ArXiv最新文献

{"title":"Femtosecond Three-Dimensional Imaging of Single-Protein with Hard X-ray Laser.","authors":"Paul Lourdu Xavier, Andrew J Morgan, Johan Bielecki, Amit K Samanta, Simon Dold, Chufeng Li, Xinyue Gao, Spencer K Passmore, Ruslan P Kurta, Tim B Berberich, Emiliano De Santis, Wenhui Zhang, Juncheng E, Faisal H M Koua, Romain Letrun, Luca Gelisio, Oleksii Turkot, Egor Sobolev, Lukas V Haas, Jannik Lübke, Jingxuan He, Madeline B F Memovich, Kevin Janson, Stefanie Lenzen, Safi Rafie-Zinedine, Mansi Butola, Oleksandr Yefanov, Fabian Trost, Chan Kim, Tomas Popelar, Tokushi Sato, Yevheniy Ovcharenko, Rebecca Boll, Sergey Usenko, Thomas M Baumann, Alberto De Fanis, Björn Senftleben, Matteo Porro, Andrea Castoldi, Ibrahym Dourki, Cyril Danilevski, Ekaterina Round, Christina Schmidt, Huijong Han, Roberto Alvarez, Ervin Chia, Cornelia Cazey, Carolin Seuring, Ruojie Sha, Maia Azubel, David A Bushnell, Sébastien Boutet, Sergey Ovchinnikov, Adrian P Mancuso, Michael Meyer, Richard Bean, Anton Barty, Filipe R N C Maia, Richard A Kirian, Thomas D Grant, N Duane Loh, Carl Caleman, Erik G Marklund, Nadia Zatsepin, Andrew V Martin, Saša Bajt, Roger D Kornberg, Jochen Küpper, Henry N Chapman","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The extremely intense pulses of X-ray free-electron lasers (XFELs) have enabled imaging of radiation-sensitive samples, such as macromolecular microcrystals, beyond radiation damage limits. These sources have the potential to deliver biomolecular single-particle imaging, similar to cryo-electron microscopy but without the need for cryo-fixation and with temporal resolution from femtoseconds to milliseconds. While this possibility was recognized before XFELs were built, the biological single-particle imaging work-flow has previously only been demonstrated on large virus particles. Based on decades of improvements in X-ray beam focusing, particle delivery, diffraction detection, and advanced analysis, here we demonstrate imaging of a single molecular complex, the giant-hemoglobin erythrocruorin (Ery) with X-ray laser pulses. Two-dimensional classes of diffraction patterns could be reconstructed to 15 Angstrom resolution, and 3D images to approximately 20 Angstrom, while the 3D merged intensity in reciprocal space extended beyond 20 Angstrom. The resolution discrepancy is likely due to heterogeneity caused by gas-phase compaction of the complexes. With increased throughput, this approach could be used to reveal in-situ structural details during mass spectrometry studies of biomolecules, while improvements in sample delivery may provide ultrafast snapshot imaging of biological single-particles in their native-state beyond the limitations of radiation damage.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TopoLIA: Topology-Inspired Latent Interaction Attention for Drug Discovery.","authors":"Xiang Liu, Yiming Ren, Mustafa Hajij, Pietro Liò, Guo-Wei Wei","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The growing sophistication of drug discovery calls for new advances in artificial intelligence (AI). Transformers have underpinned recent breakthroughs in AI, yet their conventional source-target attention mechanism can become a bottleneck for modeling complex interactions. Here, we introduce TopoLIA, a topology-inspired latent interaction attention mechanism that learns not only how much information should be exchanged between interaction partners, but also how that information should be transformed. Source-target-dependent attention is mediated through a compact latent interaction space, providing a structured representation of complex interactions. We further incorporate topological spectral representations to capture multiscale data geometry and extract algebraic relationships. TopoLIA thus incorporates topological principles into both data representation and neural network architecture. We demonstrate TopoLIA on protein-ligand binding, where persistent spectra encode multiscale topological and geometric patterns in 3D protein-ligand complexes, while latent interaction attention (LIA) enables structured, source-target-dependent information exchange. We pretrain TopoLIA by reconstructing masked multiscale spectral tokens from unlabeled molecular structures, yielding transferable structural representations without task-specific annotations. Across multiple scoring and docking benchmarks, TopoLIA achieves state-of-the-art performance and consistently outperforms its standard-attention counterpart. Rank analyses show that compact latent dimensions yield the best performance, supporting compact interaction spaces as an effective inductive bias for biomolecular learning. More broadly, LIA provides a general framework for structured, source-target-dependent information exchange that is applicable to diverse scientific and AI problems involving complex heterogeneous interactions.