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01.
arXiv (quant-ph) 2026-06-16

Experimental Observation of Dynamical Phase Transitions in a Dephased Photonic Quantum Walk

arXiv:2606.15935v1 Announce Type: new Abstract: Dynamical phase transitions in open quantum systems govern how non-equilibrium states relax toward a stationary state. We study these transitions experimentally using a discrete-time photonic quantum walk on a three-node graph. A tunable synthetic gauge flux and calibrated dephasing allow us to control time-reversal symmetry and the detailed balance properties of the effective Markovian dynamics. With detailed balance, we observe a first-order dynamical phase transition marked by a crossing of real Liouvillian eigenvalues. When detailed balance is broken, we observe a second-order dynamical phase transition at an exceptional point where eigenvalues and eigenvectors coalesce. By progressively reducing the dephasing strength, we track the crossover toward the quantum-coherent regime and determine that the transitions persist down to a finite threshold. Our results link Liouvillian spectral topology to relaxation criticality and demonstrate a controllable platform for engineered dissipative dynamics.

02.
arXiv (quant-ph) 2026-06-24

Polynomial-time exact diagonalization via sparse guided eigenwalks

arXiv:2606.23967v1 Announce Type: new Abstract: Computing quantum ground states is generically difficult, but additional structure can sometimes allow diagonalization to be recast as a more feasible problem. For example, when the desired ground state is sparse in a given basis, diagonalization can be facilitated via graph search. We make this reformulation precise by introducing the eigenwalk problem, which seeks the support of a sparse eigenvector of a Hermitian matrix by exploring the graph induced by its nonzero entries. However, it is not obvious whether the relevant support vertices must always be efficiently reachable by a search on the graph. To resolve this question, we prove that for every sparse eigenvector, there exists a (possibly different) sparse eigenvector with the same eigenvalue whose support is tightly localized in the graph, with diameter scaling only linearly in the sparsity and independently of the total number of vertices. As a consequence, if a $2^n$-dimensional, $poly(n)$-sparse Hamiltonian has an $\mathcal{O}(1)$-sparse extremal eigenvector and one support element is known, then an exact eigenvector with the same eigenvalue can be computed classically in $poly(n)$ time. The same conclusion follows when the $\mathcal{O}(1)$-sparse eigenvector is non-extremal, provided that it is sparser than every eigenvector with a different eigenvalue. These results hold with no assumptions on the degeneracy, locality, spectral width, or spectral gap of the Hamiltonian, and the underlying support-localization principle also extends to problems beyond exact diagonalization, such as sparse principal component analysis.

03.
arXiv (CS.CV) 2026-06-24

Adaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning

Few-shot image recognition requires models to adapt to new classes from a small labeled support set. Hebbian fast-weight memory can provide temporary associative information during an episode, but fixed memory behavior may not be appropriate for every few-shot task. In this work, we propose Adaptive Hebbian Routing for few-shot Vision Transformers. The method uses a lightweight MLP router to control the contribution of Hebbian memory, the strength of memory updates, and the retention of previous memory from support-set features. We study Adaptive Placement, Adaptive Plasticity, and Fully Adaptive Hebbian Routing. Experiments use ViT-Small, DeiT-Small, and Swin-Tiny under 5-way 1-shot evaluation on Omniglot, CIFAR-FS, and cross-domain transfer from CIFAR-FS to Omniglot. In the direct Swin comparison, fixed and adaptive Hebbian variants use the same memory location. Adaptive Plasticity improves the fixed Hebbian result from 96.74\% to 96.92\%, while Fully Adaptive Routing achieves the best result at 96.94\%. The fully adaptive Swin model also reduces inference time from 16.51 ms to 14.05 ms relative to fixed Hebbian Swin. On CIFAR-FS, adaptive variants improve performance across all three backbones, and the multi-shot evaluation shows that these gains remain useful as the number of support examples increases. These results show that adaptive plasticity and adaptive memory activation can improve few-shot Transformer representations beyond fixed Hebbian behavior.

