Search Results (336)

Neuroinflammation plays a central role in Alzheimer’s disease (AD). Glycyrrhizin (GL), a major component of licorice, exhibits anti-inflammatory effects, but its effects on AD pathology remain unclear. To investigate the effects of GL (18β-glycyrrhizin, 18β-GL) and its stereoisomer (18α-glycyrrhizin, 18α-GL) on cognitive function, neuroinflammation, and AD pathology in senescence-accelerated mouse prone 8 (SAMP8; P8) mice, 40-week-old P8 male mice, an AD model due to aging, and the control (senescence-accelerated mouse resistant 1, SAMR1; R1) mice were treated with 18β-GL, 18α-GL and physiological saline (control) for 12 weeks (n = 6 in each group). Cognitive function was evaluated using a step-through passive avoidance test. Plasma levels of α-Klotho, IGF-1, 2′,3′-cyclic GMP-AMP (2′,3′-cGAMP), HMGB1, IL-6, and TNF-α were measured by ELISA. Hippocampal microglial activation (Iba1), amyloid-β (Aβ) deposition, and phosphorylated tau (p-Tau) were assessed by immunohistochemistry. Aged P8 mice showed impaired memory, decreased α-Klotho and IGF-1 levels, and increased inflammatory markers compared with R1 mice. GL significantly improved memory performance, reduced inflammatory markers, and suppressed Iba1 activation, as well as Aβ and p-Tau accumulation. These effects were associated with inhibition of the cGAS–STING pathway, as indicated by reduced 2′,3′-cGAMP and HMGB1 levels. GL ameliorates AD pathology by inhibiting neuroinflammation, suggesting its therapeutic potential for AD. Full article

Fuzzy entropy (FuzzyEn) theoretically outperforms sample entropy (SampEn) in quantifying dynamic complexity; however, its anti-noise robustness remains insufficiently validated. This study compares SampEn and FuzzyEn using model simulations and real-world depression electroencephalogram (EEG) signals. The two entropy metrics are first compared using chaotic time series generated from the logistic and two-dimensional Henon maps, with additive white Gaussian noise (SNRs ranging from 1 to 10 dB). Surrogate data analysis reveals that SampEn maintains stronger nonlinear detection performance (lower than the 2.5th percentiles of surrogate data) under noisy conditions. EEG data from 46 depressed patients and 75 healthy subjects are employed to evaluate SampEn and FuzzyEn, with statistical differences corrected by the Bonferroni method. Depressed patients showed significantly increased EEG complexity in the occipital lobe (PO7, PO5, PO3 channels) under visual stimulation (p < 0.01). SampEn outperforms FuzzyEn in alpha-band feature detection, especially the low-alpha sub-band (8–10 Hz, p < 0.001). In summary, this study verifies that SampEn may be more suitable than FuzzyEn under noise conditions, thus providing valuable insights for real-world signal analysis and methodological guidance for developing EEG biomarkers of depression. Full article

Real-time fatigue classification on resource-constrained edge devices faces three unresolved computational challenges: just-in-time compilation latency spikes that violate the 50 ms inference budget, statistical moment features insensitive to temporal complexity signatures of fatigue, and binary anomaly outputs insufficient for actionable coaching decisions. A synthetic IMU dataset (9 subjects, 540,000 samples, 6 channels at 100 Hz) was generated as a reproducible computational benchmark, with fatigue signatures calibrated to published biomechanical effect sizes (sample entropy $d=+0.77$; permutation entropy $d=+0.38$). We present Safari (Stochastic Adaptive Fitness-Aware Real-time Inference), an end-to-end computational pipeline integrating: a dual-pathway entropy triplet (SampEn, PermEn, SpEn) replacing statistical moments; 16 pre-compiled polyhedral anchor kernels eliminating JIT latency; $O\left({(\Delta W)}^2\right)$-bounded runtime interpolation; subject-specific MaxEnt free-energy anomaly scoring; and a Banister fitness–fatigue adaptive threshold. Safari achieves AUC-ROC = 0.9820 (Monte Carlo 95% CI: 0.9726–0.9886), F1 = 0.8835, four-state accuracy = 83.3%, and worst-case latency = 7.2 ms on a Raspberry Pi 4. Entropy features achieve 1.55× higher discriminability than statistical moments. Safari is a computational framework for real-time fatigue monitoring, contributing a reproducible algorithmic benchmark for edge AI in movement analysis, with real-athlete validation as the recommended next step. Full article

