Search Results (110)

Two chikungunya (CHIKV) vaccines have now been licensed, but the human clinical evidence base remains fragmented across vaccine platforms, populations, follow-up periods, and safety settings, complicating product-specific interpretation of durability and benefit–risk. We conducted a PRISMA-ScR–guided scoping review of human CHIKV vaccine evidence indexed in PubMed, Embase, and Web of Science from January 2000 to June 2026. After screening 890 records, we included 77 sources of evidence and mapped them at both the record level and the candidate/product level. The included evidence clustered around a limited number of vaccine programs, including TSI-GSD-218, VRC-CHKVLP059-00-VP/PXVX0317/Vimkunya, MV-CHIK/V184, VLA1553/IXCHIQ, ChAdOx1 Chik, and mRNA-1388/VAL-181388. Late-stage and post-authorization evidence was concentrated mainly in VLA1553/IXCHIQ and PXVX0317/Vimkunya, whereas viral-vector and mRNA candidates remained largely restricted to early-phase adult studies. Evidence has expanded to adolescents and adults aged ≥65 years for selected products but remains limited or product-specific for children, pregnant individuals, immunocompromised populations, and medically complex older adults. Short-term trial safety data were characterized primarily by mild or moderate local and systemic reactogenicity, while post-authorization safety evidence remains recent and concentrated in licensed products. This scoping review provides a structured evidence map for CHIKV vaccine development and highlights priorities for standardized immunogenicity assessment, longer-term durability data, broader population representation, endemic-region effectiveness studies, and continued post-marketing surveillance. Full article

While the Triton compiler has revolutionized GPU kernel development, its deployment on general-purpose CPUs struggles to fully utilize the underlying hardware capabilities. This is primarily due to the semantic gap between Triton’s SPMD execution model and CPU vector architectures, which leads to suboptimal utilization of vector units during complex memory accesses. In this paper, we present a comprehensive compilation and runtime co-optimization framework for Triton-CPU, specifically targeting Vector Length Agnostic architectures (VLA) like ARM SVE. At the compiler level, we propose a novel semantic reconstruction and explicit base-offset decoupling strategy, enabling native VLA gather/scatter generation and eliminating scalar loop overheads. At the runtime level, we introduce a Machine Learning-driven thread scheduling model to optimally orchestrate the synergy between Thread-Level Parallelism and Vector-Level Parallelism. Extensive evaluations on an ARM-based multi-core processor demonstrate that our framework achieves up to a 2.0× throughput improvement for compute-bound GEMM operators (peaking at 346 GFLOPS), notably outperforming the hand-optimized OpenBLAS library by up to 1.54× at small-to-medium scales. Additionally, it delivers a 1.7× speedup for element-wise workloads. Furthermore, our optimizations saturate memory bandwidth (up to 55 GB/s) for memory-bound operators with zero compilation bloat, establishing a robust, high-performance foundation for deploying deep learning models on general-purpose CPUs. Full article

Background and Objectives: Blood group antigens are well known for their importance in transfusion medicine and transplant compatibility; however, their biological role extends beyond these functions and includes associations with the risk of several diseases. In this study, we investigated the relationship between ABO blood groups and the circulating levels of 73 different molecules. Patients and Methods: Fifty-six healthy donors were enrolled, including 24 individuals with blood group O, 19 with blood group A, and 13 with blood group B. Blood samples were collected and analyzed in a single laboratory using Luminex fluorescent bead-based assay panels to determine the concentrations of 73 circulating molecules. Depending on data distribution, ANOVA or Kruskal–Wallis tests and Student’s t-test or Kolmogorov–Smirnov tests were applied to identify significant differences among groups. Associations were further assessed by binary logistic regression analysis. Results: Subjects with blood group A showed significantly higher circulating levels of IL-1R1, IL-13, IL-23, PDGF-BB, VEGF-A, VEGF-D, soluble VEGF-R2 (KDR), soluble VEGF-R3 (FLT-4), VLA-4, CD141, MMP-1, syndecan-1 (SDC-1), and mannose-binding lectin (MBL) compared with the other blood groups. In contrast, individuals with blood group B exhibited significantly higher levels of IL-22, IL-23, PDGF-BB, CD62P (P-selectin), and amyloid β1–42. Several significant associations were identified by logistic regression analysis. Conclusions: Our findings indicate that ABO blood groups are associated with distinct circulating molecular profiles, supporting the existence of biological differences that may contribute to variations in disease susceptibility among individuals with different blood types. Nevertheless, given the exploratory’s nature and limited sample size of this study, further investigations are required to validate these findings, confirm the observed associations, and clarify their potential clinical implications. Full article