04.
arXiv (CS.LG) 2026-06-17

Price of metric universality in vector quantization is at most 0.11 bit

arXiv:2602.05790v2 Announce Type: replace-cross Abstract: Fast computation of a matrix product $W^\top X$ is a workhorse of modern LLMs. To make their deployment more efficient, a popular approach is that of using a low-precision approximation $\widehat W$ in place of true $W$ (``weight-only quantization''). Information theory demonstrates that an optimal algorithm for reducing precision of $W$ depends on the (second order) statistics of $X$ and requires a careful alignment of vector quantization codebook with PCA directions of $X$ (a process known as ``waterfilling allocation''). Dependence of the codebook on statistics of $X$, however, is highly impractical. This paper proves that there exist a universal codebook that is simultaneously near-optimal for all possible statistics of $X$, in the sense of being at least as good as an $X$-adapted waterfilling codebook with rate reduced by 0.11 bit per dimension in the case when $W$ is Gaussian. Such universal codebook would be an ideal candidate for the low-precision storage format, a topic of active modern research, but alas the existence proof is non-constructive. Equivalently, our result shows existence of a net in $\mathbb{R}^n$ that is a nearly-optimal covering of a sphere simultaneously with respect to all Hilbert norms.

05.
bioRxiv (Bioinfo) 2026-06-18

fuzzyfold: a high-performance framework for stochastic RNA folding kinetics

作者:

The analysis of nucleic acid secondary structures is overwhelmingly dominated by methods that analyze the thermodynamic equilibrium distribution and which ignore all dynamic aspects of nucleic acid folding. Yet, there are numerous popular examples of nucleic acid folding that rely on kinetic models, such as RNA riboswitches or DNA strand displacement systems. Here, I am presenting fuzzyfold, a Rust-based software package for nucleic acid secondary structure analysis with an explicit focus on stochastic modeling. The framework introduces three-way and four-way shift moves with a biophysically motivated rate-model parameterization, and it is developed with an emphasis on both model flexibility and performance, e.g. allowing for the generation of single co-transcriptional trajectories for thousand-nucleotide long RNA molecules in just a few minutes. The main strength of the fuzzyfold package, however, is its focus on user and developer interfaces for long-term development. It provides easily installable command-line interfaces, e.g. for aggregating data from multiple parallel trajectories efficiently into an ensemble-level dynamic analysis. For developers, the code-base supports straight-forward substitution of thermodynamic and kinetic free-energy models, and a flexible library interface with Python bindings, enabling integration of individual components into custom computational workflows.

06.
arXiv (CS.AI) 2026-06-24

VoltanaLLM: Energy-Efficient and SLO-Aware Disaggregated LLM Serving via Adaptive Frequency Control and State-Space Routing

arXiv:2509.04827v3 Announce Type: replace-cross Abstract: The energy cost of Large Language Model (LLM) inference is rapidly becoming a barrier to sustainable and scalable deployment. Although modern serving architectures expose distinct prefill and decode behaviors, existing systems fail to exploit these phase differences for energy-efficient serving under strict latency SLOs. This paper introduces VoltanaLLM, the first system that explicitly targets and reduces the energy bloat in modern prefill-decode (P/D) disaggregated LLM serving. Guided by a control-theory perspective, VoltanaLLM separates two levers: per-instance operating-point selection (GPU frequency per iteration) and system-level state-space routing of requests. We empirically observe that LLM inference exhibits a U-shaped energy-frequency curve creating "sweet spots" that depend on phase behavior and load. VoltanaLLM exploits this by combining phase-specific, iteration-level frequency selection driven by a lightweight, online-adaptive latency predictor, with a decode state-space guided router that avoids architectural granularity-induced inefficiencies, all while meeting desired SLOs. We implement VoltanaLLM using SGLang and evaluate it across multiple models and real-world workloads. Our results show VoltanaLLM reduces end-to-end energy by up to 36.3% versus a static max-frequency baseline while maintaining high SLO attainment, and generalizes to newer GPUs. These results point to sustainable LLM serving via phase-aware, iteration-level frequency selection coupled with architecture-aware routing. Source code is available in https://github.com/Supercomputing-System-AI-Lab/VoltanaLLM.

