Search Results (995)

In the Czochralski process of silicon single-crystal growth, the V/G value at the solid–liquid interface is a key parameter affecting intrinsic crystal defects. However, online V/G detection remains difficult because the temperature gradient G cannot be directly measured, while multi-condition distribution shifts and limited labeled data reduce the robustness of data-driven models. To address these issues, this paper proposes DWC-ISBiGNN, an adaptive multi-condition invariant feature extraction method based on the Invariant-Specific Bidirectional Graph Neural Network. The proposed method introduces dynamic sample graph construction with stage-aware global nodes to capture non-stationary process correlations, source-domain credibility weighting to suppress negative transfer, and a semi-supervised training framework combining stage-conditional alignment with teacher–student regression consistency to exploit unlabeled target-domain data. Experiments on industrial data from a 12-inch silicon single-crystal production line show that DWC-ISBiGNN achieves an RMSE of 0.0041, an MAE of 0.00285, and an $R^2$ of 0.9549. Compared with the original IS-BiGNN, the RMSE is reduced by 32.6%, and $R^2$ is increased by 5.43 percentage points. The results demonstrate that the proposed method provides an effective soft-sensing approach for V/G prediction under multiple operating conditions. Full article

Panax notoginseng, a valuable medicinal plant in Yunnan, suffers significant yield losses due to root rot, with Chryseobacterium indologenes MA9 as a major causal agent. Conventional chemical control methods are limited by residues and the development of bacterial resistance, underscoring the need for alternative strategies. In this study, we isolated a new lytic myovirus, vB_CinP_MA9V-2, from wastewater using MA9 as the host. MA9V-2 exhibited high lytic efficiency with 75% adsorption in 25 min and a burst size of ~100 PFU per cell, stability across pH 4 to 11 and temperatures of 4 to 50 °C, a moderately broad host range, and effective suppression of biofilm formation. Genome analysis confirmed the absence of virulence or antibiotic resistance genes, indicating its safety for application. In potted plant experiments, single-phage treatment reduced root rot incidence to 16.7% compared with 83.3% in the control, while a combined treatment with phages achieved up to 80 percent control. Curative effects post-infection were limited with a disease incidence of 61.3%, highlighting the preventive advantage of phage therapy. Overall, these results demonstrate that phage therapy, particularly using a combination of phages, shows potential for application in the management of bacterial root rot in P. notoginseng. Full article

Satellite-derived bathymetry (SDB) provides an efficient approach for shallow-water mapping because of its wide spatial coverage and repeated observation capability. However, multi-temporal bathymetric results derived from optical imagery often exhibit substantial inconsistencies due to variations in atmospheric conditions, water optical properties, bottom reflectance, and imaging geometry. Moreover, different bathymetric intervals usually exhibit distinct uncertainty characteristics, while conventional global fusion methods generally apply a single statistical strategy to the entire depth range. To address this limitation, this study proposes an ICESat-2-constrained adaptive segment-wise rank-statistic fusion framework for multi-temporal SDB. The bathymetric range is adaptively divided into multiple depth intervals using ICESat-2 bathymetric control points, and the optimal rank-statistic fusion strategy is independently selected for each interval according to local RMSE evaluation. In this way, shallow-water outliers can be effectively suppressed, while deep-water systematic underestimation can be alleviated simultaneously. Experiments conducted in Ganquan Island, Dong Island, and Key Biscayne demonstrate that the proposed framework consistently outperforms individual single-scene results as well as conventional mean and median fusion methods. Compared with conventional mean and median fusion methods, the RMSE was reduced by up to 27.5%, while the coefficient of determination (R²) reached 0.95. Significant improvements were particularly observed in deeper bathymetric intervals and complex benthic environments. The results indicate that adaptive segmented rank-statistic fusion can effectively characterize bathymetric-dependent error variations and achieve unified optimization for shallow-water outlier suppression and deep-water bias correction. Full article

