We demonstrate a spatially resolved method for imaging millimeter-wave (mmWave) electric fields using Rydberg-state fluorescence in a warm atomic vapor. By utilizing a multi-photon ladder excitation scheme, we leverage a specific decay channel that remains dark in the absence of the mmWave field, resulting in high-contrast imaging with effectively zero background. Absolute calibration of the local electric field is achieved by reconstructing the Autler-Townes splitting of the Rydberg resonance across the imaging volume. To ensure robust field extraction across a wide dynamic range-including regimes where spectral features are not fully resolved-we employ a steady-state analysis based on the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation. We apply this technique to visualize standing-wave interference patterns within a vapor cell and demonstrate the ability to engineer local field distributions using structured dielectric reflectors. This approach provides a versatile and self-calibrating platform for the diagnostic imaging of high-frequency electromagnetic fields and the characterization of mmWave-optical interfaces.

Wrist-worn devices offer a practical means of monitoring gait, yet no validated end-to-end pipeline exists for deriving digital mobility outcomes (DMOs), including cadence, stride length (SL), and walking speed (WS), in people with multiple long-term conditions (MLTC, the coexistence of two or more long-term conditions). This study presents the first modular pipeline for wrist-worn devices for DMO estimation, validated in 45 older adults with MLTC (65-90 years), whose conditions spanned four multimorbidity clusters (cardiometabolic, painful conditions, pulmonary, and cancer), across laboratory tasks, using stereophotogrammetry as the reference. Algorithms were selected independently for each block (gait sequence detection (GSD), initial contact detection (ICD), SL) using novel, fine-tuned, and adaptive versions of established methods developed on an independent cohort. Blocks were first validated independently before being integrated into a pipeline capturing cumulative error propagation. GSD achieved a recall of 0.90. ICD was robust across algorithms, with the best-performing algorithm achieving a recall of 0.76 and precision of 0.82. At the pipeline level, the best-performing pipeline achieved an SL error of 0.14 m with near-zero bias, cadence absolute error of 7.71 steps/min, and WS absolute error of 0.13 m/s. These findings support wrist-worn devices for objective gait assessment in multimorbid populations, establishing a validated open-source pipeline for real-world deployment.

A Fe5Cu5V30Ti30Nb30 high-entropy alloy film was designed as a Cr- and Al-free metallic sensing layer for thin-film strain gauges in high-pressure hydrogen environments. CALPHAD calculations predicted a BCC/B2-type phase field, while XRD, EBSD and GIXRD results supported a BCC-type structure without direct confirmation of long-range B2 ordering. Fe5Cu5V30Ti30Nb30 films deposited on Si reference substrates at 150 and 300 W retained broad BCC-type diffraction features. The 300 W film showed a more continuous cross-sectional morphology, good metallic conductivity and a comparable nanomechanical response with slightly higher hardness. Device-level tests were then performed using Cr/AlN/Fe5Cu5V30Ti30Nb30 TFSGs on 316L stainless-steel substrates. The devices exhibited average absolute apparent zero shifts of 16.08 με in 12 MPa N2 and 17.79 με in 12 MPa H2, with an additional H2-associated apparent response of only 1.71 με. Static tensile tests in 12 MPa H2 confirmed a linear strain response with a gauge factor of 1.72 ± 0.01.

Central vertigo (CV) lacks objective electrophysiological measures for severity assessment and rehabilitation monitoring. We aimed to characterize multiscale resting-state EEG alterations and identify clinically interpretable candidate features in stroke-related CV. Approach: Resting-state EEG was analyzed in 50 patients with stroke-related CV (31 moderate, 19 severe) and 31 age-matched healthy controls. The framework integrated relative spectral power, cross-frequency coupling, PLV-based sensor-level phase synchrony, graph metrics, machine-learning feature ranking, and associations with balance confidence and dizziness severity. Main results: Severe CV showed widespread relative delta-power reductions of 28.7%-29.4% versus controls. Post hoc analysis showed lower global absolute delta power in severe CV than controls (Tukey p = 0.0227; rank-based FDR q = 0.0459), although the absolute-power effect was less spatially extensive. Both patient groups showed reduced delta-theta and delta-beta amplitude-amplitude coupling, enhanced delta-alpha phase-phase coupling, and theta-band increases in PLV-derived node degree, clustering, and global efficiency; local efficiency increased only in SV. Delta-beta amplitude-amplitude coupling ranked highest across machine-learning methods and correlated moderately with balance confidence (ρ = 0.470, p = 0.001) and dizziness severity (ρ = -0.472, p = 0.001). Zero-lag-robust measures showed the same theta ordering but were nonsignificant after FDR correction and did not establish volume-conduction-independent topology, supporting cautious PLV interpretation. Significance: Stroke-related CV involves coordinated alterations across oscillatory, cross-frequency, and sensor-level network measures. This interpretable framework identifies candidate EEG features for objective characterization that require external and longitudinal validation before clinical use. .