07.
PLOS Computational Biology 2026-06-24

The transcriptional gradient in negative-strand RNA viruses suggests a common RNA transcription mechanism

by Connor R. King, Casey-Tyler Berezin, Brian Munsky, Jean Peccoud Nonsegmented negative-strand RNA viruses (NNSV) are a diverse class of medically relevant viruses which display a conserved attenuation gradient in the transcription of their genomes. This gradient has been traditionally explained by the Stop-Start model which attributes attenuation to polymerase behavior at gene junctions. In this article, we evaluate an alternative explanation where the gradient arises from polymerase dynamics during transcription. We introduce the RNA Polymerase Association Mechanism (RAM) model, a coarse-grained stochastic framework that describes transcription using two parameters related to polymerase processivity and the ability of the polymerase to backtrack. The RAM model accurately reproduces transcriptional gradients across diverse NNSVs as well as in gene-shuffled VSV variants. Additionally, the inferred polymerase processivity appears correlated to the length of the viral genomes suggesting a conserved constraint on transcription across these viruses. While the RAM model does not account for all known molecular features of NNSV transcription, it provides a parsimonious and predictive framework for relating genome architecture and transcription. These results support the view that, in tandem with the traditional junction-centric mechanisms governing transcription, nonspecific attenuation mechanisms contribute to the NNSV transcriptional gradient and warrant closer inspection in future studies which could lead to better rational genome design in viral studies and biomedical applications.

08.
arXiv (CS.CV) 2026-06-24

MM-TRELLIS: Point-Cloud Guided Multi-Modal 3D Vehicle Generation in Autonomous Driving

Recovering realistic 3D vehicle models from autonomous driving scenes is crucial for synthesizing training data and building simulation environment. However, most existing vehicle generation methods fail to fully exploit multimodal sensors i.e. multi-view images and LiDAR point clouds) and rely on neural rendering based reconstruction, leading to low-quality mesh. Recently, native 3D generative models have made significant progress, yet they are not built for arbitrary multi-view inputs and often struggle with in-the-wild driving images. In this work, we present MM-TRELLIS, a multi-modal version of TRELLIS for in-the-wild 3D vehicle generation that integrates LiDAR and image sensors from autonomous driving datasets into native 3D generative models. Specifically, multi-view images are cycled as conditioning inputs, while LiDAR point clouds provide test-time guidance to ensure geometric accuracy and cross-view consistency. During denoising, we first align the guidance point cloud with the model priors, then enforce consistency between the generated geometry and the guidance point cloud. Finally, we introduce a voxel filtering strategy based on the opacity of 3D Gaussian Splatting to suppress floaters and produce clean meshes. Comprehensive experiments on Waymo dataset demonstrate our method outperforms existing methods in high-fidelity 3D vehicle generation. Code is available at https://github.com/HongliXiao/MM-TRELLIS.

09.
Nature Medicine 2026-06-24

Automated reanalysis of genomic data for rare disease diagnostics at scale

Reanalysis of genomic data in rare disease is highly effective in increasing diagnostic yields but remains limited by manual approaches. Automation and optimization for high specificity will be necessary to ensure scalability, adoption and sustainability of iterative reanalysis. We developed Talos, an open-source tool that automates variant prioritization by integrating dynamically updated gene−disease and variant-level evidence with inheritance-aware filtering and validated its performance using data from 1,089 individuals with rare disease. Trio-based analysis identified 90% of known diagnoses, returning 1.3 variants per case on average. Variant burden reduced to one variant per 200 cases on iterative monthly reanalysis. Application to an unselected cohort of 4,735 undiagnosed individuals identified 241 diagnoses (5.1% yield): 78 (32%) due to new gene−disease relationships, 54 (22%) due to new variant-level evidence and 109 (45%) due to improved analysis strategies. Our automated, iterative reanalysis model demonstrates the feasibility of delivering frequent, systematic reanalysis at scale. Talos, a new tool for the automated analysis of genomic data, demonstrates the feasibility and diagnostic utility of systematic reanalyses of data in rare diseases.

