Clay weekly context brief for the Quantitative Biology category (ISO week 2026-W33).

Clay tracks publications from the Quantitative Biology feed list. Below are recent items from this category, each with its source and a short description of what the publication covers when one is available in the source feed.

Recent publications:
1. Multiparametric MRI Radiomics and Machine Learning Framework for Predicting Treatment Response in Glioblastoma
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2608.05733
   Distinguishing True Progression (TP) from Pseudo-Progression (PsP) after chemoradiotherapy remains a major diagnostic challenge in GBM, as both entities present near-identical appearances on conventional contrast-enhanced post-treatment MRI.
2. Frozen but Not Always Accessible: A Representation Analysis of Genomic Language Models
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2608.05329
   Genomic foundation models are increasingly reused as frozen feature extractors for downstream sequence prediction, offering a compute-efficient alternative to full fine-tuning.
3. Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2608.05464
   The pruning of network connections is key to brain function but, despite its importance, there exist few biologically-plausible pruning rules with demonstrated good performance.
4. Phylogenetic Tree Inference with Tropical Axial Attention
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2605.13894
   In this work, we introduce a Tropical Axial Attention neural reasoning architecture that replaces vanilla softmax dot-product attention with max-plus operators, inducing a piecewise-linear structure aligned with dynamic programming formulations.
5. On the abelian structure of noncompetitive chemical reaction networks
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2512.17491
   Chemical reaction networks (CRNs) are foundational models for describing complex biochemical processes.
6. A concentration-independent paradigm rendering weak interactions inherently quantifiable
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2608.02865
   A vast class of weak, millimolar-affinity molecular interactions governs cellular function, yet their quantitative characterization has remained largely beyond conventional methods.
7. A quantitative model for the emergent population dynamics of the melanoma MITF rheostat
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2607.11820
   Cancer progression is driven by the ability of cells with identical driver mutations to adopt biologically distinct adaptive phenotypes.
8. Current validation practice undermines surgical AI development
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2511.03769
   Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation as an important contributing factor.
9. TRAECR: A Tool for Preprocessing Positron Emission Tomography Imaging for Statistical Modeling
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2511.04458
   Positron emission tomography (PET) imaging is widely used in a number of clinical applications, including cancer and Alzheimer's disease (AD) diagnosis, monitoring of disease development, and treatment effect evaluation.
10. Curriculum Multiple Shooting for Robust Training of Neural and Universal Differential Equations
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2608.05777
   Neural ordinary differential equations (NODEs) and universal differential equations (UDEs) provide flexible and popular frameworks for learning interpretable dynamical systems from noisy time-series data.
11. EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2608.06022
   Epitopes determine where antibodies bind antigens and shape downstream therapeutic properties such as functional blockade and escape resistance, making epitope understanding central to antibody drug discovery.
12. From Local Learning to Global Prediction Through Layered Surprise Cascades
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2608.05481
   Hierarchical predictive coding proposes a compelling hypothesis of brain computation, suggesting that the cortex builds layered predictions to minimize surprise.
13. Inferring Phylogenetic Networks from Required and Forbidden LCA-Constraints
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2605.03827
   Least common ancestor (LCA) constraints encode relative-order information in directed acyclic graphs (DAGs) and give rise to a natural constraint-realization problem.
14. Noise-Driven Differentiation via Gene Frustration and Epigenetic Fixation
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2604.18185
   Gene expression in cells is stochastic, yet differentiation can display reproducible timing and stable fate commitment.
15. Experimental access to molarity's blind spot in macroscopic assays
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2608.02864
   Chemical kinetics has long inferred local molecular behaviour through the flask-and-molarity pairing, where well-mixed concentrations serve as the experimental readout.
16. Smart membrane: high content in situ monitoring barrier on chip with artificial neuronal network
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2608.01239
   Conventional transepithelial electrical resistance (TEER) technique provides only a low-content analysis of cell-layer conditions, necessitating repeated microscopic assessments of morphology and cell-cell contacts outside the incubator for barrier-on-chip systems.
