Search Results (605)

Seasonal dynamics in wetland landscapes are closely associated with habitat availability and are likely to influence the spatial organization and diversity of waterbird communities. However, most existing studies rely on static land-cover representations or single spatial scales, limiting our ability to characterize how waterbirds respond to seasonally shifting habitats across scales. Focusing on the Qilihai Wetland in Tianjin, China, we combined high-frequency waterbird surveys from 2019–2021 with multi-temporal, season-matched Sentinel-2 imagery and the Dynamic World dataset. Partial least squares regression (PLSR) was applied across a continuous spatial gradient (100–3000 m) to quantify scale-dependent statistical associations between landscape composition and configuration derived from satellite-mapped habitat mosaics on different functional groups. Waterbird diversity exhibited pronounced seasonal contrasts. During the breeding and post-fledging period, high-diversity assemblages were stably concentrated within core wetland areas, showing limited spatial variability. In contrast, during the wintering and stopover period, community distributions became increasingly dispersed, with elevated spatial heterogeneity and interannual variability associated with habitat reorganization. The scale of effect shifted systematically between seasons. In the breeding and post-fledging period, both waterfowl and waders responded predominantly to local-scale landscape factors (<800 m), consistent with nesting requirements and microhabitat conditions. During the wintering and stopover period, however, the characteristic response scale of waterfowl expanded to 1500–2000 m, suggesting stronger associations with broader landscape context, whereas waders remained closely linked to local-scale shallow-water and mudflat connectivity (~200 m). Functional traits played a key role in structuring these scale-dependent responses, with diving behavior and tarsus length being associated with strong constraints on habitat use. Overall, our results suggest that waterbird diversity patterns emerge from the interaction between seasonal habitat dynamics, landscape structure, and functional trait filtering, underscoring the need for phenology-informed, multi-scale conservation strategies that move beyond static spatial boundaries. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations. Full article

Glioblastoma (GBM) remains incurable due to its invasive growth and therapeutic resistance. While the neurogenic transcription factor-mediated reprogramming of glioma cells has been reported, pharmacological reprogramming offers a promising alternative due to its potential advantages for clinical translation. Using phenotype-driven screening, we identified a multi-target small-molecule cocktail DLC79 (DAPT, LDN193189, CHIR99021, I-BET762, and Isx9) that effectively reprograms human glioma cells into neuron-like cells by activating endogenous ASCL1 (174.4-fold) and remodeling the transcriptional landscape. This conversion led to the strong upregulation of neuronal markers (e.g., MAP2 and GAD67) and suppression of glial identity. Functionally, DLC79 treatment inhibited glioma malignancy in vitro, impairing proliferation, migration, invasion, and clonogenicity. In a subcutaneous xenograft model, brief pretreatment with DLC79 significantly attenuated the tumorigenic potential of glioma cells, reducing tumor bioluminescence by 56% and tumor mass by 47%. Our study establishes pharmacological reprogramming as a promising anti-glioma strategy that leverages neuronal conversion to reduce oncogenic properties, thereby initiating a novel therapeutic paradigm. Full article

Microwave sensing technology is rapidly advancing and increasingly finding its way into biomedical applications, promising significant improvements for medical care. Concurrently, the rise of artificial intelligence (AI) is enabling significant enhancements in the biomedical domain. Close scrutiny of the recent literature reveals intense activity in both fields, with particularly impactful outcomes deriving from the combined use of advanced microwave techniques and AI for biomedical monitoring. In this review, an up-to-date compilation, from the perspective of the authors, of the most significant works published on these topics in recent years is given, focusing on their integration and current challenges. With the objective of analyzing the current landscape, we survey and compare state-of-the-art biosensors and imaging systems at all healthcare levels, from outpatient contexts to specialized medical equipment and laboratory analysis tools. We also delve into the relevant applications of AI in medicine for processing microwave-derived data. As our core focus, we analyze the synergistic integration of AI in the design of microwave devices and the processing of the acquired data, which have shown notable performances, opening new avenues for compact, affordable, and multi-functional medical devices. We conclude by synthesizing the prevailing technical, algorithmic, and translational challenges that must be addressed to realize this potential. Full article

