Single-domain antibodies (sdAbs) derived from naive phage display libraries offer a time-efficient alternative to animal immunization but often exhibit suboptimal affinity, particularly for small-molecule haptens, where restricted binding interfaces limit the efficacy of traditional saturation mutagenesis. For instance, the wild-type sdAb (B1)-targeting ethoxyquin (EQ) exhibits a moderate equilibrium dissociation constant (KD) at the submicromolar level (within the 10-7 M range), restricting its practical sensitivity. To overcome the structural and energetic barriers inherent in hapten recognition, a "structure-guided directed modification" strategy was developed that focuses on conformational tuning rather than simple side-chain replacement. Integrating AlphaFold2 modeling and AutoDock mechanistic analysis identified key interactions (e.g., the ASP19-EQ-N8 hydrogen bond). Subsequently, a random single-amino acid insertion strategy was implemented within the CDR3 loop (residues 77-90) to fine-tune local loop geometry. Top candidates were screened via MM/GBSA binding free energy calculations and experimentally validated using biolayer interferometry. This approach yielded three high-affinity mutants-77A, 79C, and 85H-with calculated ΔΔG values of -10.56, -4.35, and -6.69 kcal/mol, respectively. The mutants achieved enhanced affinities in the tens-of-nanomolar range (down to 35.1 nM), representing up to approximately 3 times overall improvement. Importantly, orthogonal surface plasmon resonance analysis using nonconjugated, free EQ successfully verified this affinity maturation trend, showing that mutant 79C bound free EQ with a KD of 1.30 × 10-5 M (a 2.3 times improvement over wild-type) through simultaneously accelerated association and slowed dissociation. Mechanistically, this substantial increase in affinity is attributed not only to reinforced noncovalent networks that significantly stabilize the complex but also to favorable CDR3 geometric outward flips that alleviate steric hindrance, leading to an approximate 3.8 times acceleration in the kon. Crucially, these mutants demonstrated notable anti-interference tolerance in complex aquatic food matrices (e.g., sea bass extracts). Ultimately, this work provides a practical and efficient computational framework for assisting the rapid evolution of low-affinity hapten sdAbs into high-performing biorecognition elements with potential for next-generation biosensing architectures.

Low-Rank Adaptation (LoRA) enables foundation models to efficiently adapt to downstream tasks with minimal trainable parameters, yet it lacks the capability of continual learning (CL) from non-stationary data streams. While existing CL methods attempt to address this by storing and retrieving task-specific parameter modules, they suffer from non-trivial challenges of task identification and inconsistency in input-output feature mappings, resulting in suboptimal feature representations. Instead of keeping multiple task-specific parameter modules, we propose a novel continual LoRA mechanism that parameterizes all task-specific adaptations with a single adaptation module, naturally obviating the task identification and mapping inconsistency issues. To this end, we reformulate LoRA-based CL as a consistent feature mapping problem that mimics the behavior of the joint-training upper bound, wherein a unified adaptation parameter matrix is learned to simultaneously capture the input-output relationships established by all task-specific LoRAs. By solving this formulation, our approach facilitates approximate unified optimization over sequential tasks via progressively updating cumulative feature statistics, achieving efficient knowledge retention and renewal. Notably, the proposed arithmetic training mechanism can be applied both to the backbone of the foundation model for parameter-efficient fine-tuning and the classifier optimization, thereby enabling continual learning within an integrated architecture that closely approximates the joint learning paradigm. Extensive experiments across conventional and long-sequence class-incremental benchmarks demonstrate that our method achieves new state-of-the-art performance.

