Search Results (1,243)

The fast-growing cyanobacterium Synechococcus sp. PCC 11901 is emerging as a promising chassis for photosynthetic biomanufacturing. Here we report recombinant protein production in PCC 11901 via signal peptide-mediated secretion, enabling direct recovery of target proteins from the culture medium without cell disruption. Seven signal peptides spanning both Sec and Tat pathways are screened using eYFP as a reporter, with secretion quantified daily over seven days by fluorescence measurements. FutA, belonging to the Tat pathway from Synechocystis sp. PCC 6803, achieves 92.2% extracellular export by day 7, substantially outperforming all Sec candidates, including the best Sec signal peptide thermitase from Cyanobacterium aponinum PCC 10605 (55.7%). Signal peptide-bearing strains exhibit growth reductions of up to 26% relative to the wild-type, with FutA most affected, indicating a general metabolic cost correlated with secretion efficiency. The best-performing signal peptides from both pathways, FutA and thermitase, are validated with secretion of lichenase. Notably, the rank order of signal peptide performance is reversed for lichenase: thermitase demonstrates 2.6-fold higher extracellular activity than FutA, indicating that optimal signal peptide selection is cargo-dependent. These results establish PCC 11901 as a secretion-competent chassis and provide a rational framework for matching signal peptide pathways to target protein properties. Full article

Multi-object tracking and segmentation (MOTS) aims to jointly perform pixel-level instance segmentation and temporal identity association for multiple objects in video sequences. Existing online decoupled MOTS methods face several challenges in complex scenarios, including limited front-end mask quality, corruption of memory representations under prolonged occlusion, and unstable data association and trajectory recovery. To address these limitations, we propose TrackRefine, a plug-and-play decoupled enhancement framework. TrackRefine enhances overall performance through back-end refinement without modifying the architecture of the front-end instance segmenter or relying on additional end-to-end joint training. Specifically, we introduce a lightweight Fast GrabCut-based mask refinement module to optimize mask boundaries, a multimodal long-short-term memory bank that integrates appearance, semantic, and shape cues for identity modeling, and a progressive three-stage association strategy for stable matching and long-term trajectory recovery. Experimental results on MOTS20 show that TrackRefine achieves 69.4 sMOTSA, 82.7 MOTSA, and 478 Frag. Experimental results on KITTI MOTS show that it achieves 62.4/73.7 sMOTSA and 78.0/85.4 MOTSA for pedestrians and cars, respectively. Extensive experiments with different front-end instance segmenters verify its plug-and-play flexibility and decoupled design, while ablation studies confirm the effectiveness of each core module. These results show that TrackRefine provides an efficient and practical solution for online MOTS in complex scenarios. Full article

The increasing activity of unmanned aerial vehicles (UAVs) has intensified the demand for reliable and computationally efficient methods for passive radio-frequency (RF) signal detection. In practical RF monitoring scenarios, the environment is often non-stationary and affected by varying noise conditions. Under such circumstances, classical energy-based detectors are sensitive to noise uncertainty, while more robust approaches, such as cyclostationary analysis, require substantially higher computational resources. This work presents a burst-aware cascade method for UAV RF signal presence detection that explicitly addresses this trade-off. The proposed framework combines fast energy-based screening with temporal burst aggregation, applying spectral correlation function (SCF) analysis selectively and only when sustained signal activity is indicated. Detection is performed on fixed-length RF signal chunks, while additional segment-level duration constraints are used to characterize sustained transmissions. The method is evaluated using the publicly available DroneRF dataset and compared against six baseline detectors, including fixed-threshold energy, wavelet-based, blind cyclostationary, two adaptive energy detector variants, and a lightweight convolutional neural network. Experimental results confirm that chunk-level detection remains difficult for all considered methods. Temporal aggregation across longer intervals yields a substantial improvement: the cascade achieves $P_d$ = 1.000 and AUC = 1.000 at the segment level, matching exhaustive cyclostationary detection while reducing per-segment processing time by a factor of 2.46. An additional result is that burst-level concatenation prior to SCF estimation provides implicit coherent integration, preserving $P_d$ = 1.000 at signal amplitude reductions of up to −20 dB where standalone detectors degrade to $P_d$ = 0.995. Overall, burst-aware cascade architectures offer a practical and interpretable approach to RF-based UAV monitoring, providing a well-grounded compromise between detection reliability and computational efficiency under realistic operating conditions. Full article