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience.","authors":"Johannes Bertram, Luciano Dyballa, T Anderson Keller, Savik Kinger, Steven W Zucker","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>RSA and CKA are standard metrics for comparing neural representations across brain regions, organisms, and deep learning models. We demonstrate a fundamental weakness: these decoding-based metrics are insensitive to encoding manifold topology---the internal functional organization of a neural population. In a controlled MNIST experiment, RSA, CKA, and Procrustes $R^2$ remain statistically unchanged when encoding topology is causally manipulated via an auxiliary clustering loss, while the two model populations differ significantly in attribution patterns, weight-graph assortativity, and out-of-distribution robustness. Across biological systems and machine learning models, similar decoding behavior can arise from small, non-representative subpopulations, and alignment metrics are insensitive to encoding manifold topology even when it is fundamentally altered. These findings bear directly on mechanistic interpretability: standard alignment metrics cannot distinguish whether two networks share the same computational circuits or merely produce indistinguishable aggregate outputs. We propose encoding manifolds and Gromov-Wasserstein distance as complementary diagnostics for any decoding-based similarity claim, and provide a Neural Manifold Explorer tool at https://johannesbertram.github.io/FNN_Manifolds/index.html for further experimentation.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants.","authors":"Pratik Dutta, Matthew B Obusan, Max Chao, Rekha Sathian, Nimisha Papineni, Ramana V Davuluri","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Over 90% of disease-associated variants from genome-wide association studies fall in noncoding regulatory regions, yet their functional interpretation remains a central open problem in genomic medicine. Large language models prompted to interpret such variants routinely hallucinate transcription factor (TF) binding changes, fabricate experimental support, and assign biological significance to statistically negligible signals. We present ARGUS (Agentic Regulatory Genomics for an Uncertainty-aware Scientist), which strictly separates deterministic biological computation from LLM-mediated reasoning. ARGUS wraps 458 DNABERT-based TF binding models in a hypothesis-directed investigation loop where a planner selects evidence sources based on current uncertainty, a verifier deterministically interprets each observation, and intermediate results change the investigation path. On variant rs6983267 at the 8q24 cancer risk locus, the same planner produces four divergent trajectories for four TFs. FOXA1 is rescued in 3 steps when real ADASTRA allele-specific binding data (15 experiments, FDR = 0.030) reveals a model false negative masked by saturation. KLF6 traverses 8 steps across ADASTRA, JASPAR motif analysis, and ENCODE cCRE regulatory annotation before abstaining due to mixed indirect evidence. RAD21 abstains in 8 steps after ADASTRA returns a coverage-qualified but nonsignificant allelic test (5 experiments, FDR = 0.65), and SP1, which shares FOXA1's saturated retained prediction, abstains because no direct experimental evidence exists at this locus. All observations come from real ADASTRA, JASPAR, and ENCODE cCRE queries; none are simulated. A comparison of fixed-priority and LLM-mediated planning shows that the LLM planner reaches identical verdicts with fewer tool calls by declining evidence that cannot resolve the claim under test.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beta frequency shifts in decision making: Spectral fingerprints or communication channels?","authors":"Saskia Haegens, Julio Rodriguez-Larios, Elie Rassi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Recent evidence suggests that beta-band activity plays a key role in decision-making. Here we review our recent work in humans and non-human primates showing that beta-band frequency shifts in frontal cortex signal categorical decision outcomes. We revisit our previous proposal suggesting that content-specific beta reflects the flexible recruiting of transient neural ensembles and update it to emphasize frequency as the relevant parameter. We argue that beta frequency shifts arise from changes in connectivity between weakly coupled oscillators and that, more than a spectral fingerprint, they reflect an active mechanism to (re)-activate behaviorally relevant communication channels in the brain.