Leukocyte recruitment from blood into tissues involves sequential adhesive steps, including rolling and integrin-dependent arrest. VLA-4 can support firm adhesion and, in some settings, rolling interactions, whereas CD44–hyaluronan interactions have also been implicated in leukocyte rolling. Here, we used adhesion assays and parallel-plate flow chamber experiments to analyze CD44–hyaluronan-dependent monocyte interactions on ECV304 monolayers and to compare them with α4-integrin-sensitive adhesion on endothelial monolayers. WEHI 78/24 monocytoid cells interacted with ECV304 monolayers in a CD44- and hyaluronan-dependent manner, whereas adhesion to HMEC-1 and bEnd.3 monolayers was sensitive to α4-integrin blockade. Blocking CD44, adding soluble hyaluronan, or treating ECV304 monolayers with hyaluronidase reduced adhesion and rolling. Mixed primary human monocyte preparations also showed CD44-dependent adhesion and rolling on ECV304 monolayers. ECV304 cells are interpreted here not as endothelial cells, but as T24-derived, hyaluronidase-sensitive cellular monolayers useful for functional analysis of CD44–hyaluronan-dependent interactions. These findings support a substrate-dependent functional hierarchy in which CD44–hyaluronan-dependent monocyte rolling becomes detectable when α4-integrin-dependent adhesion is not dominant, while emphasizing the cell-model-based nature of the assay. Full article

Recent Vision–Language–Action (VLA) models have rapidly emerged as general-purpose robotic policies that integrate language understanding, visual perception, and robot control. However, prior studies and surveys have primarily emphasized backbone architectures, action decoders, training recipes, and benchmark performance, whereas relatively limited systematic attention has been given to sensor modality selection, heterogeneous signal alignment and fusion, and their connection to action generation, all of which are critical to the performance and safety of real-world robotic manipulation. This survey addresses this gap by reinterpreting VLA within the framework of a sensor–fusion–action pipeline. This study first presents a systematic taxonomy of major sensor modalities, including RGB, depth, tactile sensing, force/torque, proprioception and inertial measurement unit, multi-spectral/thermal, and event-based vision, and compares them in terms of the physical information they provide, their characteristic failure modes, and their deployment constraints. This survey further reviews teleoperation-, human video-, and simulation-based data collection pipelines, together with representative dataset configurations, and analyzes the multi-modal design space from a sensor-centric perspective, including early and late fusion, cross-attention, token-level fusion, adapters, mixture of experts, and multi-rate action representations. In addition, this study identifies a strong bias in existing benchmarks toward RGB-centric inputs and single success-rate metrics and emphasizes the need for a multidimensional evaluation framework incorporating robustness, worst-case performance, safety, latency, and efficiency. By shifting the focus away from a model-centric narrative and explicitly accounting for real-world sensor complexity, this survey seeks to establish a sensor-centered foundation for the next generation of Physical AI. Full article