Aims: Murine studies show a promising effect of high-fiber β-glucan on glycemic control and serum lipids. In addition, β-glucan has recently been found to have strong antioxidant and immunomodulatory effects. Oat flakes are a natural source of β-glucan. However, the effects of oat flakes on glycemic variability, dyslipidemia, and pancreatic duodenum homeobox-1 (PDX-1) levels in type 1 diabetes (T1D) remain unclear. Hence, this study assessed the effect of oat flakes on glycemic variability, dyslipidemia, and PDX-1 among adolescents with T1D. Materials and Methods: Sixty adolescents with T1D were divided into 2 equally matched groups. Group A received oat flakes β-glucan 6 g per day for 3 months in addition to an ordinary diet and insulin regimen. Group B received an ordinary diet and insulin regimen. This was followed by crossing over both arms for another 3 months after a two-week washout period. All participants underwent auxological assessment, continuous glucose monitoring (CGM), hemoglobin A1c (HbA1c), fasting lipids, and PDX-1 measurements at baseline, 3 months, and 6 months. Results: Oat flakes consumption resulted in a significant decrease in the coefficient of variation, HbA1c, serum cholesterol, triglycerides, and LDL-C levels (p < 0.001), with a significant increase in TIR, HDL-C, and PDX-1 levels (p < 0.001). However, all these effects waned after the stoppage of the oat flakes, except for HDL-C. Conclusions: Oat flakes have a favorable outcome on glycemic metrics, lipid profile, and PDX-1 in adolescents with T1D. Full article

Similar to a web search engine, we have developed a computer-based acoustic search engine tailored for the critical scenario of high-speed aircraft ground explosion monitoring, addressing the long-standing challenge of real-time localization for such high-impact events. Unlike conventional acoustic source localization techniques, our method uniquely resolves the separation and localization of multiple aliasing events, which are prevalent in high-speed aircraft explosion scenarios due to complex shock wave propagation and overlapping signatures. We first calculate the waveforms of all possible acoustic sources over 2D grids. Then, a dimensionality reduction method and fast search technology are applied to the database. Once a high-speed aircraft ground explosion occurs, the real-time system returns detection feedback by matching real-time data with the pre-established search database. Different from other artificial intelligence (AI)-based approaches, the acoustic search engine can handle multiple aliased acoustic events in real time and does not require any prior information or input parameters—a key advantage for emergency response to high-speed aircraft explosions where predefined parameters are often unavailable. Both synthetic tests and field data applications (using actual acoustic records from high-speed aircraft ground explosion experiments) demonstrate the method’s credibility in detecting and localizing multiple acoustic sources. Full article

Corrosion-driven section loss in steel tower structures erodes load-carrying capacity, yet field assessment still relies on subjective visual grading. This paper presents a closed-loop framework coupling quantitative image-based corrosion measurement with stochastic degradation modeling, Monte Carlo reliability simulation, and Sobol’ variance-based global sensitivity decomposition. Two complementary segmentation paths—hue–saturation–value (HSV) color-space thresholding for fleet-scale screening and DeepLabV3+ deep learning for detailed evaluation—convert imagery into calibrated section-loss estimates via nonlinear regression. Three analysis modes (single-image, multi-angle weighted-median fusion, and Oriented FAST and Rotated BRIEF (ORB) feature-matched temporal differencing) feed a Bayesian-updated power-law corrosion growth model whose outputs propagate through a time-dependent limit-state function via 10⁶-sample Monte Carlo simulation. Sobol’ indices rank each uncertain input’s contribution to the reliability-index variance. A field demonstration on a 40-year-old galvanized lattice tower in an ISO 9223 C4 coastal environment shows that the corrosion rate constant and zinc coating thickness together govern 65% of the total reliability variance and that a risk-ranked selective maintenance strategy reduces expected life-cycle cost by 71% relative to blanket intervention. Full article