17. BODIESReg: An Open-Source Pipeline for Registering 3D Body Scans Using Pose-Aligned Initialization
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2607.15463
   Biomechanical models are used to quantify and optimize human movement in clinical rehabilitation, sports science, and occupational health.
18. From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
   Source: q-bio.TO (Tissues and Organs)
   Link: https://arxiv.org/abs/2607.02564
   Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials.
19. PyOMES: an open-source framework for biochemical process modelling
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2608.06360
   PyOMES is a Python-based, Open-source Modelling Environment for (bio)chemical process Simulation that aims to simplify the modelling of dynamic (including steady state) processes.
20. An Early Warning of Emerging Biosecurity Risks in Frontier LLMs
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2607.18056
   Frontier large language models (LLMs) are increasingly integrated into scientific workflows, yet their growing biological capabilities may outpace current safeguards.
21. Transcutaneous Spinal Cord Stimulation Disrupts Conscious Ankle Proprioception and Produces a More Constrained Locomotor Pattern in Unimpaired Adults
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2608.05635
   Transcutaneous spinal cord stimulation (tSCS) modulates spinal sensorimotor circuits primarily through activation of afferent networks.
22. Cross-Country Learning for National Infectious Disease Forecasting Using European Data
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2601.20771
   Accurate forecasting of infectious disease incidence is critical for public health planning and timely intervention.
23. Variational kinetics: elementary reaction kinetics via conic optimisation
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2607.25217
   Genome-scale modelling methods primarily predict reaction fluxes, whereas established high throughput experimental technologies primarily measure molecular species concentrations.
24. Funnel-like protein energy landscapes emerge from functional evolution under thermal fluctuations
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2608.01143
   Proteins perform biological functions by folding into specific native structures that are stabilized by funnel-like energy landscapes shaped through evolution.
25. Necessary and sufficient condition for hysteresis in the mathematical model of the cell type regulation of \textit{Bacillus subtilis}
   Source: q-bio.CB (Cell Behavior)
   Link: https://arxiv.org/abs/2009.06524
   The key to a robust life system is to ensure that each cell population is maintained in an appropriate state.
26. Open Questions about Time and Self-reference in Living Systems
   Source: q-bio.OT (Other Quantitative Biology)
   Link: https://arxiv.org/abs/2508.11423
   Living systems exhibit a range of fundamental characteristics: they are active, self-referential, self-modifying systems.
27. MS-MLB: An Open Machine Learning Benchmark for Blood-Based MS Classification
   Source: q-bio.QM (Quantitative Methods)
   Link: https://arxiv.org/abs/2608.05196
   Multiple sclerosis (MS) is diagnosed through clinical assessment, magnetic resonance imaging, laboratory evidence when appropriate, and exclusion of better explanations.
28. LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling
   Source: q-bio.GN (Genomics)
   Link: https://arxiv.org/abs/2606.04552
   Genomic foundation models increasingly adopt large language model architectures, yet almost universally rely on fixed tokenization schemes such as $k$-mers, BPE, or single nucleotides, which impose arbitrary sequence boundaries that may obscure biologically relevant structure.
29. Complexity and Stability of Neural Activity Across Aging and Neurodegenerative Disease
   Source: q-bio.NC (Neurons and Cognition)
   Link: https://arxiv.org/abs/2608.05882
   Objective: EEG signals fluctuate continuously even within a fixed cognitive state, but an important question is whether the brain still reuses similar activity patterns to represent information over time.
30. Complete strategy spaces reveal hidden pathways to cooperation
   Source: q-bio.PE (Populations and Evolution)
   Link: https://arxiv.org/abs/2511.17794
   Understanding how cooperation emerges and persists is a central challenge in evolutionary game theory.
31. Omega-S: A Functional Resilience Index for LLM Fine-Tuning
   Source: q-bio.MN (Molecular Networks)
   Link: https://arxiv.org/abs/2608.03887
   Fine-tuning a large language model on new data degrades what it previously learned.
32. MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding
   Source: q-bio.BM (Biomolecules)
   Link: https://arxiv.org/abs/2607.01982
   Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery.

Sources in this brief: q-bio.BM (Biomolecules); q-bio.CB (Cell Behavior); q-bio.GN (Genomics); q-bio.MN (Molecular Networks); q-bio.NC (Neurons and Cognition); q-bio.OT (Other Quantitative Biology); q-bio.PE (Populations and Evolution); q-bio.QM (Quantitative Methods); q-bio.TO (Tissues and Organs).
Selected 32 of 435 available items for this weekly brief.