10.
arXiv (quant-ph) 2026-06-19

Quantum Dynamics from Lax Pair Theory: A Reconstruction from Spectrum Preservation

arXiv:2606.19664v1 Announce Type: new Abstract: We reconstruct unitary quantum dynamics from a minimal axiomatic foundation built on Hilbert-space observables and isospectral evolution. The only dynamical assumption is that physical time evolution is a continuous one-parameter flow of Hermitian observables that preserves their spectra, i.e. the possible outcomes of measurement. We show that this assumption is already sufficient to force the Lax form of quantum dynamics. The Heisenberg equation, the time-dependent and time-independent Schrödinger equations, conservation laws, and good quantum numbers then follow as theorems rather than postulates. In this formulation, Lax pair theory supplies the missing dynamical bridge between the measurement structure of a Hilbert space and standard quantum evolution: the Hamiltonian is not assumed, but emerges as the generator required for an isospectral observable flow.

11.
medRxiv (Medicine) 2026-06-24

Barriers and facilitators to diabetes management among adults and healthcare providers in a peri-urban Ugandan health facility: A qualitative study

Diabetes mellitus is an increasing public health challenge in Uganda and other low- and middle-income countries, where health systems face growing demands for chronic disease care. Although quantitative studies have documented poor glycemic control and health system constraints, less is known about how patients and healthcare providers experience diabetes management in peri-urban public health settings. This study explored barriers and facilitators to diabetes management among adults with diabetes mellitus and healthcare providers at a peri-urban health facility in Uganda. We conducted a qualitative descriptive study at Kasangati Health Centre IV, Wakiso District, Uganda, between February and March 2025. Data were collected through 15 in-depth interviews with adults living with diabetes mellitus and 8 key informant interviews with healthcare providers involved in diabetes care. Participants were purposively selected based on their experience with diabetes management and service delivery. Interviews were audio-recorded, transcribed verbatim, translated where necessary, and analyzed using a hybrid inductive-deductive thematic approach informed by the Theoretical Domains Framework. Five interrelated themes were identified: (1) institutional and environmental factors influencing access to diabetes care; (2) cognitive and informational factors influencing medication adherence; (3) social influences on diabetes management; (4) emotional experiences of patients and healthcare providers; and (5) self-management strategies and continuity of care. Across these themes, participants identified barriers including resource limitations, communication challenges, medication management difficulties, stigma, emotional distress, and weak follow-up systems. Facilitators included peer support, religious and community networks, health education, provider flexibility, and patient-developed adherence strategies. Diabetes management was influenced by interacting health-system, social, informational, and behavioural factors. Resource constraints, limited health literacy, stigma, and weak follow-up systems hindered effective management, while social support, health education, and patient self-management strategies facilitated continued engagement in care. Interventions that strengthen chronic care services, patient education, and community support may improve diabetes outcomes in similar resource-constrained settings.

12.
arXiv (CS.CV) 2026-06-18

Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework

Current image editing methods excel at static attributes but fail at complex Human-Object Interactions (HOI), a critical challenge unaddressed by existing benchmarks that conflate HOI with static attributes, relying on global metrics incapable of simultaneously assessing dynamic interaction validity and entangled human-object pair preservation. Thus, we first introduce HOI-Edit, a comprehensive benchmark with three progressive cognitive levels, which features an automated metric HOI-Eval that reliably evaluates instance-level interaction by letting VLM Q&A after thinking with images containing grounded Human-Object pairs. Considering the task's essence of remodeling dynamic relationships, we benchmark Image-to-Video (I2V) models, finding them inherently suited for dynamic editing due to their temporal generation capabilities. Crucially, beyond superior performance, this capability provides a "replay of the failure process," offering unique diagnosability into why errors occur. We thus propose SCPE (Self-Correcting Process Editing), a novel, agentic self-correcting framework that constrains the generation of I2V models through iteratively refined prompts, enabling the generated videos to more accurately present the target HOI. Extracted frames from these videos are the final editing results. On HOI-Edit, SCPE achieves performance competitive with state-of-the-art (SOTA) editing models like Nano Banana on interaction. Code is available at https://github.com/oceanflowlab/HOI-Edit.

