Biology subjects

Taylor, J. E.

Publications and source records attributed to Taylor, J. E..

7 recordsLinked to original sources

bioRxiv2026

Single-Molecule Proteomics via a Dynamic Translocase and Physics-Informed Machine Learning

Single-molecule protein sequencing promises to democratize clinical proteomics, but platforms retrofitting static DNA-sequencing nanopores face a fundamental biophysical bottleneck: they only measure one-dimensional excluded volume. Consequently, these static calipers struggle to resolve isobaric residues, requiring complex DNA-handle chemistries and target concentrations that exceed clinically relevant abundance ranges. Here, we introduce a dynamical, target-docking translocase engine--the anthrax toxin protective antigen (PA)--as a label-free single-molecule peptide sensor. By extracting the multi-state thermodynamic friction generated as the pore's active site dynamically "breathes" around translocating analytes, we trained a physics-informed machine learning (PIML) architecture to classify a 20-member guest-host peptide library panel representing all 20 canonical amino acids at the single-event level. Operating at low nanomolar concentrations under a 35-millisecond thermodynamic read constraint, the translocase resolved isobaric variants (leucine and isoleucine). Furthermore, we achieved 98.02 (+/-0.05)% classification accuracy on a panel of five un-tagged, native clinical biomarkers (e.g., KRAS G12D, angiotensin, bradykinin). Transitioning from static volumetric measurement to time-domain thermodynamic fingerprinting establishes the requisite protein nanopore hardware for de novo proteomics.

biophysics↗

bioRxiv2026

Beyond the Static Caliper: Dynamical Translocases and the Mathematical Imperative for Single-Molecule Proteomics

The advent of single-molecule nanopore sequencing established a powerful platform for modern genomics by using static biological pores to report the translocation of canonical nucleic acids, enabling rapid, accessible nucleic acid analysis. However, extending this strategy to single-molecule proteomics has stalled against a fundamental biophysical bottleneck. Current efforts in nanopore proteomics attempt to retrofit these static, spatial "caliper" biological nanopores (e.g., -hemolysin, MspA, CsgG, aerolysin) for protein sequencing despite the immense steric, charge, and conformational heterogeneity of proteins. Unlike the chemically uniform, polyanionic phosphodiester backbone of DNA, the proteome contains isosteric and isobaric variants that confound purely volumetric measurements made by static pores. To address this bottleneck, we propose the application of dynamical translocases - naturally evolved, protein-handling nanomachines (e.g., the anthrax toxin protective antigen). Unlike static pores that rely on passive diffusion, dynamical translocases employ target-docking clamp architectures that achieve low nanomolar sensitivity. Active-site conformational dynamics generate high-dimensional kinetic fingerprints that enable molecular discrimination during translocation. By coupling dynamical translocases with Physics-Informed Machine Learning (PIML), we demonstrate that amino-acid side-chain-dependent thermodynamic friction can be mathematically decoded, enabling >90% accurate classification of chemically distinct amino acid classes and doing so label-free without the artificial DNA-handles required by legacy platforms.

biophysics↗

bioRxiv2025

Framed RSA: Representational comparisons that honor bothgeometry and population-mean response preferences

Representational similarity analysis (RSA) characterizes the geometry of neural activity patterns elicited by different stimuli while discarding information about neural response preferences, regional population-mean activity, and the absolute location and orientation of the patterns in the multivariate response space. When evaluating alternative representational models, invariance to certain aspects of the neural code is desirable because systems might use superficially different encodings to implement the same computations. However, neural preferences and regional-mean activation are arguably physiologically and mechanistically important, and so we may want our models to predict them correctly. Here we introduce a novel analysis technique, framed RSA, which honors both geometry and population-mean preferences in evaluating model-predicted representations. To achieve this, we augment the set of patterns that define the geometry by two reference patterns: the all-zero point (origin) and an all-c (uniform constant) pattern in the multivariate response space, enabling RSA to incorporate information about the profile across stimuli of regional population-mean activations and about the global location and orientation of the ensemble of response patterns. We show that framed RSA improves model-selection accuracy when ground truth is known, considering brain-region identification (using human fMRI data from the Natural Scenes Dataset and macaque intracranial recording data from the Things Ventral Stream Spiking Dataset) and deep-neural-network-layer identification. By incorporating neural population preferences into model evaluation, framed RSA enables more mechanistically meaningful model comparisons and benefits from improved power for model-comparative inference.

neuroscience↗

bioRxiv2024

Beyond Letters: Optimal Transport as a Model for Sub-Letter Orthographic Processing

Letter processing plays a key role in visual word recognition. However, word recognition models typically overlook or greatly simplify early perceptual processes of letter recognition. We suggest that optimal transport theory may provide a computational framework for describing letter shape processing. We use representational similarity analysis to show that optimal transport cost (Wasserstein distance) between pairs of letters aligns with neural activity elicited by visually presented letters <225 ms after stimulus onset, outperforming an existing approach based on shape overlap. We additionally show that optimal transport can capture the emergence of geometric invariances (e.g., to position or size) observed in letter perception. Finally, we demonstrate that Wasserstein distance predicts neural activity similarly well to features from artificial networks trained to classify images and letters. However, whereas representations in artificial neural networks emerge in a computationally unconstrained manner, our proposal provides a computationally explicit route to modeling the earliest orthographic processes.

neuroscience↗

bioRxiv2023

Can Prediction Error Explain Predictability Effects on the N1 during Picture-Word Verification?