Vision–Language–Action (VLA) models unify visual perception, natural-language understanding, and action generation within a single foundation model, allowing a robot to follow instructions such as “fold the towel” or “fly to the red building” directly from camera images. Because VLAs inherit world knowledge from internet-scale pre-training, they have become the dominant framework for learning-based manipulation, with bimanual coordination serving as the most demanding testbed: two arms with 7+ degrees of freedom each must move in concert to fold, assemble, and reorient objects. Unmanned aerial robotics faces a structurally similar challenge: a drone must coordinate thrust, attitude, and increasingly gripper commands from visual observations under strict latency and payload constraints. This review covers 183 contributions spanning 2017–2026 and organized along seven dimensions: VLA architectures, training recipes, action representations, bimanual coordination (2022–2026), unmanned aerial vehicle (UAV) navigation and control (2017–2026), language grounding, and cross-cutting concerns including memory and world models. We show that the coordination strategies, training recipes, and action representations developed for bimanual VLAs transfer to unmanned aerial systems and identify fourteen research directions across both domains. Full article

Robotic waste sorting presents significant challenges, including object variability, cluttered environments, and the predominant reliance on deep learning and traditional computer vision techniques, which typically demand extensive datasets and task-specific training. This paper introduces a robotic waste sorting system that integrates the Gemini Vision–Language–Action (VLA) model with a KUKA LBR iiwa collaborative robot and an RGB-D camera. Our approach leverages the advanced reasoning capabilities of large, pre-trained VLA models to perform waste sorting, without requiring explicit training or dataset collection. Key contributions include the development of effective prompt engineering strategies for waste object identification, the assessment of the VLA’s performance in terms of inference time and accuracy, and the development of different grasping strategies for operation in cluttered scenarios. Our experimental tests demonstrated that the system’s inference time is between 2 and 4 s, which is suitable for collaborative robotic applications, and the system achieved a high overall classification accuracy of 89.64%. Crucially, we demonstrated that integration of RGB-D sensing enhanced the model’s ability to perceive object heights, resolve occlusions, and make informed grasping decisions in realistic, three-dimensional settings. We further validated multiple real-world grasping strategies, demonstrating tradeoffs between system efficiency and safety in heavily cluttered scenarios. This work establishes a practical and adaptable framework for deploying VLA-driven intelligence on commercial robotic platforms, highlighting the potential of VLAs for complex manipulation tasks beyond waste sorting. Full article

Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertainty-calibrated safety-gating wrapper that estimates online failure risk and routes control among policy execution, pause-and-reobserve, and a fallback planner. Using a cleaned and consistently aggregated benchmark pipeline, we evaluate two long-horizon manipulation tasks in NVIDIA Isaac Sim 5.0 under lighting, texture, occlusion, sensor, and combined shifts. Relative to an ungated VLA baseline, calibrated gating improves mean shifted success from 57.5% to 77.2% and reduces aggregate expected calibration error from 0.303 to 0.100. The largest success gains occur under occlusion and combined shift, including improvements from 48.3% to 85.2% on the drawer task and from 59.4% to 87.8% on clutter sort. The results also expose a systems trade-off: an aggressive uncalibrated threshold baseline attains stronger raw success and collision metrics, but requires nearly twice as many interventions per shifted episode (21.6 vs. 11.5). The main contribution is, therefore, an empirical characterization of the reliability–intervention trade-off created by calibrated supervision, not a claim that the calibrated supervisor is universally the best terminal controller. We frame calibrated gating as a better-calibrated, lower-intervention supervisor that materially improves robustness relative to an ungated VLA while revealing the open problem of mapping calibrated risk into efficient intervention policies. Additional threshold-sensitivity, signal-diagnostic, overhead, and residual-failure analyses show that the selected operating point is meaningful but not universal: the calibrated risk threshold captures most shifted failures in retrospective logs, yet residual contacts still arise during pause and fallback states. These findings provide controlled simulation evidence for trustworthy VLA supervision under distribution shift and clarify the reliability–intervention frontier that future embodied-control systems must navigate. Full article