Plant communities in reservoir drawdown zones experience highly altered hydrological regimes, and responses of locally dominant species shape the biodiversity and restoration trajectories of these artificial wetlands. The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir (TGR) is exposed to alternating flooding and drought, which strongly constrains both its vegetation and the biodiversity that depends on it. Cynodon dactylon dominates the herbaceous cover of the TGR WLFZ, but evidence on its stress responses remains fragmented across single-site studies. Following a PRISMA 2020 literature search and screening procedure, we synthesized 169 effect sizes from 12 qualifying experimental studies, covering biomass and morphological traits, photosynthetic gas-exchange parameters, chlorophyll content, and oxidative-stress indicators. Effect sizes were calculated as natural log response ratios (lnRR) and pooled with random-effects models; shallow and deep flooding were compared using subgroup analyses with bootstrap 95% confidence intervals. Flooding effects varied with water depth. Shallow flooding increased total biomass (+47.2%), whereas deep flooding reduced plant height (−46.5%) and root length (−22.3%). Plant height showed significant between-group heterogeneity (Q_between = 5.60, p = 0.045), indicating sensitivity to submergence depth. Flooding also increased malondialdehyde content (MDA) by 31.7%, whereas peroxidase activity (POD), superoxide dismutase activity (SOD), and photosynthetic gas-exchange parameters showed no consistent responses. Drought effects on total biomass, plant height, and total chlorophyll were non-significant, although inference was limited by a few drought-related entries. Deep flooding, therefore, appears to be a stronger constraint than drought for Cynodon dactylon in the TGR WLFZ, mainly through morphological suppression and increased oxidative damage. Given the dominant role of this species in the herbaceous layer, its depth-dependent decline is relevant both for biodiversity conservation in this artificial wetland and for elevation-based restoration planning. Full article

Background: HCC remains one of the leading causes of cancer-related mortality worldwide, and the therapeutic efficacy of sorafenib is limited by the development of acquired resistance. Increasing evidence indicates that the tumor microenvironment, particularly HSCs, plays a pivotal role in modulating drug response; however, the underlying molecular mechanisms remain incompletely elucidated. Methods: Co-culture systems, mouse models, and biochemical assays were employed to evaluate the effects of HSCs on sorafenib sensitivity and ferroptosis in HCC cells. Transcriptomic analyses of data from The Cancer Genome Atlas were performed to identify key long non-coding RNAs (lncRNAs), followed by gain- and loss-of-function experiments to determine their biological roles. The underlying molecular mechanisms were further investigated through expression profiling, correlation analyses, and RNA stability assays. Results: HSCs markedly reduced the sensitivity of HCC cells to sorafenib by inhibiting ferroptosis, as evidenced by decreased levels of ferrous iron, reactive oxygen species, and lipid peroxidation, accompanied by increased glutathione content and activation of the NRF2 signaling pathway. LINC00152 was identified as a critical lncRNA that was upregulated in both HCC tissues and HCC cells co-cultured with HSCs, and its high expression was associated with poor prognosis. Functional studies demonstrated that LINC00152 promoted sorafenib resistance and suppressed ferroptosis both in vitro and in vivo. Mechanistically, LINC00152 enhanced HSPB1 expression by stabilizing its mRNA. Notably, HSPB1 knockdown reversed the effects of LINC00152, restoring ferroptosis and drug sensitivity to sorafenib. Conclusions: These findings reveal a novel HSCs–LINC00152–HSPB1 axis that promotes ferroptosis resistance and sorafenib tolerance in HCC. Targeting this pathway may represent a promising therapeutic strategy for overcoming drug resistance and improving clinical outcomes in patients with HCC. Full article

To address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance estimation and adaptive diagonal loading. The forgetting-factor recursion assigns larger weights to recent jammer-plus-noise snapshots to track nonstationary interference, while the adaptive loading coefficient is jointly controlled by sample deficiency and covariance condition-number degradation to improve inversion stability. Unlike many robust adaptive beamforming methods that require steering-vector uncertainty sets, mismatch distributions, or subspace information, the proposed method relies only on secondary data and a small set of scalar design parameters. Simulation results based on a synthetic dual-polarized array model show that the proposed method achieves competitive output SINR, effective jammer suppression, and improved robustness to moderate DOA and polarization mismatch under limited-snapshot and time-varying interference conditions. Complexity analysis indicates that the proposed method has the same dominant computational order as standard covariance-based MVDR beamforming, apart from condition-number evaluation. The present validation is simulation-based, and further verification using measured polarimetric radar data, realistic propagation models, or hardware experiments is still required. Full article

