Search Results (14,451)

Introduction: Bovine leukemia virus is a single-stranded RNA virus that targets B cell CD5⁺ lymphocytes in cattle. Only a tiny percentage of individuals develop malignant lymphoproliferative disorders, while most remain healthy carriers or experience persistent lymphocytosis. The exact mechanisms leading to lymphoma development are complex and not fully understood. RNA-seq analysis of cows’ peripheral blood leukocytes (PBLs) with and without Bovine leukemia virus (BLV) antibodies was conducted to gain a deeper understanding of molecular events beyond BLV infection. Method: Eighteen samples were selected, and their RNA was sequenced. For gene expression analysis and protein–protein network interactions, three groups were selected, including healthy negative samples (CT, n = 7), asymptomatic carriers (AC, n = 5), and persistent lymphocytosis (PL, n = 6), to provide the differentially expressed gene (DEG) and protein–protein interaction network (PPIN) outputs. Results: Our results demonstrated that in comparison to CT, ACs upregulated TLR7 and transcription activation factors. In the CT vs. PL group, MHC class II, transcription activation factors, and anti-inflammatory cytokines increased, while the acute-phase proteins, antiviral receptors, and inflammatory cytokines decreased. Additionally, antiviral receptors, acute-phase proteins, and inflammatory receptors were downregulated in the PL versus the AC groups. Moreover, PPINs analysis suggested that nuclear receptor corepressor 1 (NCOR1), serine/arginine repetitive matrix 2 (SRRM2), LUC7 like 3 pre-mRNA splicing factor (LUC7L3), TWIST neighbor (TWISTNB), U6 small nuclear RNA and mRNA degradation associated (LSM4), eukaryotic translation elongation factor 2 (EEF2), ubiquitin C (UBC), CD74, and heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNP A2B1) are possible hub gene candidates in the PL group. Conclusions: Our results suggest that innate and cellular immune responses are more loose in severe BLV infectious conditions, while the PPINs revealed that new protein interactions are necessary for oncogenesis. Full article

Traditional Software-Defined Wide Area Network (SD-WAN) solutions lack adaptive load-balancing mechanisms, leading to inefficient traffic distribution, increased latency, and performance degradation. This paper presents an Application-Aware Dynamic Load Balancing (AADLB) framework designed for open-source SD-WAN environments. The proposed solution enables dynamic traffic routing based on real-time network performance indicators, including CPU utilization, memory usage, connection delay, and packet loss, while considering application-specific requirements. Unlike conventional load-balancing methods, such as Weighted Round Robin (WRR), Weighted Fair Queuing (WFQ), Priority Queuing (PQ), and Deficit Round Robin (DRR), AADLB continuously updates traffic weights based on application requirements and network conditions, ensuring optimal resource allocation and improved Quality of Service (QoS). The AADLB framework leverages a heuristic-based dynamic weight assignment algorithm to redistribute traffic in a multi-cloud environment, mitigating congestion and enhancing system responsiveness. Experimental results demonstrate that compared to these traditional algorithms, the proposed AADLB framework improved CPU utilization by an average of 8.40%, enhanced CPU stability by 76.66%, increased RAM utilization stability by 6.97%, slightly reduced average latency by 2.58%, and significantly enhanced latency consistency by 16.74%. These improvements enhance SD-WAN scalability, optimize bandwidth usage, and reduce operational costs. Our findings highlight the potential of application-aware dynamic load balancing in SD-WAN, offering a cost-effective and scalable alternative to proprietary solutions. Full article