Implant-related biofilm infections remain a major challenge in orthopedic surgery. Prior in vitro comparisons have focused on arthroplasty surfaces using static models; fracture fixation implants remain unstudied. We compared six antiseptic solutions against mature S. aureus biofilms on stainless steel, the most common fracture fixation metal, using a continuous-flow CDC biofilm reactor, more closely approximating hydrodynamic surgical conditions than static systems. Mature biofilms were created on stainless steel coupons using a continuous-flow reactor for 72 h, then washed for 3 min with one of six irrigation solutions: normal saline, 10 % PI (povidone-iodine), 0.35 % PI, a 10 % PI + 3 % hydrogen peroxide mixture, hypochlorous acid, or 0.05 % chlorhexidine gluconate. Colony-forming units (CFUs) were quantified following standardized sonication and plating, and mixed-effects negative binomial regression was used to estimate treatment effects. All antiseptic solutions produced statistically significant reductions in biofilm-associated CFUs relative to untreated controls. Ten percent PI demonstrated the greatest reduction (3.46- log⁡ 10), with 0.35 % PI producing a similar 3.42- log⁡ 10 reduction. The PI-hydrogen peroxide mixture achieved a 3.24- log⁡ 10 reduction without evidence of synergistic activity. Hypochlorous acid and chlorhexidine produced 2.95- log⁡ 10 and 2.46- log⁡ 10 reductions, respectively, while saline demonstrated minimal effect. These findings indicate that PI solutions, even at dilute concentrations, are highly effective at disrupting established biofilms on stainless steel. As an in vitro single-organism model, these findings establish a comparative efficacy baseline on a fracture-fixation-relevant surface but require validation in multispecies, multi-material, and mechanically realistic irrigation systems before clinical translation.

Childhood stunting remains a critical indicator of chronic undernutrition and a major barrier to achieving Sustainable Development Goals 1 and 2. Despite gradual improvements, substantial geographic disparities in stunting risk persist across India. This study characterised the spatial and temporal heterogeneity of childhood stunting using harmonized district-level data from NFHS-4 (2015-16) and NFHS-5 (2019-21).

We present results for a photon-free exchange-correlation functional within the local density approximation (pxcLDA) for quantum electrodynamics density functional theory (QEDFT), which efficiently describes the electron density of many-electron systems across weak to strong light-matter coupling. Building on previous work [Lu et al., Phys. Rev. A 109, 052823 (2024)] that captured electron-photon correlations via an exchange-correlation functional derived from the nonrelativistic Pauli-Fierz Hamiltonian and tested on one-electron systems, we use a simple procedure to compute a renormalization factor describing electron-photon correlations and inhomogeneity in the weak-coupling regime by comparing it with quantum electrodynamics coupled-cluster and previous QEDFT optimized effective potential methods. Across various atoms and molecules, pxcLDA reproduces cavity-modified densities in close agreement with these references. The renormalization factor approaches unity as the system size or collective coupling increases, reflecting an electron-photon exchange-dominated behavior and improved accuracy for larger systems. This approach now offers a practical route to applying QEDFT functionals based on electron density to realistic electron systems.

Spontaneous parametric down-conversion is the primary source of position-correlated and momentum-anti-correlated photon pairs that form the canonical Einstein-Podolsky-Rosen (EPR) state. Their transverse spatial correlations are usually analyzed within the thin-crystal approximation, where the two-photon wavefunction is assumed to factorize into independent functions of the sum and difference coordinates. In practice, however, birefringence-induced transverse walk-off breaks this factorization and couples these degrees of freedom. Here, we show that this coupling persists even for nominally thin crystals once the free-space propagation of the joint spatial intensity is taken into account. This sum-difference coordinate coupling leads to a distinctive tapering of the transverse correlations near the crystal image plane - an effect that standard factorized models cannot capture. Numerical simulations and experimental data clearly confirm this novel behavior. Our findings provide a more complete description of photon-pair generation in birefringent nonlinear media and clarify fundamental limits on spatially resolved quantum imaging and spatial-mode quantum information processing with EPR states.