Background: Autoimmune diseases such as primary Sjögren’s syndrome and type 1 diabetes are frequently complicated by hepatic injury, yet therapies that simultaneously target inflammation and parenchymal damage remain limited. Mitochondrial dysfunction with excessive reactive oxygen species (ROS) production drives a self-amplifying pathogenic loop by activating the cGAS-STING innate immune pathway. We previously observed that a Chinese herbal formula preserved mitochondrial ultrastructure in autoimmune NOD mice, and computational screening identified trifolirhizin—a natural pterocarpan flavonoid—as the candidate active constituent mediating this protection. Here, we investigated the hepatoprotective effects and underlying mechanisms of trifolirhizin in autoimmune-associated liver injury. Methods: Female NOD mice received trifolirhizin (5, 10, or 20 mg/kg/day) for four weeks, with C57BL/6J mice as healthy controls. Hepatic histopathology, inflammatory cytokines, mitochondrial ultrastructure (TEM), mitochondrial membrane potential (ΔΨm), and ROS levels were evaluated. Integrated transcriptomic and metabolomic profiling was performed to unbiasedly characterize protective mechanisms. In vitro, H₂O₂-induced oxidative stress was established in HepG2 cells. Cells were treated with trifolirhizin (15–25 µM) and assessed for antioxidant enzyme activities, ΔΨm, ROS production, glycolytic and mitochondrial respiration (Seahorse analysis), and cGAS-STING pathway protein expression. Pharmacological rescue experiments using the cGAS agonist cGAMP were conducted to test pathway dependency. Results: Trifolirhizin dose-dependently alleviated hepatic pathological damage and reduced pro-inflammatory cytokine levels in NOD mice. Multi-omics profiling revealed that oxidative stress responses, the mitochondrial electron transport chain, and glutathione metabolism were the most significantly restored pathways. Trifolirhizin preserved mitochondrial ultrastructure, restored ΔΨm, and attenuated ROS accumulation both in vivo and in vitro. Functionally, Seahorse analysis demonstrated that trifolirhizin rescued overall cellular bioenergetics, restoring both glycolytic capacity and mitochondrial respiratory parameters (basal respiration, ATP production, maximal respiration, and spare respiratory capacity). Mechanistically, trifolirhizin suppressed the cGAS-STING-TBK1-IRF3 axis, as evidenced by reduced expression of cGAS, p-STING, ZBP1, p-TBK1, and p-IRF3. Importantly, the cGAS agonist cGAMP abrogated the protective effects of trifolirhizin, confirming that the cGAS-STING pathway is functionally required for its action downstream of mitochondrial protection. Conclusion: Trifolirhizin attenuates liver injury in the nod mouse by preserving mitochondrial integrity, maintaining cellular energy metabolism, and thereby suppressing the ROS/cGAS-STING inflammatory cascade. These findings position trifolirhizin as a promising mitochondria-targeted therapeutic candidate for pSS-related hepatic complications and provide a mechanistic framework for discovering active compounds from mitochondrially active herbal formulations. Full article

Accurate attitude measurement of non-cooperative space targets is essential for on-orbit servicing, active debris removal, and autonomous rendezvous missions. To address the challenges associated with unknown geometry, rapid tumbling motion, and the limited observability of single-view systems, this study proposes a cooperative multi-view measurement framework for three-dimensional reconstruction and attitude estimation. Multiple spacecraft are deployed to form a stable observation configuration, and multi-view image sequences are acquired to strengthen geometric constraints. A learning-based multi-view stereo reconstruction module is used to estimate depth information and reconstruct point clouds, which are further processed through iterative closest point (ICP) registration to derive inter-frame attitude variations. An extended Kalman filter (EKF) is then introduced to improve temporal consistency and suppress measurement noise. Validation is conducted in a numerical simulation using a simplified Fengyun-1 (FY-1) satellite model under a three-spacecraft cooperative fly-around scenario. The simulation results demonstrate that the proposed method achieves high-precision attitude estimation, with attitude errors below $0.3^{\circ }$ and positional errors within $0.05\mathrm{m}$. Comparative experiments show that the method maintains stable measurement performance under varying observation distances and viewing configurations. The proposed framework provides a reliable and robust measurement solution for dynamic attitude determination of free-tumbling space targets. Full article

