIAL SELECTION DIVERSIFIES BIRDSONG THROUGH POLYGENIC CHANGES ACROSS SENSORY AND MOTOR SYSTEMS

Vocal learning, or the ability to acquire and produce contextualized sounds through social interaction, underlies human speech yet has evolved in a few animal lineages, including songbirds. In songbirds, learned song is produced by a dedicated neural circuit and depends on auditory feedback for its acquisition and maintenance. Roughly four centuries of artificial selection on the domestic canary (Serinus canaria) have produced song breeds whose vocalizations diverge as much as those of distinct species, offering a rare intraspecific system in which to identify the genetic and neural changes underlying vocal divergence. Here we show that selection for song reshaped the canary song control system. Compared with breeds selected for plumage or body form, song breeds show increased neural progenitor proliferation and breed-specific changes in neuronal recruitment, sensorimotor brain region volume, and auditory sensitivity. Genome-wide analyses reveal a polygenic architecture underlying song divergence, enriched for transcriptional regulation and synaptic transmission genes. Among these, we find a coding variant in ADGRV1, the gene mutated in human Usher syndrome type 2C, associated with low-frequency singing and elevated auditory thresholds in canary. Disrupting ADGRV1 in zebrafish alters hair cell dynamics, linking adgrv1 mutation to a peripheral sensory mechanism. These findings demonstrate that learned vocal behavior can diversify rapidly through changes spanning sensory, neurogenic, and motor systems, with hearing itself as a substrate for selection.

evolutionary biology↗

bioRxiv2026

Decoding Bispecific Antibody Developability: Design Rules and Predictive Models from a 160-Member Library

Bispecific antibodies deliver functional outcomes that monospecific antibodies cannot, yet emergent self-association, polyreactivity, and aggregation often degrade their developability relative to their parental arms. Whether bispecific developability inherits from the parents or is driven by the format has not been tested at scale. We characterized 160 bispecific antibodies and their 65 parental arms on a uniform knobs-into-holes CrossMab IgG1 scaffold across 10 assays on the PROPHET-Ab high-throughput platform. Bispecific developability separates into three classes of inheritance. Hydrophobicity and surface charge inherit cleanly from the parents (Spearman {rho} {approx} 0.85 to 0.95), so parental-level screening predicts bispecific fate. Self-association and polyreactivity inherit partially ({rho} {approx} 0.60 to 0.88), with mechanistically interpretable emergent outliers driven in part by Fv-Fv charge complementarity and a parental biophysical ceiling on the hydrophobicity (HIC) by surface-charge (HAC) plane. Thermostability is poorly predicted from parental antibodies ({rho} < 0.4), so it requires bispecific-level testing. The class framework yields actionable selection rules: triage hydrophobicity and charge at the parental level, avoid pairing two high-HIC x high-HAC arms, pair opposite-sign Fv charges to suppress self-association but re-validate at the formulation buffer, and measure thermostability on the bispecific itself. This work charts a tractable path from monospecific sequence to bispecific developability prediction. SignificanceBispecific antibodies are a fast-growing therapeutic class, yet the rational design of well-behaving bispecific antibodies from validated monospecific antibody building blocks remains challenging. A key bottleneck is the lack of comprehensive, high-quality public datasets linking parental antibody developability properties to corresponding bispecific antibody developability properties. We address this gap by releasing a dataset comprising 160 bispecific antibodies and the 65 parental monospecific antibodies profiled in 10 developability assays. The data show that bispecific antibody developability is complex. Some properties are easily predictable from the parents, whereas others emerge in the bispecific format or from the bispecific format itself. The factors that govern each property can be identified empirically and used to make practical selection decisions. The mechanistic explanations and predictive models reported here establish a compact set of actionable rules. Together, they define a framework for using computational pipelines to convert monospecific antibodies into bispecific antibodies with drug-like developability properties, enabling faster and more effective generation of high-quality bispecific antibodies for diverse therapeutic applications.

