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01.
medRxiv (Medicine) 2026-06-24

Projected epidemiologic and economic impact of the 7-1-7 outbreak response framework in Uganda: a stochastic modelling study of Bundibugyo Ebola virus

The 7 1 7 framework (detection 7 days, notification & 1 day, response & 7 days) is a global target for epidemic preparedness, but its prospective value during an active cross border outbreak has not been quantified. Using a stochastic SEIR model parameterised for Uganda with the Bundibugyo Ebola strain and three daily importation probabilities (10%, 30%, and the observed 56%), we compared a rapid 3 1 5 response (detection 3 days, notification 1 day, response 5 days) against a delayed counterfactual (detection 11 days, notification 10 days, response 12 days). The rapid response reduced median cumulative cases by 60 to 66% (26 to 31 cases vs. 76 to 80 cases) and deaths by 62 to 63% (3 deaths vs. 8 deaths) across all import levels, with total costs of USD 29.1 to 29.9 million compared to USD 37.4 to 38.1 million for the delayed scenario. The rapid response was strictly dominant (cost saving and life saving). Variance based Sobol sensitivity analysis identified the case fatality rate, import probability, and basic reproduction number as the most influential parameters, with detection and response delays contributing through interactions. Institutionalising the 7 1 7 framework in Uganda is projected to be highly cost effective and should be supported with sustainable domestic financing, community based surveillance at unofficial border points, three consecutive PCR laboratory capacity, and multilingual risk communication.

02.
medRxiv (Medicine) 2026-06-12

Home-based binocular serious games in virtual reality to treat visual acuity and stereovision in residual amblyopia: AMBER study

Objectives: Amblyopia is a pediatric visual disorder traditionally treated by patching the fellow eye, though many patients retain residual amblyopia post-treatment. Increasing evidence suggests that visual plasticity allows treat-ment beyond the classical therapeutic window. AMBER evaluated the efficacy of binocular serious games in virtual reality (VR) in residual amblyopia. Methods and Analysis: The monocentric, prospective, randomized, crossover trial (reported as case series) includ-ed 14 anisometropic, strabismic, or mixed residual amblyopia patients (6-35 years; 5 children, 9 adults). Participants underwent two 2-month intervention phases: optical correction (standard care) and standard care plus VR games (2.5 h/week), each with a 2-month follow-up. Best-corrected visual acuity (BCVA), stereoacuity, and reading speed were assessed (5 timepoints) using the Sloan and Landolt charts, the Titmus, TNO, Lang II, Asteroid, and Mnread tests. Compliance and adverse events (AE) were recorded. Results: VR training improved BCVA in 10 amblyopic eyes (Landolt and Sloan), with more pronounced effects in anisometropic patients. Six patients showed improved stereoacuity (Titmus; 4x mixed, 1x anisometropic, 1x stra-bismic amblyopia), persistent only in children (1x strabismic, 1x mixed amblyopia). Four improvements were ob-served with TNO (1x), Lang II (1x), Asteroid (0x), and MNread (1x). Despite positive trends, when comparing re-sults of individual patients, between both eyes, and with standard treatment, consistency of improvements cannot be conclusively demonstrated. One non-severe AE (dizziness) was reported. Conclusions: Following individual cases, VR training improved BCVA and stereoacuity, particularly in children and patients with high compliance. However, considering the cohort as a whole, consistency of effects has to be confirmed in larger groups. Thus, the methodologically sophisticated AMBER study revealed differences in VR treatment efficacy between amblyopia types, children/adults, endpoints and tests, offering precious data for the design of meaningful future studies. It shows that neurovisual plasticity gauged by VR-games offers safe, engaging treatment options for residual amblyopia.

03.
arXiv (CS.CL) 2026-06-15

Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results

AI evaluations are widely used for testing and understanding progress. However, the diverse evaluators bring with them inconsistencies that challenge analysis and comparison. First, results are saved in incompatible formats, scattered across leaderboards, papers, blog posts, evaluation harness logs, and custom repositories. Second, results are created by different evaluation frameworks, which produce divergent scores for nominally identical evaluations and record metadata inconsistently, hindering comparison, cross-community evaluation science, cost reduction, and reuse. We introduce Every Eval Ever, the first shared schema and community-crowdsourced repository for AI evaluation results. The schema standardizes how evaluations are represented in a unified, single JSON document. It is source-agnostic by design, ingesting results from evaluation harnesses and papers alike, and optionally stores per-instance outputs for fine-grained analysis. We contribute: (i) a community-governed metadata schema with a companion instance-level schema, the first standardization effort of its kind; (ii) automatic converters from popular formats, evaluation harnesses, and leaderboards to the unified schema; and (iii) a crowdsourced community database hosted on Hugging Face, currently spanning to date 22,235 models, 2,273 unique benchmarks, and 31 evaluation formats.

04.
arXiv (CS.AI) 2026-06-19

Review of Machine Learning Models for Solar Energetic Particle Prediction

arXiv:2606.19539v1 Announce Type: cross Abstract: Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.