13.
arXiv (CS.LG) 2026-06-12

Extracting Governing Equations from Latent Dynamics via Multi-View Contrastive Learning

arXiv:2606.13260v1 Announce Type: new Abstract: Identifying latent dynamical systems from noisy, high-dimensional measurements is a central problem at the intersection of representation learning, system identification, and scientific discovery. We present DYSCO, a multi-view temporal contrastive learning algorithm that jointly recovers latent trajectories and the governing dynamics from such observations, by leveraging multiple independent noisy views of the same underlying process to disentangle signal from noise. By parameterizing the dynamics in a structured functional basis, our framework further enables symbolic recovery of the governing equations within an affine gauge. We offer theoretical guarantees for strong identification up to an affine indeterminacy, extending prior identifiability results to the realistic setting of noisy nonlinear observations. Empirically, we demonstrate accurate recovery of both latent trajectories and flow fields across a diverse set of dynamical regimes (e.g., chaotic, oscillatory, and metastable) under both Gaussian and Poisson observation noise, the latter being particularly relevant for neural recordings.

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

Experimental violation of a Bell-like inequality for causal order

arXiv:2506.20516v2 Announce Type: replace Abstract: Quantum mechanics is compatible with scenarios where physical processes happen in an indefinite order. In theory, this feature could be detected through violations of inequalities on the observed correlations, analogous to Bell inequalities. However, experimental demonstrations of such violations have been missing until recently due to the complexity of the required setup. Here we report an experimental violation of a Bell-like inequality involving the correlations of four parties, one of which is spacelike separated from the others. Our demonstration employs 3 km fiber spools to simulate spacelike separation, and achieves high-speed operations in photonic time-bin encoding, nanosecond synchronization, and accurate temperature stabilization. These experimental advances enable a violation by 5.7 standard deviations and open a path towards a certification of indefinite order in conditions that guarantee spacelike separation with existing state-of-the-art devices. However, the certification is not device-independent, as it relies on knowledge about the setup to exclude bidirectional signaling–a loophole inherent to implementations in classical acyclic spacetimes, which may be resolved in future quantum-spacetime tests.

16.
arXiv (CS.CV) 2026-06-24

Ingredient-Level Food Image Segmentation for Nutrition Awareness

Food images often contain several visible ingredients, so assigning one dish label to an entire image hides important visual structure. This work studies ingredient-level semantic segmentation on FoodSeg103, where the model predicts an ingredient class for each pixel. Two SegFormer variants were fine-tuned and evaluated under a controlled setup: SegFormer-B0 as the smaller baseline model and SegFormer-B1 as the larger final model. Both models use ImageNet-pretrained MiT backbones with newly initialized 104-class output layers. On the held-out FoodSeg103 test split of 2,135 images, B0 achieved 0.7709 pixel accuracy and 0.2521 mean IoU, while B1 achieved 0.7929 pixel accuracy and 0.3204 mean IoU. B1 improved every saved test metric, including a +0.0683 absolute gain in mean IoU. The system also converts predicted masks into visible ingredient-area percentages, giving a simple visual composition summary of the predicted meal. This summary can serve as a first-pass nutrition-awareness cue by providing a visual alternative to detailed food tracking similar to plate-based meal guidance, but it is not a direct estimate of calories, macronutrients, food mass, volume, density, or true portion size.