biophysics↗

bioRxiv2026

SIMOFF: Discovering the metabolic objective of the cell

There are huge variations in metabolic complexity between the different kingdoms of life. Whilst it has been shown that some simple, unicellular organisms such as E. coli direct their energetic resources towards maximising proliferation, the metabolic goals of more complex organisms are unclear. This is an especially important topic for engineered organisms, such as Chinese Hamster Ovary (CHO) cells, that have been modified to produce therapeutically relevant compounds. This metabolic goal is reflected in the objective function of a constraint-based model (CBM) and has a direct impact on the metabolic flux distribution that is predicted using Flux Balance Analysis (FBA). However, there is no broadly applicable approach to infer this objective function from experimental data, to ensure CBMs represent real growth conditions. Here, we developed SIMOFF (SIMulated annealing Objective Function Finder) to infer the most appropriate objective function from minimal experimental flux data. Our applications of SIMOFF to S. cerevisiae demonstrated that the most suitable objective function is dependent on key metabolic phenotypes, even when the same organism and conditions are being modelled. Furthermore, we demonstrated the translatability of SIMOFF through application to CHO cells, where we showed that a SIMOFF-inferred objective function improved the accuracy of gene essentiality simulations, resulting in more reliable experimental target predictions.

Systems Biology↗

bioRxiv2026

Characterization Standard for In-situ Cryo-electron Tomography

Standardized biological specimens are essential for optimizing cryoEM workflows and benchmarking instrument performance. While apoferritin fulfills this role for single-particle analysis, no equivalent exists for cryo-electron tomography. Ribosomes are frequently used but require large datasets due to C1 symmetry and structural heterogeneity, limiting rapid optimization and standardized comparison of workflows. Here, we present PP7 virus-like particles (VLPs) overexpressed in E. coli as a scalable in situ benchmark. VLPs have high orders of symmetry enabling rapid, high-resolution validation of tomographic pipelines from minimal datasets, while their distinct structural features across low to high resolutions provide a practical resolution metric.

cell biology↗

bioRxiv2026

High Accuracy Fluorescence Guided Focused Ion Beam Milling

Cryo-electron tomography (cryo-ET) is a powerful approach for visualizing macromolecular structures directly within cells, but its broader application is limited by the difficulty of reliably targeting specific structures during sample preparation. In particular, capturing small or rare objects within cryo-focused ion beam (cryo-FIB) milled lamellae remains a major bottleneck. Here, we present two fluorescence-guided cryo-FIB milling workflows that overcome key sources of targeting error and enable routine capture of structures across a broad range of spatial scales. For larger targets (>500 nm), we present an improved registration-based strategy combining FIB-milled fiducials with depth correction for refractive index mismatch. For smaller targets (150-500 nm), we implement real-time fluorescence guided milling on a commercially available cryo-FIB-SEM platform enabling consistent recovery of small, low-copy organelles such as centrosomes and primary cilia. Both workflows are implemented in a new open-source package fibsemOS and together expand the range of cellular structures accessible to cryo-ET and establish a broadly deployable framework for targeted in situ structural biology.

cell biology↗

bioRxiv2026

Intuitive knowledge of object acoustics enables perceptual separation of physical variables from impact sounds

Upon hearing objects collide, humans can estimate physical attributes such as material and mass. Although the physics of sound generation is well established, the inverse problem that listeners solve - of inferring physical parameters from sound - remains poorly understood. Classical accounts posit the use of acoustic cues that correlate with physical variables, but do not explain how humans might distinguish multiple concurrent physical causes. To study this problem, we built a probabilistic generative model of impact sounds, combining theoretical acoustics with statistics of object resonances measured from hundreds of everyday objects, and used it to synthesize and manipulate experimental stimuli. Humans accurately judged object properties from collision sounds. However, when both of the colliding objects varied, performance was impaired if the distribution of object resonances deviated from those measured in real-world objects. The results suggest that listeners use internal physical models to separate the acoustic contributions of objects in the world.

