Search Results (295)

Modern distribution networks undergo frequent topology reconfiguration, volatile bi-directional flows, and noisy measurements, making five-class line-loss anomaly classification both valuable and challenging. In this study, PC-LossGNN is proposed—a physics-consistent spatiotemporal graph neural network for edge-level classification into Normal, Infrastructure, Documentation, Metering, and Theft. A static topology prior is fused with a measurement-adaptive graph and confidence-aware multi-source features; power-flow physics is injected via residual-guided attention using active/reactive balance, voltage-drop, and ohmic-loss residuals. A dual-path decoder is employed to yield calibrated probabilities and interpretable class evidence, trained under an uncertainty-weighted curriculum objective. On six months of real utility data, macro-F1 of 0.8503 and accuracy of 0.9915 are achieved, surpassing XGBoost, LSTM, GCN, STGCN, and two recent physics-aware spatiotemporal GNN baselines including ST-RGNN and PA-STGCN. Ablation indicates that physics-consistent regularization is pivotal, while adaptive topology and interactive temporal encoding further improve performance. Robustness tests with injected Gaussian noise show more graceful degradation than baselines. These results suggest that PC-LossGNN provides accurate, physically plausible, and interpretable five-way line-loss diagnostics suitable for real-world operations. Full article

Background: In rodent models of intracranial tumor development, evaluating the actual burden experienced by animals beyond procedural severity is essential for ethical and legal compliance. This study examined whether voluntary wheel running (VWR) could serve as a sensitive indicator of post-surgical burden following subcutaneous transmitter implantation, tumor cell injection, and tumor resection. It also assessed whether VWR supports the detection of humane endpoints. VWR outcomes were compared with body weight, clinical scores, heart rate, and activity levels recorded via telemetry. Methods: Fourteen male BDIX rats were housed individually in cages equipped with a running wheel. Under general anesthesia, telemetric devices to monitor heart rate and activity were subcutaneously implanted. After recovery, glioblastoma BT4Ca cells were stereotaxically injected into the right frontal cortex. Eight days later, the resulting tumors were microsurgically resected. Body weight, VWR, heart rate, and general activity were continuously monitored until the animals reached humane endpoint criteria, indicated by sudden weight loss and clinical deterioration. Results: On average, body weight and VWR declined significantly after all surgical procedures, with tumor resection causing the most pronounced effect. As animals approached the endpoint, a marked drop in these parameters was observed, along with an increased clinical score (p < 0.05). Activity measures supported these findings, though less consistently than weight and VWR. Conclusions: Monitoring body weight and VWR enables an effective assessment of the actual postoperative burden experienced by rats undergoing surgeries of different procedural complexity. Moreover, VWR is a valuable supplementary tool for identifying humane endpoints alongside body weight and clinical scoring. Full article