Diabetes mellitus (DM) and osteoporosis are among the most common non-communicable diseases worldwide. Beyond their considerable socio-economic burden, both conditions significantly impair quality of life and reduce life expectancy, representing major causes of disability. DM-induced osteoporosis has recently emerged as a notable and frequent complication. Patients with type 2 DM have a twofold increased risk of fragility fractures, while those with longstanding type 1 DM exhibit a fivefold higher risk of hip, vertebral, and non-vertebral fractures. Bone mineral density (BMD) assessed by Dual Energy X-ray Absorptiometry (DXA) often fails to predict fracture risk in this population, as bone mass tends to be normal, slightly reduced, or even elevated. However, DXA-derived indices can offer additional clinical value. The Trabecular Bone Score (TBS), which reflects bone microarchitecture, is frequently reduced in patients with DM and is associated with increased fracture risk, particularly in postmenopausal women. TBS is also linked to glycemic control and microvascular complications and can improve with bone-active medications, thus aiding follow-up assessments. Another useful DXA-based tool is the Bone Strain Index (BSI), which evaluates load resistance and has been shown to be degraded in diabetic patients, offering further predictive value for fractures. Additionally, Hip Structural Analysis (HSA) provides information on the mechanical integrity of the proximal femur, which may be compromised in DM. Based on the available evidence, this review aims to highlight the clinical utility of DXA-derived tools in DM-induced osteoporosis, emphasizing their ability to provide quantitative and qualitative information on bone health and to predict the risk of fragility fractures. Full article

Closing resistors in ultra-high-voltage (UHV) gas-insulated circuit breakers (GCBs) are critical components designed to suppress inrush currents and transient overvoltages during switching operations. However, in practical service, these resistors are subjected to repeated mechanical impacts and transient electrical stresses, leading to degradation of their electrical contact interfaces, fluctuating resistance values, and potential failure of the entire breaker assembly. Existing studies mostly simplify the closing resistor as a constant resistance element, neglecting the coupled electro-thermal–mechanical effects that occur during transient events. In this work, a comprehensive modeling framework is developed to investigate the dynamic electrical contact characteristics of a 750 kV GCB closing resistor under transient closing impacts. First, an electromagnetic transient model is built to calculate the combined inrush and power-frequency currents flowing through the resistor during its pre-insertion period. A full-scale mechanical test platform is then used to capture acceleration signals representing the mechanical shock imparted to the resistor stack. These measured signals are fed into a finite element model incorporating the Cooper–Mikic–Yovanovich (CMY) electrical contact correlation to simulate stress evolution, current density distribution, and temperature rise at the resistor interface. The simulation reveals pronounced skin effect and current crowding at resistor edges, leading to localized heating, while transient mechanical impacts cause contact pressure to fluctuate dynamically—resulting in a temporary decrease and subsequent recovery of contact resistance. These findings provide insight into the real-time behavior of closing resistors under operational conditions and offer a theoretical basis for design optimization and lifetime assessment of UHV GCBs. Full article

Copper selenide (Cu₂Se) has garnered significant attention as an exceptional thermoelectric material due to its high thermoelectric figure of merit (ZT values > 2). This remarkable efficiency makes it a strong candidate for various applications. However, the practical deployment of thermoelectrics often requires operation in an oxygen-containing atmosphere, which poses a significant challenge for Cu₂Se due to its environmental instability. This work investigates the environmental behavior of high-purity Cu₂Se, which was synthesized via a direct high-temperature reaction and spark plasma sintering (SPS). Our Temperature-Programmed Oxidation (TPO) studies determined that the onset of oxidation occurs at a temperature as low as 623 K. Further analysis using SEM–EDS confirmed the formation of copper oxides, Cu₂O and CuO. Critically, thermogravimetric analysis (TGA) revealed that the SeO₂ formation and sublimation process is an equally profound degradation mechanism, alongside copper oxidation, particularly within the optimal 673–973 K temperature range. Complementary XRD studies of samples annealed in air underscore this severe material degradation, which is especially devastating between 873 and 973 K. Ironically, this is the precise temperature window where Cu₂Se’s highest ZT values have been reported. Our findings demonstrate that the direct application of Cu₂Se in air is impractical, highlighting the urgent need for developing robust protective layers to unlock its full potential. Full article