neuroscience↗

bioRxiv2025

Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography

The difficulty of particle picking in cryo-electron tomography remains a barrier to routine in situ structure determination. Machine learning is well-suited to overcome this bottleneck with efficient algorithms that generalize across molecular species. To spur new algorithm development, we held a three-month Kaggle challenge that tasked contestants with annotating five molecular species across hundreds of experimental tomograms. This competition successfully engaged >1000 participants from diverse fields and delivered particle pickers that outperformed existing state-of-the-art. Systematic comparisons of the contestants submissions revealed the tolerance of subtomogram averaging to moderate but not severe over-picking and underscored the need for more robust measures of annotation quality. The winning models also highlighted the importance of data augmentation to overcome limited training data. All competition tomograms along with the ground truth and winning teams annotations have been released on the CryoET Data Portal as a resource to benchmark current and future tools for particle picking.

cell biology↗

bioRxiv2025

Outdoor Air Pollution, Perivascular Space Morphology, and Cognition in Preadolescence

BackgroundAmbient air pollution exposure is associated with structural brain differences and poorer cognition in children; however, mechanisms of toxicity remain unclear. Perivascular spaces (PVS), key for brain waste clearance, may play a role in the neurotoxicity of air pollution. This study explored associations between air pollution exposure, PVS morphology, and cognition in preadolescents. MethodsWe analyzed cross-sectional Adolescent Brain Cognitive DevelopmentSM (ABCD) Study(R) data from 6,949 9-10-year-old participants. Annual average exposures to PM2.5, O3, NO2, and 15 PM2.5 components were estimated using spatiotemporal models mapped to residential addresses. PVS count and volume were derived from T1w and T2w MRI, and cognition was estimated using NIH Toolbox scores. Linear mixed-effects models examined independent associations between air pollution, PVS, and cognition; weighted quantile sum regression assessed co-exposure effects of PM2.5 mixtures. FindingsLinear models revealed that exposures to Zn, NH4 +, and Br were positively associated with PVS count in several regions. Higher PVS count in five key regions was associated with poorer cognitive performance across several NIH Toolbox domains. Higher Ca, Zn, and NH 4 + exposures were associated with poorer cognition (PFDR < 0.01). Higher frontal lobe PVS count mediated the association between Zn exposure and poorer total cognition (P < 0.01). Co-exposure models revealed that PM2.5 mixtures were associated with higher temporal and cingulate PVS count, and poorer working memory and crystallized intelligence (P < 0.01). InterpretationOutdoor air pollution was associated with higher PVS count and reduced cognition, suggesting that brain clearance may be a novel mechanism linking pollution to neurodevelopmental harm in preadolescents. FundingThis work was supported by the National Institutes of Health (NIH) National Institute of Environmental Health Sciences (NIEHS) (Grant Nos. R01ES032295 and R01ES031074 [to MMH]; T32ES013678 [to JM]; P30ES07048 [to JM and MAR]; 3P30ES000002-55S [to MAR]), National Institute of Mental Health (NIMH) (Grant RF1MH123223 [to JC]), National Institute of Neurological Disorders and Stroke (Grant R01NS128486 [to JC]), and EPA grants (Grant Nos. 83587201 and 83544101 [to JS]).

neuroscience↗

bioRxiv2025

Sources of outdoor air pollution exposure and child brain network development across the United States