05.
arXiv (CS.CL) 2026-06-15

ClaimFlow: Tracing the Evolution of Scientific Claims in NLP

Scientific papers advance $claims$ that later work supports, extends, or sometimes refutes. Yet existing methods for citation and claim analysis capture only fragments of this dialogue. In this work, we make these interactions explicit at the level of individual scientific claims. We introduce $\texttt{ClaimFlow}$, a claim-centric view of the NLP literature, built from $1{,}617$ ACL Anthology papers $(1979 - 2025)$ that are manually annotated with $5{,}689$ claims and $4{,}871$ cross-paper claim relations, indicating whether a citing paper $\texttt{supports}$, $\texttt{extends}$, $\texttt{qualifies}$, $\texttt{refutes}$, or references a cited claim as $\texttt{background}$. Building on $\texttt{ClaimFlow}$, we define a new task – $Claim Relation Classification$ – which requires models to infer the scientific stance toward a cited claim from the text and citation context. Evaluating neural models and large language models on this task, we report baseline performance of $0.81$ macro-F1, suggesting that the task is tractable while leaving room for improvement. We then scale this framework to $\sim$$13k$ NLP papers to study claim evolution across decades of NLP research. We show that $63.5\%$ claims are never reused; only $11.1\%$ are ever challenged. Widely propagated claims are more often $reshaped$ through qualification and extension than supported or refuted. Overall, $\texttt{ClaimFlow}$ offers a lens for examining how ideas shift and mature within NLP.

06.
medRxiv (Medicine) 2026-06-24

Biochemical fingerprinting of human scalp hair reveals endocannabinoid related compounds as potential biomarker indicators of altered mitochondrial bioenergetics in immune cells from female patients with major depressive disorder

Major depressive disorder (MDD) is a severe psychiatric disorder that affects more than 350 million people worldwide, yet its biomolecular mechanisms are incompletely understood, and clinically applicable markers remain elusive. To shed new light on the underlying pathophysiology of MDD across multiple research disciplines, we first used a biochemical fingerprinting approach with human hair (the first 3 cm cut from the scalp) to identify changes in the total set of detectable metabolites and lipids (metabolipidomics) using quadrupole time-of-flight mass spectrometry (qToF-MS). In this study, we focused on endocannabinoid (ECB)-related lipid compounds and identified 7 candidate markers that differed between depressed and non-depressed female participants. Two phosphatidylinositols, namely PI 24:0 and PI 37:4, showed dose-dependent associations with the severity of depressive symptoms. Finally, to bridge hair findings with previously reported results in blood, we tested associations between changes in identified ECB-related compounds and parameters of mitochondrial respiratory activity in peripheral blood mononuclear cells. We found 17 significant associations, with the strongest effects for the lipids PI 24:0, MGDG-O 16:3, PG 12:0, and PI 37:4. Our approach not only identified novel associations between endocannabinoid (ECB)-related lipid dysregulation and impaired mitochondrial energy metabolism in MDD but also revealed ECB-related lipids as a possible surrogate marker of impaired bioenergetic metabolism in MDD, at least in immune cells. More research is needed to replicate these findings, ideally by testing reversibility in longitudinal intervention studies and by including both sexes in larger cohorts.

07.
arXiv (CS.CL) 2026-06-16

FraudSMSWalker: Benchmarking Agentic Large Language Models for SMS-to-Webpage Fraud Detection

SMS fraud is increasingly cross-channel: a message directs the user to a webpage, and the final risk depends on how the SMS claim aligns with the page content and requested user action. However, existing evaluations either focus on message-only smishing classification or expose URL and domain cues that allow models to rely on reputation shortcuts. To address this gap, we introduce FraudSMSWalker, a controlled benchmark for URL-masked SMS-to-webpage fraud judgment. FraudSMSWalker contains 699 bilingual chains, including 332 fraudulent and 367 benign cases, across ten service scenarios. The model-visible input consists of the SMS context and sanitized webpage evidence, while raw URLs, hosts, domains, IPs, redirects, and reputation metadata are withheld. The benchmark further includes hard benign cases whose pages contain login, payment, verification, or account-management elements that are plausible under the service context but also appear in scam flows. We evaluate nine web agents under masked browser-agent protocols and conduct URL-visibility ablations. The results show that current agents can detect suspicious cues, but struggle to preserve benign recall and often produce positive predictions that are weakly supported by the observed evidence. These findings position FraudSMSWalker as a benchmark for measuring whether web agents can make fraud judgments that remain both accurate and evidence-grounded when direct reputation shortcuts are suppressed. The associated code and dataset are accessible at the \href{https://anonymous.4open.science/w/FraudMessageWalker-Bench}{anonymous link}.

08.
arXiv (quant-ph) 2026-06-16

Quantum coherence and Leggett-Garg inequality

arXiv:2606.15717v1 Announce Type: new Abstract: In this paper, we attempt to establish the relationship between quantum coherence and the violation of the Leggett-Garg inequality. In particular, employing the Lindblad equation, we obtain the pseudo-density matrix for a damping system to study the effect of environment interaction on the violation of this inequality in a two-state quantum system. It is shown that the violation of the Leggett-Garg inequality can be observed as long as temporal evolution does not induce decoherence. This statement is independent of the initial state of the system. Furthermore, similar to the Horodecki criterion for the CHSH inequality (R. Horodecki et al. Phys. Lett. {\bf A200}, 340), we study necessary and sufficient conditions for violating the Leggett-Garg inequality. Hereby, under the circumstance that the inequality violation occurs, an upper bound for the time interval between consecutive measurements with respect to the time scale of interaction with the environment (the relaxation time) is obtained.