17.
arXiv (quant-ph) 2026-06-25

A Candidate Framework for Free-Space Quantum Key Distribution based on Geometrical-Configuration Modulation

arXiv:2606.25807v1 Announce Type: new Abstract: This paper proposes a candidate framework for free-space quantum key distribution (QKD) based on geometrical-configuration modulation (GM). In the minimal implementation considered here, Alice coherently splits a single photon emitted from one source into two spatial output modes with a tunable separation, and uses the source separation $R$ as the GM variable that defines the prepared single-photon spatial superposition state. Bob records the single-photon detection coordinate in the far field or Fourier plane, providing the correlated data used for soft-input information reconciliation. Based on this physical mechanism, we first establish an $R-x$ protocol model in which the source separation $R$ and the single-photon detection coordinate $x$ are random variables, and further propose an $R-\Delta x$ extension based on the difference variable $\Delta x$ between adjacent accepted detection events to mitigate slowly varying center drift in free-space links. The framework specifies state preparation, far-field conditional probabilities, soft-input information generation, parameter estimation, reconciliation, and asymptotic candidate key-rate formulas. A complete composable security analysis further requires derive an explicit computable upper bound on Eve's information from experimentally observed parameters, together with finite-key analysis and experimental validation under free-space conditions. The proposed candidate framework (GM-QKD) provides a modulation approach based on spatial degrees of freedom in which the source geometry serves as the modulation variable.

18.
Nature Medicine 2026-06-11

Clinical Profile and Genomic Characterization of the 2026 Bundibugyo Virus Index Case in Uganda

Bundibugyo virus disease (BVD) remains a high-consequence threat in Eastern and Central Africa, where cross-border mobility, nonspecific early symptoms, and delayed recognition can obscure transmission. In this case report, we describe Uganda’s 2026 BVD index case: a male patient who traveled from the Democratic Republic of the Congo to Uganda and was admitted to a private hospital in Kampala on 11 May 2026 after more than two weeks of vomiting and diarrhea, with epigastric pain, weakness, and hiccups. He deteriorated rapidly, developing acute kidney injury, pulmonary edema, hepatic dysfunction, hypoxemia, delirium, atrial flutter, possible disseminated intravascular coagulation, and multiorgan failure, and died on 14 May. A posthumous EDTA whole-blood specimen tested at the Central Emergency Response and Surveillance Laboratory was positive for orthoebolavirus RNA and confirmed as Bundibugyo virus (BDBV) by RT-qPCR. Sequencing achieved 99% genome coverage at ≥100× depth. The 2026 BDBV genome formed a distinct lineage approximately equidistant from the 2007–2008 Butalya and 2012 Isiro variants, differing by 216–227 nucleotides (~1.2% sequence divergence). Here, we demonstrate the value of fatality surveillance, private-sector surveillance, diagnostic optimization through national specimen referral, and rapid molecular-genomic diagnostics for early detection, transmission chain interruption, and public health response coordination.

19.
arXiv (quant-ph) 2026-06-24

Initial-state-dependent dephasing effect in non-Hermitian Su-Schrieffer-Heeger models

arXiv:2606.24185v1 Announce Type: new Abstract: Understanding the dynamical evolution of non-Hermitian systems under extra external dissipation is essential. Dephasing, a major realistic dissipation, is conventionally considered detrimental to information processing. However, its impact on non-Hermitian systems remains largely unexplored. Here, we focus on finite-sized non-Hermitian Su-Schrieffer-Heeger (SSH) lattice models with alternating gain and loss in real space and examine the dynamical evolution of the trace distance under pure dephasing. By tuning system parameters, this model supports phases with either parity-time or anti-parity-time symmetries, enabling us to explore the interplay between dephasing and different non-Hermitian symmetries. While the trace distance exhibits distinct dynamical behaviors across the different phases in the absence of dephasing, its response to dephasing is largely symmetry-independent but instead initial-state dependent. By varying initial states, we observe that increasing the dephasing strength can either merely accelerate the decay of the trace distance or stabilize it. Interestingly, we reveal two kinds of dephasing-induced stabilization that differ in the strong dephasing limit: a partial stabilization, where the trace distance approaches a finite value smaller than its initial value in the long-time limit, and a complete stabilization, where the trace distance remains at its initial value throughout the entire evolution. By analyzing the equation of motion, we attribute the initial-state dependent dephasing effect to the alternating gain and loss in the system and confirm its absence in Hermitian counterparts. Furthermore, in the anti-parity-time symmetry unbroken phase, we identify a continuous suppression-upon increasing the dephasing strength-of the otherwise exponential decay of the trace distance seen in the absence of dephasing.