Ambient fine particulate matter (PM2.5) pollution is a heterogeneous mixture of chemicals with documented neurotoxic effects. Developmental neuroimaging literature has linked childhood PM2.5 exposure to alterations in brain morphology, microarchitecture, and function, with implications for cognition and psychopathology. However, the extant literature remains largely cross-sectional and often considers PM2.5 a single pollutant, rather than a heterogeneous mixture of chemicals from different sources. This work addresses these gaps by leveraging estimates of exposure to six PM2.5 sources derived from positive matrix factorization, and longitudinal neuroimaging data from a large, geographically-diverse sample of Adolescent Brain Cognitive Development Study youth (N = 6,291) from across the United States (U.S.). To identify exposure-related differences in brain function and assess their geographical generalizability, we used a predictive modeling approach to assess both differences in functional brain network connectivity during childhood (9-11 years of age) and changes in functional brain network connectivity during the transition to adolescence (9-13 years of age) related to PM2.5 exposure. Childhood PM2.5 exposure from traffic emissions and industrial/residual fuel burning were linked to mixed patterns of both stronger and weaker connectivity of sensorimotor networks at ages 9-11 years. Conversely, childhood exposures to secondary pollutants (i.e., ammonium sulfates, nitrates) were linked to largely stronger connectivity of brain networks underlying higher-order cognition that decreased over the following two years. However, these patterns of exposure-related functional connectivity identified in youth across the U.S. better represented youth living in the northeast as compared to youth living in the west. Altogether, this work provides insights into the neurotoxicity of outdoor air pollution exposure in developing sensory and motor systems and potential for biomarkers of eventual psychopathology.

neuroscience↗

bioRxiv2025

Epigenome-wide association study meta-analysis of wellbeing

Wellbeing is associated with both behavioral phenotypes as well as several key life outcomes, such as health, employment, and coping with stressful events. These phenotypes associated with wellbeing could be potential indicators of differential epigenetic patterns between individuals that differ in their levels of wellbeing. We performed the largest epigenome-wide (EWAS) meta-analysis of wellbeing to date by combining whole blood DNA methylation data (Illumina 450k array) from 13 cohorts from Europe, Australia, and the USA (N = 10,757 participants). After correcting for smoking and BMI, no epigenome-wide significant methylation sites were identified. We tested whether a weighted methylation score (MS) based on leave-one-cohort-out EWAS meta-analysis summary statistics predicted wellbeing in an independent cohort, and whether prediction was significant over and above the polygenic score (PGS) for wellbeing. The MS was associated with wellbeing (variance explained=0.22%, p=0.03) and was no longer significant after adding the polygenic score (PGS; variance explained=0.43%, p=0.0046, MS; variance explained=0.07%, p=0.2842). We further compared DNA methylation levels in 16 pairs of monozygotic twins discordant for wellbeing. These analyses revealed no significant within-pair DNA methylation differences at the top-sites from the meta-analysis or in MS. Our results suggest that larger EWAS meta-analyses with uniform phenotype assessment are required to identify methylation sites associated with wellbeing.

genomics↗

bioRxiv2025

Histone H3 lysine methyltransferase activities control compartmentalization of human centromeres.

Centromeres are essential chromosomal regions that ensure accurate genome segregation during cell division. They are organized into epigenetically discrete compartments: a Centromere Protein A (CENP-A)-rich core for microtubule attachment and surrounding heterochromatic pericentromeres that promote cohesion. Despite their importance, the mechanisms that define, enforce and partition these chromatin domains remain poorly understood. To address this, we disrupted key H3K9 methyltransferases- SUV39H1, SUV39H2, and SETDB1- that establish heterochromatin in humans. We find that SETDB1 is required for H3K9 dimethylation at core centromeres, while SUV39H1/2 complete trimethylation. Unexpectedly, depleting all three enzymes results in aberrantly high H3K9me3, driving CENP-A expansion into pericentromeres. This promiscuous deposition is mediated by G9a/GLP methyltransferases, which selectively reestablish H3K9me3 within the centromere core. SETDB1, regardless of its enzymatic activity, blocks G9a/GLP-mediated heterochromatin deposition and CENP-A expansion, revealing a novel, catalytic-independent function in safeguarding centromeres. Overall, our work defines the molecular logic governing centromeric repression, and uncovers foundational principles of epigenetic compartmentalization.

genomics↗

bioRxiv2025

Ambient Pollution Components and Sources Associated with Hippocampal Architecture and Memory in Pre-Adolescents