09.
medRxiv (Medicine) 2026-06-17

Macrophage-targeted glucocorticoid prodrug resolves acute inflammation while preserving HPA axis function: mechanistic, preclinical, and Phase II/III clinical evidence

Glucocorticoids (GCs) remain the fastest-acting anti-inflammatory agents but are constrained by systemic exposure that suppresses the hypothalamic pituitary adrenal (HPA) axis, silences adaptive immunity, and drives chronic toxicities. Chronic inflammatory diseases are sustained by long-lived CD206+ macrophages containing immune-resistant pathogenic material not cleared physiologically. We developed 101-PGC-005 ('005), a macrophage-targeted type 1a dexamethasone prodrug engineered for low-affinity, recycling-compatible uptake via CD206, with intracellular release triggered by acidic endosomes. We evaluated '005 in mechanistic assays, pathogen-diverse preclinical models, three human pharmacokinetic (PK) studies, and an adaptive-design randomized Phase II/III trial in 309 hospitalized patients with moderate COVID-19. In two completed Phase I human studies, a first-in-human dose-escalation and repeated-dose study and a dedicated single/multiple-dose PK and safety study; '005 circulated as intact prodrug with rapid systemic clearance (Tmax ~0.5 h; terminal half-life ~1.9 h), with no measurable free dexamethasone after single dosing and only low, clinically non-significant free dexamethasone after repeated dosing, and intact prodrug recovered unchanged in urine. Morning cortisol and ACTH were preserved after 30 mg once daily for three consecutive days (1.5 times the intended therapeutic dose). A cerebrospinal fluid PK study is evaluating central-compartment penetration. In the Phase II/III trial, powered for non-inferiority, conducted across six sites in India under GCP with Ministry of Health approval and independent DSMB oversight; '005 (20 mg IV daily for 3 days) was superior to dexamethasone (6 mg IV daily for 3 -10 days) on the primary endpoint of time to > a 2-point improvement on the WHO ordinal scale (HR 2.31; 95% CI 1.83-2.93; p < 0.0001; median 3 vs. 4 days). '005 was also superior on viral clearance (HR 1.47; 95% CI 1.17-1.84; p = 0.0001), hospital discharge rate, SpO2; recovery, and fever resolution. Zero patients in the '005 arm received investigator-initiated corticosteroid supplementation despite protocol allowance. All 309 randomized patients completed the study (ITT = per-protocol). Safety profiles were equivalent (TEAEs 54.8% vs 54.5%; p = 0.958), with no Grade 3+ events, SAEs, deaths, or discontinuations in either arm. Mechanistically, '005 delivered dual benefit: acute debulking of inflammatory macrophages and selective depletion of chronically activated pathology-sustaining macrophages, while preserving CXCL10 antiviral signaling and physiologic HPA control. Critically, HPA preservation is not merely a safety feature, it is a core efficacy mechanism: by clearing the pathogenic macrophage burden that was overriding HPA regulation, '005 restores the conditions for endogenous cortisol to resume its pulsatile, demand-responsive anti-inflammatory role across all GR-expressing cells, lymphocytes, endothelial cells, neurons, and newly differentiated macrophages, that '005 itself cannot reach. These findings support regulatory-grade evidence for macrophage-targeted corticosteroid therapy and provide the foundation for further development across acute inflammatory indications (sepsis, viral pneumonia, cytokine-release syndromes) and chronic macrophage-driven diseases (atherosclerosis, metabolic steatohepatitis, neurodegeneration, tumor-associated macrophages).

10.
arXiv (CS.CV) 2026-06-17

Impact of Hand Impairment and Occlusions on Hand Pose Estimation Accuracy in Augmented Reality Applications

Mixed reality applications can be designed for hand rehabilitation. Augmented reality (AR) head mounted displays (HMDs) specifically allow for ecologically valid tasks because individuals can see their real environment and interact with real objects while receiving additional cues on the HMD. While these applications rely on accurate hand pose estimation, there is a gap in investigating the influence of hand impairment or occlusion from real-object interactions on pose estimation accuracy. Further, comparisons between AR HMD predictions and state-of-the-art pose estimation methods have not been established. The current study assessed pose estimation accuracy of the HoloLens 2 HMD and state-of-the-art pose estimation algorithms (WiLoR, HaMeR, WildHands, and MediaPipe) while individuals with cervical spinal cord injury (cSCI; n = 13, Neurological Level of Injury: C3-C6; American Spinal Injury Association Impairment Scale: A-D) and 15 uninjured controls interacted with clear and opaque objects. Ground truth estimates of 3D joint positions were generated via triangulation from a multi-camera setup. Pose estimation accuracy did not differ between the cSCI and uninjured control groups suggesting that 3D joint predictions from the HoloLens 2 and pose estimation algorithms can generalize to populations with hand impairment. Further, clear objects provided a small accuracy advantage over opaque objects (0.1 mm) and predictions from both WiLoR and HaMeR were slightly more accurate than the HoloLens 2 (2 mm). Overall, these results suggest that the HoloLens 2 may be viable for hand rehabilitation applications and the dataset generated can be used to refine pose estimation methods for hand-impaired populations.