This paper presents the application of a structural finite element model (FEM) of a light patrol aircraft for numerical flutter analysis. The thin-walled structure was developed using 2D shells and additional 1D beam elements. The virtual structure was supplemented with additional point elements imitating lumped masses of non-structural on-board components. The model was subjected to validation for qualities such as the mass distribution, its CG location, the structural stiffness of its airframe units, and the similarity of natural modes. The comparative analyses showed satisfactory consistency of the mass and stiffness properties of the FEM with the actual aircraft. Numerical flutter analysis was then performed with the MD Nastran for an integrated aeroelastic model consisting of the FEM and the simplified aerodynamic model. The critical velocities of basic flutter modes were determined. Using simplified kinematic models of flight control systems built into the FEM, an analysis of the sensitivity of control surface flutter due to the pilot’s influence was carried out. The stick grip and the support of control pedals with the pilot’s legs cause specific conditions related to the imposition of additional stiffness and mass on the control manipulators. These conditions directly affect the natural frequencies of control surface modes, which translates into a change in the critical flutter speed of the tail. For the established range of changes in stiffness and mass added to the stick and pedals, a series of analyses of natural vibrations and flutter were carried out. The influence of the change in the support conditions of control manipulators was illustrated in graphs. Full article

Edge generative artificial intelligence (AI) increasingly combines language, perception, reasoning, audio, and action on resource-constrained devices. This paper profiles public GPT-Generated Unified Format (GGUF) checkpoints from the Hugging Face Hub (HFH) across conversational, instruct, thinking, audio, vision-language (VL), and vision-language-action (VLA) categories using a shared parser-based deployment-envelope workflow. The main category-specific run retained 21,039 profiled entries and estimated the minimum memory bandwidth, compute throughput, and unified-memory architecture (UMA) footprint needed to satisfy category-specific target throughput values. The resulting measurement protocol was symmetric, but the deployment envelopes were asymmetric: VL and thinking workloads were the heaviest on the compute–bandwidth axis, VLA formed a smaller elevated multimodal branch, and audio, instruct, and conversational workloads were lighter on average. A unified 10-tokens-per-second (TPS) sensitivity run compressed the compute–bandwidth gaps, showing that service-rate assumptions contributed strongly to cross-category separation. Welch/Games–Howell and Kruskal/Dunn analyses confirmed large category effects for bandwidth and compute in the category-specific regime, but only small memory effects. The results show that edge-model feasibility cannot be inferred from parameter count alone; throughput target, backbone family, modality, and memory budgeting must be considered jointly. Full article

Integrins and other cell adhesion molecules play a critical role in the migration and homing of leukocytes. This study investigates whether metastatic tumor cells can exploit leukocyte-like rolling and arrest mechanisms during early vascular steps of metastatic dissemination. B16 melanoma cell adhesion to activated bEnd.3 endothelial monolayers or immobilized VCAM-1 were analyzed under defined shear flow using a parallel-plate chamber. Function-blocking antibodies, divalent cation modulation, pertussis toxin, and low-temperature conditions were used as classical controls. B16-BL6 melanoma cells exhibited robust VLA-4-dependent rolling and arrest on activated endothelial monolayers and on immobilized VCAM-1 under physiological shear stresses (0.7–2 dyn/cm²), independent of chemokine-related Gαi signaling. These findings identify a chemokine-independent mechanism of VLA-4-mediated vascular capture by melanoma cells under shear flow, providing a potential mechanistic basis for early steps in metastatic dissemination. Full article