Analytically deriving equilibria, which often govern the long-term behavior of biochemical reaction networks, is essential for understanding cellular decision-making and robustness, yet remains computationally challenging for large and complex systems. The COMPILES framework of Hernandez et al. addresses this problem via network decomposition but is limited by a computationally intensive translation step and by its restriction to networks with zero kinetic deficiency. We introduce CRITERIA (Computing paRametrized posITive EquilibRIA), a new framework that overcomes these limitations through two key advances. First, it replaces the translation mechanism of COMPILES with a more efficient graph-theoretic formulation based on the work of Johnston and Burton, which generates a reaction-to-reaction graph from elementary flux modes and identifies directed cycles of the constructed graph via a binary linear program. Second, CRITERIA computes equilibria on a single unified translated network rather than solving subnetworks independently, thereby eliminating interdependencies that previously required extensive symbolic manipulation. Across a benchmark set of 26 biochemical models, CRITERIA achieves consistent and often substantial speed improvements while expanding applicability beyond the restricted class of zero kinetic deficiency systems. We demonstrate the biological utility of the framework by analyzing multistationarity, which underlies cellular decision-making, and absolute concentration robustness, a key mechanism for maintaining stable biochemical outputs, in the EnvZ-OmpR signaling pathway and a large-scale CRISPRi toggle switch, respectively. By improving both scalability and applicability, CRITERIA enables a systematic equilibrium analysis in biochemical networks of realistic size and complexity, providing a practical tool for studying long-term dynamical behavior in systems biology.

Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring target-domain retraining, fine-tuning, or dataset-specific metric parameter tuning for new environments. We identify three critical limitations: (a) fixed user-defined parameters (e.g., voxel size, search radius) that fail to generalize across varying scales, (b) learned keypoint detectors exhibit poor cross-domain transferability, and (c) absolute coordinates amplify scale mismatches between datasets. To address these three issues, we present BUFFER-X, a registration framework whose descriptor model is trained once and then applied without parameter tuning or target-domain retraining, which achieves zero-shot generalization through: (a) geometric bootstrapping for automatic hyperparameter estimation, (b) distribution-aware farthest point sampling to replace learned detectors, and (c) patch-level coordinate normalization to ensure scale consistency. Our approach employs hierarchical multi-scale matching to extract correspondences across local, middle, and global receptive fields, enabling robust registration in diverse environments. For efficiency-critical applications, we present BUFFER-X-Lite from a holistic system perspective, integrating the BUFFER-X front-end and adaptive early exit with the fast and robust pose solver to reduce total computation time by 43% relative to BUFFER-X while preserving comparable accuracy. We evaluate on a comprehensive benchmark comprising 12 datasets spanning object-scale, indoor, and outdoor scenes, including cross-sensor registration between heterogeneous LiDAR configurations. Results demonstrate that our approach generalizes effectively across diverse scales, scenes, and sensor setups. Code: https://github.com/MIT-SPARK/BUFFER-X.

We investigate the thermodynamic and structural properties of divalent patchy hard rods confined to a one-dimensional channel by modeling the bonding sites as attractive square-well (SW) patches located at the rod tips. The zero-range sticky limit is recovered by letting the well width vanish while keeping the stickiness parameter finite. While Wertheim's first-order thermodynamic perturbation theory becomes exact in this sticky limit, it fails for finite-range site-site interactions. Because the present model is mathematically equivalent to an exactly solvable one-dimensional nearest-neighbor fluid, we use the exact solution to reformulate the thermodynamics in terms of association-theory variables, including the fraction of unbonded sites and a generalized law of mass action. Finite-range SW sites produce a richer structural behavior than sticky sites, including monotonic and oscillatory asymptotic decay of the pair correlation function, separated by the Fisher-Widom line. In the monotonic regime, the correlation length exhibits an absolute maximum defining the Widom line, while in the oscillatory regime, it may display a local maximum and minimum, whose locus defines the "Extrema of the Correlation length under Oscillatory decay" line. These features disappear in the sticky limit, where the system remains entirely in the oscillatory regime. We also show that the high-pressure behavior of the correlation length changes from ξ ∼ p2 for finite-range SW sites to ξ ∼ p3 in the sticky limit.