11.
arXiv (CS.LG) 2026-06-12

One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data

arXiv:2606.10678v2 Announce Type: replace Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies. Despite their success, these single-stage forecasting architectures exhibit persistent systematic residual biases arising from structural discrepancies, unmodeled stochastic components, or inadequate multi-scale temporal representations. This limitation persists when residuals are treated as irreducible noise, precluding adaptive correction of structured error patterns. To address this limitation, we introduce a two-stage, model-agnostic framework that explicitly decouples forecasting and residual learning into distinct stages of representation learning. A base transformer first generates the initial predictions. Subsequently, a dedicated meta-corrector dynamically models structured error patterns across multivariate channels, preserves cross-variable dependencies, and iteratively refines the residual bias of the base transformer. By formalizing this pipeline as a hypothesis space expansion, our framework addresses approximation limitations inherent in single-stage architectures, removes reliance on restrictive assumptions, and enables end-to-end learning of complex error dynamics. Evaluated on eight popular benchmark datasets using established protocols, our approach achieves state-of-the-art performance, with significant improvements in standard metrics (MSE, MAE). The results demonstrate the framework's ability to mitigate systematic biases and enhance robustness to complex temporal dynamics, advancing the practical applicability of transformer-based forecasting models.

12.
medRxiv (Medicine) 2026-06-17

LLM-Driven Extraction of NI-RADS and Imaging Tumor Characteristics to Enhance Oropharyngeal Cancer Survivorship Surveillance

Abstract Purpose Radiologic surveillance is essential for oropharyngeal cancer (OPC) survivors, guiding recurrence detection and follow-up strategies. The Neck Imaging Reporting and Data System provides a standardized framework for post-treatment risk reporting at both the primary tumor site (pNI-RADs) and cervical lymph nodes (nNI-RADS). Comprehensive surveillance additionally requires assessment of disease status, including the primary tumor, nodal involvement, and distant metastases. These clinical results are often embedded as unstructured data within free-text radiology reports. We hypothesized that a large language model (LLM) can reliably extract NI-RADS score criteria and summarize key imaging features from unstructured radiology text, achieving high concordance with expert review. Methods Previously untreated OPC patients who received definitive cancer therapy were identified. Eligible imaging reports included post-treatment head and neck CT, MRI, or FDG PET/CT scans containing narrative and impression text. Examinations lacking narrative or impression text, containing pre-existing NI-RADS annotations, or involving non-surveillance imaging modalities were excluded. A total of 200 reports were randomly selected from 7,076 eligible examinations for manual abstraction using a three-reviewer consensus framework to establish a reference dataset. Using the Palantir Foundry Pipeline Builder, a GPT-5-based LLM was deployed to extract pNI-RADS and nNI-RADS scores, and key imaging features of disease status from these reports. Performance was evaluated using exact agreement and F1-based metrics. Results Agreement for no evidence of disease (score of 1) was 93.3% (126/135; F1 = 0.94) and 90.3% (130/144; F1 = 0.93) for pNI-RADS and nNI-RADS, respectively. For NI-RADS [&ge;]2, exact category agreement was 73.1% (38/52; macro-F1 = 0.75) for pNI-RADS and 64.3% (27/42; macro-F1 = 0.56) for nNI-RADS. Quadratic weighted {kappa} was 0.81 and 0.59, respectively. For post-treatment disease surveillance variables, agreement was 94.9% (149/157; F1 = 0.87) for primary tumor presence, 89.1% (164/184; F1 = 0.87) for nodal disease presence, and 94.7% (126/133; F1 = 0.70) for distant metastasis detection. Specificity was high across disease-status variables (0.95-0.99), with negative predictive values of 0.95 for primary tumor, 0.87 for nodal disease, and 0.99 for distant metastasis. Conclusions Our LLM-based information retrieval and classification approach for radiographic treatment response from unstructured, multidimensional imaging reports achieved high performance for disease exclusion and moderate performance for detecting suspected residual and/or new disease. This pipeline supports scalable and standardized surveillance data capture for longitudinal monitoring, clinical analytics, and survivorship research in head and neck oncology.