Partial discharge (PD) signals acquired from gas-insulated switchgear (GIS) are often severely contaminated by discrete-spectrum interference and periodic narrowband noise, which impairs the accuracy of subsequent fault diagnosis. This paper proposes a hybrid denoising method that integrates Spearman coefficient-optimized variational mode decomposition (S_VMD), spatially related recursive sample entropy (Sdr_SampEn) for intrinsic mode function (IMF) classification, an improved wavelet threshold function, and Savitzky–Golay (SG) filtering. First, the Spearman correlation coefficient between the original signal and the reconstructed signal is used to adaptively determine the optimal mode number K of VMD, avoiding the over- and under-decomposition problems of conventional VMD. Second, Sdr_SampEn, which characterizes signal irregularity along both the Chebyshev distance and spatial direction of a recurrence plot, is employed to classify the obtained IMFs into noise-dominant and PD-dominant components, with the discrimination threshold calibrated as p = 1.94 at 0 dB. Third, an improved wavelet threshold function—continuous at the threshold and asymptotically unbiased—is applied to the noise-dominant components, while SG filtering is applied to the PD-dominant components, after which the denoised signal is reconstructed. The results demonstrate that the proposed method effectively suppresses both white and narrowband noise while preserving the detailed morphology of PD pulses. Full article

Next-day S&P 500 direction forecasting matters for allocation, hedging, and risk management because broad-index movements transmit quickly across portfolios. Does structured news sentiment help predict next-day S&P 500 direction? We test four feature sets over 2008–2023 in an ablation sequence: technical indicators only (Set A), with FinBERT headline sentiment (Set B), with BERTopic topic-linked sentiment (Set C), and with realized-volatility weighting (Set D). This design makes two contributions: it separates the incremental value of increasingly structured sentiment features, and it tests whether sentiment value is state-dependent across volatility regimes. CatBoost, XGBoost, LightGBM, LSTM, and GRU are evaluated under walk-forward cross-validation, nested cross-validation, and formal statistical tests. On the full sample, sentiment does not deliver a measurable forecasting edge. Walk-forward AUCs sit near 0.50 for every feature set, and pairwise tests find no significant differences. However, this average masks a consistent pattern. Sentiment becomes more informative during high-volatility periods, suggesting that its value is state-dependent rather than uniform. Rolling AUC swings from 0.28 to 0.71 depending on the market period. When we split by VIX regime, Set D reaches 0.5684 AUC during high-volatility episodes ($n=50$, permutation $p=0.213$) while adding almost nothing in calm markets. Set D also has the lowest fold-to-fold variance and the shallowest drawdown in trading simulations. These results imply that the relevant question is not whether sentiment works in general, but when it does. Sentiment does not help on average; whether it helps during stress is suggestive but unconfirmed and needs more crisis-period data to settle. Full article

Age-related osteoporosis is characterized by progressive loss of bone mass and deterioration of bone microarchitecture, leading to enhanced skeletal fragility. Cytokines regulate bone remodeling through distinct signaling pathways. Baricitinib, a selective JAK1/2 inhibitor effective in inflammatory disorders such as rheumatoid arthritis, suppresses cytokine signaling, but its role in age-related osteoporosis remains insufficiently defined. In our study a total of 60 eight-month-old female SAMP8 mice were randomized to receive baricitinib (10 mg/kg) or vehicle twice daily by oral gavage for six weeks. Bone outcomes were evaluated by high-resolution micro-computed tomography (µCT) and static histomorphometry. Intracellular cytokine production by splenocytes was determined via flow cytometry. We found that baricitinib substantially reduced T-cell cytokine production, decreasing IL-6, IL-17, IFN-γ, and IL-21 in CD4⁺ T cells and IL-6 in CD8⁺ T cells, accompanied by lower IFN-γ/IL-17 and IL-21/IL-6 ratios, respectively. µCT analyses showed no significant intergroup differences in BV/TV, whereas histomorphometry demonstrated higher BV/TV in the baricitinib group. Overall, baricitinib was found to effectively suppressed proinflammatory cytokines in aged SAMP8 mice but did not consistently enhance bone parameters, indicating reduced skeletal responsiveness during aging. Full article