Near-field multistatic radar imaging assumes that the transmitter, receiver, and target positions, as well as the receiver time references, are known exactly. In practice they are known only approximately: receiver positions and clocks carry small survey and synchronization errors, and target locations used to initialize or refine an image are themselves approximate. This paper develops a joint self-calibration framework that estimates small corrections to receiver positions, receiver clock biases, and target positions from the same bistatic echo delays used for imaging, and ties the correction directly to image sharpness rather than to parameter accuracy alone. We derive the linearized observation model relating delay residuals to these corrections, and give a regularized (maximum a posteriori) weighted least-squares estimator that explicitly separates measurement noise from prior parameter uncertainty. We characterize the identifiability of this estimator progressively, from a single anchor (the transmitter alone, which leaves an exact three-dimensional rotational null space) to two anchors (transmitter plus one additional point, which reduces the null space to a one-parameter rotation about a fixed axis) to three anchors (transmitter, one target, and one receiver, in general position, which removes the continuous ambiguity entirely). We additionally treat the dual problem of localizing an unknown transmitter from a small number of exactly known anchors-receivers, targets, or time samples of a single moving platform-and show that collinear or coplanar anchor geometries leave an exact, uncorrectable continuous or discrete ambiguity, respectively, regardless of how many such anchors are used, with the coplanar case notably invisible to a standard rank or conditioning check. We then reformulate the calibration objective directly in terms of coherent multistatic image sharpness, evaluated using the matched-filter score already used for image formation, and propose a two-stage algorithm: a coarse linear delay-residual solve followed by phase-coherent sharpness refinement. Numerical experiments verify the predicted identifiability transitions via the singular value spectrum of the linearized system, demonstrate quadratic convergence of the proposed estimator, verify the transmitter-localization ambiguity structure-including an exact mirror-twin solution for coplanar anchors, reproducing all range measurements to floating-point precision-and demonstrate the effect of self-calibration on a simulated multistatic image of an extended (eagle-shaped) target, including the incremental effect of bandwidth, aperture/frequency windowing, and CLEAN deconvolution on the recognizability of the resulting image, as well as on the resolvability of multiple simultaneous discrete targets (two instances of the same target). We relate this formulation to, and distinguish it from, the existing literature on time-of-arrival sensor network self-calibration and on joint target-localization/clock-bias estimation, which largely target single moving targets, anchor-free minimal-data solvability, or localization accuracy rather than multistatic image focus.

Point clouds are widely used to represent real-world objects in the fields of augmented/virtual reality, robotics, etc. However, they are often corrupted with noise, hindering downstream tasks such as mesh surface reconstruction, rendering, etc. In this paper, we revisit sparse point cloud representation for denoising. We observe that existing works generally assume global sparsity of noise across point cloud surfaces, leading to over-smoothing, and typically adopt multiple stages to recover features, inevitably introducing biases and structural inconsistencies. To address these challenges, we propose a structure-aware joint sparse optimization approach for point cloud denoising. Specifically, considering that sparsity primarily lies in feature areas, we propose a novel group-level sparsity-based approximate deviation to characterize surface distortion for different structures. Based on that, we develop an optimization model that can protect structural integrity. We further derive a linear approximate solution and provide a parallel denoising algorithm. Experimental results on different types of datasets demonstrate that the proposed approach outperforms the state-of-the-art works.

Achieving robust, humanlike whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittle process of tuning controllers. We introduce ZEST (zero-shot embodied skill transfer), a streamlined motion-imitation framework that trains policies via reinforcement learning from diverse sources-high-fidelity motion capture, noisy monocular video, and non-physics-constrained animation-and deploys them to hardware zero-shot. ZEST generalizes across behaviors and platforms without relying on contact labels, reference or observation windows, state estimators, or extensive reward shaping. Its training pipeline combines adaptive sampling, which focuses training on difficult motion segments, and an automatic curriculum using a model-based assistive wrench, together enabling dynamic, long-horizon maneuvers. We further provide a procedure for selecting joint-level gains from approximate analytical armature values for closed-chain actuators, along with a refined model of actuators. Trained entirely in simulation with moderate domain randomization, ZEST demonstrated broad generality. On Boston Dynamics' Atlas humanoid, ZEST learned dynamic, multicontact skills (army crawl and breakdancing) from motion capture. It transferred expressive dance and scene-interaction skills, such as box climbing, directly from videos to Atlas and the Unitree G1. Furthermore, it extended across morphologies to the Spot quadruped, enabling acrobatics, such as a continuous backflip, through animation. Together, these results demonstrate robust zero-shot deployment across heterogeneous data sources and embodiments, establishing ZEST as a scalable interface between biological movements and their robotic counterparts.