13.
medRxiv (Medicine) 2026-06-19

The Impact of Pregnant Womens Dietary Behavior on the Physiological Adaptation Paradox and Maternal-Fetal Resource Conflict in Conflict Settings: A Predictive Analytical Study

This scientific study aims to assess the level of awareness, nutritional knowledge, and actual behavioral practices among pregnant women in the Capital District of Sanaa, Republic of Yemen, and to determine their impact on the health and clinical indicators of the mother and fetus under complex conflict conditions. The study employed a descriptive-analytical approach based on a simple random sample of 200 pregnant women attending government-run hospitals and specialized medical centers in the Capital District. Field data were collected during December 2025 using a structured and validated questionnaire consisting of 42 items measuring demographic variables, awareness, practices, barriers, and health outcomes. The results of the statistical analysis using SPSS software showed a high level of nutritional awareness (87%) and healthy dietary practices (80%) among the sample participants. Simple and multiple linear regression tests revealed a statistically significant effect of awareness and practices in explaining 20.2% of the variance in the health status of the mother and fetus (R{superscript 2}= 0.204, p < 0.001). The study demonstrated that actual behavioral practices have greater predictive power ({beta}=0.316, p=0.001) compared to theoretical cognitive awareness ({beta}=0.232, p=0.005) in determining clinical outcomes for the mother and fetus, highlighting the widening gap between knowledge and behavior under structural pressures. "Morning sickness" (80%) and the deterioration of "family economic status" (71%) emerged as the greatest physiological and material barriers to proper nutrition. With their inferential impact established as an extension of the maternal-fetal resource allocation conflict in a physiologically and economically challenging environment, the study also identified significant differences in nutritional behavior and health outcomes in favor of housewives and mothers who are more educated and have higher incomes, while no significant differences were recorded attributable to obstetric variables such as stage or order of pregnancy. The study offers a unique theoretical and practical contribution by formulating an integrated causal model that demonstrates that the fetus acts as a biological drain on the mothers cellular and mineral reserves in a war environment, which necessitates directing antenatal care and support programs toward effective behavioral empowerment and nutritional support to overcome the structural and material barriers faced by pregnant women.

14.
arXiv (quant-ph) 2026-06-16

Generative modelling powered by room-temperature polariton condensates

arXiv:2606.15344v1 Announce Type: cross Abstract: Generative modelling requires efficient stochastic nonlinear transformations and physical platforms that can naturally realise them. We experimentally demonstrate that nonlinear optical systems operating in the strong light-matter coupling regime can serve as physical transformation layers for conditional generative modelling. Specifically, we develop a workflow in which room-temperature exciton-polariton condensates formed in organic dye microcavities act as a physical stochastic transform within a generative adversarial network and enable conditional digit-to-image translation. By using the nonlinear many-body dynamics and intrinsic stochasticity of polariton condensates, the workflow outperforms baseline approaches based on digitally injected perturbations. We find that polariton-enabled sampling via generative adversarial network (Polariton GAN) yields improved inception score, digit preservation accuracy and structural similarity compared with both digital sampling and laser-based systems. We further show that spatially correlated output variations can naturally regularise adversarial training and enhance output diversity. Our results establish polariton condensation as a new computational resource for generative modelling, opening a pathway towards physics-enhanced machine learning systems.

15.
arXiv (quant-ph) 2026-06-15

Conditional squeezing induced by a two-level system: arbitrary-time Magnus coefficients in the quantum Rabi model

arXiv:2508.03506v5 Announce Type: replace Abstract: We present a systematic Magnus expansion treatment of the quantum Rabi model beyond the Rotating Wave Approximation. We show that at the second order of Magnus series, the second-order evolution operator contains a term that induces conditional squeezing of the field mode depending on the state of the atom, in addition to the energy shifts. We analyze the scaling behavior of the conditional squeezing coefficient for $^{87}\mathrm{Rb}$ $5^2S_{1/2}\rightarrow5^2P_{1/2}$ transition line and show that the slow envelope of the squeezing coefficient is maximized at half-detuning cycles, and that it scales with $\frac{4g^2}{\omega_0|\Delta|}$. We also show that the quadrature squeezing angle suggests a possible route towards quantum non-demolition readouts, while further investigation is required for a full first-order suppression. We then connect our work to the well-studied AC-Stark shift and Bloch-Siegert shift using the effective Hamiltonian theory. Finally, we show how the energy shifts and the conditional squeezing arise, as a whole $\mathrm{SU}(1,1)$ algebra, and how they can be disentangled as individual unitary evolutions.

16.
bioRxiv (Bioinfo) 2026-06-11

A multi-agent system for spine MRI report generation from multi-sequence imaging

Spinal pathology is a leading cause of pain and disability worldwide. Spine magnetic resonance imaging (MRI) is central to clinical evaluation, yet its interpretation remains complex and time-consuming, requiring integration of information across multiple imaging sequences and anatomical regions. Despite recent advances in automated MRI analysis, effectively combining multi-sequence data while preserving sequence-specific diagnostic information remains an open challenge. Here we present SpineAgent, a multi-agent framework for spine MRI report generation built upon a multi-sequence foundation model trained on routine clinical data from 32,047 patients and 453,683 MRI series, comprising a total of 13,441,191 MRI slices. To accommodate diverse modalities of sequences, we first pre-train two DINOv3-based encoders separately on T1- and T2-weighted sequences. We then introduce a continual training strategy that learns a synthesizer to embed images of other sequences using the T1 and T2 encoders, producing patient-level embedding that integrates various signals across MRI sequences. Using these embeddings, SpineAgent achieves state-of-the-art performance, with mean 10.8% AUROC improvement across 17 spinal condition-prediction tasks compared to the best competing method, and demonstrates strong generalizability under cross-manufacturer and cross-cohort evaluation. Beyond classification, SpineAgent enables pathology localization by identifying findings-relevant slices and segmenting pathological regions. It also supports multimodal image-report retrieval, providing a solid foundation for scalable and explainable MRI report generation. We further integrate these validated capabilities of SpineAgent into 37 specialized agents for condition diagnosis, pathological-region localization, and clinically-similar-cases retrieval. Finally, we incorporate their outputs as structured tokens within a Medical Report Agent trained end-to-end for report generation. Through both automated metrics and expert evaluation by five radiologists, SpineAgent achieves leading performance in spine MRI report generation. Together, SpineAgent introduces a continual training approach for multi-sequence spine MRI understanding. By decomposing report generation into clinically grounded subtasks addressed by specialized agents, the SpineAgent framework enables accurate, interpretable and generalizable spine MRI reporting across diverse imaging sequences and anatomical regions.