BackgroundAmbient air pollution poses significant risks to brain health. The hippocampus may be particularly vulnerable, yet the extent to which it is impacted in children remains unclear. MethodsUsing partial least squares correlation, we cross-sectionally analyzed air pollution, brain, and cognitive data from the Adolescent Brain Cognitive Development Study to examine how multi-pollutant exposure influences hippocampal structure and memory in 9-11-year-olds (n= 7,940). Annual average air pollution exposures included PM2.5 (total mass, 15 components, and 6 source factors), NO2, and 8-hour maximum O3. Hippocampal outcomes included microstructure measured using Restriction Spectrum Imaging and hippocampus longitudinal-axis (i.e., head, body, tail) volumes. We examined hippocampal-dependent list-learning using the Rey Auditory Verbal Learning Test. Models were adjusted for demographic, socioeconomic, and neuroimaging factors. FindingsPM2.5 total mass was associated with hippocampal microstructure, but not long-axis volume or list-learning ability. Component and source analyses provided greater specificity: higher bromine, sulfate, and vanadium exposure was related to microstructure (72% shared variance), while higher copper and zinc exposure correlated with smaller left head and right body and tail volumes (75% shared variance). Source models implicated biomass burning and traffic pollution in microstructure (61% and 32% shared variance) and industrial and traffic sources in smaller hippocampal volumes (77% shared variance). Higher exposure to several components were also linked to poorer list-learning (67% shared variance). DiscussionCo-exposure to multiple pollutants is linked to differences in hippocampal structure and memory, showing that associations are driven not only by PM2.5 total mass but also by specific components and sources. This evidence underscores the necessity of targeting source-specific (e.g., biomass burning, traffic, and industrial emissions) and constituent components (e.g., metals) of air pollution during critical developmental windows to safeguard brain health.

neuroscience↗

bioRxiv2025

Upstream open reading frame inactivation augments GATA4 translation and cardiomyocyte hypertrophy in mice

Upstream open reading frames (uORFs) are short peptide-encoding sequences located in the 5 untranslated region (5 UTR) of mRNAs, enabling translational repression of main (m)ORFs. While uORFs are found in [~]50% of mRNAs in humans, our understanding of their biological function remains limited. This study aims to elucidate the role of the uORF in the 5 UTR of the Gata4 (GATA binding protein 4) gene in cardiac biology by inactivating its start codon ({Delta}uORF) in the mouse genome. Our investigation reveals that mice with Gata4 uORF inactivation manifest spontaneous cardiac hypertrophy without apparent fibrosis as they age. Utilizing single-nucleus RNA sequencing (snRNA-seq), we uncovered significant transcriptional variations between wild-type (WT) and {Delta}ORF mice. Notably, mRNAs associated with sarcomeres and contractile functions show heightened expression levels, reflecting the hypertrophic phenotype. Notably, at least nine upregulated genes are GATA4-bound targets in mouse ventricles. Functional assessments of isolated primary adult cardiomyocytes confirmed enhanced hypertrophy and contractility in {Delta}ORF mice. Additionally, we employed single-nucleus transposase-accessible chromatin (snATAC)-seq to investigate changes in chromatin accessibility. Our results indicated increased accessibility within specific transcription-regulatory elements linked to elevated gene transcription. These putative cis-regulatory elements (pCREs) are significantly enriched in MEF2 (myocyte enhancer factor 2) binding motifs. In vitro luciferase reporter assays further supported the regulatory potential of three of these pCREs, highlighting their role in the transcriptional enhancement of three GATA4 target genes bound by MEF2 and GATA4. These findings illuminate the role of uORF in negatively regulating GATA4 protein expression and cardiomyocyte hypertrophy at the organismal level and provide a novel therapeutic target for cardiac pathogenesis.

biochemistry↗

bioRxiv2025

Oral Cavity Serves as Long-Term COVID-19 Reservoir with Increased Periodontal and Viral Disease Risk