We developed OmicIntegrator, a broadly adaptable pipeline designed to standardize and integrate publicly available transcriptomic, proteomic, and phosphoproteomic datasets. We applied this workflow to Arabidopsis thaliana etiolated seedlings to identify protein kinases and phosphatases relevant to skotomorphogenic development, a phase during which seedlings rely on tightly regulated signaling networks to ensure survival in darkness. This meta-analysis provided a comprehensive view of gene and protein expression, revealing discrepancies between transcript and protein abundance, suggesting post-transcriptional and post-translational regulation. By integrating multiple datasets, OmicIntegrator reduces experimental bias and enables the detection of phosphorylation events that may be missed in single-condition studies. Distinct phosphorylation patterns were detected across different protein kinase families. Motif enrichment analysis showed a strong overrepresentation of RxxS motifs among phosphosites in protein phosphatases and microtubule-associated proteins, consistent with potential regulation by calcium-dependent protein kinases (CPKs). Across omics layers, CPK3 and CPK9 repeatedly emerged as prominent candidates, highlighting them as priorities for future functional studies in skotomorphogenesis. Overall, our results demonstrate the power of OmicIntegrator as a flexible framework to contextualize signaling landscapes and identify robust patterns and candidate genes and for generating testable hypotheses from integrated multi-omics data in plant developmental biology. Full article

Extreme wildfires are becoming increasingly frequent and severe across many regions worldwide, driven by climate change, land-use transitions, and long-standing fire-suppression legacies. In this context, Nature-based Solutions (NbS)—defined as actions that work with ecological processes to address societal challenges while providing biodiversity and socio-economic benefits—offer a promising yet underdeveloped pathway for enhancing wildfire resilience. This Special Issue brings together eleven contributions spanning empirical ecology, landscape configuration, simulation modelling, spatial optimisation, ecosystem service analysis, governance assessment, and community-based innovation. Collectively, these studies demonstrate that restoring ecological fire regimes, promoting multifunctional landscapes, and integrating advanced decision support tools can substantially reduce wildfire hazard while sustaining ecosystem functions. They also reveal significant governance barriers, including fragmented policies, limited investment in prevention, and challenges in incorporating social demands into territorial planning. By synthesising these insights, this editorial identifies several strategic priorities for advancing NbS in fire-prone landscapes: mainstreaming prevention within governance frameworks, strengthening the science–practice interface, investing in long-term socio-ecological monitoring, managing trade-offs transparently, and empowering local communities. Together, the findings highlight that effective NbS emerge from the alignment of ecological, technological, institutional, and social dimensions, offering a coherent pathway toward more resilient, biodiverse, and fire-adaptive landscapes. Full article

Enhancer of zeste homologs inhibitory protein (EZHIP) is a eutherian-specific protein, with poorly defined developmental functions and physiological expression restricted to germ cells. Its aberrant re-expression characterizes posterior fossa ependymoma subtype A and a subset of diffuse midline gliomas with wild-type histone H3—aggressive pediatric brain tumors marked by global loss of the repressive H3 lysine 27 trimethylation (H3K27me3). Functionally analogous to the H3 lysine 27 to methionine (H3K27M) oncohistone, EZHIP inhibits Polycomb repressive complex 2 (PRC2), altering genome-wide H3K27me3 distribution and fate commitment. Unlike H3K27M, EZHIP is epigenetically silenced under physiological conditions yet inducible, suggesting context-dependent oncogenic roles. Its intrinsically disordered structure enables multifunctional interactions and biological versatility. Beyond brain tumors, EZHIP has emerged as an oncogenic driver in osteosarcoma, underscoring broader relevance across cancers. This review integrates current insights into EZHIP—from gene discovery and the mechanism of PRC2 inhibition to its emerging roles in metabolism, DNA repair, 3D chromatin regulation, and development. We outline EZHIP’s clinico-pathological significance in pediatric and adult malignancies, with an emphasis on EZHIP-driven hindbrain tumors. Finally, we discuss therapeutic opportunities, from the direct targeting of intrinsically disordered proteins to the indirect modulation of EZHIP-associated epigenetic and metabolic landscapes, highlighting implications for tumor evolution and precision oncology. Full article

Under the dual pressures of global climate change and accelerating urbanization, landscape design has been tasked with the critical mission of enhancing urban environmental resilience and ecological livability. However, conventional design practices often struggle to efficiently integrate complex sustainability norms with aesthetic creativity, leading to a disconnect between form and function. To address this issue, this study proposes and validates an AI-enabled sustainability decision-support framework. The framework is based on a “Generative-Critical” multi-agent workflow that enables “Self-Correcting” iterative optimization of design schemes through a built-in expert knowledge base and a quantitative scorecard. The framework’s effectiveness was validated through a cultural park case study and a blind evaluation by 10 experts. It guided a design from an initial concept with only aesthetic forms and lacking effective stormwater management, to an ecologically integrated scheme that strategically incorporated bioretention ponds at key nodes and converted hard plazas into permeable pavements. This transformation significantly elevated the scheme’s sustainability score from 59.3 to 88.0 (p < 0.001), while the framework itself achieved a high system usability scale (SUS) score of 85.5. These results confirm that the proposed “Generative-Critical” mechanism can effectively guide AIGC to adhere to ecological-technical norms and constraints while pursuing aesthetic innovation, thereby achieving a scientific integration of aesthetic form and ecological function at the early conceptual design stage. This study offers a scalable methodology for AI-assisted sustainable design and provides a novel intelligent tool for creating resilient urban landscapes that possess both environmental performance and aesthetic value. Full article