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692666/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention when you need.","authors":"Grayson Matthew, Lokesh Boominathan, Yizhou Chen, Matthew McGinley, Xaq Pitkow","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Paying attention improves performance, but attention is metabolically costly, so how should a resource-efficient agent allocate it? We study optimal allocation strategies using a normative model of a signal detection task in which attention comes at a cost. The model reveals that optimal attention is temporally structured in one of two patterns depending on task conditions: when attention costs penalize intense focus, optimal attention ramps up as evidence for the signal accumulates, but with less prohibitive attention costs, optimal attention fluctuates rhythmically. In cases with rhythmic attention, allocation frequency increases with task parameters such as reward magnitude for successful detection, signal brevity, and signal frequency. We argue that rhythmic attention emerges naturally in an agent whose belief updates are dominated by a stable temporal prior and not by the likelihood of new sensory observations. Our results characterize the conditions under which rhythmic vs. ramping attention is optimal and offer a normative account of attentional fluctuations observed in sustained attention tasks.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692668/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mean Field Theory Based on the Spike Time Response Curve for Synchronization Within and Between Two Alternating Populations of Neural Oscillators with Delays.","authors":"Ananth Vedururu Srinivas, Carmen C Canavier","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In order to study oscillators coupled by pulses emitted at discrete times, it is generally assumed that the effect of each pulse is an instantaneous phase shift, with a possible conduction delay between emission and receipt. However, we are interested in neural oscillators in which each pulse (action potential) activates a biexponential synapse, and at high frequencies, the duration of the synaptic conductance may be greater than the network period. Therefore, the effect of received pulses can summate over the course of several cycles. To overcome this limitation, we previously defined a mean field approach for a single synchronous population. Here, we extend it to two alternating synchronous populations by assuming a perturbation of a single oscillator from one of the synchronous populations which is represented by a single self-connected neural oscillator. The train of delayed biexponential synapses emitted by each oscillator is divided into a tonic and a phasic component. Because of the continuous rather than pulsatile nature of the input, we had to drop the concept of oscillator phase and derive self-consistent criteria for the existence and stability of an alternating firing pattern between two synchronous populations based solely on the perturbation of time intervals from their steady state values. Simulations showed that for high frequency oscillations as defined above, the mean field approach predicts the existence and stability of phase-locking between two synchronous populations better than the instantaneous phase shift approach. This approach may generalize to other forms of coupling amongst non-neural oscillators.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polarization of the Drosophila oocyte by active cortical exchange.","authors":"Mohammadhossein Firouznia, Wen Lu, Margot Lakonishok, Reza Farhadifar, Vladimir I Gelfand, Michael J Shelley","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Anterior-posterior polarization in the Drosophila oocyte requires posterior localization of oskar mRNA. oskar mRNA is produced in the anteriorly situated nurse cells, moved into the oocyte, and assembled into oskar-Staufen ribonucleoprotein particles (RNPs). These RNPs then move through the oocyte and interact with the cortex, where myosin-V binds them to the actin network and kinesin-1 removes them along cortically anchored microtubules. This cortical tug-of-war takes place against an anterior-to-posterior decrease in microtubule density. How the interplay between cytoplasmic transport and cortical binding and unbinding produces a stable posterior cap is unclear. Here, we combine quantitative microscopy, photoconversion experiments, experimental perturbations, and biophysical modeling to show that posterior localization emerges from position-dependent cortical exchange coupled to cytoplasmic diffusion. Our model, which accounts for the tendency of RNPs to demix, suggests that two key parameters that determine the observed localization pattern are the total amount of oskar and the ratio of cortical unbinding to binding rates (the exchange equilibrium constant). Given oskar volume fractions in wild-type, the measured exchange constant is such that oskar localizes to the posterior cortex without demixing. If the balance is broken, say by increasing unbinding rates, oskar irreversibly demixes into cytoplasmic droplets. This is consistent with observations from our experimental perturbations. Our results show how bulk diffusion and cortical exchange, in the face of oskar RNPs' self-attraction, convert a cytoskeletal asymmetry into robust developmental polarity.