Search Results (1,887)

Redox signaling is central to plant adaptation, influencing metabolic regulation, stress responses, and developmental processes through thiol-based oxidative post-translational modifications (oxiPTMs) of redox-sensitive proteins. These modifications, particularly those involving cysteine (Cys) residues, act as molecular switches that alter protein function, structure, and interactions. Advances in mass spectrometry-based redox proteomics have greatly enhanced the identification and quantification of oxiPTMs, enabling a more refined understanding of redox dynamics in plant cells. In parallel, the emergence of computational modeling, artificial intelligence (AI), and machine learning (ML) has revolutionized the ability to predict redox-sensitive residues and characterize redox-dependent signaling networks. This review provides a comprehensive synthesis of methodological advancements in redox proteomics, including enrichment strategies, quantification techniques, and real-time redox sensing technologies. It also explores the integration of computational tools for predicting S-nitrosation, sulfenylation, S-glutathionylation, persulfidation, and disulfide bond formation, highlighting key models such as CysQuant, BiGRUD-SA, DLF-Sul, and Plant PTM Viewer. Furthermore, the functional significance of redox modifications is examined in plant development, seed germination, fruit ripening, and pathogen responses. By bridging experimental proteomics with AI-driven prediction platforms, this review underscores the future potential of integrated redox systems biology and emphasizes the importance of validating computational predictions, through experimental proteomics, for enhancing crop resilience, metabolic efficiency, and precision agriculture under climate variability. Full article

The construction of large-scale, dynamic datasets for specialized domain models often suffers with problems of low efficiency and poor consistency. This paper proposes a method that integrates multi-role collaboration with automated annotation to address these issues. The framework introduces two new roles, data augmentation specialists and automatic annotation operators, to establish a closed-loop process that includes dynamic classification adjustment, data augmentation, and intelligent annotation. Two supporting tools were developed: an image classification modification tool that automatically adapts to changes in categories and an automatic annotation tool with rotation-angle perception based on the rotation matrix algorithm. Experimental results show that this method increases annotation efficiency by 40% compared to traditional approaches, while achieving 100% annotation consistency after classification modifications. The method’s effectiveness was validated using the WATER-DET dataset, a collection of 1500 annotated images from the water conservancy engineering field. A model trained on this dataset achieved an F1-score of 0.9 for identifying water environment problems in rivers and lakes. This research offers an efficient framework for dynamic dataset construction, and the developed methods and tools are expected to promote the application of artificial intelligence in specialized domains. Full article

As an emerging energy-saving approach, bio-inspired drag reduction technology has become a key research direction for reducing energy consumption and greenhouse gas emissions. This study introduces the latest research progress on bio-inspired microstructured surfaces in the field of underwater drag reduction, focusing on analyzing the drag reduction mechanism, preparation process, and application effect of the three major technological paths; namely, bio-inspired non-smooth surfaces, bio-inspired superhydrophobic surfaces, and bio-inspired modified coatings. Bio-inspired non-smooth surfaces can significantly reduce the wall shear stress by regulating the flow characteristics of the turbulent boundary layer through microstructure design. Bio-inspired superhydrophobic surfaces form stable gas–liquid interfaces through the construction of micro-nanostructures and reduce frictional resistance by utilizing the slip boundary effect. Bio-inspired modified coatings, on the other hand, realize the synergistic function of drag reduction and antifouling through targeted chemical modification of materials and design of micro-nanostructures. Although these technologies have made significant progress in drag reduction performance, their engineering applications still face bottlenecks such as manufacturing process complexity, gas layer stability, and durability. Future research should focus on the analysis of drag reduction mechanisms and optimization of material properties under multi-physical field coupling conditions, the development of efficient and low-cost manufacturing processes, and the enhancement of surface stability and adaptability through dynamic self-healing coatings and smart response materials. It is hoped that the latest research status of bio-inspired drag reduction technology reviewed in this study provides a theoretical basis and technical reference for the sustainable development and energy-saving design of ships and underwater vehicles. Full article