Robotics optimization is essential for improving the performance, efficiency, and reliability of robotic systems, especially when dealing with complex engineering problems involving multiple conflicting objectives. Algorithm-specific parameter-free metaheuristic algorithms have gained attention in such applications because they eliminate the need for problem-specific parameter tuning. However, their performance can be further enhanced by improving convergence and maintaining solution diversity in multi-objective optimization. This paper proposes three multi-objective variants—archive, opposition, and self-adaptive multi-population (SAMP)—for the algorithm-specific parameter-free BxR algorithms such as Best–Mean–Random (BMR), Best–Worst–Random (BWR), and Best–Mean–Worst–Random (BMWR). The proposed variants are evaluated on five robotic optimization problems spanning two to six objectives, including Autonomous Underwater Vehicle shape optimization, power line inspection robot design, inverse kinematics of a 4-DOF manipulator, wall-building robot trajectory planning, and optimization of a reconfigurable parallel cutting and grinding mechanism. Their performance is compared with several established multi-objective algorithms using metrics such as GD, IGD, SPC, and HV, supported by rigorous statistical testing involving Friedman tests, Conover post hoc analysis with Holm correction, and Vargha–Delaney A₁₂ effect sizes over 30 independent runs. The results show that archive variants achieve the best IGD rank in four of the five case studies and the best HV rank in three of them, with the five-objective trajectory planning problem being the sole exception where SAMP and base BxR variants show improved IGD performance. The base BxR algorithms prove to be strong competitors, consistently outperforming established parameter-dependent methods on IGD across all five problems. The opposition variants do not provide consistent improvement; however, they also do not cause catastrophic degradation, suggesting that refined opposition strategies warrant further investigation. The study demonstrates the effectiveness of the proposed algorithms as practical optimization tools for complex robotic optimization problems. Full article

Background: Metabolic syndrome (MetS) leads to alterations in cardiac autonomic control that can be detected from electrocardiogram (ECG)-derived markers, particularly when the cardiovascular system is challenged during an oral glucose tolerance test (OGTT). Methods: In this paper, we present an automated framework for MetS identification using RR intervals and heart rate variability (HRV) features extracted from 12-lead ECG recordings acquired during the five OGTT stages in 40 male participants (15 with MetS, 10 controls, and 15 endurance-trained marathon runners). RR intervals were first derived using a multilead Pan-Tompkins approach with fusion-based validation. From these RR series, HRV descriptors were computed from time-domain statistics (RR mean, SDNN, rMSSD, pNN50), spectral indices (VLF, LF, HF, LF/HF), and nonlinear measures (SD1, SD2, SampEn, DFA-${\alpha }_1$). Conventional HRV analysis revealed pronounced physiological differences between groups: MetS subjects exhibited reduced parasympathetic activity, reflected by lower rMSSD and SD1, lower HF power, and higher LF/HF ratios, whereas marathoners showed greater vagal modulation, higher HF power, and increased signal complexity. Healthy controls showed an intermediate autonomic profile. Using RR sequences and HRV descriptors (256 samples per stage), we trained three multimodal classifiers: a CNN-MLP model with a softmax output, a CNN-MLP model with an SVM head, and a CNN + LSTM-MLP + SVM architecture. Results: All models achieved strong discriminative performance, with accuracies ranging from 0.92 to 0.95, F1-macro values from 0.92 to 0.95, and macro-AUC values from 0.96 to 0.97. The CNN-MLP model achieved the best overall performance, whereas the CNN + LSTM-MLP + SVM model showed strong class discrimination, particularly for endurance athletes, while maintaining competitive recall for MetS. Conclusions: These findings support the feasibility of ECG-based autonomic assessment as a complementary non-invasive approach for early metabolic risk detection in clinical and preventive cardiometabolic screening settings. Full article