20.
medRxiv (Medicine) 2026-06-24

Breaking The Pain-Stiffness Cycle- Supraclavicular Catheter Facilitated Rehabilitation Of Post-Surgical Elbow stiffness- A Retrospective Observational Study

ABSTRACT Background: Post-traumatic elbow stiffness is a recognised complication following orthopaedic trauma surgery, occurring in 10-15% of trauma patients sustaining injuries. Pain remains the primary barrier to physiotherapy compliance, with surgical arthrolysis carrying recurrence rates of up to 34%. The supraclavicular brachial plexus block, referred to as the 'spinal of the arm', provides anaesthesia and analgesia to the entire upper limb below the shoulder. A structured non-surgical approach combining continuous catheter analgesia with timed rehabilitation was identified as an unmet need in this patient group. Methods: A single-centre retrospective observational study was conducted on data of patients treated for post-surgical upper limb stiffness between January 2022 and April 2026. Of 30 patients identified, 28 with elbow involvement formed the primary analysis group following exclusion of 2 patients with isolated wrist stiffness and complex regional pain syndrome. Ultrasound- guided supraclavicular brachial plexus catheters were inserted using the Contiplex system. Patients received 0.5% Bupivacaine (10-15ml) for initial blockade, followed by daily top-up doses of 0.2% Ropivacaine(20ml) given 30 minutes prior to structured physiotherapy and CPM sessions for up to 5 days. The primary outcome was change in arc of elbow motion in degrees, measured by the attending orthopaedic consultant using standard goniometry. Results: Complete pre- and post- intervention data were available for all 28 patients. Mean pre-intervention arc of elbow motion was 39.1{degrees}(SD+/-23.2{degrees}), improving to 104.2{degrees}(SD+/- 30.0{degrees}) post-intervention. Mean improvement was 65.1{degrees}(SD+/- 30.6{degrees} ); 95% CI 53.8{degrees} to 76.4{degrees} ; range 10{degrees}-140{degrees} ; paired t-test t=-11.27, p

21.
medRxiv (Medicine) 2026-06-18

Empirical Validation and Predictive Utility of the Perinatal Grief Scale in Men after Perinatal Loss

Background. The Perinatal Grief Scale (PGS) is a widely used instrument for assessing grief following pregnancy loss, yet no study has validated it specifically in men despite documented use in several studies. This gap is critical given fathers' persistent underrepresentation in perinatal bereavement research and the absence of empirically supported screening thresholds for this population. Methods. This cross-sectional validation study used data from the OPALE project (Observatory on PerinatAL hEalth) conducted by the CiaoLapo Foundation in Italy. Among 276 fathers who experienced stillbirth or miscarriage, we examined criterion validity by testing the association between PGS scores and trauma-related symptomatology assessed via three validated instruments: the Revised Impact of Event Scale (RIES, n=103), National Stressful Events Survey Short Scale (NSESSS, n=95), and SCL-90 (n=173). We systematically tested multiple threshold combinations to identify optimal discriminative performance. Results. The PGS demonstrated excellent criterion validity. The optimal threshold (PGS >=92) showed sensitivity 81.0%, specificity 81.8%, and Youden's J index 0.628. Fathers scoring >=92 had 19.12 times the odds of high trauma symptoms (95% CI: 9.35 to 39.14, p

