Search Results (857)

Accelerating environmental degradation and the continued overshoot of planetary boundaries highlight the urgent need for scientifically grounded sustainability assessments that operate across scales. While the planetary boundaries framework provides a global reference for safe environmental limits, its translation to regional and local contexts remains a methodological and practical challenge. In response, this study presents a novel scalable framework for conducting regionally explicit assessments of absolute environmental sustainability, grounded in the planetary boundaries framework. The central objective is to enable scientifically robust and globally comparable evaluations that remain sensitive to local environmental and socioeconomic conditions. The method integrates historical environmental datasets, and satellite-based Earth observation, to assess environmental impacts at the regional scale. A structured three-step process is introduced: (1) regional thresholds are derived from historical reference conditions; (2) thresholds are validated using Earth observation; and (3) environmental impacts are quantified against the validated thresholds to detect transgressions. The framework was tested in the urban core of Kiruna, northern Sweden, across five planetary boundary indicators. The results reveal substantial boundary transgressions, most notably for genetic diversity, which reaches 269 extinctions per million species-years, and for land system change, where the regional threshold is fully exceeded. These findings illustrate both the analytical value and the methodological challenges of applying planetary boundaries at fine spatial scales. Kiruna, northern Sweden, was selected as a case study due to its role as a European mining center, its location within Sámi territories, and the overlap between resource extraction and settlement. The case study illustrates the difficulty of applying planetary boundaries at fine spatial scales. This highlights the need for careful interpretation and improved calibration when downscaling global thresholds to local conditions. Ultimately, the framework reveals the potential and limitations of regionalizing planetary boundaries, highlighting the importance of methodological transparency and contextual nuance in sustainability assessment. Full article

Background/Objectives: Adipose tissue depots located at different anatomical sites exert differential functions in response to adiposity and glucose intolerance. These fat depots exhibit distinct metabolic signaling patterns that may influence pathological fat accumulation, thereby affecting the efficacy of anti-obesity interventions. Nonetheless, the mechanisms underpinning depot-specific signaling and pathway responsiveness remain insufficiently understood. Methods: Kinase activity was characterized during the progression of adiposity across five adipose tissue depots in obese versus lean mice using the advanced PamGene kinome technology. Furthermore, kinase pathways in human preadipocytes and mouse 3T3-L1 preadipocytes were analyzed and compared with those in their differentiated, mature adipocytes. The kinases most significantly altered across adipose tissue depots were identified, revealing depot-specific combinations of hyperactive and hypoactive kinase pathways involved in adiposity. Results: Our findings demonstrate distinct kinase families that regulate specific fat depots, with potential implications for drug discovery and therapeutic resistance. Conclusions: This research presents a comprehensive adipokinome atlas, elucidates potential targets for developing fat-depot-specific anti-obesity therapies, and offers novel insights into the functional heterogeneity of adipose tissues. Full article

To address the limitations of zero-shot generalization in Vision-Language Navigation (VLN), this paper proposes a novel knowledge graph-driven reinforcement learning approach. Our method constructs a hierarchical, dynamically updated knowledge graph online during the agent’s real-time interaction with the environment, seamlessly aligning external semantic priors with continuous visual perception. By leveraging a Chain-of-Thought (CoT) prompting mechanism, the agent performs multi-hop reasoning to precisely locate target objects. Furthermore, we design an end-to-end optimized reinforcement learning framework that fuses multi-modal features and employs a task-oriented composite reward function. Extensive experiments in the AI2-THOR simulation environment demonstrate that the proposed method significantly improves navigation success rates in zero-shot settings. The results validate its robust generalization capabilities, particularly for unseen object categories and complex scene layouts. Full article