Background/Objectives: People aging with HIV (PAWHs) face distinct health challenges, including early onset of aging and heightened risk for chronic comorbidities despite effective antiretroviral therapy (ART). However, significant gaps persist in understanding the lived experience and how PAWHs perceive the interplay between their controlled HIV and the aging process. This study examined PAWHs’ illness perceptions of aging, health, and relationship of HIV to other health conditions. Methods: Semi-structured interviews were conducted with a convenience sample of 25 PAWHs (mean age 63.5; mean time living with HIV 22.3 years; 24 virally suppressed) recruited through an academic HIV specialty clinic. Demographic and clinical data were collected from Electronic Health Records (EHRs), and interviews were analyzed using inductive thematic analysis. Results: A central finding was the disconnect between participants’ illness perceptions of controlled HIV and other aging-related health concerns. Absence of acute somatic symptoms and sustained viral suppression fostered a view of HIV as chronologically remote, leading to an apparent unawareness of HIV’s systemic links to accelerated aging and comorbidities. Two primary themes around aging emerged: acceptance/disengagement and fear of future debility (prevalent among older, socially isolated individuals concerned about dementia and finances). Conclusions: This pervasive disconnect, understandable through the lens of the Common Sense Model of Self-Regulation, highlights a critical need to adjust health counseling strategies for PAWHs. Clinicians can leverage existing trusted provider relationships to explicitly address and refine PAWHs’ illness models, clarifying that viral suppression is not a cure and educating on HIV’s systemic links to chronic conditions (e.g., ‘inflammaging’). Tailored educational interventions are crucial for fostering shared decision-making, encouraging early screening, and improving health outcomes for this vulnerable and growing population. Generalizability may be limited by sample characteristics. Full article

Hydraulic supports in coal mining faces require continuous pressure monitoring to detect anomalies indicative of roof instability or equipment failure. Existing reconstruction-based methods rely on standard convolutional or recurrent encoders whose limited receptive fields or coarse temporal representations restrict detection accuracy; static per-window thresholding further discards temporal continuity during online deployment. This study proposes a temporal convolutional network autoencoder (TCN-AE) coupled with a Cumulative Sum (CUSUM) control chart for online anomaly detection in hydraulic support pressure data. The TCN encoder uses dilated convolutions with symmetric padding and residual connections, producing an exponentially expanding receptive field that captures temporal patterns at multiple scales. The CUSUM chart accumulates sustained positive deviations in the reconstruction error sequence, improving detection sensitivity while suppressing isolated false alarms. Component analysis experiments on synthetic anomalies show TCN-AE achieves an AUC of 0.811, outperforming CNN, LSTM, GRU, and fully connected autoencoder variants, along with Isolation Forest and One-Class SVM. On a manually curated real fault test set, where per-window reconstruction scores carry negligible discriminative information (AUC = 0.586, near chance), the CUSUM strategy exploits temporal continuity to improve F1 from 0.213 to 0.905 for TCN-AE. This +0.692 gain is driven entirely by temporal accumulation rather than model discriminability, demonstrating that the CUSUM framework is most valuable precisely when per-window signals are weakest. Full article

