Search Results (230,278)

Waterlogging and drought have become major challenges in many regions worldwide. Under water stress, plants exhibit a range of physiological and electrical responses, including changes measurable by electrical impedance spectroscopy (EIS). Monitoring these parameters can provide valuable insights into plant growth status under adverse conditions. This study investigated changes in relative chlorophyll content (SPAD), maximum photochemical efficiency (Fv/Fm), relative water content (RWC), non-structural carbohydrates (NSC), and EIS parameters in apple rootstocks subjected to different water stress treatments. Results indicated that all physiological indicators, except NSC, showed a declining trend under two water stress episodes. Critically, the initial water stress episode elicited significantly greater physiological disruption than its subsequent counterpart. This suggests that plants developed a degree of physiological adaptation—such as osmotic adjustment and enhanced antioxidant activity—reducing their sensitivity to subsequent stress. Correlation analysis revealed that high-frequency resistivity (r) and intracellular resistivity (r_i) were strongly associated with key physiological parameters. Thus, r and r_i may serve as effective indicators for assessing plant water stress status. Furthermore, classification algorithms—Fuzzy K-Nearest Neighbors (FKNN) and sparse Linear Discriminant Analysis (sLDA)—were applied to distinguish water status in apple rootstocks, achieving high classification accuracy. These findings provide a theoretical basis for improved water management in apple cultivation. Full article

Forest fires pose severe ecological, climatic, and socio-economic threats, destroying habitats and emitting greenhouse gases. Early and timely warning is particularly challenging because fires often originate from small-scale, low-temperature ignition sources. Traditional monitoring approaches primarily rely on single-source satellite imagery and empirical threshold algorithms, and most forest fire monitoring tasks remain human-driven. Existing frameworks have yet to effectively integrate multiple data sources and detection algorithms, lacking the capability to provide continuous, automated, and generalizable fire monitoring across diverse fire scenarios. To address these challenges, this study first improves multiple monitoring algorithms for forest fire detection, including a statistically enhanced automatic thresholding method; data augmentation to expand the U-Net deep learning dataset; and the application of a freeze–unfreeze transfer learning strategy to the U-Net transfer model. Multiple algorithms are systematically evaluated across varying fire scales, showing that the improved automatic threshold method achieves the best performance on GF-4 imagery with an F-score of 0.915 (95% CI: 0.8725–0.9524), while the U-Net deep learning algorithm yields the highest F-score of 0.921 (95% CI: 0.8537–0.9739) on Landsat 8 imagery. All methods demonstrate robust performance and generalizability across diverse scenarios. Second, data-driven scheduling technology is developed to automatically initiate preprocessing and fire detection tasks, significantly reducing fire discovery time. Finally, an integrated framework of multi-source remote sensing data, advanced detection algorithms, and a user-friendly visualization interface is proposed. This framework enables all-weather, fully automated forest fire monitoring and early warning, facilitating dynamic tracking of fire evolution and precise fire line localization through the cross-application of heterogeneous data sources. The framework’s effectiveness and practicality are validated through wildfire cases in two regions of Yunnan Province, offering scalable technical support for improving early detection of and rapid response to forest fires. Full article

Spinal cord injury (SCI) results in significant motor, sensory, and autonomic dysfunction. The pathophysiology of SCI develops during the primary and secondary phases. Inflammation contributes to the secondary phase through the non-specific activation of the innate immune response. Glial scar formation (gliosis), a reactive cellular mechanism facilitated by astrocytes, also occurs during this phase. Synthetic steroids such as tibolone (Tib) have been proposed as a treatment for SCI since they exert neuroprotective effects in various models of central nervous system (CNS) injury. We studied the effect of Tib on locomotor functional recovery and the regulation of neuroinflammation and gliosis in an SCI model. We performed an SCI at the thoracic vertebrae nine in male Sprague Dawley rats. The animals received daily doses of Tib (1 or 2.5 mg per kg of body weight) administered orally. We quantified pro- and anti-inflammatory cytokine levels at the injury site and determined motor recovery using the Basso, Beattie, and Bresnahan (BBB) scale. Finally, we investigated the effect of Tib on the expression of glial fibrillary acidic protein (GFAP) and ionized calcium-binding adaptor molecule 1 (Iba-1), two markers of gliosis, using an immunohistochemistry assay. Our findings showed that Tib regulated pro- and anti-inflammatory cytokine levels at 3 h and 3, 7, and 14 days post-SCI. Furthermore, Tib administered orally for 15 days reduced gliosis markers and favored tissue preservation and motor function recovery after SCI. Full article