Background/objectives: Although informative, current insights into the inflammatory nature of colorectal cancer (CRC) have yet to have a meaningful impact on the prevention of, and development of novel therapies for, the treatment of this prevalent and challenging disease. Accordingly, the current study was focused on identifying putative, key, systemic, mostly pro-inflammatory biomarkers of metastatic CRC (mCRC) prognosis and outcome. Methods: Patients with mCRC (n = 38) and matched healthy controls (n = 30) were recruited to the study. A multiplex magnetic bead array system and an ELISA procedure were used to measure the plasma concentrations of selected cytokines (n = 25) and that of C-reactive protein (CRP) by immunonephelometry. Systemic inflammatory indices (n = 5) were derived from the hematological data. Results: Plasma levels of 17/25 of the cytokine biomarkers and CRP were found to be significantly elevated, while the neutrophil/lymphocyte ratio proved to be the most useful of the various inflammatory indices. Subgroup analysis of the data derived from the group of mCRC patients revealed that the intensity of the systemic inflammatory response was mostly unaffected by tumor location, age, gender, and treatment line. The exception was time to progression, with a shorter time (<120 days) being associated with increased levels of IL-6, IL-8 and TNF-α. Hierarchical cluster analysis of the data revealed a possible association with a small group of four cytokines, comprising IL-1β, IL-13, IL-6/CRP and TGF-β1. Conclusions: This study confirms a strong association of established mCRC with cytokine-driven systemic inflammation. Four of these cytokines, IL-1β/IL-13 IL-6/CRP, and TGF-β1, appear prominent and are possibly indicative of novel targetable therapeutic options. Full article

A growing number of studies have highlighted the various sensory interactions involved in the musical experience, as relationships between music and dimensions of taste, olfaction, sound, and visual qualities, such as associations between pitch and the size of images or objects, spatial location and frequency, and instrumental timbres and visual shapes. These studies share the premise that the way we relate to the musical phenomenon, whether in the processes of production, perception, or understanding, emerges from an integrated and intrinsically multisensory perceptual event. Nevertheless, because music is present daily in everyday life and because this experience is inherently subjective, such interactions tend to occur so naturally and seem so obvious that they have been relegated to common sense. On the other hand, evidence indicates that sensory interactions constitute a fundamental ancestral mechanism for cognitive and neuronal development governed by non-arbitrary tendencies, multiple variables, and patterns of predictability. The novel contribution of this review is to advance a dynamic theoretical model of multisensory musical experience that takes crossmodal correspondences as its central organising axis, articulated through three structuring principles (universality, congruence effect, hierarchical tendency) and their interaction with musical organisation, cognitive structure, and the sensory systems mobilised by music. A future research agenda is also proposed to broaden and deepen investigations in the field of music psychology and human development. Full article

Agrivoltaic systems co-locate photovoltaic (PV) arrays and crops, offering land-use efficiency and potential microclimate benefits, yet they introduce new challenges for computer-vision-based crop monitoring. PV structures produce strong, spatially varying shadows, specular reflections, and periodic occlusions that confound visual cues for diagnosing crop diseases and abiotic stresses. Meanwhile, agrivoltaic deployments are often distributed across farms and operators, making centralized data collection impractical due to privacy, ownership, and regulatory concerns. This paper proposes PrivAgriVolt, a novel privacy-preserving learning framework for agrivoltaic crop issue recognition that explicitly models PV-induced illumination and enables collaborative training without sharing raw images. The core algorithm integrates (i) a PV-geometry-conditioned shadow normalization module that fuses estimated array layout and sun-angle priors into a shadow-aware appearance canonization network, reducing illumination-induced domain shift across times and sites; (ii) a federated contrastive stress learner that aligns stress semantics across farms via prototype-based contrastive objectives while remaining robust to heterogeneous sensors and crop stages; and (iii) an adaptive privacy layer that combines secure aggregation with budget-aware gradient perturbation and client-level clipping to provide formal privacy guarantees while preserving fine-grained diagnostic performance. Extensive experiments on real agricultural vision benchmarks and agrivoltaic shadow variants demonstrate that PrivAgriVolt improves stress recognition and segmentation under PV shading while maintaining strong privacy–utility trade-offs. Full article

