Biology subjects

Jay, M.

Publications and source records attributed to Jay, M..

7 recordsLinked to original sources

bioRxiv2026

BioVault: A privacy-first data visitation platform for equitable global collaboration in biomedicine

Biomedical datasets representing diverse populations are essential for advancing precision medicine, yet remain siloed due to regulatory, sovereignty, and privacy constraints. Existing data-sharing solutions remain limited. Centralized repositories and Trusted Research Environments (TREs) require data migration into external infrastructure, introducing governance complexities, cost barriers, and constraints on accountable reuse. Secure computation frameworks from modern cryptography, and federated learning models demand deep domain expertise and substantial engineering effort, limiting their accessibility in resource-constrained environments. These barriers disproportionately impact under-resourced institutions, limiting equitable participation in global collaborations. Here, we introduce BioVault (https://www.biovault.net), an open-source platform for privacy-first biomedical collaboration through a peer-to-peer data visitation network, where analysis code travels to data rather than data being transferred to centralized systems. BioVault can be deployed as a desktop application or command-line interface, enabling out-of-the-box use for participants in diverse resource settings. BioVault supports both clinical and research workflows, and with built-in permissioning, audit trails, and local governance controls, it enables data holders and collaborators to retain oversight and control while participating in collaborative research. We demonstrate BioVaults utility in enabling two cross-border collaborations with under-resourced communities: a genome-wide association study of Type 2 Diabetes in Circassian and Chechen populations in Jordan, and allele frequency estimation across Caribbean cohorts. In both cases, analyses were executed locally without data export. We further demonstrate compatibility for user-defined secure, federated computation protocols built on state-of-the-art cryptographic tools (multiparty computation and homomorphic encryption), by lowering barriers to their deployment. Together, these results establish BioVault as a general-purpose framework for decentralized analytics that reduces the technical and financial constraints to biomedical collaboration - enabling diverse institutions to participate as equal partners in global discovery, while preserving privacy and data sovereignty.

genomics↗

bioRxiv2024

Attenuation of Atherosclerosis with PAR4 Deficiency: Differential Platelet Outcomes in apoE-/- vs. Ldlr-/- Mice

ObjectiveCardiovascular disease (CVD) is a significant burden globally and, despite current therapeutics, remains the leading cause of death. Platelet inhibitors are of interest in CVD treatment to reduce thrombus formation post-plaque rupture as well their contribution to inflammation throughout the progression of atherosclerosis. Protease activated receptor 4 (PAR4) is a receptor highly expressed by platelets, strongly activated by thrombin, and plays a vital role in platelet activation and aggregation. However, the role of PAR4 Approach and ResultsMice on a low-density lipoprotein receptor-deficient (Ldlr-/-) background were bred with Par4 deficient (Par4-/-) mice to create Ldlr-/-/Par4+/+ and Ldlr-/-/Par4-/- cousin lines. Mice were fed high fat (42%) and cholesterol (0.2%) Western diet for 12 weeks for all studies. Bone marrow transplant (BMT) studies were conducted by irradiating Ldlr-/-/Par4+/+ and Ldlr-/- /Par4-/- mice with 550 rads (2x, 4 hours apart) and then repopulated with Par4+/+ or Par4-/- bone marrow. To determine if the effects of thrombin were mediated solely by PAR4, the thrombin inhibitor dabigatran was added to the Western diet. Ldlr-/-/Par4-/- given dabigatran did not further decrease their atherosclerotic burden. Differences between apolipoprotein E deficient (apoE-/-) and Ldlr-/- platelets were assessed for changes in reactivity. We observed higher PAR4 abundance in arteries with atherosclerosis in human and mice versus healthy controls. PAR4 deficiency attenuated atherosclerosis in the aortic sinus and root versus proficient controls. BMT studies demonstrated this effect was due to hematopoietic cells, most likely platelets. PAR4 appeared to be acting independent of PAR1, as there werer no changes with addition of dabigatran to PAR4 deficient mice. apoE-/- platelets are hyperreactive compared to Ldlr-/- platelets. ConclusionsHematopoietic-derived PAR4, most likely platelets, plays a vital role in the development and progression of atherosclerosis. Specific targeting of PAR4 may be a potential therapeutic target for CVD. HighlightsO_LIDeficiency of protease-activated receptor 4 attenuates the development of diet-induced atherosclerosis in a Ldlr-/- mouse model. C_LIO_LIPAR4 deficiency in hematopoietic cells is atheroprotective. C_LIO_LIPAR4 deficiency accounts for the majority of thrombin-induced atherosclerosis in a Ldlr-/- mouse model. C_LIO_LIThe examination of platelet-specific proteins and platelet activation should be carefully considered before using the apoE-/- or Ldlr-/- mouse models of atherosclerosis. C_LI