Immune checkpoint inhibitors (ICIs) have transformed the landscape of cancer therapy by reactivating immune surveillance mechanisms against tumor cells. In the context of oral squamous cell carcinoma (OSCC) and broader head and neck squamous cell carcinoma (HNSCC), agents such as pembrolizumab, durvalumab, and ipilimumab target PD-1, PD-L1, and CTLA-4, respectively. This review comprehensively examines their clinical efficacy, safety profiles, mechanisms of action, and therapeutic potential in OSCC management, with an emphasis on strategies to overcome therapeutic resistance. A systematic analysis of the literature was conducted, focusing on clinical outcomes, ongoing trials, and emerging combination therapies. Pembrolizumab has demonstrated significant improvements in overall survival (OS) and progression-free survival (PFS) in OSCC patients. Durvalumab, mainly utilized in locally advanced or recurrent disease, has shown survival benefit, particularly in combination or maintenance settings. Ipilimumab exhibits durable responses in advanced OSCC, with enhanced efficacy observed when used alongside nivolumab in dual checkpoint blockade regimens. Although both pembrolizumab and nivolumab target PD-1, they differ in clinical indications and regulatory approvals. Notably, ICIs are associated with immune-related adverse events (irAEs), requiring careful monitoring. Collectively, these agents represent promising therapeutic options in oral cancer, though future studies must prioritize the identification of predictive biomarkers and the development of optimized combination strategies to maximize therapeutic benefit while minimizing toxicity. Full article

Clubroot disease, caused by Plasmodiophora brassicae, poses a serious threat to global Brassica crop production. Orphan genes (OGs), which are species or lineage-specific and lack detectable homologs in other taxa, have been implicated in various biotic stress responses. Here, we identified a novel Brassica rapa-specific orphan gene, designated CROG1, that confers resistance to clubroot. Heterologous overexpression of CROG1 in Arabidopsis thaliana significantly enhanced resistance to P. brassicae. Transcriptomic profiling of CROG1-overexpressing lines highlighted the essential role of the phenylpropanoid biosynthesis pathway, showing upregulation of key lignin synthesis genes (including CCoAMT, CAD6, PER4, and AZI1) and defense-related regulators (RBOHC and WAKs). Weighted co-expression network analysis further corroborated the link between CROG1-mediated resistance and enhanced lignin deposition and cell wall reinforcement. Our findings establish CROG1 as a Brassica-specific orphan gene that enhances clubroot resistance via phenylpropanoid pathway activation. These results highlight the potential of orphan genes as novel genetic resources for breeding clubroot-resistant Brassica varieties, offering a sustainable strategy to mitigate yield losses caused by this devastating disease. Full article

Epidermal growth factor receptor (EGFR) inhibitors remain a cornerstone in the treatment of metastatic colorectal cancer with RAS and BRAF wild-type cancer. Yet, primary and acquired resistance limit their benefit for many patients. A growing body of evidence reveals that resistance is not random but rather driven by a complex network of molecular alterations that sustain tumor growth independent of EGFR signaling. These include amplification of ERBB2 (HER2) and MET, activation of the PI3K and AKT pathways, EGFR extracellular domain mutations, and rare kinase fusions. The concept of negative hyperselection has emerged as a powerful strategy to refine patient selection by excluding tumors with these resistance drivers. Multiple clinical trials have consistently shown that patients who are hyperselected based on comprehensive molecular profiling achieve significantly higher response rates and improved survival compared to those selected by RAS and BRAF status alone. Liquid biopsy through circulating tumor DNA has further transformed this landscape, offering a noninvasive tool to capture tumor heterogeneity, monitor clonal evolution in real time, and guide rechallenge strategies after resistance emerges. Together, negative hyperselection, ctDNA-guided monitoring, and emerging therapeutics define a precision-oncology framework for identifying, tracking, and overcoming resistance to anti-EGFR therapy in mCRC, moving the field toward more effective and individualized care. Looking ahead, the development of innovative therapeutics such as bispecific antibodies, antibody drug conjugates, and RNA-based therapies promises to further expand in this challenging clinical scenario. These advances move precision oncology in colorectal cancer from concept to clinical reality, reshaping the standard of care through molecular insights. Full article