This paper applies physics-informed neural networks (PINNs) to laterally excited liquid sloshing in a two-dimensional rectangular tank, where near-resonant forcing (${\omega }_e/{\omega }_1=0.9$) produces a multi-frequency beating response with a period of approximately $10T_1$. Linearized potential flow theory governs the problem; the network learns the velocity potential $\phi (x,z,t)$ while the free-surface elevation $\eta$ is injected analytically. Two training obstacles specific to forced sloshing are analyzed. First, a zero-solution trap arises because the trivial solution $\widehat{\phi }=0$ satisfies all equations except the free-surface conditions, whose residuals are roughly ${10}^{-4}$ times smaller than the Laplace residual; characteristic-scale normalization combined with loss weighting (${\lambda }_D={\lambda }_K=100$) breaks this trap. Second, spectral bias prevents standard MLPs from resolving the three co-existing frequencies (${\omega }_1$, ${\omega }_e$, $\mathsf{\Delta }\omega$); a Fourier time embedding that augments the input from 3 to 9 dimensions overcomes this limitation. Two additional techniques further reduce errors: a hard-wall boundary condition enforced exactly via a $\cos (\pi x/B)$ spatial embedding, which eliminates wall collocation points; and a gradient-enhanced Laplace regularizer (${\parallel \nabla \left({\nabla }^2\widehat{\phi }\right)\parallel }^2$) that constrains velocity smoothness through third-order automatic differentiation. An ablation study shows that these four techniques progressively reduce the horizontal velocity error from ${\epsilon }_u=12.46\%$ to $0.84\%$. Results are validated against a viscous finite-difference benchmark. Over one beating cycle the errors are ${\epsilon }_{\eta }=0.15\%$, ${\epsilon }_u=0.84\%$, and ${\epsilon }_w=1.65\%$. A frequency parameter study across ${\omega }_e/{\omega }_1$ = 0.5–1.1 gives ${\epsilon }_{\eta }<0.25\%$ and ${\epsilon }_u<2.3\%$ for all near-resonance cases. For long-time simulation, a time-domain decomposition strategy with transfer learning partitions the domain into one-beat windows; extending to five beating cycles ($50T_1$) yields ${\epsilon }_u=3.43\%$ and ${\epsilon }_{\eta }=0.30\%$ with no monotonic error accumulation across windows. The methodology is then extended to a three-dimensional rectangular tank ($B\times W\times H$) with bi-directional lateral excitation. The 3-D formulation introduces the y-dimension into the Laplace equation (${\nabla }^2\phi ={\phi }_{xx}+{\phi }_{yy}+{\phi }_{zz}=0$), adds transverse wall boundary conditions ($\partial \phi /\partial y=0$) enforced exactly via a $\cos (\pi y/W)$ embedding, and extends the Fourier time embedding from 9 to 16 dimensions to accommodate six physical frequencies. The bi-directional excitation excites both $(m,0)$ and $(0,n)$ modal families, producing a genuinely three-dimensional beating response. Experimental results verify that the proposed methods can be well generalized to three-dimensional scenarios. Within a single beating cycle, the relative errors reach ${\epsilon }_{\eta }=0.24\%$, ${\epsilon }_u=1.31\%$, ${\epsilon }_v=1.78\%$ and ${\epsilon }_w=2.32\%$, with a total training time of 2499 s. By applying time domain decomposition to carry out two-cycle three-dimensional simulations, the model can steadily maintain satisfactory prediction precision across segmented time intervals, achieving overall errors of ${\epsilon }_{\eta }=0.30\%$ and ${\epsilon }_u=1.32\%$. Full article

This study aimed to evaluate the protective effects of soluble dietary fiber (SDF) derived from Polygonatum cyrtonema Hua residues on cyclophosphamide (CTX)-induced intestinal injury in mice. A total of 60 C57BL/6 mice (6–8 weeks old; body weight, 23.8 ± 0.5 g) were randomly allocated to six groups (n = 10 per group): a control group (CON), a CTX model group (CTX), a levamisole-treated positive control group (PC), and low-, medium-, and high-dose SDF groups (125, 250, and 500 mg/kg body weight, respectively). Mice received oral administration of SDF or an equal volume of water for 21 consecutive days and were intraperitoneally injected with CTX (80 mg/kg body weight) on days 19–21 to induce intestinal injury. The results demonstrate that SDF possessed a porous, sponge-like network structure and comprised multiple monosaccharides. SDF intervention, particularly at medium and high doses, significantly attenuated CTX-induced body weight loss and immune organ atrophy; restored villus height and the villus-to-crypt ratio; increased the numbers of goblet cells and intraepithelial lymphocytes; elevated intestinal levels of sIgA, β-defensins, and lysozyme; and reduced serum levels of LPS, D-lactic acid, and DAO (p < 0.05). In conclusion, SDF derived from Polygonatum cyrtonema effectively mitigates CTX-induced intestinal injury by enhancing intestinal mucosal immunity and preserving intestinal barrier integrity, thereby highlighting its potential as a functional ingredient for promoting gut health. Full article