Grassland degradation influences arthropod community structure and abundance, which, in turn, modulate element cycling in grassland ecosystems through predation and soil structure modification. In order to explore the influence of degradation on arthropods in Bayinbuluk alpine grassland, we selected four degraded transects (i.e., non-degraded: ND, lightly degraded: LD, moderately degraded: MD, and heavily degraded: HD) to collect soil samples and determine their composition, spatial distribution, and diversity patterns, in addition to the factors driving community change. Following identification and analysis, the following results were obtained: (1) A total of 342 soil arthropods were captured in this study, belonging to 4 classes, 11 orders, and 24 families. (2) With the intensification of degradation, the dominant groups exhibited significant alteration: the initial dominant groups were Pygmephoridae and Microdispidae; however, as the level of degradation became more severe, the dominant groups gradually shifted to Campodeidae and Formicidae, as these groups are more adaptable to environmental changes. (3) Common groups included six families, including Parasitoididae and Onychiuridae, and rare groups included 16 families, such as Macrochelidae. (4) As degradation intensified, both the species diversity and population size of the arthropod community increased. Our Redundancy Analysis (RDA) results demonstrated that the key driving factors affecting the arthropod community were soil organic carbon (SOC), electrical conductivity (EC), soil total nitrogen (TN), and available nitrogen (AN). The above results indicate that grassland degradation, by altering soil properties, increases arthropod diversity, induces alterations in the dominant species, and reduces mite abundance, with these changes being closely related to soil carbon and nitrogen contents. The results of this study provide basic data for understanding the changes in soil arthropod communities during the degradation of alpine grasslands and also offer support for the sustainable development of soil organisms in grassland ecosystems. Full article

CRISPR technology, which is derived from the bacterial adaptive immune system, has transformed traditional genetic engineering techniques, made strain engineering significantly easier, and become a very versatile genome editing system that allows for precise, programmable modifications to a wide range of microbial genomes. The economies of fermentation-based manufacturing are changing because of its quick acceptance in both academic and industry labs. CRISPR processes have been used to modify industrially significant bacteria, including the lactic acid producers, Clostridium spp., Escherichia coli, and Corynebacterium glutamicum, in order to increase the yields of bioethanol, butanol, succinic acid, acetone, and polyhydroxyalkanoate precursors. CRISPR-mediated promoter engineering and single-step multiplex editing have improved inhibitor tolerance, raised ethanol titers, and allowed for the de novo synthesis of terpenoids, flavonoids, and recombinant vaccines in yeasts, especially Saccharomyces cerevisiae and emerging non-conventional species. While enzyme and biopharmaceutical manufacturing use CRISPR for quick strain optimization and glyco-engineering, food and beverage fermentations benefit from starter-culture customization for aroma, texture, and probiotic functionality. Off-target effects, cytotoxicity linked to Cas9, inefficient delivery in specific microorganisms, and regulatory ambiguities in commercial fermentation settings are some of the main challenges. This review provides an industry-specific summary of CRISPR–Cas9 applications in microbial fermentation and highlights technical developments, persisting challenges, and industrial advancements. Full article

The Republican era of China witnessed three primary positions regarding Buddhist monastic robe reform. Taixu advocated preserving canonical forms (法服) for ritual garments while adapting regular robes (常服) to contemporary needs; Dongchu proposed diminishing ritual distinctions by establishing a tripartite hierarchical system—virtue-monk robes (德僧服), duty-monk robes (職僧服), and scholar-monk robes (學僧服); and Lengjing endorsed the full secularization of monastic robes. As a reformist leader, Taixu pursued reforms grounded in both doctrinal authenticity and contextual responsiveness. His initial advocacy for robe modifications, however, rendered him a target for traditionalists like Cihang, who conflated his measured approach with the radicalism of Dongchu’s faction. Ultimately, the broader Buddhist reform collapsed, with robe controversies serving as a critical lens into its failure. The reasons for its failure include not only wartime disruption and inadequate governmental support, but also the structural disadvantages of the reformists compared to the traditionalists, which proved decisive. This was due to the fact that the traditionalists mostly controlled monastic economies, wielded institutional authority, and commanded discursive hegemony, reinforced by lay Buddhist alignment. These debates crystallize the core tension in Buddhist modernization—the dialectic between “disenchantment” and “preservation of monastic discipline”. This dynamic of negotiated adjustment offers a vital historical framework for navigating contemporary Buddhism’s engagement with modernity. Full article