Phase unwrapping, the recovery of a continuous signal from measurements known only modulo $2\pi$, is a ubiquitous problem in coherent imaging, from medical MRI to radar remote sensing. In Interferometric Synthetic Aperture Radar (InSAR), phase unwrapping is both critical and computationally demanding: current methods require minutes to hours per interferogram and frequently fail on large images. We present FAUST-ADMM (Fast ADMM Unwrapping via Spectral Transforms), an algorithm that formulates phase unwrapping as a weighted $L_1$ optimization and solves it efficiently on GPU using the Alternating Direction Method of Multipliers (ADMM). Each iteration reduces to a Poisson equation solved in closed form via the Discrete Cosine Transform, followed by element-wise soft thresholding, both trivially parallel. On 500 synthetic earthquake interferograms, FAUST-ADMM achieves 99% accuracy with reference-point correction, matching SNAPHU, MCF, and PUMA, while running 10 to 100× faster. On a full three-subswath Sentinel-1 interferogram of the 2019 Ridgecrest M7.1 earthquake (∼6500 × 8500 pixels), FAUST-ADMM agrees with SNAPHU on 99.7% of pixels in 35 s, a $74\times$ speedup. Our method makes batch unwrapping of large InSAR time series practical on a single consumer GPU. Full article

In large-scale road environments, constructing and maintaining high-precision maps is challenging, while GNSS-denied conditions exacerbate accumulated drift due to the lack of global references. Additionally, existing methods largely rely on LiDAR data but inadequately preprocess the data, which often leads to degraded accuracy and instability. To address these issues, this study proposes large-scale drift-resilient localization via multi-sensor fusion and topological map matching. The method leverages digital maps to extract topological road networks, eliminating the need for high-precision map construction. Accumulated drift is corrected by matching the odometry trajectory with the topological map, while localization accuracy and stability are further improved through precise ground point filtering and the integration of wheel odometry into a LiDAR-inertial odometry. Experiments on two campus datasets and KITTI 05 demonstrate the high accuracy and generalization of the proposed method in large-scale localization. Notably, on the longer School Dataset (3645 m), the mean error drops by 48.1% relative to LIO-SAM and 44.2% relative to FAST-LIO2. Repeated ablation trials further confirm the stability of the proposed method. These results demonstrate accurate and stable large-scale localization without high-precision prior maps. Full article

Objectives: Treadmill walking is often employed for tightly controlled gait and energetics research, but growing evidence suggests that treadmill-based metabolic and biomechanical measurements may not directly reflect the ecologically valid mode of overground walking. While many previous studies focused on older adults, much less is known about how treadmill walking influences gait energetics and spatiotemporal parameters in young healthy adults across matched speeds. We investigated energy expenditure, metabolic cost of walking and spatiotemporal gait parameters in healthy young adults walking overground and on a treadmill at three speeds (slow—1.0, comfortable—1.3, fast—1.5 m/s). Our hypothesis was that at the comfortable speed, treadmill and overground energetics and gait parameters would be comparable. However, at slow and fast speeds, there would be a significant energetic penalty, accompanied by significant differences in spatiotemporal parameters. Methods: Twenty young participants (10 males and 10 females) completed a randomized cross-over walking protocol with a minimum of ten minutes treadmill familiarization at 1.3 m/s. Breath-by-breath oxygen consumption ($\dot{V}{O}_2$) and Respiratory Exchange Ratio were measured using a portable indirect calorimetry system and gait parameters were calculated from Inertial Measurement Units. Gross and net energy expenditures, costs of walking, cadence, average step and stride lengths, and walk ratio were calculated. A three-way mixed ANOVA was used for primary statistical analyses. Results: Treadmill walking was characterized by higher gross and net energy expenditures and metabolic costs (p < 0.001, η_p² = 0.6) across all speeds compared to overground. It was also characterized by faster cadence and shorter average step and stride lengths (p < 0.001, η_p² = 0.9). Additionally, there was an effect of sex (p = 0.01, η_p² = 0.3) on the gait parameters, with females exhibiting a faster cadence and shorter average step and stride lengths than males. Conclusions: Our findings show that treadmill walking imposes a medium-to-large metabolic penalty even in healthy young adults, with compensatory gait adaptations, possibly reflecting increased stabilization demands and altered neuromuscular control strategies. These results underscore the limits of generalizing treadmill derived gait data to overground walking and we caution against the uncritical use of treadmills, especially while trying to understand ecologically relevant human walking mechanics and energetics. Full article