Increasing disinfectant usage in urban water systems has heightened concerns regarding the enrichment of disinfection-resistant bacteria, particularly those capable of forming robust biofilms. Although chlorine-resistant bacteria and their associated risks are relatively well characterized, the characteristics and control strategies of ozone-resistant bacteria (ORB) remain insufficiently explored. Here, we examined seven previously classified ORB strains with varying ozone resistance to experimentally validate their biofouling risks and investigate efficient control methods. Cellular surface hydrophobicity and growth-related traits were associated with between-strain variation in biofilm formation potential. After investigating the inhibitory patterns of methylisothiazolinone (MIT) and 2,2-dibromo-3-nitrilopropionamide (DBNPA), we developed a synergistic control strategy using low concentrations of them, which effectively inhibited the biofilm formation of ORB and reduced extracellular polymeric substances. A likely synergistic mechanism involved the different targets in the cellular proteins, which may cause more severe damage to ORB. This approach was subsequently evaluated in a typical scenario, a water reclamation reverse osmosis system. When the reclaimed water contained ORB, the normalized flux of reverse osmosis membrane decreased by 12% after ozone treatment but increased by 13% after subsequent synergistic treatment with MIT and DBNPA. The synergistic treatment was also associated with a lower approximated relative abundance of fouling-related ORB and a lower predicted biofilm-forming potential of the microbial community. In summary, our findings bridged the gap between fundamental characteristics of ORB and practical biofouling risk, highlighted the potential of a synergistic biofilm control approach, and provided preliminary insights for mitigating the risks posed by ORB in advanced water treatment processes.

Nonlinear internal waves (NIWs) in the ocean can produce substantial effects on acoustic propagation, including three-dimensional reflection, refraction, and diffraction in the sound ducts bounded and formed by individual NIWs. Feature models for analyzing such effects frequently use a well approximation, which neglects the finite width of NIWs. This paper examines consequences of more realistic conditions including finite wave width and curvature. This curved duct model produces acoustic horizontal modes in the radial direction with a distinct characteristic of enhanced horizontal tunneling through the curved NIW that forms the sound duct. Also, because of the proximity of their modal wavenumbers to those of classical horizontal trapped modes, these enhanced tunneling modes are named companion modes. Conditions for the occurrence of these modes can be derived as physically interpretable bounds on acoustic modal wavenumber values. Furthermore, companion modes decay in sufficiently long distances, representing proper mode contributions to the acoustic total field. Theoretical and numerical analyses presented in this paper further suggest that the phenomena of enhanced tunneling is a result of joint wave tunneling and whispering gallery effects.

Patients undergoing maintenance hemodialysis often experience multiple concurrent symptoms that may affect psychosocial functioning. This study aimed to identify symptom-clusters and examine the associations among symptom-cluster severity, fear of disease progression (FoP), and social alienation, including the statistical mediating role of FoP. This analytical cross-sectional study included 321 patients receiving maintenance hemodialysis at a tertiary hospital in Shenyang, China. Symptoms, social alienation, and FoP were assessed using the Chinese versions of the Dialysis Symptom Index, General Alienation Scale, and Fear of Progression Questionnaire-Short Form, respectively. Principal component analysis with Varimax rotation was used to identify symptom-clusters. Correlation analyses and structural equation modeling were performed, and indirect effects were tested using 5000 bootstrap resamples. Five symptom-clusters were identified: water and electrolyte, sleep disorder, gastrointestinal, skin, and psychological symptom-clusters, collectively explaining 55.50% of the total variance. The severity scores of all 5 symptom-clusters were positively correlated with FoP and social alienation (all P < .01), with the sleep disorder symptom-cluster showing the strongest correlation with total social alienation (correlation coefficient = 0.703, P < .01). The partial-mediation model showed better fit than the competing full-mediation model and demonstrated acceptable model-fit (χ2/df = 2.604, root mean square error of approximation = 0.072). Symptom-clusters were positively associated with FoP (B = 4.910, β = 0.705, P < .001), and FoP was positively associated with social alienation (B = 0.112, β = 0.358, P < .001). The direct association between symptom-clusters and social alienation remained significant after FoP was included in the model (B = 1.610, β = 0.737, P < .001). The indirect effect through FoP was statistically significant (B = 0.551, β = 0.252, 95% confidence interval: 0.180-1.258), accounting for 25.5% of the total effect. Greater symptom-cluster severity was associated with greater FoP and social alienation, with FoP showing a significant statistical indirect effect. Integrated assessment of symptom-clusters, disease-related fears, and psychosocial needs may help identify patients requiring comprehensive support. Given the cross-sectional design, these findings should not be interpreted as evidence of temporal or causal relationships.