pathology↗

bioRxiv2023

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

neuroscience↗

bioRxiv2022

Thalamus sends information about arousal but not valence to the amygdala

RationaleThe basolateral amygdala (BLA) and medial geniculate nucleus of the thalamus (MGN) have both been shown to be necessary for the formation of associative learning. While the role that the BLA plays in this process has long been emphasized, the MGN has been less well-studied and surrounded by debate regarding whether the relay of sensory information is active or passive. ObjectivesWe seek to understand the role the MGN has within the thalamoamgydala circuit in the formation of associative learning. MethodsHere, we use optogenetics to dissect the MGN-BLA circuit and explore the specific subpopulations for evidence of learning and synthesis of information that could impact downstream BLA encoding. We employ various machine learning techniques to investigate function within neural subpopulations. We introduce a novel method to investigate tonic changes across trial-by-trial structure, which offers an alternative approach to traditional trial-averaging techniques. ResultsWe find that the MGN appears to encode arousal but not valence, unlike the BLA which encodes for both. We find that the MGN and the BLA appear to react differently to expected and unexpected outcomes; the BLA biased responses toward reward prediction error and the MGN focused on anticipated punishment. We uncover evidence of tonic changes by visualizing changes across trials during inter-trial intervals (baseline epochs) for a subset of cells. ConclusionWe conclude that the MGN-BLA projector population acts as both filter and transferer of information by relaying information about the salience of cues to the amygdala, but these signals are not valence-specified. PrefaceIn tribute to Nadia Chaudhri, and her discoveries regarding how contexts can modulate the representation of cues in the amygdala (Sciascia et al., 2015), and other mesolimbic circuit components (Chaudhri et al., 2009, 2008; Valyear et al., 2020), here we explore what information processing precedes entry into the basolateral amygdala (BLA) from the thalamus and develop visualization approaches for exploring across-trial temporal dynamics in addition to within-trial population activity.

neuroscience↗

bioRxiv2018

Multicenter validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU

ObjectivesWe validate a machine learning-based sepsis prediction algorithm (InSight) for detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings.\n\nDesignA machine learning algorithm with gradient tree boosting. Features for prediction were created from combinations of only six vital sign measurements and their changes over time.\n\nSettingA mixed-ward retrospective data set from the University of California, San Francisco (UCSF) Medical Center (San Francisco, CA) as the primary source, an intensive care unit data set from the Beth Israel Deaconess Medical Center (Boston, MA) as a transfer learning source, and four additional institutions datasets to evaluate generalizability.\n\nParticipants684,443 total encounters, with 90,353 encounters from June 2011 to March 2016 at UCSF.\n\nInterventionsnone\n\nPrimary and secondary outcome measuresArea under the receiver operating characteristic curve (AUROC) for detection and prediction of sepsis, severe sepsis, and septic shock.\n\nResultsFor detection of sepsis and severe sepsis, InSight achieves an area under the receiver operating characteristic (AUROC) curve of 0.92 (95% CI 0.90 - 0.93) and 0.87 (95% CI 0.86 - 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 -0.98), and severe sepsis with an AUROC of 0.85 (95% CI 0.79 - 0.91).\n\nConclusionsInSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions.\n\nStrengths and limitations of this studyO_LIMachine learning is applied to the detection and prediction of three separate sepsis standards in the emergency department, general ward and intensive care settings.\nC_LIO_LIOnly six commonly measured vital signs are used as input for the algorithm.\nC_LIO_LIThe algorithm is robust to randomly missing data.\nC_LIO_LITransfer learning successfully leverages large dataset information to a target dataset.\nC_LIO_LIRetrospective nature of the study does not predict clinician reaction to information.\nC_LI

bioinformatics↗

bioRxiv2017

Pediatric Severe Sepsis Prediction Using Machine Learning

Early detection of pediatric severe sepsis is necessary in order to administer effective treatment. In this study, we assessed the efficacy of a machine-learning-based prediction algorithm applied to electronic healthcare record (EHR) data for the prediction of severe sepsis onset. The resulting prediction performance was compared with the Pediatric Logistic Organ Dysfunction score (PELOD-2) and pediatric Systemic Inflammatory Response Syndrome score (SIRS) using cross-validation and pairwise t-tests. EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Patients (n = 11,127) were 2-17 years of age and 103 [0.93%] were labeled severely septic. In four-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.912 for discrimination between severely septic and control pediatric patients at onset and AUROC of 0.727 four hours before onset. Under the same measure, the prediction algorithm also significantly outperformed PELOD-2 (p < 0.05) and SIRS (p < 0.05) in the prediction of severe sepsis four hours before onset. This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction for pediatric inpatients.

bioinformatics↗

bioRxiv2017

Mapping protein interactions of sodium channel NaV1.7 using epitope-tagged gene targeted mice

The voltage-gated sodium channel NaV1.7 plays a critical role in pain pathways. Besides action potential propagation, NaV1.7 regulates neurotransmitter release, integrates depolarizing inputs over long periods and regulates transcription. In order to better understand these functions, we generated an epitope-tagged NaV1.7 mouse that showed normal pain behavior. Analysis of NaV1.7 complexes affinity-purified under native conditions by mass spectrometry revealed 267 NaV1.7 associated proteins including known interactors, such as the sodium channel {beta}3 subunit (Scn3b) and collapsin response mediator protein (Crmp2), and novel interactors. Selected novel NaV1.7 protein interactors membrane-trafficking protein synapototagmin-2 (Syt2), G protein-regulated inducer of neurite outgrowth 1 (Gprin1), L-type amino acid transporter 1 (Lat1) and transmembrane P24 trafficking protein 10 (Tmed10) together with Scn3b and Crmp2 were validated using co-immunoprecipitation and functional assays. The information provided with this physiologically normal epitope-tagged mouse should provide useful insights into the pain mechanisms associated with NaV1.7 channel function.

neuroscience↗