Do early effects of predictability in visual word recognition reflect prediction error? Electrophysiological research investigating word processing has demonstrated predictability effects in the N1, or first negative component of the event-related potential (ERP). However, findings regarding the magnitude of effects and potential interactions of predictability with lexical variables have been inconsistent. Moreover, past studies have typically used categorical designs with relatively small samples and relied on by-participant analyses. Nevertheless, reports have generally shown that predicted words elicit less negative-going (i.e., lower amplitude) N1s, a pattern consistent with a simple predictive coding account. In our preregistered study, we tested this account via the interaction between prediction magnitude and certainty. A picture-word verification paradigm was implemented in which pictures were followed by tightly matched picture-congruent or picture-incongruent written nouns. The predictability of target (picture-congruent) nouns was manipulated continuously based on norms of association between a picture and its name. ERPs from 68 participants revealed a pattern of effects opposite to that expected under a simple predictive coding framework.

neuroscience↗

bioRxiv2023

TorchLens: A Python package for extracting and visualizing hidden activations of PyTorch models

Deep neural network models (DNNs) are essential to modern AI and provide powerful models of information processing in biological neural networks. Researchers in both neuroscience and engineering are pursuing a better understanding of the internal representations and operations that undergird the successes and failures of DNNs. Neuroscientists additionally evaluate DNNs as models of brain computation by comparing their internal representations to those found in brains. It is therefore essential to have a method to easily and exhaustively extract and characterize the results of the internal operations of any DNN. Many models are implemented in PyTorch, the leading framework for building DNN models. Here we introduce TorchLens, a new open-source Python package for extracting and characterizing hidden-layer activations in PyTorch models. Uniquely among existing approaches to this problem, TorchLens has the following features: (1) it exhaustively extracts the results of all intermediate operations, not just those associated with PyTorch module objects, yielding a full record of every step in the models computational graph, (2) it provides an intuitive visualization of the models complete computational graph along with metadata about each computational step in a models forward pass for further analysis, (3) it contains a built-in validation procedure to algorithmically verify the accuracy of all saved hidden-layer activations, and (4) the approach it uses can be automatically applied to any PyTorch model with no modifications, including models with conditional (if-then) logic in their forward pass, recurrent models, branching models where layer outputs are fed into multiple subsequent layers in parallel, and models with internally generated tensors (e.g., injections of noise). Furthermore, using TorchLens requires minimal additional code, making it easy to incorporate into existing pipelines for model development and analysis, and useful as a pedagogical aid when teaching deep learning concepts. We hope this contribution will help researchers in AI and neuroscience understand the internal representations of DNNs.

neuroscience↗

bioRxiv2019

A reciprocal inhibition model of alternations between non-dissociative and dissociative states in patients with PTSD

ObjectiveTraumatic life-events can leave individuals with contrasting posttraumatic stress disorder (PTSD) symptoms, including re-experiencing and avoidance. Notably, patients with PTSD are known to periodically switch between two opposing attentional biases; namely, toward threat and away from threat. We hypothesized that reciprocal inhibition between the amygdala and ventromedial prefrontal cortex (vmPFC) may induce alternations between these attentional biases, which in turn may contribute to the re-experiencing and avoidance symptoms, respectively.\n\nMethodsTo test this reciprocal inhibition model, we performed an experiment to measure the attentional biases of patients with PTSD. We examined the differential relationships between PTSD symptom clusters (re-experiencing/avoidance) and attentional biases (toward/away from threat). Additionally, we performed a meta-regression analysis to examine the role of amygdala reactivity in the imbalance between re-experiencing and avoidance symptoms.\n\nResultsWe found that attentional bias toward threat was selectively associated with re-experiencing symptoms whereas attentional bias away from threat was selectively associated with avoidance symptoms. Meta-regression analysis based on twelve participant populations (total N = 316) revealed that left amygdala activity was positively correlated with the severity of re-experiencing symptoms relative to avoidance symptoms.\n\nConclusionsOur findings support the hypothesis that reciprocal inhibition of common neural circuits may underlie the switch between attentional biases toward and away from threat as well as that between re-experiencing and avoidance symptoms. Re-experiencing and avoidance/emotional numbing are the core symptoms used to distinguish between the non-dissociative and dissociative PTSD subtypes. The reciprocal inhibition mechanism may help elucidate the mechanisms underlying those PTSD subtypes.

neuroscience↗