To address the problem of low-frequency broadband vibration and noise encountered in engineering, a method for expanding the low-frequency band gap of locally resonant acoustic metamaterials is proposed based on the multi-mode coupling effect. A computational method for the band gap characteristics of second-order multi-mode acoustic metamaterials has been derived. By incorporating the vibrational modes obtained from band structure calculations, a systematic investigation of the formation mechanisms of multiple band gaps was conducted, revealing that the emergence of these multiple band gaps stems from the coupled resonance between elastic waves and distinct vibrational modes of the local resonator units. Furthermore, the influence of design parameter variations on the bandgap was investigated, and the strategy of realizing low-frequency multi-order bandgaps by increasing the order of local resonance units was examined. Finally, vibration tests were conducted on the second-, third-, and fourth-order multi-mode coupled acoustic metamaterials. The results demonstrated that these materials exhibit an expanded vibration band gap within the low-frequency range, and the measured frequency response aligns closely with the theoretical calculations. This type of acoustic metamaterial offers viable applicability for controlling low-frequency broadband vibrations. Full article

The recycling of glass presently poses several challenges, predominantly to the heterogeneous chemical compositions of various glass types, along with the waste glass particle size distribution, both of which critically influence the efficiency and feasibility of recycling operations. Numerous studies have elucidated the potential of converting non-recyclable glass waste into valuable materials thanks to the up-cycling strategies, including stoneware, glass wool fibres, glass foams, glass-ceramics, and geopolymers. Among the promising alternatives for improving waste valorisation of glass, alkali-activated materials (AAMs) emerge as a solution. Waste glasses can be employed both as aggregates and as precursors, with a focus on its application as the sole raw material for synthesis. This overview systematically explores the optimisation of precursor selection from a sustainability standpoint, specifically addressing the mild alkali activation process (<3 mol/L) of waste glasses. The molecular mechanisms governing the hardening process associated with this emerging class of materials are elucidated. Formulating sustainable approaches for the valorisation of glass waste is becoming increasingly critical in response to the rising quantities of non-recyclable glass and growing priority on circular economy principles. In addition, the paper highlights the innovative prospects of alkali-activated materials derived from waste glass, emphasising their emerging roles beyond conventional structural applications. Environmentally relevant applications for alkali-activated materials are reported, including the adsorption of dyes and heavy metals, immobilisation of nuclear waste, and an innovative technique for hardening as microwave-assisted processing. Full article

Cloud Data Centers (CDCs) are the foundation of the digital economy, enabling data storage, processing, and connectivity for different academia/industry/commerce activities and digital services worldwide. As a result, their consistent power supply and reliable performance are critical factors; however, few works have considered power consumption optimization based on intelligent workload allocation. To this end, the proposed paper presents a Federated Learning (FL)-based server workload allocation model for optimal power optimization. In this strategy, the servers are modeled based on their Central Processing Unit (CPU), memory, storage, and network usage. A global server is considered as the global model responsible for final workload allocation decisions. Each server acts as a client in the federated learning framework, sharing its derived parameters with the global model securely and federatedly. Finally, after ten epochs of the system running, the model could optimize the system, decrease the overall power consumption, and reduce the workload pressure in each server by distributing it to other servers. The model is evaluated using different Key Performance Indicators (KPIs), and an appendix is provided, including the full performance results, workload shifting logs, and server resource status. Overall, the suggested FL allocator model shows promise in significantly lowering power consumption and alleviating server workload efficiently. Full article