Mechanical construction of metro-connected aisles is a novel construction method in the field of metro engineering, and it is being gradually applied to practical projects at present. However, current research predominantly focuses on the mechanical response of tunnel structures, with insufficient theoretical investigations into ground settlement. To study the ground settlement law caused by the mechanical construction of the metro-connected aisle, the ground settlement was divided into the superposition of settlement caused by the construction of the main shield tunnels and the connected aisle. The modified Peck formula was used to calculate the ground settlement caused by tunnel excavation. Based on the integration of the Mindlin solution, the ground settlement caused by the jacking force of the cutterhead was solved, and the three-dimensional calculation formula for ground settlement was derived. Taking the NO. 1 connected aisle of Shenzhen Metro Line 8 as the research object, the accuracy of the calculation formula was verified through comparative analysis with three-dimensional numerical simulation results and in situ monitoring data, and good agreement was observed. The research results indicate that after the construction of a connected aisle, a wedge-shaped surface appears on the settlement surface at the location of the connected aisle. The surface settlement curve presents a “U”—shaped distribution; as the depth increases, the stratum settlement curve presents a “W”—shaped distribution. The stratum disturbance caused by the connected aisle is more significant in its longitudinal direction than in the transverse direction. The theoretical calculation results show that the maximum surface settlement generated by the construction of the connected aisle is 0.61 mm, accounting for about 15.6% of the total settlement value (3.9 mm), and is far below the control value adopted by Shenzhen Metro. The calculation formula proposed in this article can be used to evaluate the surface settlement caused by the construction of connected aisles. Full article

A systematic assessment of the human settlement suitability (HSS) and its structural resilience in metropolitan areas, from a spatial network perspective, is essential for understanding the spatial organization and evolutionary mechanisms of regional human settlement systems. It also supports the high-quality development of metropolitan areas. This study considers the Shenyang Metropolitan Area as the research object and constructs a comprehensive evaluation model of HSS from two dimensions: natural environmental suitability (NES) and human environmental suitability (HES). This study systematically analyzes the spatial distribution pattern of HSS, characteristics of its spatial association network, and its structural resilience, by integrating a modified gravity model, social network analysis (SNA), and structural resilience measurement methods. The results indicate that NES exhibits a high-west to low-east gradient, with high-value areas primarily located in peripheral regions with better ecological conditions. HES reveals a pronounced core–periphery structure, with high suitability concentrated in core cities and their adjacent suburban areas. Under the combined influence of NES and HES, the HSS forms a layered differentiation pattern dominated by core cities. The spatial association network of HSS has an overall low density and displays the coexistence of a core–periphery structure and proximity dependence, in which the HES network demonstrates strong cross-node transmission capacity, while the NES network is significantly constrained by geographical proximity. The structural resilience of the network is characterized by a moderate hierarchy, predominantly homophilic matching, limited transmission efficiency, and pronounced spatial differentiation in aggregation, indicating an overall pattern of highly connected cores with low aggregation and moderately or weakly connected nodes with high aggregation. The findings provide a scientific basis for optimizing the human settlements and enhancing regional resilience governance in metropolitan areas, while offering a novel analytical perspective for research on human settlement systems. Full article