Redox (reduction–oxidation) processes underlie all forms of life and are a universal regulatory mechanism that maintains homeostasis and adapts the organism to changes in the internal and external environments. From capturing solar energy in photosynthesis and oxygen generation to fine-tuning cellular metabolism, redox reactions are key determinants of life activity. Proteins containing sulfur- and selenium-containing amino acid residues play a crucial role in redox regulation. Their reversible oxidation by physiological oxidants, such as hydrogen peroxide (H₂O₂), plays the role of molecular switches that control enzymatic activity, protein structure, and signaling cascades. This enables rapid and flexible cellular responses to a wide range of stimuli—from growth factors and nutrient signals to toxins and stressors. Mitochondria, the main energy organelles and also the major sources of reactive oxygen species (ROS), play a special role in redox balance. On the one hand, mitochondrial ROS function as signaling molecules, regulating cellular processes, including proliferation, apoptosis, and immune response, while, on the other hand, their excessive accumulation leads to oxidative stress, damage to biomolecules, and the development of pathological processes. So, mitochondria act not only as a “generator” of redox signals but also as a central link in maintaining cellular and systemic redox homeostasis. Redox signaling forms a multi-layered cybernetic system, which includes signal perception, activation of signaling pathways, the initiation of physiological responses, and feedback regulatory mechanisms. At the molecular level, this is manifested by changes in the activity of redox-regulated proteins of which the redox proteome consists, thereby affecting the epigenetic landscape and gene expression. Physiological processes at all levels of biological organization—from subcellular to systemic—are controlled by redox mechanisms. Studying these processes opens a way to understanding the universal principles of life activity and identifying the biochemical mechanisms whose disruption causes the occurrence and development of pathological reactions. It is important to emphasize that new approaches to redox balance modulation are now actively developed, ranging from antioxidant therapy and targeted intervention on mitochondria to pharmacological and nutraceutical regulation of signaling pathways. This article analyzes the pivotal role of redox balance and its regulation at various levels of living organisms—from molecular and cellular to tissue, organ, and organismal levels—with a special emphasis on the role of mitochondria and modern strategies for influencing redox homeostasis. Full article

Type 2 diabetes mellitus (T2DM) involves chronic hyperglycemia, insulin resistance, low-grade inflammation, and oxidative stress that drive cardiometabolic and renal damage despite current therapies. Sodium–glucose cotransporter (SGLT) inhibitors have reshaped the treatment landscape, but residual risk and safety concerns highlight the need for new agents that combine glucose-lowering efficacy with redox–inflammatory modulation. LASSBio-1986 is a synthetic N-acylhydrazone (NAH) derivative designed as a gliflozin-like scaffold with the potential to interact with SGLT1/2 while also influencing oxidative and inflammatory pathways. Here, we integrated in silico and in vivo approaches to characterize LASSBio-1986 as a multifunctional antidiabetic lead in murine models of glucose dysregulation. PASS and target class prediction suggested a broad activity spectrum and highlighted transporter- and stress-related pathways. Molecular docking indicated high-affinity binding to both SGLT1 and SGLT2, with a modest energetic preference for SGLT2, and ADME/Tox predictions supported favorable oral drug-likeness. In vivo, intraperitoneal LASSBio-1986 improved oral glucose tolerance and reduced glycemic excursions in an acute glucose challenge model in C57BL/6 mice, while enhancing hepatic and skeletal muscle glycogen stores. In a dexamethasone-induced insulin-resistance model, LASSBio-1986 improved insulin sensitivity, favorably modulated serum lipids, attenuated thiobarbituric acid-reactive substances (TBARS), restored reduced glutathione (GSH) levels, and rebalanced pro- and anti-inflammatory cytokines in metabolic tissues, with efficacy broadly comparable to dapagliflozin. These convergent findings support LASSBio-1986 as a preclinical, multimodal lead that targets SGLT-dependent glucose handling while mitigating oxidative and inflammatory stress in models relevant to T2DM. Chronic disease models, formal toxicology, and pharmacokinetic studies, particularly with oral dosing, will be essential to define its translational potential. Full article