Background: Serum cystatin C is a promising biomarker for vascular risk, yet its nonlinear dose–response relationships and prognostic value in general populations remain unclear, particularly for stroke-specific outcomes. Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted in 1999–2002 cycles. A total of 11,610 participants were included in the primary analysis examining the cross-sectional association between cystatin C and stroke morbidity, using multivariate logistic regression models and odds ratios (ORs). Analyses utilized complete-case data (n = 11,610 for morbidity; n = 11,598 for mortality). Subsequently, 11,598 adults were retained for mortality endpoint analyses, which focused on the longitudinal association between cystatin C and stroke mortality, using cause-specific weighted multivariable Cox models and ratios (HRs). Restricted cubic splines identified nonlinear thresholds, and piecewise regression quantified risk gradients. Models were adjusted for sociodemographic/clinical/behavioral confounders. Results: Serum cystatin C exhibited a nonlinear dose–response relationship with stroke morbidity (p for nonlinear < 0.001), with an inflection point at 1.24 mg/L; below this threshold, each 0.1 mg/L increase conferred 13.84-fold higher odds (95% CI: 7.11–27.03, p < 0.001). For mortality, nonlinear thresholds were identified at 1.24 mg/L for all-cause/cause-specific mortality (HR = 6.73–10.60 per 0.1 mg/L increase, p < 0.001) and 1.81 mg/L for stroke-specific mortality. Conversely, cerebrovascular mortality demonstrated a linear association (HR = 1.43 per 1 mg/L increase, p = 0.008), though cystatin C independently predicted risk (HR = 1.38/continuous, p = 0.034 in fully adjusted models). Conclusions: This study identifies serum cystatin C as an independent predictor after full adjustment of stroke morbidity and all-cause and cardio-cerebrovascular mortality. Consequently, cystatin C emerges as a dual-purpose biomarker for early vascular injury detection in subclinical populations and integrated mortality risk stratification. Future research should validate these thresholds in prospective neuroimaging-confirmed cohorts and investigate interventions targeting cystatin C pathways to optimize preventive strategies. Full article

The pathogenic plant fungus Bipolaris maydis is responsible for southern corn leaf blight (SCLB), a widespread agricultural disease that significantly reduces maize yield in various agroecological zones. The present research focuses on characterizing the role of Trichoderma harzianum cellobiohydrolase (CBH) Thph2 in induced maize resistance to SCLB by triggering the production of reactive oxygen species (ROS) in leaves. First of all, we demonstrated the potential activities of Thph2 in triggering ROS burst and PDC in a model plant, Nicotiana benthamiana. Cell death, ROS burst, and programmed cell death (PCD) were observed in N. benthamiana leaves following transient expression of Thph2, indicating its defensive role against Sclerotinia sclerotiorum infection. The removal of the signal peptide from Thph2 resulted in the complete loss of the cell death phenotype and the accumulation of reactive oxygen species (ROS), confirming that Thph2 functions as a microbial elicitor that primes host plant immunity through ROS-mediated signaling, thereby inducing systemic resistance (ISR). Furthermore, the Thph2 protein conferred resistance against B. maydis in maize, significantly increasing reactive oxygen species (ROS) accumulation (1.5-fold compared to the control) at 48 h post-inoculation (hpi),and leading to the reduction in the lesion area of SCLB by 15.9% at 2 days post-inoculation (dpi). Our results demonstrated that the Thph2 protein markedly enhanced the expression of lox5, aos, and hpl in maize leaves, thereby confirming its function in triggering plant defense mechanisms primarily via the jasmonic acid signaling pathway. This research reveals new molecular mechanisms by which T. harzianum enhances plant defense and showcases the biocontrol efficacy of Thph2 against southern corn leaf blight (SCLB). Full article

In software development, software requirement engineering (SRE) is an essential stage that guarantees requirements are clear and unambiguous. However, incomplete inconsistency, and ambiguity in requirement documents often occur, which can cause project delay, cost escalation, or total failure. In response to these challenges, this paper introduces a machine learning method to automatically identify the risk levels of software requirements according to ensemble classification methods. The labeled textual requirement dataset was preprocessed utilizing conventional preprocessing techniques, label encoding, and oversampling with the synthetic minority oversampling technique (SMOTE) to handle class imbalance. Various ensemble and baseline models such as extra trees, random forest, bagging with decision trees, XGBoost, LightGBM, gradient boosting, decision trees, support vector machine, and multi-layer perceptron were trained and compared. Five-fold cross-validation was used to provide stable performance evaluation on accuracy, area under the ROC curve (AUC), F1-score, precision, recall, root mean square error (RMSE), and error rate. The bagging (DT) classifier achieved the best overall performance, with an accuracy of 99.55%, AUC of 0.9971 and an F1-score of 97.23%, while maintaining a low RMSE of 0.03 and error rate of 0.45%. These results demonstrate the effectiveness of ensemble-based classifiers, especially bagging (DT) classifiers, in accurately predicting high-risk software requirements. The proposed method enables early detection and mitigation of requirement risks, aiding project managers and software engineers in improving resource planning, reducing rework, and enhancing overall software quality. Full article