Fairness is a fundamental virtue in machine learning systems, alongside with four other critical virtues: Accountability, Transparency, Ethics, and Performance (FATE + Performance). Ensuring fairness has been a central research focus, leading to the development of various mitigation strategies in the literature. These approaches can generally be categorized into three main techniques: pre-processing (modifying data before training), in-processing (incorporating fairness constraints during training), and post-processing (adjusting outputs after model training). Beyond these, an increasingly explored avenue is the direct modification of existing algorithms, aiming to embed fairness constraints into their design while preserving or even enhancing predictive performance. This paper presents a comprehensive survey of classical machine learning models that have been modified or enhanced to improve fairness concerning sensitive attributes (e.g., gender, race). We analyze these adaptations in terms of their methodological adjustments, impact on algorithmic bias and ability to maintain predictive performance comparable to the original models. Full article

As tumor research has deepened, the deregulation of cellular metabolism has emerged as yet another recognized hallmark of cancer. Tumor cells adapt different biochemical pathways to support their rapid growth, proliferation, and invasion, resulting in distinct anabolic and catabolic activities compared with healthy tissues. Certain metabolic shifts, such as altered glucose and glutamine utilization and increased de novo fatty acid synthesis, are critical early on, while others may become essential only during metastasis. These metabolic adaptations are closely shaped by, and in turn remodel, the tumor microenvironment, creating favorable conditions for their spread. Anticancer metabolic strategies should integrate pharmacological approaches aimed at inhibiting specific biochemical pathways with well-defined dietary interventions as adjunctive therapies, considering also the role of gut microbiota in modulating diet and treatment responses. Given the established link between the consumption of foods rich in saturated fatty acids and sugars and an increased cancer risk, the effects of diet cannot be ignored. However, current evidence from controlled and multicenter clinical trials remains insufficient to provide definitive clinical recommendations. Further research using modern omics methods, such as metabolomics, proteomics, and lipidomics, is necessary to understand the changes in the metabolic profiles of various cancers at different stages of their development and to determine the potential for modifying these profiles through pharmacological agents and dietary modifications. Therefore, clinical trials should combine standard treatments with novel approaches targeting metabolic reprogramming, such as inhibition of specific enzymes and transporters or binding proteins, alongside the implementation of dietary restrictions that limit nutrient availability for tumor growth. However, to optimize therapeutic efficacy, a precision medicine approach should be adopted that balances the destruction of cancer cells with the protection of healthy ones. This approach, among others, should be based on cell type-specific metabolic profiling, which is crucial for personalizing oncology treatment. Full article

Background/Objectives: The Jackson/Cubbin scale is a recommended tool to assess the risk of pressure injury in intensive care unit (ICU) patients. This scale is deemed to have superior predictive validity compared to the Braden scale. Many Italian nurses struggle with reading and applying the tool in English. This language barrier results in a lack of use of the Jackson/Cubbin scale clinically, meaning that patients potentially experience worse outcomes. This study aims to translate the original English version of the Jackson/Cubbin scale into the Italian language, conduct a cultural adaptation, and verify its content validity. Methods: An observational study was conducted using Beaton’s five-step methodology: (1) forward translation, (2) synthesis, (3) back-translation, (4) expert committee approval using Fleiss’ Kappa (κ) index, and (5) pre-testing, where participants assessed item clarity on a dichotomous scale (clear/unclear). Items deemed unclear by 20% or more of the sample were revised. Content validity was assessed using the Content Validity Index (CVI). Results: Fleiss’ κ index was 0.74. Item 3 “PMH-affecting condition” was unclear to 36% of the sample and required revision. The item-level CVI (I-CVI) was >0.78 for each item. The scale-level CVI (S-CVI) and the scale-level CVI using the average method (S-CVI-Ave) were 0.92 and 0.94, respectively. Conclusions: The translation process resulted in a linguistically accurate scale requiring content modifications to reflect current evidence and reduce inter-rater variability. This may improve implementation of the Jackson/Cubbin scale in clinical practice for Italian nurses and reduce the incidence of pressure injury for ICU patients. Full article