Uninterrupted traffic flow monitoring is a prerequisite for optimal resource allocation and minimizing vehicular emissions in smart cities. However, centralized traffic management architectures are highly vulnerable to single points of failure. When structural sensor malfunctions occur, the resulting network unobservability paralyzes dynamic signalization, triggering cascading traffic congestion, extended idling times, and severe greenhouse gas emissions. To address this cyber-ecological vulnerability, we propose the Hybrid Multi-Agent State Estimation (H-MASE) protocol, a fully decentralized decision-support framework designed from an applied systems reliability engineering perspective. By deploying PSAs and VLAs directly onto IoT-enabled edge devices at smart intersections, H-MASE leverages a hop-by-hop edge computing topology to collaboratively track macroscopic route flow dynamics. Mathematically, this distributed estimation process is formulated as a network-wide least-squares convex optimization problem, where local projection operators function as exact Distributed Gradient Descent steps to minimize the global residual sum of squares. The distributed consensus mechanism acts as a spatial variance reduction tool, effectively dampening measurement noise and stochastic demand fluctuations. Furthermore, we introduce an autonomous anomaly detection logic that isolates severe structural faults rapidly, which is mathematically structured to prevent false alarms under bounded disturbance conditions. Numerical simulations demonstrate that the protocol yields a highly resilient optimality gap (e.g., a Root Mean Square Error of merely 0.81 vehicles per estimated state) even under catastrophic hardware failures. Ultimately, H-MASE provides a robust, fail-safe data foundation for sustainable urban logistics and green-wave signalization, ensuring that smart cities maintain ecological resilience and optimal resource utilization under severe structural disruptions. Full article

Chikungunya virus (CHIKungunya Virus, CHIKV) is a mosquito-borne plus-stranded RNA virus. Adaptive mutations such as A226V in the E1 envelope protein of CHIKV significantly enhance the transmission efficiency of the virus in Aedes albostriae, leading to multiple rounds of epidemics around the world including the large-scale outbreak in Guangdong Province in 2025. After a viral infection, a significant proportion of patients will progress from acute arthralgia to chronic arthritis that persists. The pathogenesis of the disease involves the persistence of the virus in joint tissues, the persistent inflammatory response with IL-1β, IL-6 and IL-17 as the core mediated by macrophages, possible autoimmune cross-reactions, and individual genetic susceptibility. At present, there is no specific antiviral drug, but important progress has been made in vaccine development against the virus. Vaccines based on live attenuated virus (VLA1553) and virus-like particle (VLP) platforms have been approved for the market and provide a tool to prevent and control this important public health threat. This review synthesizes current knowledge on CHIKV-induced chronic arthritis pathogenesis and recent vaccine advances, providing a framework for understanding disease mechanisms and guiding future prevention strategies. Full article

Vision–language–action (VLA) models have shown strong potential for enabling robots to interpret goals and perform complex manipulation tasks by integrating perception, language, and control. However, existing VLAs rely heavily on large-scale, diverse demonstration datasets, which are difficult and expensive to collect. When trained with limited data, they often overfit to irrelevant visual cues such as background, lighting, or viewpoint, resulting in weak generalization. To overcome this limitation, we propose a simple yet effective object-centric learning framework for VLA. For each sub-task, the framework leverages an instance segmentation foundation model to identify and track task-relevant objects, and trains the policy on both the original RGB scene and two object-focused representations: (i) a masked image emphasizing the target object and (ii) an object-only crop. These multiple visual inputs share the same action supervision, encouraging the policy to attend to the manipulated object rather than the surrounding context. Furthermore, a distance-based chunk alignment mechanism ensures smooth control transitions between consecutive predicted action segments. Experiments conducted in both simulation and real hardware demonstrate that the proposed method achieves robust performance and stable trajectories across various manipulation tasks, validating its practicality and efficiency in training object-aware robotic behaviors. Full article

This paper proposes a unified vision–language framework that translates user instructions into navigation for the mobile base and actions for the manipulator in indoor environments. In general, occupancy grid maps constructed via SLAM capture solely the geometric layout of the environment. This renders the robot incapable of leveraging the semantic information required for object distinction. The proposed method encodes semantic information from vision–language models and the robot’s pose in a textual format, referred to as a semantic topological graph. Specifically, the models including GroundingDINO, LG EXAONE, and SAM2 extract object-level semantic information, which is subsequently used to identify room characteristics. A large language model then interprets user instructions to identify the final destination for navigation within the semantic topological graph, followed by reasoning to determine the suitable action network. Notably, the proposed text-based representation facilitates a substantial reduction in inference time, and its effectiveness is validated through real-world experiments. Full article