The ecotoxicity induced by macrolides has attracted widespread attention, but their impacts on the nitrogen metabolism and symbiotic environmental bacteria of microalgae remain unclear. This study examined the effects of roxithromycin (ROX) on the growth, chlorophyll levels, and nitrogen metabolism of Chlorella pyrenoidosa; investigated the changes in the composition and functions of environmental bacterial communities; and finally, analyzed the relationship between microalgae and environmental bacteria. The results indicated that all concentrations of ROX (0.1, 0.25, and 1 mg/L) inhibited microalgae growth, but the inhibition rates gradually decreased after a certain exposure period. For instance, the inhibition rate in the 1 mg/L treatment group reached the highest value of 43.43% at 7 d, which then decreased to 18.93% at 21 d. Although the total chlorophyll content was slightly inhibited by 1 mg/L ROX, the Chl-a/Chl-b value increased between 3 and 21 d. The nitrate reductase activities in the three treatments were inhibited at 3 d, but gradually returned to normal levels and even exceeded that of the control group at 21 d. Under ROX treatment, the consumption of NO₃⁻ by microalgae corresponded to the nitrate reductase activity, with slower consumption in the early stage and no obvious difference from the control group in the later stage. Overall, the diversity of environmental bacteria did not undergo significant changes, but the abundance of some specific bacteria increased, such as nitrogen-fixing bacteria (unclassified-f-Rhizobiaceae and Mesorhizobium) and organic contaminant-degrading bacteria (Limnobacter, Sphingopyxis, and Aquimonas). The 0.25 and 1 mg/L ROX treatments significantly enhanced the carbohydrate metabolism, cofactor and vitamin metabolism, amino acid metabolism, and energy metabolism of the environmental bacteria, but significantly downregulated nitrogen denitrification. This study provides new insights into the environmental bacteria-driven recovery mechanism of microalgae under antibiotic stress. Full article

Water contamination by emerging pollutants poses a significant environmental challenge, demanding innovative treatment technologies beyond conventional methods. Advanced oxidation processes (AOPs) utilizing niobium-based catalysts, particularly niobium oxide (Nb₂O₅) and its modified forms, are prominent due to their high chemical stability, effective reactive oxygen species (ROS) generation, and versatility. This review systematically examines recent advancements in Nb₂O₅-based catalysts across various AOPs, including heterogeneous photocatalysis, electrocatalysis, and Fenton-like reactions, highlighting their mechanisms, material modifications, and performance. Following PRISMA and InOrdinatio guidelines, 381 papers were selected for this synthesis. The main findings indicate that niobium incorporation enhances pollutant degradation by extending light absorption, reducing electron–hole recombination, and increasing ROS generation. Structural modifications such as crystalline phase tuning, defect engineering, and the formation of heterostructures further amplify catalytic efficiency and stability. These catalysts demonstrate considerable potential for water treatment, effectively degrading a broad range of persistent contaminants such as dyes, pharmaceuticals, pesticides, and personal care products. This review underscores the environmental benefits and practical relevance of Nb₂O₅-based systems, identifying critical areas for future research to advance sustainable water remediation technologies. Full article

Heparanase is the only human enzyme responsible for heparan sulfate (HS) breakdown, an activity that remodels the extracellular matrix (ECM) and strongly drives cancer metastasis and angiogenesis. Compelling evidence implies that heparanase promotes essentially all aspects of the tumorigenic process, namely, tumor initiation, vascularization, growth, metastasis, and chemoresistance. A key mechanism by which heparanase accelerates cancer progression is by enabling the release and bioavailability of HS-bound growth factors, chemokines, and cytokines, residing in the tumor microenvironment and supporting tumor growth and metastasis. The currently available heparanase inhibitors are mostly HS/heparin-like compounds that lack specificity and exert multiple off-target side effects. To date, only four such compounds have progressed to clinical trials, and none have been approved for clinical use. We have generated and characterized an anti-heparanase monoclonal antibody (A54 mAb) that specifically inhibits heparanase enzymatic activity (ECM degradation assay) and cellular uptake. Importantly, A54 mAb attenuates xenograft tumor growth and metastasis (myeloma, glioma, pancreatic, and breast carcinomas) primarily when administered (syngeneic or immunocompromised mice) in combination with conventional anti-cancer drugs. Co-crystallization of the A54 Fab fragment and the heparanase enzyme revealed that the interaction between the two proteins takes place adjacent to the enzyme HS/heparin binding domain II (HBDII; Pro271-Ala276), likely hindering heparanase from interacting with HS substrates via steric occlusion of the active site cleft. Collectively, we have generated and characterized a novel mAb that specifically neutralizes heparanase enzymatic activity and attenuates its pro-tumorigenic effects in preclinical models, paving the way for its clinical examination against cancer, inflammation, and other diseases. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