17.
bioRxiv (Bioinfo) 2026-06-11

TMO: ASYMMETRIC CROSS-MODAL ATTENTION FOR LEARNINGCELL-STATE-DEPENDENT REGULATORY LAGS FROM SINGLE-CELL MULTIOMIC DATA

Abstract Background: Single-cell multi-omics technologies simultaneously measure chromatin accessibility (ATAC) and gene expression (RNA), providing a unique window into the temporal ordering of regulatory events during differentiation. However, most computational models treat the two modalities symmetrically, ignoring the directional relationship between chromatin and transcription, and existing lag-aware methods estimate a single global lag per gene, failing to capture cell-state-dependent dynamics. Methods and Results: We introduce Temporal Multi-Omics (TMO), a deep learning framework that learns signed, cell-state-conditional regulatory lags ({Delta}{tau}) using asymmetric cross-modal attention. TMO projects RNA and ATAC into 50 latent components each, tokenises each cell as a sequence of 100 tokens, and uses a two-pass transformer in which a data-driven lag prior - derived from a sliding-window cross-correlation function - directly biases attention asymmetrically. On four independent 10x Multiome datasets (mouse brain, human brain, mouse kidney, human PBMC), the asymmetric model achieves Lag Concordance Scores (LCS) of 0.988-0.999, compared to 0.048-0.108 for an architecturally identical symmetric baseline. A stratified 80/20 held-out experiment confirms that the learned component-lag ordering generalises to unseen cells (held-out LCS 0.85-0.99). Clustered {Delta}{tau} heatmaps show positive {Delta}{tau} (ATAC-led priming) in early pseudotime and negative {Delta}{tau} (RNA-led, activity-dependent regulation) in late pseudotime; the ATAC-RNA correlation heatmap exhibits a U-shaped pattern indicative of developmental decoupling. Components with the most positive {Delta}{tau} are enriched for chromatin organization and stem cell differentiation (FDR < 0.05), while those with the most negative {Delta}{tau} are enriched for synaptic signalling and immune activation. Ablating the cell-state information from the lag predictor reduces the LCS and collapses per-component temporal dynamics (KS p [&le;] 0.039 in all four tissues), proving that TMOs dynamic lag patterns depend on cell-state conditioning. Independent ChIP-seq validation for four transcription factors (PAX5, Pax6, ASCL1, Hnf4) confirms highly significant separation between target genes and expression-matched background (p < 10-4 in all cases). Two Multiome Perturb-seq screens provide causal validation: SMARCB1 knockout shows a directional trend (1.5-fold target shift, p = 0.056, n = 147 perturbed cells), and SMARCE1 knockout reaches statistical significance (p = 0.0089, n = 3,394 perturbed cells). Gene-level cross-correlation independently validates that the regulatory lag signal is present in the raw data, and TMO further identifies rare, statistically significant biphasic gene programs where the regulatory direction reverses across pseudotime. Conclusions: TMO is the first method to make regulatory lag a learnable, cell-state-conditional, and architecturally encoded parameter. It is scalable, interpretable, and open-source, providing a powerful tool for studying regulatory timing in development, disease, and perturbation screens.

18.
arXiv (quant-ph) 2026-06-25

Analytic Approach to Quantum Control Using Quantum Signal Processing

arXiv:2606.26085v1 Announce Type: new Abstract: Realizing coherent quantum computation requires precise and robust manipulation of quantum systems through quantum control protocols. Most quantum control techniques rely on heuristic methods for designing the driving pulses that steer the system towards a target state. Such methods are often based on brute-force optimization and offer limited understanding of the solution landscape. In contrast, quantum algorithms offer a rich body of analytical methods with rigorous error guarantees for implementing unitary and non-unitary transformations, which suggests a promising direction for developing new approaches to quantum control. Among various such algorithms, quantum signal processing (QSP) has emerged as a powerful framework for quantum algorithm design, implementation, and optimization. However, its potential for quantum control remains largely unexplored. In this work, we establish QSP-Control, an analytical framework for quantum control of qubit-oscillator dynamics. We focus on dispersively coupled qubit-oscillator systems and employ the QSP formalism to mitigate unwanted nonlinear effects arising from cross-Kerr interactions. In addition, we develop constructions for precise manipulation of Fock states by designing Fock-state-selective operators, based on structural parallels between the Jaynes-Cummings interaction and QSP. These findings demonstrate how several practically relevant problems in quantum control can be mapped to forms amenable to QSP, offering both a systematic design framework and an interpretable perspective on quantum control.