Real-time monitoring of the pile foundation pouring status is the key to ensuring the quality and reliability of cast-in-place bored pile foundation structures. In response to the technical challenge of difficult real-time monitoring and accurate evaluation of pile top morphology during concrete pouring, this paper proposes a method for detecting the cast-in-place bored pile top surface based on full waveform inversion. Firstly, a coupling equation between concrete sound waves and viscoelastic waves inside the borehole is constructed, forming a full waveform inversion method that considers multiple parameters of the complex environment inside the borehole. Subsequently, a pile top flatness factor that simultaneously considers the elevation and undulation characteristics of the pile top is constructed to achieve a comprehensive evaluation of the elevation between the center position and the center peripheral position of the bored pile top. Finally, the feasibility and accuracy of the proposed method are verified through indoor experiments. The results indicate that the detection method proposed in this article can not only accurately reflect the actual elevation of the pile top, ensuring the accuracy of the measurement data, but also achieve a comprehensive evaluation of the quality of the pile top considering the differences in the center and edge positions of the pile top, which can provide a new analysis method for quality control of bored piles. Full article

With the rise of environmental protection and health topics in recent years, microbial production of red pigments has gradually become a research hotspot. Red pigment possesses biological properties such as anticancer and antioxidant activities and has a wide range of potential applications in the fields of food and medicine. In this paper, a red pigment-producing strain was screened from rice soil to provide a reserve for obtaining natural and safe red pigments. Methods: The strain LSY1-2 was identified using morphological and 16S rDNA molecular biological identification. The fermentation conditions for red pigment production were optimised to improve pigment yield, and the best conditions were analysed using response surface methodology. Finally, the stabilisation conditions of red pigment were analysed to determine the difficulty of retention. Results: The molecular ecology was identified as the bacterium Arthrobacter sp. of the genus Arthrobacter. The optimal red pigment production medium for the strain was determined by a one-way test with the carbon source beef extract, the nitrogen source peptone, the inoculum size 2%, the temperature 27 °C, the pH value 7, and the rotational speed 160 rpm. Response surface optimisation determined the optimal red pigment production conditions as the incubation temperature of 26.43 °C, the pH value of 6.89, and the rotational speed of 162.77 rpm, which resulted in the yield of red pigment under these optimal conditions as 0.883 U/mL. The stability of red pigment was best under the condition without light, and poorer under conditions of above 50 °C, strong acid, strong alkali, and more than 3% oxidant, and Fe³⁺ had a greater effect on the stability. Conclusions: Strain LSY-1 can produce stable red pigment under the optimised red pigment-producing conditions, which provides a reference for the large-scale production of natural red pigment and subsequent related research. Full article

We investigated synchronization behavior using an experimental setup consisting of two metronomes placed on a platform floating over water. By setting the metronomes to oscillate perpendicular to the line between them, we observed three distinct modes of movement: in-phase synchronization, anti-phase synchronization, and synchronization with a fixed phase difference. While this last mode resembles phase-locking, it is important to distinguish that phase-locking typically refers to an oscillator’s response to external pacing, whereas the fixed phase difference observed in our study emerges from the mutual interaction between two metronomes. The frequencies of oscillations, and the placement of the metronomes are also changed to check the reliability of the new phenomenon. Even if we changed the material of the platform to a heavier one or turned around one of the metronomes, synchronization with a fixed time delay still was still observed. Drawing on previous research, we developed mathematical equations to model the coupled metronomes and performed numerical simulations that successfully reproduced all three observed phenomena. The simulation results showed excellent agreement with our experimental observations. These findings contribute to our understanding of coupled oscillators and may have potential applications in various fields. Full article