The cGAS-STING pathway initiates the core cascade of innate immune defense by recognizing pathogen-associated and self-derived abnormal nucleic acids, and key molecules (such as cGAS, STING, downstream IFN-β, IL-6, etc.) may serve as biomarkers in various diseases. The diverse mechanisms by which distinct nucleic acids activate this pathway provide novel insights for therapeutic strategies targeting infectious diseases, cancer, and autoimmune disorders. To prevent aberrant cGAS-STING pathway activation, cells employ multiple regulatory mechanisms, including restricting self-DNA recognition and terminating downstream signaling. Strategies to mitigate pathological activation involve reducing nucleic acid accumulation through nuclease degradation (e.g., of mitochondrial DNA or neutrophil extracellular traps, NETs) or directly inhibiting cGAS or STING. This review elucidates the molecular mechanism of nucleic acid-mediated regulation of cGAS-STING and its role in disease regulation. Full article

The construction sector is a major contributor to environmental degradation due to high energy consumption and CO₂ emissions. This study presents a low-tech, sustainable construction system based on the manual fabrication of curved laminated bamboo beams, assembled with screws and steel straps, without adhesives or heavy machinery. The case study is part of a bamboo roof structure built within Jericoacoara National Park, Brazil, using Dendrocalamus asper for its mechanical strength and carbon storage capacity. The construction process of three vertical lower laminated curved beams (Vig.CLIV-1, CLIV-2, and CLIV-3) was systematized into two main phases—preparation and construction. Due to the level of detail involved, only Vig.CLIV-1 is fully presented, broken down into work items, processes, and sub-processes to identify critical points for quality control and time efficiency. Comparative analysis of the three beams complements the findings, highlighting differences in logistics, labor performance, and learning outcomes. The results demonstrate the potential of this handcrafted system to achieve high geometric accuracy in complex site conditions, with low embodied energy and strong replicability. Developed by bamboo specialists from Colombia and Peru with support from local assistants, this experience illustrates the viability of low-impact, appropriate construction solutions for ecologically sensitive contexts and advances the integration of sustainable, replicable practices in architectural design. Full article

This study assessed the critical fire resistance of bridge cables by investigating post-fire tensile degradation of high-strength steel wires (1860, 1960, 2100 MPa) heated under initial stress (10% and 40% of design strength) to 300 °C, 400 °C, 500 °C, and 600 °C followed by cooling to an ambient temperature. Tensile tests determined reduction coefficients (RCs) for proportional limit (σ_p), yield strength (σ_y), ultimate strength (σ_u), elastic modulus (E), and elongation (δ) relative to ambient values, with constitutive models for σ_p, σ_y, and σ_uRCs integrating temperature, stress, and grade. Visual observation showed intensified wire hue and reduced necking with increasing temperature. All RCs remained stable up to 300 °C; beyond this temperature, σ_p, σ_y, and σ_uRCs progressively decreased, averaging 77.5%, 65.7%, and 61.9% at 600 °C under lower-level initial stress (10%) and 74.1%, 63.8%, and 60.6% at 400 °C under higher-level initial stress (40%). Elastic modulus exhibited minimal variation, whereas elongation reached its minimum at 400 °C under lower-level initial stress but its maximum at 400 °C under higher-level initial stress. The impact of lower-level initial stress on mechanical properties was negligible, showing a less than 3.2% average RC decrease at 600 °C. Higher-level initial stress accelerated mechanical property degradation with increasing temperature, with comparable degradation patterns observed across different steel wire strength grades. The models confirm maximum temperature dominance in degradation, supporting a recommended critical fire-resistant temperature of approximately 400 °C for bridge cable wires. Full article