Aging is closely associated with oxidative stress, which contributes to functional decline and increased vulnerability to neurodegenerative diseases. Natural antioxidants, such as Chlorella Growth Factor (CGF) and γ-polyglutamic acid (γ-PGA), possess antioxidant and anti-aging properties; however, their combined effects remain unknown. This study investigated the potential synergistic effects of CGF and γ-PGA supplementation in senescence-accelerated mouse-prone 8 (SAMP8) mice, a model characterized by early cognitive decline, locomotor deficits, and elevated oxidative DNA damage. Three-month-old male SAMP8 mice (n = 40) were divided into four groups: control, CGF (49.2 mg/kg BW/day), γ-PGA (20.5 mg/kg BW/day), and combined CGF + γ-PGA (69.7 mg/kg BW/day), and were treated for 13 weeks. Behavioral and physiological assessments included locomotor activity, aging index, and cognitive function (passive and active avoidance tests). Biochemical analysis focused on brain 8-hydroxy-2′-deoxyguanosine (8-OHDG) as a biomarker of oxidative DNA damage. Supplementation with CGF and γ-PGA, particularly in combination, significantly improved locomotor activity, aging scores, and cognitive functions. Notably, the combined treatment yielded the greatest reduction in brain 8-OHDG levels. These findings indicate that CGF and γ-PGA, when administered together, exert enhanced protective effects against functional and molecular aging. In conclusion, long-term supplementation with CGF and γ-PGA protects against aging-related decline in SAMP8 mice. This study highlights the potential of CGF and γ-PGA as safe, natural candidates for the development of functional foods or nutraceuticals aimed at promoting healthy aging and reducing oxidative stress-associated disorders. Full article

The appearance of antibiotic-resistant strains of Helicobacter pylori (H. pylori) is leading to a decreased eradication rate of H. pylori infection. There is an urgent need to find new agents with antimicrobial mechanisms different from those of antibiotics, with therapeutic potential to clear colonization of H. pylori in the stomach. Some antimicrobial peptides (AMPs) possess bactericidal activity by enhancing the permeability of the outer membrane and damaging the integrity of the cell membrane. Bacteria are not susceptible to drug resistance through this antimicrobial mechanism. In this study, 28 short peptides containing 12 amino acid residues were designed based on nine amino acid fragments (KRIVQRIKD) from human cathelicidin LL-37, which is stable in gastric juice, and 3 amino acids were added at the C-terminus of the peptide. These designed peptides were not digested and degraded by pepsin at low pH values. The peptides were predicted using the online tool platform. Then, the strongest antimicrobial peptide, named SAMP-12aa (KRIVQRIKDVIR), was screened from 28 short peptides. Further studies found that SAMP-12aa retained anti-H. pylori activity after incubation in simulated gastric juice. The MIC and MBC of SAMP-12aa were 8 μg/mL and 32 μg/mL, respectively. SAMP-12aa showed good bactericidal kinetics. SAMP-12aa was found to have cell selectivity, penetrating and damaging bacterial cell membranes and exhibiting almost no toxicity to human cells at a relatively high concentration (128 μg/mL). Regulatory T (Treg) cells express CD25^High with immunosuppressive activity that induces immune tolerance in response to H. pylori. Molecular docking prediction revealed that SAMP-12aa could target the active center of Foxp3. Flow cytometry analysis revealed that SAMP-12aa can inhibit Foxp3 activity and downregulate CD25 protein expression on CD4⁺ T cells, thereby reducing the development and differentiation of CD4⁺Foxp3⁺CD25^High Treg cells with immunosuppressive effects. Further research revealed that the levels of the cytokine interferon-γ (IFN-γ), which activates CD8⁺ T-cell activity, were significantly elevated, and the levels of transforming growth factor-β (TGF-β), which inhibits CD8⁺ T-cell activity, were significantly reduced. The results of this study reveal that SAMP-12aa not only possesses antibacterial activity but also has immunomodulatory effects. Full article