Optical and Synthetic Aperture Radar (SAR) image registration is a fundamental task in remote sensing information fusion, yet it remains challenging due to significant differences in imaging mechanisms, radiation characteristics, and noise properties between the two modalities. Existing public datasets suffer from limited resolution, small scale, and insufficient scene diversity, and these limitations have hindered algorithm development. This paper constructs a large-scale, high-resolution optical–SAR registration dataset based on the HongTu-1 satellite 3-m SAR imagery and Google Earth optical imagery at zoom level 17, covering diverse scenes across China with a standardized pipeline including terrain correction, geometric alignment, standardized slicing, and quality filtering. Building upon this dataset, a hand-crafted keypoint-based cross-modal registration method is proposed, incorporating multi-level edge filtering and hybrid feature detection. Unlike conventional hand-crafted methods such as RIFT, SRIF, and LNIFT, which mainly refine keypoint detection, description, or matching within a SIFT-style pipeline, the core novelty of this work lies in SAR-specific preprocessing and multi-level hybrid filtering. These components are designed to suppress speckle while extracting more stable and discriminative shared edge responses for cross-modal registration. An improved Log-domain Total Variation (Log-TV) denoising model is introduced for SAR preprocessing. A hybrid edge filtering framework combining phase congruency analysis and Structured Random Forest (SRF) edge detection is constructed within a Gaussian scale space. A dual-branch feature detection scheme integrating blob and corner features is designed with a robust orientation assignment strategy. Feature description uses the Gradient Location–Orientation Histogram (GLOH) descriptor with Principal Component Analysis (PCA) reduction, while geometric estimation employs the Fast Sample Consensus (FSC) algorithm. Experiments on the self-constructed HT dataset and on the public OSdataset and SAR2Opt benchmarks show that the proposed method consistently achieves low RMSE and high success rates. It also maintains competitive efficiency among hand-crafted methods while retaining strong robustness to scale and rotation variations. Full article

The convergence of artificial intelligence, biotechnology, and the Internet of Bio-Nano Things (IoBNT) is enabling the creation of a new generation of intelligent in-body medical devices for continuous diagnosis and monitoring. In this context, a compact, wideband, circularly polarized conformal microstrip antenna is proposed for capsule endoscopy applications. The antenna is integrated along the inner wall of a 10 mm-diameter capsule and achieves an impedance bandwidth of 2.06–5.39 GHz (89.39%), maintaining stable matching under varying biological tissue conditions. A 3 dB axial ratio bandwidth (ARBW) of 2.31–3.14 GHz (30.45%) ensures reliable circular polarization and robust wireless communication in lossy and dynamic in-body environments. To extend beyond conventional electromagnetic analysis, a physics-informed neural network (PINN) framework is introduced to model the thermal response of biological tissues based on the governing bioheat equation. This AI-driven approach enables fast and generalizable prediction of temperature rise under varying operational conditions without repeated numerical simulations. At 2.45 GHz, the antenna exhibits a maximum gain of $-31.1$ dBi with a radiation efficiency of approximately $-34$ dB, consistent with in-body propagation constraints. Simulation and experimental results in realistic tissue phantoms, including muscle, small intestine, large intestine, and stomach, confirm stable wideband and polarization performance. Specific absorption rate (SAR) analysis demonstrates compliance with IEEE C95.1-2019 safety limits, while link budget evaluation validates reliable telemetry over a 1–3 m communication range. The integration of advanced antenna design with physics-informed machine learning provides a scalable framework for intelligent, safe, and adaptive IoBNT-enabled capsule endoscopy systems. Full article