The "train low, deploy high" paradigm offers significant practical advantages by minimizing training overhead while enabling high-fidelity inference through increased spatial resolutions. However, Vision Transformers (ViTs) often suffer from poor zero-shot generalization to unseen resolutions compared to their convolutional counterparts. We attribute this deficiency to two fundamental phenomena: intra-patch spectral drift, where image resizing suppresses discriminative mid-to-high frequency components due to interpolation-induced low-pass filtering, and inter-patch positional awareness collapse, where the interpolation of absolute position embeddings distorts spatial priors and causes the effective receptive field to degenerate into isolated patches at larger scales. To mitigate these issues, we propose the Multi-Scale Vision Transformer (MSViT), which integrates Spectral-Constrained Convolution for adaptive frequency-weighted patch embedding, Horizontal-Vertical Separable Attention to enforce a full-span cross-shaped effective receptive field, and Reparameterized Convolutional Position Embedding to provide boundary-aware spatial bias without the need for interpolation. When trained exclusively at 224 × 224, MSViT demonstrates remarkable robustness across a broad range of test resolutions, maintaining consistent and stable accuracy as the input scales from 128 × 128 up to 640 × 640. Our work underscores that explicit modeling of spectral stability and spatial structure is essential for developing resolution-flexible vision transformers.

Brazil has a rich population of 435 snake species, 112 of which occur in the Caatinga biome. The genus Hepatozoon comprises over 300 species, with 36 described in Brazilian snakes. The objective of this study was to characterize morphologically and molecularly a new species of Hepatozoon that infects Philodryas nattereri in the Caatinga, Pernambuco, Brazil. Between 2022 and 2025, five individuals of P. nattereri were collected. Blood samples were obtained for smear preparation, blood and liver samples were obtained and preserved in absolute ethanol, and viscera were collected for histopathological analyses; this material was used for the extraction of genetic material from the hemoparasites. Prevalence and parasitism indices were calculated. Amplification of the 18S rDNA gene was performed using the primers HepF300 and ER. All specimens (100%) were parasitized by Hepatozoon univasfensis sp. nov. Gametocytes in different stages of maturation were observed inside erythrocytes. Thus, based on morphological, morphometric, and molecular analyses, a new species of Hepatozoon was described in Philodryas nattereri from the municipality of Petrolina, Pernambuco, Northeast Brazil. Hepatozoon univasfensis sp. nov. represents the second species described in Philodryas and the second species described in snakes (and in the general herpetofauna) of the Caatinga.

Cobb angle measurement is essential for diagnosing and monitoring scoliosis; however, its accuracy varies with observer experience. In many healthcare systems, including Oman, initial measurements are commonly performed by residents and influence referral urgency, follow-up intervals, and bracing decisions, where even small inaccuracies may alter clinical pathways. This study evaluated the inter- and intra-observer reliability of Cobb angle measurement among orthopaedic residents and spine specialists. A prospective comparative reliability study was conducted including 18 OMSB orthopaedic residents (PGY2-5) and five spine specialists. Each participant measured Cobb angles on five standardised standing scoliosis radiographs on two occasions, four weeks apart. Inter- and intra-observer reliability were assessed using the intraclass correlation coefficient (ICC) with a two-way random-effects, single-measurement, absolute-agreement model [ICC(2,1)], and Bland-Altman plots were used to assess intra-observer reproducibility. Mean Cobb angles ranged from 45° to 56°. Inter-observer reliability was poor among residents (ICC = -0.219; 95% CI -1.41 to 0.485) and remained low among specialists (ICC = 0.120; 95% CI -2.28 to 0.898). Intra-observer variation averaged approximately 5°, with a Bland-Altman mean bias close to zero and limits of agreement of ±5%. Negative ICC values indicated that between-observer variability exceeded within- observer consistency. Residents demonstrated markedly poorer inter-observer reliability compared to specialists, highlighting the impact of clinical experience on accurate end-vertebra selection and angle measurement. Targeted training modules and structured exposure during residency may improve measurement consistency, referral accuracy, and overall scoliosis management pathways.