Lung cancer is a leading cause of death worldwide and is often accompanied by declines in musculoskeletal health (i.e., cachexia). Despite affecting a majority of lung cancer patients, cachexia remains understudied and currently has no cure. We have previously demonstrated that liver metastases (LMs) exacerbate cachexia in murine models of colorectal cancer, and, while the liver represents a common site of metastases and is associated with poor prognosis in patients with lung cancer, whether LMs heighten musculoskeletal wasting in mice bearing lung cancer is unknown. Here, we aimed to characterize the impact of LMs on musculoskeletal health in a mouse model of lung cancer cachexia. C57BL/6J male mice were injected with LLC tumor cells either subcutaneously or intrasplenically (LMs) to mimic hepatic metastases (n = 6–9/group). Upon sacrifice, skeletal muscle, bone, and plasma were collected for morphological and molecular analyses. Consistently, compared to healthy controls, metastatic tumor hosts displayed greater reductions in muscle weights (~17%), in line with decreased muscle torque (~23%) and reduced muscle cross-sectional area (~10%). On a molecular level, skeletal muscle from mice bearing LMs had elevated levels of pStat3, Murf1, and Atrogin-1, suggesting enhanced protein catabolism. Similar to skeletal muscle, metastatic tumor hosts displayed greater losses in trabecular bone and increased skeletal fragility. Plasma proteomics identified 211 and 131 differentially expressed proteins in metastatic hosts compared to control animals and subcutaneous LLC hosts, respectively. Top regulated pathways in mice bearing LMs included neutrophil degranulation, BAG2 signaling, and cachexia signaling. Overall, our findings demonstrate that LMs are accompanied by accelerated musculoskeletal wasting and weakness in a mouse model of lung cancer cachexia. This work highlights the need for animal models that mimic advanced cancer, thus providing a better understanding of the mechanisms that mediate cachexia. Full article

Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary embolism (PE), is a prevalent condition with a significant annual incidence, particularly increasing with age. Its pathophysiology is explained by Virchow’s triad (venous stasis, vascular injury, and hypercoagulability). Tirzepatide, a dual receptor agonist of glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), is approved for type 2 diabetes mellitus (T2DM) and obesity, showing efficacy in lowering HbA_1c and promoting weight loss. Recent case reports have linked tirzepatide to VTE events, particularly in patients experiencing significant weight loss, raising concerns about its safety profile. We present a case of a male T2DM subject who developed PE after five injections of tirzepatide in a patient with grade I obesity. We also review emerging literature on VTE associated with tirzepatide, emphasizing the need for further research to clarify the drug’s risk and underlying mechanisms. Full article

Zero-shot anomaly segmentation (ZSAS) has significantly advanced with the emergence of vision–language models such as CLIP. Among recent approaches for ZSAS, VCP-CLIP introduced visual context prompting (VCP) and demonstrated impressive zero-shot localization capability without class-specific training. However, we revisit VCP-CLIP and find room for supplementation and improvement in the VCP-CLIP framework. In this study, we upgrade VCP-CLIP with simple yet effective modifications designed to enhance pixel-level localization and image-level reliability. Specifically, we propose: (1) a fixed temperature scaling scheme that improves consistency in similarity estimation and stability in training; (2) a learnable anomaly map fusion scheme that adaptively and optimally aggregates anomaly cues from complementary branches; (3) an adaptive loss weighting mechanism that balances segmentation objectives; and (4) an image-conditioned direct prompting module that directly injects visual context information to the text prompts. With minimal architectural changes, our upgraded model, dubbed VCP-CLIP+, achieved high performance improvements over VCP-CLIP on the ZSAS benchmark datasets, outperforming other state-of-the-art CLIP-based ZSAS methods in both pixel-level and image-level anomaly detection. Full article