Background: Dalbavancin (DBV) is a long-acting lipoglycopeptide with activity against Gram-positive pathogens approved for the treatment of acute bacterial skin and skin structure infections (ABSSSI). Its pharmacological profile supports use in infections requiring prolonged therapy, yet its role in osteomyelitis without implantable devices (OM-WoID) remains off-label. This study aims to describe real-world DBV use in a large tertiary care hospital, focusing on its effectiveness in OM-WoID. Methods: This is a monocentric, retrospective analysis including all patients receiving DBV at ASST Spedali Civili di Brescia, Italy, from April 2017 to July 2023. The statistical analysis focused on patients who received DBV for either ABSSSI or OM-WoID, with the latter transitioning to DBV after traditional daily intravenous therapy. Clinical, microbiological, and treatment data were extracted from electronic records and stored in REDCap. Effectiveness was defined as infection resolution or improvement; treatment failure encompassed clinical worsening, recurrence or suppressive therapy. Predictors of failure were identified through univariate and stepwise multivariate logistic regression. Results: During the study period, 157 patients (63.0% male; mean age 62.5 ± 20 years) received at least one dose of DBV, predominantly for off-label indications (66.2%). Early discharge was the most common reason for switching to DBV (66.3%). Focusing specifically on patients treated for ABSSSI (53) and OM-WoID (43), treatment success was achieved in 81.1% of ABSSSI and 90.7% of OM-WoID cases. In the stepwise multivariate logistic regression, older age was independently associated with an increased risk of treatment failure (OR 1.07, 95% CI 1.01–1.13; p = 0.028), while the presence of multimorbidity significantly reduced the risk (OR 0.07, 95% CI 0.01–0.77; p = 0.029). Discussion: Our study offers a comprehensive real-world analysis of DBV use in both approved and off-label indications. Although current clinical experience with DBV remains limited, DBV emerges as a valuable step-down option for the management of invasive Gram-positive infections in our setting. Consistent with previous evidence, older age independently increased the risk of treatment failure, whereas multimorbidity appeared protective, likely due to selection bias and the more intensive monitoring, earlier interventions, and tailored management such patients often receive. Our results support a broader range of approved indications for DBV to allow earlier discharge and more efficient use of healthcare resources. Full article

Conversion from distributed acoustic sensing (DAS) measurements to geophone-equivalent data is important for integrating DAS into conventional seismic workflows. This is because most established seismic-processing algorithms are designed for particle-velocity or acceleration data, whereas DAS measures strain or strain rate. Recovering geophone-equivalent particle velocity from DAS strain-rate measurements requires inversion of a gauge-length-dependent spatial-difference operator, which can amplify measurement noise, particularly in field data with low signal-to-noise ratios (SNRs). Existing single-regularization methods often trade noise attenuation against waveform fidelity and the preservation of weak coherent events. To address these limitations, we propose an inverse reconstruction framework combining high-order L_p (HOLp) and overlapping group sparsity (OGS) regularizations. HOLp promotes a compact representation of second-order differences and suppresses incoherent fluctuations, whereas OGS exploits local coherence to reduce isolated artifacts and preserve weak continuous events. The resulting objective function is solved using the alternating direction method of multipliers, with iteratively reweighted L₁ minimization for the HOLp subproblem and a majorization–minimization strategy for the OGS subproblem. Numerical and field experiments confirm that the method restores amplitude and waveform fidelity under low SNR conditions, demonstrating robust and reliable DAS-to-geophone conversion. Full article

Accurate extraction of transmission towers from Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) point clouds is a prerequisite for overhead transmission line (OTL) acceptance. This task remains challenging because tower points are heavily entangled with ground, vegetation, conductors, and insulators, especially in complex terrain. To address this issue, we propose a shape prior-guided coarse-to-fine framework for tower extraction from UAV LiDAR point clouds. First, candidate tower regions are localized from the scene point cloud through preprocessing, near-ground suppression, and density-based clustering. Second, the least-disturbed central body of each candidate tower is identified in a slice-wise manner and used to estimate the tower orientation and four principal structural axes. Third, side-view and front-view structural envelopes are progressively inferred to suppress non-tower points around the tower body and tower head. Finally, a base-constrained filtering strategy is introduced to remove residual ground and low-vegetation points within the tower footprint. Experiments conducted on multiple OTL datasets acquired in different regions of China, including plains and mountainous areas, demonstrate that the proposed method achieves robust and efficient tower extraction across diverse scenarios. The results indicate that explicit structural priors offer a promising complement to feature-driven and data-intensive approaches, particularly in scenarios with limited annotated data and strict real-time requirements. The proposed method processes scene point clouds containing tens to hundreds of millions of points, with an average extraction time of approximately 100 to 300 s per tower depending on scene density. Full article