Accurate detection of cotton pests and diseases is essential for agricultural productivity yet remains challenging due to complex field environments, the small size of pests and diseases, and significant occlusions. To address the challenges presented by these factors, a novel cotton disease and pest detection method is proposed. This method builds upon the YOLOv8 baseline model and incorporates a Multi-Scale Sliding Window Attention Module (MSFE) within the backbone architecture to enhance feature extraction capabilities specifically for small targets. Furthermore, a Depth-Separable Dilated Convolution Module (C2f-DWR) is designed to replace the existing C2f module in the neck of the network. By employing varying dilation rates, this modification effectively expands the receptive field and alleviates the loss of detailed information associated with the downsampling processes. In addition, a Multi-Head Attention Detection Head (MultiSEAMDetect) is introduced to supplant the original detection head. This new head utilizes diverse patch sizes alongside adaptive average pooling mechanisms, thereby enabling the model to adjust its responses in accordance with varying contextual scenarios, which significantly enhances its ability to manage occlusion during detection. For the purpose of experimental validation, a dedicated dataset for cotton disease and pest detection was developed. In this dataset, the improved model’s mAP50 and mAP50:95 increased from 73.4% and 46.2% to 77.2% and 48.6%, respectively, compared to the original YOLOv8 algorithm. Validation on two Kaggle datasets showed that mAP50 rose from 92.1% and 97.6% to 93.2% and 97.9%, respectively. Meanwhile, mAP50:95 improved from 86% and 92.5% to 87.1% and 93.5%. These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection. Full article

In the context of intensified construction and stricter requirements for the energy efficiency of buildings, the use of thermal insulation materials and technologies is becoming particularly important. One promising area in this field is the use of thermal insulation mixtures, which are versatile, adaptable, and highly reliable in operation. Mixtures based on fillers with a porous structure and materials that impart thermal insulation properties, which provide higher thermal insulation properties, are of great interest. However, the development of dry thermal insulation mixtures is hampered by insufficient study of their physical, mechanical, and operational characteristics. This article presents the results of research work on the development and study of dry building thermal insulation mixtures. A distinctive feature of the work is the creation of a composition of dry building thermal insulation mixtures based on local raw materials, such as diatomite, its thermal modification at a temperature of 900 °C, the use of expanded perlite sand, lime, and Portland cement. Research into the properties of modified diatomite has shown that its surface after thermal treatment differs from the surface of unburned diatomite in that it becomes more active and has a 3–4 times higher increase in strength. Modified diatomite and expanded perlite sand have low thermal conductivity, and this property was used in the creation of building thermal insulation mixtures, which was confirmed by research, as the thermal conductivity coefficient ranged from 0.128 to 0.152 W/m °C. The developed dry thermal insulation lime–cement mixture is intended for both interior and exterior finishing works, which is confirmed by the results obtained for determining the frost resistance of the solution and the frost resistance of the contact zone, and corresponds to the F35 grade and has a strength of up to 3.59 MPa. Full article