19.
arXiv (CS.LG) 2026-06-16

Temporal Validation Changes the Apparent Public-Health Utility of Under-Five Mortality Prediction in Bangladesh: A Four-Round DHS Machine-Learning Study

arXiv:2602.03957v2 Announce Type: replace Abstract: Background: Under-five mortality in Bangladesh remains uneven despite national progress. DHS-based prediction models may guide targeted follow-up, but only if validation reflects future use. We examined how validation design changes apparent prediction performance. Methods: Four BDHS rounds (2011-2022; 33,962 children; 1,290 deaths) were analysed with a 26-feature pipeline and three model classes under four validation regimes, including cross-survey temporal validation (train 2011+2014, calibrate 2017, test 2022). A 32-unit ELU multilayer perceptron was selected via genetic-algorithm neural architecture search. AUROC used 2,000 bootstrap resamples; screening utility used sensitivity, PPV, and number needed to screen (NNS) at fixed capacity. Results: Validation regime altered public-health interpretation more than model class. NAS MLP AUROC ranged from 0.669 (2022-only random) to 0.775 (pooled random), with temporal AUROC 0.730. At the top-10% temporal threshold, NAS identified 152/355 deaths in 2022 (sensitivity 42.8%, PPV 13.2%, NNS 7.6). NNS across designs ranged from 5.6 to 11.0. Conclusions: Validation-regime choice changed screening workload and apparent policy value more than architecture. Temporal validation supports defensible estimates of follow-up and referral demand; DHS child-mortality studies should report sensitivity, PPV, and NNS before programmatic use.

20.
arXiv (quant-ph) 2026-06-25

A Short Note on the Generators of Controlled Quantum Gates

arXiv:2606.25789v1 Announce Type: new Abstract: We present the analytical generators for arbitrary multi-qubit controlled gates. Closed forms for the generating Hamiltonians are given for gates with both multiple control and target qubits, as well as for arbitrary control conditions. This allows us to go beyond gate-based simulations of quantum circuits and incorporate decoherence and other noise in simulations of quantum computers. We exemplify this by simulating the impact of a harmonic oscillator interacting with two qubits during the application of a controlled NOT gate.

21.
medRxiv (Medicine) 2026-06-16

Risk beliefs, intensive digital information and demand for a new preventative health product in public clinics: Evidence from an experiment in Zimbabwe.

Demand for preventative health care is weak in low-income settings. In a field experiment in a low-income, high-risk setting, we evaluated whether demand for a new bio-medical preventative health product, offered free at public health clinics, responds to digital feedback-based intensive information on health risks and benefits of prevention along with a clinic referral enabling access to the product. In our sample of women aged 18-24 years, we find a large correction in risk beliefs sustained six months after the intervention. Against a background of very low baseline usage, within six months we find a 5.8 percentage point increase in take up of the prevention method, a level of uptake which is very large relative to the control group. Reassuringly, there is no meaningful difference in up-take amongst baseline high- risk and low-risk individuals.

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medRxiv (Medicine) 2026-06-17

High burden of subclinical TB in Africa revealed from a postmortem cohort.

Tuberculosis (TB) is increasingly recognised as a spectrum of infection and disease, yet the prevalence of viable, asymptomatic Mycobacterium tuberculosis (M.tb) infection remains uncertain. Subclinical Tuberculosis (scTB), defined as microbiologically confirmed M.tb infection in the absence of recognised symptoms, is under detected by symptom, sputum and imaging-based approaches. We conducted postmortem examinations of 94 adults who died from non-infectious causes, none of whom were clinically suspected of TB or reported TB related symptoms prior to death. Lung and extrapulmonary tissues were cultured for M.tb. Viable M.tb was confirmed in six individuals, corresponding to a prevalence of 6.4% (95% CI: 2.4 to 13.4%). These findings provide direct tissue-based evidence that viable, asymptomatic M.tb infection can persist beyond the reach of conventional clinical detection. Our data suggest that a biologically active reservoir of infection may exist undetected within high-burden settings, with implications for surveillance strategies aimed at TB elimination.

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medRxiv (Medicine) 2026-06-11

Electrical signatures of divergent connectivity in the human subgenual cingulate cortex