Background: Glucagon-like peptide-1 receptor agonist (GLP-1 RA) use has increased exponentially as studies show significant benefits in cardiovascular and renal diseases and obesity. Accessibility to the public also increased after compounding pharmacies began direct-to-consumer distribution. Gastrointestinal side effects are common; however, hypersensitivity reactions are rare. Case Presentation: A 50-year-old female with a history of obesity, hypertension, and lisinopril-induced angioedema presented to the Emergency Department with swelling of the lips, tongue, and throat developing four hours after her first injection of compounded semaglutide for weight loss. She was treated with epinephrine, corticosteroids, and antihistamines, but due to progressive airway edema, she required intubation and mechanical ventilation for four days. After extubation, she reported accidentally injecting a ten-fold higher dose (2 mg) of semaglutide than was appropriate for the first dose. The hospitalization was complicated by hypoglycemia requiring dextrose infusion, but was otherwise unremarkable, and she was discharged home on day 7. Based on the temporal onset after semaglutide injection, this presentation was most consistent with GLP-1 RA-induced angioedema. While she also had a history of lisinopril-induced angioedema five years earlier, and had been taking valsartan for hypertension, the remoteness of the lisinopril exposure made this less likely. Conclusions: Semaglutide use may be associated with severe angioedema within hours of administration. Given the overlapping indications and patient populations, angioedema appearing in patients taking both GLP-1 RAs and ACE inhibitors may become increasingly common and present a diagnostic dilemma. Diagnosis of hypersensitivity to GLP-1 RAs can be supported with history and positive skin testing. Clinicians should be aware that inexperienced patients are at the highest risk of dosing errors. Full article

Accurate JIC (Japanese Investigation Committee) classification of osteonecrosis of the femoral head (ONFH) is critical for collapse risk prediction and hip-preserving treatment. However, clinical classification faces challenges: indistinct lesion boundaries, limited annotated medical data, and the black-box inference issue of purely data-driven deep learning models. To address these, a Prior Knowledge-Guided CNN-Swin Transformer Hybrid Network (PGCT-Net) is proposed for high-accuracy classification with interpretable decision support. A cascaded dual-branch structure is adopted: the CNN branch extracts fine-grained local features from MRI images, while the Swin Transformer branch captures multi-scale global semantics and long-range dependencies between necrotic lesions and the acetabular weight-bearing region. A lesion mask-guided learning module injects expert-annotated clinical prior knowledge to focus the model on pathological regions and suppress background interference. Grad-CAM is used to visualize attention distribution for better interpretability. The network is trained end-to-end with a composite loss function combining cross-entropy loss and L1 sparse regularization. On the JLU-ONFH dataset, PGCT-Net achieves 94.38% accuracy, 94.15% F1-score and 93.97% AUC, significantly outperforming mainstream models. Cross-task validation on the BT dataset verifies the architecture’s generalizability. This work provides an effective, interpretable scheme for ONFH JIC classification, with promising clinical auxiliary diagnosis potential. Full article

The obesity–metabolic syndrome–diabetes continuum is driven by interconnected mechanisms including insulin resistance, dysfunctional adiposity, chronic inflammation and progressive cardio–renal–metabolic injury. This triggered a need for therapies that extend beyond glucose lowering alone. The benefits of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) as disease-modifying drugs include weight loss, cardiovascular risk reduction, glycemic control and renal protection. However, treatment burden, adherence issues and access restrictions may limit the long-term effects of injectable formulations. One significant translational development that aims to close this gap is oral GLP-1-based treatments. In this review, we examine the mechanistic rationale, formulation science and clinical development of oral GLP-1 RAs. Oral semaglutide is presented as the first validated proof of concept for systemic peptide delivery by the gastrointestinal route. The biological barriers to oral peptide absorption, including enzymatic degradation, low epithelial permeability, pharmacokinetic variability and epithelial safety constraints, are critically discussed. Enabling technologies such as SNAC-based gastric absorption, nanocarriers, mucoadhesive systems and stability-optimization platforms are evaluated. Evidence from the PIONEER program and related studies demonstrating meaningful glycemic and weight-loss efficacy, acceptable safety and clinical utility in patients with type 2 diabetes and chronic kidney disease is further synthesized. Beyond first-generation oral peptide platforms, we discuss the emerging landscape of non-peptide oral GLP-1 RAs, dual and triple incretin agonists, precision dosing strategies and model-informed drug development. Oral GLP-1-based therapeutics are shifting from a formulation breakthrough to a broader translational strategy for disease modification across the obesity–metabolic syndrome–diabetes continuum. Long-term renal outcomes, access and implementation barriers remain important priorities for future research. Full article