Background/Objectives: Glioblastoma multiforme (GBM) treatment is limited by tumor hypoxia and poor specificity of therapeutic agents. To address these challenges, we developed brain-targeted liposomes co-encapsulating 5-aminolevulinic acid (5-ALA) and catalase (CAT), termed brain-targeted 5-ALA-CAT liposomes (BACL), which were surface-modified with the Angiopep-2 ligand to enhance blood–brain barrier penetration and achieve multimodal therapy combining targeted delivery and oxygen generation. Methods: BACL was prepared and characterized. Tumor targeting was verified by flow cytometry and in vivo imaging. In vitro antitumor activity was evaluated by wound-healing assay, colony formation assay, live/dead staining, MTT assay, and Western blotting. In vivo efficacy, apoptosis, and safety were assessed in a subcutaneous xenograft model. Transcriptome sequencing and qRT-PCR were employed to identify molecular mechanisms and novel targets. Results: BACL exhibited favorable physicochemical properties (size: 122.4 nm, PDI: 0.189, zeta potential: −12.3 mV) and spherical morphology as observed by TEM, with encapsulation efficiencies of 51.2% for 5-ALA and 43.8% for CAT. Compared with unmodified 5-ALA, BACL increased the cellular uptake efficiency by 1.6-fold in glioma cells while maintaining catalytic stability for sustained oxygen generation. In vitro experiments demonstrated that BACL significantly inhibited glioma cell migration, colony formation, and cell viability, and induced apoptosis. In a subcutaneous xenograft tumor model, BACL-mediated photodynamic therapy (PDT) achieved a tumor growth inhibition rate of 52%, with apoptosis induction via regulation of Bcl-2, Bax, and p53 expression, and no obvious toxicity to major organs was observed. Transcriptomic analysis combined with qRT-PCR validation revealed that BACL activates multiple antitumor signaling pathways, including targeted inhibition of IL-10 and CXCL13 to disrupt cytokine–receptor interactions, as well as coordinated regulation of S100A3 and IGSF-9 expression to suppress glioma progression. Conclusions: These multimodal actions enhanced PDT efficacy while remodeling the tumor microenvironment. Our findings position BACL as a promising therapeutic platform integrating targeted delivery, hypoxia alleviation, and immunomodulation for GBM therapy. Full article

A common limitation of existing multi-AGV cooperative systems is their reliance on the obstacle-agnostic Manhattan distance as the basis for reward signals. This causes agents to receive misleading feedback, engage in excessive futile exploration, and ultimately achieve poor training quality. To address this, we introduce an A*-distance guidance mechanism for multi-agent reinforcement learning (MARL) path planning, built on the precise path distance computed via the A* algorithm (A*-distance). Within the QMIX framework, we incorporate an A*-distance-based guiding function into the action selection mechanism. This function evaluates candidate actions by quantifying their immediate effect on the A*-distance, providing positive incentives for actions that bring the agent closer to the goal and applying negative penalties for those that lead it farther away. This effectively biases exploration towards actions that genuinely shorten the obstacle-aware path to the goal, suppresses ineffective exploration, and accelerates policy convergence. Experiments in four warehouse environments (simple obstacles, complex obstacles, large-scale, and congested) show that, compared with standard QMIX, the proposed method achieves higher global average reward and faster convergence. The advantage grows as environment scale and obstacle density increase. In the large-scale and congested environments, standard QMIX and the other MARL baselines fail to solve the task, whereas the proposed method still succeeds. It is the only learning-based method to solve these hardest tasks while keeping path length close to that of dedicated search-based solvers. Ablation experiments further show that the A*-distance-guided action selection is the primary contributor to these gains, while the A*-distance reward plays a supporting role. Full article