BackgroundSARS-CoV-2 infection can lead to long-term health problems affecting multiple body systems termed long COVID. Currently, limited information exists about long-term oral health manifestations in COVID-19 patients with limited healthcare access. MethodsWe conducted a sequential, cross-sectional study (December 2020-March 2024) to assess how racial/ethnic differences (Black/Hispanic vs White/Asian) and health disparities affect oral and non-oral long COVID symptoms and their relationship with COVID-19 vaccination. We retrospectively reviewed patients oral health record from University of Illinois Chicago dental clinics before vaccination (December 2020; N=1150; Covid+/- N=575/group) and after vaccination (December 2021; N=592; Covid+/- N=292/group). Participants were recruited in two separate prospective groups of COVID-19 positive subjects (February-April 2021; pre-vaccination: N=158; January-March 2024; post-vaccination: N=171), we examined clinical indicators of oral (periodontal and salivary glands) and non-oral (neurologic) sequalae 3-6 months after initial exposure. We measured viral S protein by flow cytometry and quantified inflammatory markers, viral entry receptors, and oral viral load to correlate molecular, and cellular changes in COVID-19 positive subjects before and after vaccination. ResultsOur results identified racial disparity indicating oral associated post-acute sequelae (PASC) primarily manifested as periodontal (gum) disease (COVID-19 positive: 73.1{+/-}18.9% vs COVID-19 negative: 33.1{+/-}14.3%) and correlated with higher rates of dry mouth (57.5%), taste disturbance (47%), and smell loss (20%). Vaccination reduced oral PASC in COVID-19 positive subjects; however, periodontal disease indicators persisted compared to the COVID-19 negative group. Notably, 3-6 months post-infection, while SARS-CoV-2 Spike (S) transcript was rarely detected in saliva ([~]6%), its protein was commonly detected ([~]70%) in the COVID-19 positive subjects indicating incomplete viral clearance. This correlates with significantly higher salivary expression of viral entry receptors (ACE2, and TRMPSS2), and inflammatory mediators (IL-6, IL-8 and MMP-8), in COVID-19 positive subjects. This finding was further supported by higher prevalence of other oral viruses including Epstein-Barr Virus (70.5%), Herpes Simplex Virus (8.1%), and Human Papillomavirus (17.5%) in COVID-19 positive subjects. InterpretationCOVID-19 history significantly correlates with severe oral health complications in predominantly Black communities, while vaccination reduced but did not eliminate these issues. The oral cavity serves as a long-term viral reservoir, and periodontal inflammation with increased oral viral presence in COVID-positive patients may increase susceptibility to oral and non-oral viral diseases and identify risk for long COVID.

microbiology↗

bioRxiv2025

Sleep duration and efficiency moderate the effects of prenatal and childhood ambient pollutant exposure on global white matter microstructural integrity in adolescence

BackgroundAir pollution is a ubiquitous neurotoxicant associated with alterations in structural connectivity. Good habitual sleep may be an important protective lifestyle factor due to its involvement in the brain waste clearance and its bidirectional relationship with immune function. Wearable multisensory devices may provide more objective measures of sleep quantity and quality. We investigated whether sleep duration and efficiency moderated the relationship between prenatal and childhood pollutant exposure and whole-brain white matter microstructural integrity at ages 10-13 years. MethodsWe used multi-shell diffusion-weighted imaging data collected on 3T MRI scanners and objective sleep data collected with Fitbit Charge 2 from the 2-year follow-up visit for 2178 subjects in the Adolescent Brain Cognitive Development Study(R). White matter tracts were identified using a probabilistic atlas. Restriction spectrum imaging was performed to extract restricted normalized isotropic (RNI) and directional (RND) signal fraction parameters for all white matter tracts, then averaged to calculate global measures. Sleep duration was calculated by summing the time spent in each sleep stage; sleep efficiency was calculated by dividing sleep duration by time spent in bed. Using an ensemble-based modeling approach, air pollution concentrations of PM2.5, NO2, and O3 were assigned to each childs residential addresses during the prenatal period (9-month average before birthdate) as well as at ages 9- 10 years. Multi-pollutant linear mixed effects models assessed the associations between global RNI and RND and sleep-by-pollutant interactions, adjusting for appropriate covariates. ResultsSleep duration interacted with childhood NO2 exposure and sleep efficiency interacted with prenatal O3 exposure to affect RND at ages 10-13 years. Longer sleep duration and higher sleep efficiency in the context of higher pollutant exposure was associated with lower RND compared to those with similar pollutant exposure but shorter sleep duration and lower sleep efficiency. ConclusionsLow-level air pollution poses a risk to brain health in youth, and healthy sleep duration and efficiency may increase resilience to its harmful effects on white matter microstructural integrity. Future studies should evaluate the generalizability of these results in more diverse cohorts as well as utilize longitudinal data to understand how sleep may impact brain health trajectories in the context of pollution over time.