23.
Nature (Science) 2026-06-10

Structural basis for chaperone-guided assembly of RNA-induced silencing complex

The RNA-induced silencing complex (RISC), comprising an Argonaute (AGO) protein and a small RNA, is the central effector in RNA silencing. Small RNAs are loaded onto AGO as bulky duplexes in an HSP70- and HSP90-dependent process1–3, but the molecular mechanism remains poorly understood. Here we identify the human AGO–HSP90–p23 complex, which captures AGO in an RNA-free state, termed the AGO maturation complex (AMC). The purified AMC enables RNA loading and AGO folding, faithfully recapitulating de novo RISC assembly. Using cryogenic electron microscopy, we determined the structure of AMC bound to a microRNA duplex. In contrast to its conformation in the RISC, AGO adopts a highly open conformation in the AMC: the N domain and the RNA-binding module (PAZ–MID–PIWI) are fully detached and anchored to opposite sides of the HSP90 dimer, connected solely by the unfolded L1 linker. This arrangement exposes a positively charged cleft that accommodates an RNA duplex. AGO folding is facilitated by a small RNA duplex containing a 5′-terminal phosphate—but not by single-stranded RNAs—revealing a role for the RNA duplex as a chaperone-like cofactor that directs AGO domain assembly. These findings elucidate the RISC assembly mechanism and establish the AMC as a molecular tool for probing optimal RNA features and chemical modifications for the rational design of small interfering RNA therapeutics. Our study also sheds light on how chaperones, together with ligands, can guide the folding of client proteins. Structures of the AGO maturation complex reveal how chaperones and an RNA duplex drive assembly of the RNA-induced silencing complex.

24.
arXiv (CS.CV) 2026-06-11

SCAIL-2: Unifying Controlled Character Animation with End-to-end In-Context Conditioning

Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate representations, including pose skeletons to represent motion or masked background to represent environment, which inevitably leads to information loss. To address this, we present SCAIL-2, a framework that bypasses those intermediates and achieves end-to-end character animation. By directly concatenating driving videos to the sequence, the model can obtain all the required visual information from the input video. To address the lack of end-to-end data, we unify sub-tasks of character animation with decoupled conditions and then curate a pipeline to synthesize MotionPair-60K, an end-to-end motion transfer dataset containing heterogeneous tasks of character animation. To achieve the unification, we utilize in-context mask conditioning and mode-specific RoPE as soft guidance beyond textual instructions and raw visual information. To address synthetic discrepancy in detailed regions, we propose Bias-Aware DPO to construct preference items to mitigate the errors. Extensive experiments demonstrate that our method substantially outperforms existing state-of-the-art approaches in various character animation tasks. A large subset of synthetic data as well as model weights will be released at our project page: https://teal024.github.io/SCAIL-2/.

25.
arXiv (CS.AI) 2026-06-12

Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering

arXiv:2606.12422v1 Announce Type: cross Abstract: The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices. While automated scoring systems and machine learning techniques have existed for decades, generative AI (GenAI) now enables educators to implement standards-based grading (SBG) with unprecedented efficiency and scale. This paper examines the theoretical foundations and evaluates an LLM grader that uses commercially available foundation models with context and prompt engineering to score student work against a rubric. Drawing on an empirical interrater agreement study using Massachusetts Comprehensive Assessment System (MCAS) data, we observed the Quadratic Weighted Kappa (QWK) and Proportional Reduction in Mean-Squared Error (PRMSE) across mathematics, science, and ELA, using Claude Sonnet 4, Haiku 4.5, GPT-5, and GPT-5 Mini. The results demonstrate that LLM graders, especially when based on foundational models with more parameters, achieve substantial agreement with human raters in mathematics and science assessments, while the performances vary in ELA, suggesting generic foundation models can be effective at scoring in given contexts. Additional analysis of teacher and student feedback reveals strong acceptance of AI-generated narrative feedback but skepticism toward numerical scores, suggesting that LLMs function most effectively as formative tools rather than summative evaluators. Our findings indicate that thoughtfully designed hybrid models that combine AI efficiency with teacher judgment can reduce workload, enhance feedback quality, and support equitable assessment practices without displacing professional expertise.