Background: Hashimoto’s thyroiditis (HT) is a common autoimmune disorder characterized by chronic inflammation and metabolic alterations. Mitochondria-derived peptides (MDPs), particularly mitochondrial open-reading frame of the 12S rRNA-c (MOTS-c), have emerged as key regulators of cellular metabolism, insulin sensitivity, oxidative stress, and inflammatory responses. This study aimed to investigate the association between circulating MOTS-c levels and HT and to explore its potential role in thyroid autoimmunity and metabolic regulation. Methods: In this cross-sectional study, patients diagnosed with HT (n: 90) were compared with age- and sex-matched healthy controls (n: 90). Results: A total of 180 participants were included, comprising 90 patients with HT and 90 age- and sex-matched healthy controls. Circulating MOTS-c levels were significantly lower in patients with HT compared to controls (p < 0.001). MOTS-c levels demonstrated significant inverse correlations with body mass index, fasting glucose, HbA1c, HOMA-IR, thyroid-stimulating hormone, C-reactive protein, and thyroid autoantibody levels (all p < 0.05). In subgroup analyses, these associations remained significant within the HT cohort, particularly for HOMA-IR and thyroid autoantibodies. Multivariable regression analysis identified HT (β = −30.04, p < 0.001) and HOMA-IR (β = −0.85, p < 0.001) as independent determinants of reduced circulating MOTS-c levels. Levothyroxine (LT4) use was not associated with significant differences in MOTS-c concentrations. Conclusions: Circulating MOTS-c levels are markedly reduced in patients with HT and are independently associated with insulin resistance and autoimmune burden. These findings suggest that impaired mitochondrial signaling may play a role in the pathophysiology of thyroid autoimmunity and highlight MOTS-c as a promising biomarker linking metabolic dysfunction and immune dysregulation. Full article

Precise assessment of commercial service facilities (CSFs) is a vital pillar for megacity governance. However, existing evaluations rely on static population and 2D metrics, overlooking behavioral heterogeneity and 3D spatial supply at the micro scale. This study constructs a “3D Supply–Group Demand–Matching” framework at the community level. On the supply side, a Building Coupling Entropy (BCE) model integrates 3D volume and morphology to characterize service capacity. On the demand side, a dynamic behavioral model measures multi-group needs. Mismatch patterns are identified using the Entropy-modified Spatial Disparity Ratio (ESDR). Using Guangzhou as a case, the results reveal three paradigms: (1) Core districts exhibit rigid path dependency, where first-tier sub-districts rose from 48 to 51, and elderly service shortages in old areas plummeted by nearly 80% via micro-regeneration; (2) Growth poles show spatial fragmentation, with core labor demand spilling over but infrastructure lagging, creating a fast production–slow urbanism mismatch; (3) Far-suburban areas reduced extreme-shortage sub-districts from 38 to 34, identifying resource islands besieged by residential demand. Overall, the framework elucidates the shape–flow mismatch mechanism and provides a transferable basis for precision zonation governance, supporting a shift from static quantity-based allocation to dynamic quality-oriented provision in high-density megacities. Full article

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot ($300\times 180$ mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at $2.5$ Hz, (iii) continuous platform translation at $0.3$–$0.8$ m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight $\lambda \left(t\right)$; and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3° attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm). Full article

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem remains a challenging task, particularly in the presence of noisy density maps and in large, topologically complex α/β proteins. To address this issue, we propose a fully automated, classification-based framework that infers protein secondary-structure topology directly from medium-resolution cryo-EM density maps. Specifically, we cast topology determination as a supervised classification problem in three-dimensional space, leveraging geometric learning on model-derived Cα coordinate representations to establish SSE correspondences, and a Dynamic Time Warping (DTW)-based procedure to resolve density-stick directionality. Validation on a benchmark of 38 proteins spanning both simulated and experimental cryo-EM maps and covering diverse fold classes (α, β, and α/β) demonstrates strong and consistent performance. Among the evaluated predictors, the Voronoi (1-NN) classifier achieves the highest average correspondence quality, with a mean F1-score of 96.82% across the full benchmark. The framework also scales to large, topologically dense targets containing up to 65 secondary-structure elements while preserving very fast correspondence inference (<3 ms), offering a substantial improvement over prior baselines in both accuracy and computational cost. Overall, the classification-driven strategy provides reliable SSE-to-density matching and, when coupled with DTW-based direction selection, yields stronger topology constraints that directly support model building and refinement from medium-resolution cryo-EM reconstructions, while remaining easy to integrate into existing structural interpretation pipelines. Full article