Water quality monitoring is critical for public health, ecology, and economic sustainability, but traditional methods are limited by temporal-spatial coverage and cost, failing to meet real-time assessment needs. Deep learning for water quality prediction is often hindered by high complexity and noise in raw time series. This study aims to address the high complexity and noise of hydrological time series by proposing a prediction framework integrating sliding window feature enhancement, principal component analysis (PCA), and a two-layer regularized gated recurrent unit (TLR-GRU). The core goal is to achieve high-precision real-time prediction of four key water quality parameters (dissolved oxygen (DO), ammonia nitrogen (${\mathrm{NH}}_3\text{-}\mathrm{N}$), total phosphorus (TP), and total nitrogen (TN)) for aquaculture and irrigation. Sample entropy (SampEn, $m=2$, $r=0.2 \times \mathrm{std}\left(X\right)$), a univariate complexity metric capturing intra-series pattern repetition, quantifies time series regularity, showing sliding windows reduce SampEn by filtering transient noise while retaining ecological patterns. This optimization synergizes with TLR-GRU’s regularization (L2, Dropout) to avoid overfitting. A total of 4970 water quality records (2020–2023, 4 h sampling interval) were collected from a monitoring station in a typical aquaculture-irrigated water body. After dimensionality reduction via PCA, experimental results demonstrate that the TLR-GRU model outperforms six state-of-the-art deep learning models (e.g., TLD-LSTM, WaveNet) on both the base dataset and the sliding window-enhanced dataset. On the latter, DO and TP test set $R^2$ rise from 0.82 to 0.93 and 0.81 to 0.92, with RMSE decreasing by 49.4% and 55.6%, respectively. This framework supports water resource management, applicable to rivers and lakes beyond aquaculture. Future work will optimize the model and integrate multi-source data. Full article

Hyperspectral reconstruction (SR) refers to the computational process of generating high-dimensional hyperspectral images (HSI) from low-dimensional observations. However, the superior performance of most supervised learning-based reconstruction algorithms is predicated on the availability of fully labeled three-dimensional data. In practice, this requirement demands complex optical paths with dual high-precision registrations and stringent calibration. To address this gap, we extend the fully supervised paradigm to a semi-supervised setting and propose SSHSR, a semi-supervised SR method for scenarios with limited spectral annotations. The core idea is to leverage spectrally aware mini-patches (SA-MP) as guidance and form region-level supervision from averaged spectra, so it can learn high-quality reconstruction without dense pixel-wise labels over the entire image. To improve reconstruction accuracy, we replace the conventional fixed-form Tikhonov physical layer with an optimizable version, which is then jointly trained with the deep network in an end-to-end manner. This enables the collaborative optimization of physical constraints and data-driven learning, thereby explicitly introducing learnable physical priors into the network. We also adopt a reconstruction network that combines spectral attention with spatial attention to strengthen spectral–spatial feature fusion and recover fine spectral details. Experimental results demonstrate that SSHSR outperforms existing state-of-the-art (SOTA) methods on several publicly available benchmark datasets, as well as on remote sensing and real-world scene data. On the GDFC remote sensing dataset, our method yields a 6.8% gain in PSNR and a 22.1% reduction in SAM. Furthermore, on our self-collected real-world scene dataset, our SSHSR achieves a 6.0% improvement in PSNR and a 11.9% decrease in SAM, confirming its effectiveness under practical conditions. Additionally, the model has only 1.59 M parameters, which makes it more lightweight than MST++ (1.62 M). This reduction in parameters lowers the deployment threshold while maintaining performance advantages, demonstrating its feasibility and practical value for real-world applications. Full article