This research examines how false data injection attacks (FDIAs) impact the trajectory tracking control of underactuated surface vessels (USVs). The internal uncertain dynamics of the system are reconstructed using radial basis function neural networks (RBFNNs). In order to avoid the computational pressure of the RBFNNs on the system, the neural network weights, external disturbances, and FDIAs are converted into a single parameter learning form using the minimum learning parameters (MLPs). Next, a nonlinear feedback function is constructed and introduced into the controller design process, thereby avoiding the controller accuracy loss caused by MLPs. Within the backstepping method framework, the adaptive laws leverage deep information robust adaptive technology to estimate the upper limits of the uncertainty term. The closed-loop system is provided with a rigorous theoretical analysis by combining the Lyapunov stability theory. Finally, the effectiveness of the control scheme is verified by simulation. The results show that the proposed controller guarantees boundedness of all closed-loop signals and drives the tracking errors into a small neighborhood of the reference trajectory even under the attack of FDIAs and the influence of internal and external uncertainties. Full article

Background/Objectives: Cancer cachexia (CC) is a metabolic syndrome characterized by the progressive loss of skeletal muscle and adipose tissue during tumor progression. Despite its clinical prevalence, effective therapeutic options are currently lacking. Phloretin, a natural flavonoid with potent anti-inflammatory and antioxidant properties, has unclear efficacy against CC. This study investigates the therapeutic potential of phloretin in ameliorating cancer cachexia. Methods: Mouse models of CC were established using BALB/c mice implanted with C26 colon carcinoma cells and C57BL/6 mice implanted with Lewis lung carcinoma (LLC) cells. Upon the detection of palpable tumors, phloretin (10 mg/kg) was administered daily via intraperitoneal injection. At the endpoint, hind limb skeletal muscle, inguinal white adipose tissue (iWAT), and hearts were harvested and weighed. Lean body mass was assessed by analyzing the weight of the carcass following the excision of skin, subcutaneous fat, and visceral organs. Gene expression and protein levels in muscle tissues were subsequently quantified. Results: Phloretin administration significantly alleviated tumor-induced loss of tumor-free body weight. It effectively preserved skeletal muscle mass in both C26 and LLC cachexia models, while significantly attenuating adipose tissue depletion in the C26 model. In vitro, phloretin treatment mitigated myotube atrophy induced by C26 conditioned medium. Mechanistically, phloretin inhibited STAT3 activation in skeletal muscle. This inhibition suppressed the expression of the E3 ubiquitin ligases MuRF-1 and Atrogin-1. Furthermore, phloretin concurrently modulated the autophagy pathway. Conclusions: Phloretin effectively ameliorates cancer cachexia-induced muscle wasting by targeting STAT3-mediated protein degradation and autophagy pathways. These findings suggest that phloretin represents a promising therapeutic agent for the clinical management of cancer-associated cachexia. Full article

Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbations to bypass detection. Existing defenses based on conventional adversarial training often recover robustness against known perturbation patterns at the cost of degraded detection accuracy on canonical attack categories—a robustness–accuracy trade-off that remains an open challenge in the field. In this paper, we propose GT-CSAT (Game-Theoretic Cost-Sensitive Adversarial Training), a novel defense framework tailored for cloud security environments. GT-CSAT couples an improved Wasserstein GAN with Gradient Penalty (WGAN-GP) threat generator—conditioned on attack semantics to simulate functionally consistent and highly covert traffic variants—with a minimax adversarial training loop governed by a game-theoretic cost-sensitive loss function. The proposed loss function assigns asymmetric misclassification penalties derived from a two-player zero-sum payoff matrix, enabling the detector to maintain vigilance over both novel adversarial variants and well-characterized conventional threats simultaneously. Specifically, misclassifying an adversarially perturbed attack as benign incurs a strictly higher penalty than the symmetric cross-entropy baseline, while the cost weights are dynamically adapted via a Nash equilibrium-inspired update rule during training. We conduct comprehensive experiments on the Cloud Vulnerabilities Dataset (CVD), CICIDS-2017, and UNSW-NB15, which encompass diverse cloud-specific attack scenarios including denial-of-service, port scanning, brute-force, and SQL injection traffic. Under six representative evasion strategies—FGSM, PGD, C&W, BIM, DeepFool, and IDSGAN-style black-box perturbations—GT-CSAT achieves an average robust accuracy of 94.3%, surpassing standard adversarial training by 6.8 percentage points and the undefended baseline by 21.4 percentage points, while preserving clean-traffic detection at 97.1%. These results confirm that the game-theoretic cost structure effectively decouples robustness from accuracy, yielding a Pareto-superior detection profile relative to competing baselines across all evaluated threat models. The source code and experimental configurations have been publicly released to facilitate reproducibility. Full article

Silicon carbide (SiC) power converters offer superior switching performance but generate severe broadband electromagnetic interference (EMI) that challenges regulatory compliance. Existing prediction methods face a fundamental trade-off between physical fidelity and computational efficiency, while conventional suppression strategies lack adaptability to varying operating conditions. This paper proposes a frequency-band-aware physics-informed generative adversarial network (FBA-PIGAN) that integrates electromagnetic domain knowledge into data-driven generative modeling for joint EMI prediction and adaptive suppression in SiC power converters. The framework employs a Wasserstein GAN with gradient penalty as the adversarial backbone and introduces feature-wise linear modulation (FiLM) to inject converter operating parameters into the generator through learned affine transformations. A hierarchical physics-informed loss function enforces three frequency-dependent constraints, namely, harmonic structure consistency, parasitic resonance characterization, and high-frequency envelope regularization, coordinated by a curriculum-based weight-scheduling strategy. An end-to-end differentiable suppression module maps predicted spectra to optimal passive filter parameters through an analytically embedded transfer function. Experimental validation on a 10 kW SiC inverter platform with 5120 measured spectra across 32 operating conditions demonstrates that FBA-PIGAN achieves a mean spectral error of 2.1 dB, 93.8% peak frequency accuracy, and a physical consistency score of 0.93, improving prediction accuracy by 56% over conventional conditional GANs while maintaining sub-millisecond inference latency. The integrated suppression pipeline attains 19.2 dB average attenuation with 98.5% CISPR 25 compliance, and the framework generalizes to unseen operating conditions with only 19% performance degradation, compared with 56% for data-driven baselines. Full article

Mechanistic tumor growth models are widely used to describe disease progression and treatment response, but their utility depends on accurate estimation of parameters governing the underlying biological processes. In this study, we employ a Physics-Informed Neural Network (PINN) to estimate the parameters of a tumor growth model that captures both tumor dynamics and drug effects. We introduce a piecewise PINN that splits the time domain at dosing events to handle non-smooth dose-driven dynamics, and we incorporate drug injection by representing the pharmacokinetic subsystem analytically via an impulse-response function. The approach is evaluated on synthetic tumor-volume trajectories generated from known parameter sets and dosing schedules from an experimental cohort of 54 mice. Across the cohort, the PINN accurately reconstructs total tumor volume and robustly estimates the tumor proliferation rate a, with inferred values closely aligned with the true values ($R^2=0.841$). The framework was also able to estimate the drug killing effect parameter b. This consistency is further supported by forward ODE simulations using the PINN-estimated parameters. Within the evaluated setting, performance depended on the model structure, parameter identifiability, and training configuration, underscoring the need for careful loss weighting and further validation. Overall, the results demonstrate the feasibility of piecewise PINNs for parameter inference in tumor growth models and support their further study in realistic therapeutic settings. Full article