Paper List
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MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare
This paper addresses the critical gap in healthcare AI systems that lack contextual reasoning, long-term state management, and verifiable workflows by...
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Model Gateway: Model Management Platform for Model-Driven Drug Discovery
This paper addresses the critical bottleneck of fragmented, ad-hoc model management in pharmaceutical research by providing a centralized, scalable ML...
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Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species
This paper addresses the fragmentation of tree-based inference methods across biological scales by identifying shared algorithmic principles and stati...
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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
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Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
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Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
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Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
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Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
Single-cell directional sensing at ultra-low chemoattractant concentrations from extreme first-passage events
University of Notre Dame | University of Utah
30秒速读
IN SHORT: This work addresses the core challenge of how a cell can rapidly and accurately determine the direction of a chemoattractant source when the signal is extremely weak (femto- to attomolar), and receptor binding events are discrete and rare.
核心创新
- Methodology Derives the first analytic expressions for the joint asymptotic distribution of the earliest k hitting times and their angular locations on a 2D circular cell, revealing that θ_k,N ~ N(θ_0, σ²_k,N) where σ²_k,N ∝ ( (R-1)² / (R W) ) * (1 + (2 log k)/(1+W) ) and W ~ log N.
- Theory Quantitatively demonstrates that early binding events (e.g., the first few arrivals) carry disproportionately more directional information than later arrivals, providing a theoretical basis for rapid cellular decision-making before a steady-state gradient is established.
- Methodology Proposes and rigorously analyzes the performance of several source-direction estimators (from simple averaging of early impact locations to more complex MLEs), deriving explicit formulas for their expected error and variance (e.g., E[ρ_k^res] ≈ (D/R)(b_N + a_N(log k - 1))).
主要结论
- The angular location θ_k of the k-th arriving molecule follows a normal distribution centered on the true source direction θ_0, with a variance that increases logarithmically with k (σ²_k,N ∝ log k), formally proving that earlier arrivals provide more precise directional cues.
- A simple estimator averaging the first k impact locations (n_res) can achieve accurate directional sensing with small k; its error grows with k while its variance decreases (Var[ρ_k^res] ≈ 4D²/(R²k)*((a_N log k + b_N - a_N)² + a_N²)), highlighting a trade-off.
- The theoretical framework successfully links key physical parameters (source distance R, initial molecule number N ~ 10³-10⁶, number of observed events k) to sensing performance, showing that accurate directional inference is possible even for R > 1 (source placed multiple cell radii away).
摘要: We investigate single-cell directional sensing from diffusing chemoattractant signals released by a localized source. We focus on the low-concentration regime in which receptor activity is discrete and cellular decisions are made on timescales far shorter than those required for steady-state concentration profiles or receptor occupancy to emerge. We derive analytic expressions for the joint distribution of receptor binding times and binding locations, conditional on the position of the source. We show that early binding events carry disproportionately more information about source directionality than later arrivals. Motivated by this observation, we propose and analyze several source-localization estimates that exploit early receptor binding statistics. Our results demonstrate that, even with a small number of binding events, cells possess sufficient information to rapidly and accurately infer the directionality of a diffusing chemoattractant source.