neuroscience↗

bioRxiv2024

Annotating CryoET Volumes: A Machine Learning Challenge

Cryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understanding protein complexes in their native cellular environments. Presently, 3D volumes of cellular environments can be acquired in the thousands in a few days where each volume provides a rich and complex cellular landscape. Despite numerous innovations, localizing and identifying the vast majority of protein species in these volumes remains prohibitively difficult. Machine learning based methods provide an opportunity to automate the process of labeling and annotating cryoET volumes. Due to current bottlenecks in the annotation process, and a lack of large standardized datasets, training datasets for machine learning algorithms have been scarce. Here, we present a defined "phantom" sample, along with "ground truth" annotations, that will be the basis of a machine learning challenge to bring cryoET and ML experts together and spur creativity to address this annotation problem. We have also set up a cryoET data portal that provides additional diverse sets of annotated 3D volumes from cryoET experts across the world for the machine learning challenge.

cell biology↗

bioRxiv2024

Open-source Tools for CryoET Particle Picking Machine Learning Competitions

We are launching a machine learning (ML) competition focused on particle picking in cryo-electron tomography (cryoET) data, a crucial task in structural biology. To support this, we have created a comprehensive suite of open-source tools to develop resources for our competition, including copick for dataset management, napari plugins for interactive visualization, utilities for converting particle picks to segmentation masks, and PyTorch tools for custom dataset sampling. These resources streamline the processes of data handling, labeling, and visualization, allowing participants to focus on model development. By leveraging these tools, competitors will be better equipped to tackle the unique challenges of cryoET data and push forward advancements in particle picking techniques.

bioinformatics↗

bioRxiv2024

Outdoor Air Pollution Relates to Amygdala Subregion Volume and Apportionment in Early Adolescents

BackgroundOutdoor air pollution is associated with an increased risk for psychopathology. Although the neural mechanisms remain unclear, air pollutants may impact mental health by altering limbic brain regions, such as the amygdala. Here, we examine the association between ambient air pollution exposure and amygdala subregion volumes in 9-10-year-olds. MethodsCross-sectional Adolescent Brain Cognitive DevelopmentSM (ABCD) Study(R) data from 4,473 participants (55.4% male) were leveraged. Air pollution was estimated for each participants primary residential address. Using the probabilistic CIT168 atlas, we quantified total amygdala and 9 distinct subregion volumes from T1- and T2-weighted images. First, we examined how criteria pollutants (i.e., fine particulate matter [PM2.5], nitrogen dioxide, ground-level ozone) and 15 PM2.5 components related with total amygdala volumes using linear mixed-effect (LME) regression. Next, partial least squares correlation (PLSC) analyses were implemented to identify relationships between co-exposure to criteria pollutants as well as PM2.5 components and amygdala subregion volumes. We also conducted complementary analyses to assess subregion apportionment using amygdala relative volume fractions (RVFs). ResultsNo significant associations were detected between pollutants and total amygdala volumes. Using PLSC, one latent dimension (LD) (52% variance explained) captured a positive association between calcium and several basolateral subregions. LDs were also identified for amygdala RVFs (ranging from 30% to 82% variance explained), with PM2.5 and component co-exposure associated with increases in lateral, but decreases in medial and central, RVFs. ConclusionsFine particulate and its components are linked with distinct amygdala differences, potentially playing a role in risk for adolescent mental health problems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/617429v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@e9f2ecorg.highwire.dtl.DTLVardef@14b677forg.highwire.dtl.DTLVardef@177f8daorg.highwire.dtl.DTLVardef@172dc0c_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