Balance is a fundamental quality for trampoline athletes, the basis for completing complex skills. We aimed to compare balance control strategies between elite trampolinists (ETs) and sub-elite trampolinists (Sub-ET) by integrating linear and nonlinear center of pressure (COP) measures across stable and unstable surfaces. Twenty-four male athletes (12 ET, 12 Sub-ET) participated. Each participant performed 15-s static standing trials with eyes closed on a firm surface (FI) and a foam surface (FO). COP parameters were extracted, including ellipse area, sway velocity, sway range, and sample entropy (SampEn) in the medio-lateral (ML) and antero-posterior (AP) directions. Repeated-measures ANOVA was applied to examine the effects of group and surface condition. Linear analyses indicated that ET athletes exhibited greater sway amplitudes and faster velocities than Sub-ET athletes, with both groups showing larger sway on FO compared with FI. Nonlinear analyses revealed that ET athletes demonstrated lower SampEn, suggesting more structured and automatized control strategies. ET athletes maintained consistent entropy across both conditions, reflecting stronger adaptability to unstable surfaces. These results emphasize the importance of combining linear and nonlinear measures in balance assessment and suggest that incorporating unstable or trampoline-like surfaces into training may enhance adaptability, improve performance, and reduce injury risk. Full article

Background: Although luteolin (Lut) is well recognized for its anti-inflammatory and antioxidant effects, its potential role in preventing vascular senescence remains underexplored in primary vascular aging. This study aimed to investigate the anti-vascular-aging effects of Lut in both cellular and murine aging models and to elucidate its conserved molecular mechanisms across species. Methods: Canine and feline vascular endothelial cells (cVECs and fVECs) were subjected to doxorubicin-induced senescence, while senescence-accelerated mice prone 8 (SAMP8) received an 8-week dietary supplementation with Lut. Senescence markers, inflammatory cytokines, antioxidant activities, vascular biomechanics, and histological changes were assessed. Transcriptomic and metabolomic analyses were combined to identify molecular pathways. Statistical significance was determined by one-way analysis of variance with Tukey’s or Games–Howell post hoc tests (p < 0.05). Results: Lut markedly reduced senescence-associated β-galactosidase activity, suppressed interleukin-6 and matrix metalloproteinase expression (p < 0.05), and enhanced superoxide dismutase activity and nicotinamide adenine dinucleotide levels (p < 0.05) in cVECs, fVECs, and SAMP8 sera. In aged mice, Lut alleviated arterial wall thickening and vascular inflammation, improved vascular biomechanics and systemic oxygenation (p < 0.05), and attenuated cardiac and hepatic inflammatory infiltration. Multi-omics analyses in cVECs revealed that Lut targets aldehyde dehydrogenase 1 to increase 9-cis retinoic acid, thereby activating the retinol X receptor–peroxisome proliferator-activated receptor (PPAR) network, which accelerates lipid clearance and oxidation. Consistent activation of this pathway was validated in murine vascular transcriptomes. Conclusions: These findings demonstrate that Lut delays vascular aging by activating the retinoic acid–PPAR axis and reprogramming lipid metabolism. This conserved mechanism was consistently observed in doxorubicin-induced cVEC senescence and the SAMP8 model, underscoring the robustness of Lut’s action across distinct contexts of vascular aging. Full article

This study investigated the anti-fatigue and anti-aging benefits of continuous intake of resveratrol (RES)-rich beverages. Locomotion and forelimb grip strength performance were significantly improved in medium- and high-dose RES groups. In terms of aging indices, the scores for the medium- and high-dose groups were significantly lower than those of the control group. In the PAT and active shuttle avoidance tests, the three RES groups performed better than the control group. A significant increase in SOD and catalase activity in the liver and a reduction in TBARS and 8-OHdG levels in the brain were observed in the medium- and high-dose groups. Thus, supplementation with RES-rich beverages for 13 weeks significantly improved fatigue, locomotor performance, learning and memory abilities, and liver antioxidant activity and reduced brain peroxide levels in SAMP8 mice. Full article