Bolted connections are extensively utilized in aerospace, civil, and mechanical systems for structural assembly. However, inevitable structural vibrations can induce bolt loosening, leading to preload reduction and potential structural failure. Early-stage preload degradation, particularly during initial loosening, is often undetectable by conventional monitoring methods due to limited sensitivity and poor noise resilience. To address these limitations, this study proposes an intelligent bolt preload monitoring framework that combines electromechanical impedance (EMI) signal analysis with a parallel deep learning architecture. A multiphysics-coupled model of flange joint connections is developed to reveal the nonlinear relationships between preload degradation and changes in EMI conductance spectra, specifically resonance peak shifts and amplitude attenuation. Based on this insight, a parallel convolutional neural network (P-CNN) is designed, employing dual branches with 1 × 3 and 1 × 7 convolutional kernels to extract local and global spectral features, respectively. The architecture integrates dilated convolution to expand frequency–domain receptive fields and an enhanced SENet-based channel attention mechanism to adaptively highlight informative frequency bands. Experimental validation on a flange-bolt platform demonstrates that the proposed P-CNN achieves 99.86% classification accuracy, outperforming traditional CNNs by 20.65%. Moreover, the model maintains over 95% accuracy with only 25% of the original training samples, confirming its robustness and data efficiency. The results demonstrate the feasibility and scalability of the proposed approach for real-time, small-sample, and noise-resilient structural health monitoring of bolted connections. Full article

The low efficiency of the microbial gasification of coal limits the application of bio-coal bed methane technology. The co-fermentation of coal and biomass provides a new approach for improving the degradation rate of coal. In this study, a co-fermentation system comprising five different coal orders with five microalgae was constructed in the laboratory, and the methanogenic characteristics of coal–algae co-fermentation and its microbiological mechanism were systematically investigated in terms of gas production, soluble organic matter, and microbial community characteristics. The results showed that the combination of lignite and Nannochloropsis exhibited optimal methane production, with a methane yield of 26.43 mL/g coal. Biogenic methane yields for lignite–Porphyra and anthracite–Porphyra were 23.43 mL and 21.28 mL, respectively, demonstrating the potential for algae to enhance gas production even in high-rank coals. pH monitoring revealed that algal species played a critical role in the acidification process. Dunaliella caused a continuous pH decrease, reaching 3.76 by day 30, while Nannochloropsis maintained a neutral pH of 6.95, optimizing the fermentation environment. Significant differences in soluble organic matter were observed between the lignite and anthracite fermentation systems, with lignite systems producing more volatile fatty acids, including acetic and butyric acids. Microbial community analysis revealed that Methanosarcina, an acetic acid-utilizing methanogen, was dominant in lignite and anthracite systems, while Syntrophomonas played a key role in lignite–Nannochloropsis co-fermentation. These findings provide valuable insights into optimizing coal microbial gasification and selecting appropriate algal species to enhance methane production efficiency. Full article

Phytoremediation is widely acknowledged as a viable method for soil remediation; however, its efficacy remains limited in soils co-polluted with petroleum hydrocarbons and heavy metals. To overcome this constraint, the present study explored an innovative approach utilizing ferrate-modified biochar (FeBC) to augment phytoremediation efficiency. Experimental findings revealed that ferrate treatment markedly modified the physicochemical characteristics of biochar, yielding thinner, smoother-surfaced structures with pronounced iron enrichment. At a 5% application rate alongside ryegrass cultivation, FeBC exhibited superior remediation performance, achieving 52.0% degradation of petroleum hydrocarbons (notably within the meso-aggregate fraction) and a 19.2% decline in zinc bioavailability via immobilization, thereby reducing zinc uptake in ryegrass tissues. Furthermore, FeBC amendment induced significant shifts in rhizosphere soil biochemistry and microbial ecology, characterized by diminished catalase activity but elevated urease and alkaline phosphatase activities. Phospholipid fatty acid profiling indicated a substantial rise in bacterial biomass (encompassing both Gram-positive and Gram-negative groups), particularly in meso- and micro-aggregates, whereas soil bacterial α-diversity declined markedly, accompanied by distinct compositional changes across aggregate size fractions. These results offer mechanistic insights into the synergistic interaction between FeBC and ryegrass in co-contaminated soil rehabilitation, the aggregate-dependent distribution of remediation effects, and microbial community adaptations to FeBC treatment. Collectively, this study advances the understanding of ferrate-modified biochar’s role in phytoremediation enhancement and clarifies its operational mechanisms in petroleum-zinc co-contaminated soil systems. Full article