Marine fog is a key non-rainfall water source that sustains microbial activity and transports dissolved nutrients inland, influencing early soil development in hyperarid ecosystems. However, the mechanisms through which sustained fog inputs drive soil surface modification and biocrust formation remain poorly understood. This study evaluated the effects of long-term fog augmentation on soil surface development, biocrust dynamics, and associated microbial communities in the Atacama Desert. We implemented a four-year fog addition field experiment with three sampling times (T0, T24, T48) to assess changes in soil physicochemical properties, biocrust composition, and the integrated multi-diversity of archaea, bacteria, fungi and protist. Sustained fog input transformed bare soils into biological soil crusts, particularly lichen- and moss-dominated stages. This transition was accompanied by increases in soil nitrogen, variations in organic matter accumulation, a shift from alkaline to near-neutral pH, and improvements in soil stability and water retention. Multi-diversity increased over time and was positively associated with ecosystem variables linked to water availability, structural stabilization, and decomposition. These functions, integrated into an ecosystem multifunctionality index, also increased under prolonged fog input, revealing a positive relationship between multifunctionality and multi-diversity. Overall, the results demonstrate that sustained fog input strongly enhances early soil surface development and biocrust establishment, highlighting the ecological importance of marine fog in shaping biodiversity and ecosystem functioning in hyperarid landscapes. Full article

The adaptive reuse of industrial heritage is increasingly recognized as an effective low-carbon strategy that reduces resource consumption, lowers embodied carbon emissions, and supports sustainable urban transitions. Developing appropriate reuse strategies, however, requires a robust understanding of heritage value. As material evidence of China’s modern industrialization, railway-associated industrial heritage possesses the characteristics of linear cultural heritage. Yet systematic and multi-scalar value assessments from a linear heritage perspective remain limited. Focusing on the Guanzhong Section of the Longhai Railway—one of the most representative industrial development axes in Northwest China—this study establishes a two-level value assessment framework and conducts a comprehensive evaluation of fourteen textile industrial heritage units. At the individual level, five dimensions—historical significance, architectural features, structural integrity, authenticity, and rarity—were assessed through field investigation, and type-specific weights were introduced to correct structural imbalances between quantity and value across building categories. At the unit level, the Analytic Hierarchy Process (AHP) was employed to determine the weights of spatial–functional integrity, process completeness, railway connectivity, industrial landscape characteristics, and the integrated individual-level value. The results show that factory workshops and warehouses consistently exhibit the highest value, whereas structures and residential buildings, despite their numerical dominance, contribute relatively little. Spatially, a clear west–east gradient emerges: high-value units cluster in Baoji and Xi’an, medium-value units in Xianyang, and low-value units mainly in Weinan and surrounding counties. The findings indicate that textile industrial heritage along the Guanzhong Section forms a railway-linked linear cultural heritage system rather than isolated sites. The proposed evaluation framework not only supports heritage identification and conservation planning but also provides a theoretical basis for promoting low-carbon adaptive reuse of existing industrial buildings. Full article

Understanding the formation logic and spatial organization of vernacular settlements requires analytical approaches that capture both morphological structures and the cognitive rules underlying residents’ interactions with space. However, existing research on rural spatial patterns has paid limited attention to the perceptual and cognitive mechanisms through which spatial genes are recognized, maintained, and reproduced. This gap limits the development of generalizable and bottom-up methods for interpreting and transmitting rural spatial characteristics. To address this gap, this study proposes a cognition-driven analytical framework supported by spatial analysis for rural space gene extraction and transmission. The framework consists of five interrelated components: environmental cognition, spatial element identification, system coupling, space gene extraction, and transmission mechanisms. The Guanzhong Region in Northwest China is selected as a representative case to examine the multi-scale spatial structure of vernacular settlements. The results reveal three major findings. (1) The proposed framework effectively links physical spatial features with local perceptual structures, enabling the identification of key elements constituting rural space gene. (2) Three categories of representative space gene and seven core morphological and functional factors are extracted through the coupled analysis of nature–settlement systems. (3) Three adaptive transmission mechanisms—element replication and reinforcement, recombination of disrupted elements, and controlled adjustment of characteristic elements—are identified to support spatial renewal while maintaining local distinctiveness. This research contributes a structured, scalable, and replicable workflow for rural space gene analysis and enhances the application of cognitive principles in geospatial modeling. The findings provide methodological and practical support for rural revitalization, cultural landscape conservation, and vernacular settlement planning in inland agrarian regions undergoing rapid transformation. Full article

The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images from sources (MERIS, AVHRR, SPOT, PROBA, and Sentinel-3). In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions under the Common Agricultural Policy (CAP) 2023–2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protecting biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes. Our study highlights that the CAP for mountain farms should be targeted, long-term, and compensatory, so as to compensate for the naturally unfavorable farming conditions and support their multifunctional role. The most important assumptions of CAP for mountain farms are a fair system of compensatory payments (LFA/ANCs), support for local and high-quality production, income diversification, and investments adapted to mountain conditions. Full article