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Hill numbers synthesize and generalize measures of trait polygenicity.","authors":"Maike L Morrison","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Characterizing the genetic architecture of phenotypic variation is central to human genetics, yet trait polygenicity---the diversity of genetic variants responsible for a heritable trait's variation in a sample---is rarely quantified formally. O'Connor and Sella (2026) addressed this need by proposing four polygenicity measures that are quasi-arithmetic means and therefore satisfy a set of principles they argue are desirable for polygenicity measures. Here, I show that these four measures are points on a continuum corresponding to the Hill numbers, a family of ecological diversity statistics offering a tunable emphasis on variants with small versus large contributions. I further show that the Hill numbers are the only quasi-arithmetic means suitable for measuring polygenicity because they are the only ones that are \"effective numbers\" of variants. The connection to the Hill numbers clarifies the relationships among O'Connor and Sella's four polygenicity measures: all four are effective numbers of variants, and those that give greater weight to variants with small contributions yield higher estimates of polygenicity. It also connects polygenicity to results in the ecological diversity literature, including the replication principle, a criterion for how such measures should behave when independent sets of variants are combined. Together, these results offer a unified framework for interpreting, comparing, and selecting measures of trait polygenicity.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Reliable Patient-Specific Aortic Strain Mapping from 4D CTA: Validation, Spectral Structure, and Clinical Potential.","authors":"Tricia Lobo, Wensi Wu, Harold I Litt, Nimesh D Desai, Alison M Pouch","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate a framework for spatially resolved ascending aortic strain estimation from 4D computed tomography angiography (CTA) and characterize reliability across segmentation sources and spatial scales.</p><p><strong>Methods: </strong>We constructed patient-specific aortic strain maps using nnU-Net segmentation, optimized surface remeshing, deformable registration, and mesh-based strain estimation. Finite element (FE)-derived synthetic 4D CT sequences provided controlled reference deformations for registration validation. Strain reliability was characterized across segmentation sources and Laplace-Beltrami (LB) spectral scales. Associations with diameter, aortic height index (AHI), and age were assessed.</p><p><strong>Results: </strong>Registration-derived median strain showed minimal bias relative to FE-reference strain for observer-derived (0.0013) and nnU-Net-derived meshes (0.0056). Median strain exhibited low bias and narrow 95% limits of agreement (LoA) across segmentation sources (interobserver: bias = -0.0045, LoA [-0.0490, 0.0399]; nnU-Net versus observer: bias = -0.0052, LoA [-0.0340, 0.0237]). Differences were greater for 95th-percentile strain, particularly in the interobserver comparison. LB spectral analysis showed preferential preservation of low-frequency strain organization, with k = 5 reconstructions reducing segmentation-related disagreement by 52-56%. The 95th-percentile areal strain was inversely associated with maximum ascending aortic diameter (r = -0.493, p = 0.005), AHI (r = -0.488, p = 0.005), and age (r = -0.479, p = 0.006). Three high-grade AR cases showed heterogeneous regional strain patterns.</p><p><strong>Conclusion: </strong>This framework reproducibly estimates ascending aortic strain from 4D CTA, characterizes its reliability across segmentation sources and spatial scales, and demonstrates its potential for noninvasive assessment of patient-specific aortic mechanics.</p>","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13692674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149278584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}