Background: Major depressive disorder remains a leading cause of disability. While subgenual cingulate cortex (sgCC) deep brain stimulation (DBS) shows promise for medically refractory depression, clinical outcomes have been heterogeneous, suggesting that individual differences in neural circuitry engagement may critically influence therapeutic efficacy. We aimed to define the electrophysiological signatures of sgCC efferent connectivity using single-pulse electrical stimulation (SPES) with intracranial stereo-EEG (sEEG) to inform rational targeting and physiological biomarkers for sgCC-DBS. Methods: In four patients undergoing clinically indicated sEEG for seizure mapping, SPES was delivered through sgCC pairs, while distributed brain stimulation-evoked potentials (BSEPs) were recorded across cortical and subcortical sites. Responses were characterized using Canonical Response Parameterization to extract reproducible waveforms and per-trial reliability. Results: sgCC stimulation elicited reproducible, spatially organized BSEPs across frontal, limbic, and paralimbic networks, aligning with known anatomical pathways. Frontal recruitment featured robust, lateralized orbitofrontal activation favoring the ipsilateral central, medial OFC and bilateral ventromedial prefrontal responses. Limbic effects demonstrated bilateral cingulate activation with stronger ipsilateral recruitment and lateralized amygdala and hippocampal responses. Paralimbic engagement included insular responses with subject-specific anterior predominance and bi-hemispheric temporal-polar slow-wave deflections. Conclusion: These findings provide direct electrophysiological evidence of distributed, lateralized sgCC divergent network connectivity in the human brain, offering physiologic confirmation of its role in affective circuitry. The observed topography and laterality have direct applications for sgCC-DBS targeting and implicate BSEP signatures as candidate biomarkers to guide patient-specific therapy.

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medRxiv (Medicine) 2026-06-22

A Controlled Human Malaria Infection model for relapsing Plasmodium vivax

Background Plasmodium vivax malaria relapses are a major source of morbidity and onward transmission of infection. The underlying mechanisms are poorly understood and current therapies sub-optimal. We examined the safety and feasibility of a controlled human malaria infection (CHMI) model for relapsing P. vivax. Methods We conducted an open-label, proof-of-concept, CHMI study of relapsing P. vivax. Healthy, malaria-naive, Duffy-positive adults aged 18-45 years with extensive CYP2D6 metaboliser phenotype and normal blood glucose-6-phosphate dehydrogenase (G6PD) levels were recruited in Oxford, UK. Mosquito-bite CHMI was performed in Nijmegen, The Netherlands, using Anopheles stephensi mosquitoes infected with PvW1, a clonal isolate of P. vivax from Thailand. All follow-up visits were conducted in Oxford, UK. Primary P. vivax infections (qPCR > 500 genome copies/mL) were treated with artemether-lumefantrine (80mg/480mg at 8, 24, 36, 48 and 60 hours). From Day 28 following CHMI, participants attended a fortnightly clinic for clinical review and qPCR blood sampling, with additional assessments performed for any reported symptoms. P. vivax relapse infections (qPCR > 500 genome copies/mL) were treated with artemether-lumefantrine as per primary infection. Definitive anti-malarial treatment with atovaquone-proguanil (1000mg/400mg once daily for three days) and primaquine (0{middle dot}5 mg/kg/day for 14 days) was administered six months following CHMI, regardless of parasitaemia or symptoms. The primary objective was to assess the safety, feasibility and frequency of relapsing P. vivax after CHMI. Remote follow-up (5 years) is ongoing. The study is registered with ISRCTN registry (ISRCTN48625883). Findings 20 participants were screened for eligibility from 21 January 2025. Five participants (median age 22 years) underwent CHMI (five infected mosquitoes per participant) on 15 April 2025. All participants developed primary P. vivax infection and experienced at least one relapse infection. Two participants experienced a second relapse. Overall incidence rate was 3{middle dot}6 relapse infections per person-year. Solicited adverse events were mild or moderate and there were no serious adverse events. Definitive anti-malarial treatment was administered to all participants. One participant experienced primaquine-induced methaemoglobinaemia, resolving with early discontinuation of treatment (total dose 5{middle dot}3 mg/kg). To date, more than six months after primaquine treatment, no further relapses have been recorded. Interpretation CHMI of relapsing P. vivax is safe and feasible, allowing exploration of the mechanisms underlying relapse infections and providing a platform for future anti-relapse efficacy studies. Funding European Union Horizon Europe programme and UK Research and Innovation (UKRI) via OptiVivax consortium; UK National Institute for Health and Care Research Biomedical Research Centre: Oxford; and UK Medical Research Council.

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medRxiv (Medicine) 2026-06-11

Association between depressive symptoms and physical function among participants with heart disease in the Reasons for Geographic And Racial Differences in Stroke (REGARDS) study.

Background: Depression and heart disease frequently co-occur in the aging population and are associated with functional decline and poor health outcomes. Understanding how depressive symptoms relate to different aspects of physical function among adults with heart disease may help identify high-risk subgroups. Objective: To examine the association of depressive symptoms with self-reported and observed physical function measures among participants with heart disease in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study and assess whether associations differ by sex and race?sex groups. Methods: We conducted a cross-sectional analysis using data from REGARDS study second in-home visit (2013?2016). Depressive symptoms were measured with the 10-item Center for Epidemiologic Studies Depression scale (CES D 10), considering scores ?10 as clinically significant. Physical function measures were instrumental activities of daily living (IADL), activities of daily living (ADL), chair stand time (5 repetitions), and gait speed. Linear regression models estimated associations of depressive symptoms with function, adjusting for sociodemographic, health behavior, antidepressant medications, body mass index, and social support. Effect modification by sex and race?sex group was evaluated. Results: Among 3,055 participants, 11.7% had CES D 10 ?10. Compared to CES-D-10 scores