Although asphalt mixtures can be applied to railway tracks due to their viscoelastic properties, caution is required, as their ductility and brittleness are highly sensitive to temperature variations. In recent years, interest in the application of asphalt in railway infrastructure has increased, driven by the development of modified mixtures and the broader availability of performance-enhancing additives. Additionally, evaluation methods for railway tracks should be adapted to account for the distinct loading mechanisms involved, which differ from those of conventional roadways. In this study, the comprehensive properties of asphalt binders, mixtures, and testing methods—including physical and engineering characteristics—were assessed to improve the performance of asphalt concrete layers for potential applications in railroad infrastructure. The results of this study indicate that (1) the higher the performance grade (PG), the higher the indirect tensile strength (ITS) value achieved by the 13 mm mixture using PG76-22, which is higher than that of the PG64-22 mixture. This indicates that higher PG grades and modification contribute to improved tensile strength, beneficial for upper layers subjected to dynamic railroad loads. (2) The tensile strength ratio (TSR) increased from the unmodified mixture to over 92% in mixtures containing crumb rubber modifier (CRM) and styrenic thermoplastic elastomer (STE), demonstrating enhanced durability under freeze–thaw conditions. (3) Wheel tracking test results showed that modified mixtures exhibited more than twice the rutting resistance compared to PG64-22. The 13 mm aggregate mixtures also generally performed better than the 19 mm mixtures, indicating reduced permanent deformation under repeated loading. (4) It was concluded that asphalt is a suitable material for railroads, as its overall characteristics comply with standard specifications. Full article

Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data. Full article

The expansion of human activities can degrade natural habitats, thereby increasing threats to wildlife conservation. The wild populations of many species have declined due to the modification of natural habitats by humans. The Arabian wolf (Canis lupus arabs) is a subspecies of the gray wolf that is of conservation concern across its distribution range. The Arabian wolf is understudied in certain habitats (e.g., mountainous areas), which limits understanding of its overall ecology. Given its vulnerable conservation status, this study aimed to collect relevant data and information on incidents and potential threats facing this predator in the rugged mountainous habitats of western Tabuk province, Saudi Arabia, and how the effects of these threats can be minimized. In these mountain habitats Arabian wolves encounter various severe threats that challenge relevant conservation efforts. Observations of such threats—some of which result in wolf mortality—represent serious challenges to the survival of wild Arabian wolves. Conflicts with humans and livestock represent considerable threats that must be appropriately managed. Additionally, the potential association between Arabian wolves and free-ranging dogs requires further investigation. Various conservation scenarios and mitigation approaches can be applied to help reduce negative impacts on Arabian wolf populations and maximize their likelihood of survival. Overall, ensuring the persistence of such a unique desert-adapted apex predator in this ecosystem must become a conservation priority. Full article

The dynamic control of vibrations in skyscrapers is a critical consideration in sustainable building design, particularly in response to environmental excitations such as wind impact or seismic activity. Effective vibration neutralisation plays a crucial role in providing the safety of high-rise buildings. This research introduces an innovative concept for an active vibration damper that operates based on fluid dynamic transport to adaptively alter a skyscraper’s natural frequency, thereby counteracting resonant vibrations. A distinctive feature of this system is an adjustable-length cable mechanism, allowing for the dynamic modification of the pendulum’s effective length in real time. The structure, based on cable length adjustment, enables the PTMD to precisely tune its natural frequency to variable excitation conditions, thereby improving damping during transient or resonance phenomena of the building’s dynamic behaviour. A comprehensive mathematical model based on Lagrangian mechanics outlines the governing equations for this system, capturing the interactions between pendulum motion, fluid flow, and the damping forces necessary to maintain stability. Simulation analyses examine the role of initial excitation frequency and variable damping coefficients, revealing critical insights into optimal damper performance under varied structural conditions. The findings indicate that the proposed pendulum damper effectively mitigates resonance risks, paving the way for sustainable skyscraper design through enhanced structural adaptability and resilience. This adaptive PTMD, featuring an adjustable-length cable, provides a solution for creating safe and energy-efficient skyscraper designs, aligning with sustainable architectural practices and advancing future trends in vibration management technology. The study presented in this article supports the development of modern skyscraper design, with a focus on dynamic vibration control for sustainability and structural safety. It combines advanced numerical modelling, data-driven control algorithms, and experimental validation. From a sustainability perspective, the proposed PTMD system reduces the need for oversized structural components by providing adaptive, efficient damping, thereby lowering material consumption and embedded carbon. Through dynamically retuning structural stiffness and mass, the proposed PTMD enhances resilience and energy efficiency in skyscrapers, lowers lifetime energy use associated with passive damping devices, and enhances occupant comfort. This aligns with global sustainability objectives and new-generation building standards. Full article