Lycoris plants are known for their diverse flower colors, but the molecular mechanisms behind these variations remain unclear. In this study, we first used the CIELAB system to precisely measure flower color. We objectively defined the petals of Lycoris sprengeri as blue-purple (Bp) and compared them with the white petals of Lycoris longituba (W) and the red petals of Lycoris radiata var. pumila (R). Metabolomic analysis showed that specific kaempferol glycosides, including kaempferol-3-O-sophoroside and lonicerin, accumulated significantly in the blue-purple petals. Transcriptomic analysis revealed that genes related to flavonoid biosynthesis were generally more active in the colored petals (Bp and R). However, different expression patterns of key hydroxylase genes created a metabolic split. Specifically, the blue-purple petals showed high expression of LrF3′5′H (directing synthesis toward delphinidin) and LrFLS (promoting kaempferol accumulation), whereas the red petals mainly expressed LrF3′H (leading to cyanidin synthesis). Further investigation identified LrWRKY70 as a core transcription factor highly correlated with these flavonoid pathway genes. Crucially, we discovered a new long non-coding RNA, LncRNA401, located downstream of the LrWRKY70 antisense strand. It showed a strong positive correlation with LrWRKY70. Functional verification through transient overexpression demonstrated that LncRNA401 significantly increased the expression of LrWRKY70. This, in turn, broadly activated downstream flavonoid biosynthesis genes, including LrCHS, LrF3′5′H, LrFLS, and LrDFR. This cascade ultimately promoted the synthesis of anthocyanins and kaempferol derivatives, resulting in the unique blue-purple phenotype. Our results reveal a novel LncRNA401-LrWRKY70 regulatory module. This module plays a key role in metabolic reprogramming for flower color formation in Lycoris, providing important insights into plant secondary metabolism and valuable targets for breeding specific flower colors. Full article

Background/Objectives: Klebsiella pneumoniae (K. pneumoniae) is a leading cause of serious hospital-acquired and community-acquired infections, with limited treatment options, especially for immunocompromised and critically ill patients. No licensed vaccine is currently available. The FimA antigen, a key fimbrial subunit essential for bacterial adhesion and invasion, represents a promising vaccine target. However, little is known about the immunodominant antibody responses against invasive K. pneumoniae. This study aimed to evaluate the immunogenicity and protective efficacy of recombinant FimA protein, to fine-map its immunodominant linear B-cell epitopes, and to assess the individual and combined protective capacity of these epitopes against both standard and clinically isolated K. pneumoniae strains. Methods: A murine model of lethal K. pneumoniae challenge was used. Recombinant FimA protein was administered to evaluate immunogenicity and protective efficacy. Immunodominant linear B-cell epitopes were identified by overlapping peptide ELISA using immune antisera. The identified epitopes were synthesized and conjugated to keyhole limpet hemocyanin (KLH). Mice were immunized with individual epitope-KLH conjugates or a mixture of all four, then challenged with the standard strain ATCC700721 or with multiple clinical isolates of distinct multilocus sequence types (MLST). Epitope-specific antibody responses (total IgG and IgG subclasses) and survival rates were measured. Results: Immunization with full-length recombinant FimA conferred 90% protection against lethal challenge with the standard strain ATCC700721 and induced robust IgG1-dominant antibody responses. Four novel immunodominant linear B-cell epitopes were identified: FimA_97–114, FimA_103–120, FimA_109–126, and FimA_145–160. Structural mapping revealed that the first three epitopes reside within the α-helical region, while FimA_145–160 is located in the β-sheet domain. These epitopes are highly conserved, exhibiting 100% sequence identity across 36 diverse K. pneumoniae strains. Among individual epitope-KLH conjugates, FimA_109–126-KLH induced the highest epitope-specific antibody titers, followed by FimA_103–120-KLH. Immunization with a mixture of all four epitope-KLH conjugates elicited significant cross-protection against multiple clinical isolates, achieving survival rates of 60%, 50%, 50%, and 40% against strains 10CYZ, 13LGY, 19ZXQ, and 22CZY, respectively. Protective immunity was primarily associated with IgG1 subtype responses. Conclusions: This study provides the first fine-mapping and protective evaluation of immunodominant linear B-cell epitopes within K. pneumoniae FimA. The identification of highly conserved, functionally relevant B-cell epitopes and the demonstration of cross-protection conferred by a multi-epitope formulation underscore the potential of FimA-based epitope-driven vaccines. These findings offer a promising strategy for the development of broadly protective vaccines against K. pneumoniae infections. Full article

This study investigates the multi-criteria route optimization problem within complex urban expressway networks. The primary objective is to develop and evaluate a novel pathfinding approach by integrating a cost function weighted by the Delphi-Analytic Hierarchy Process (AHP) into the A* algorithm, thereby dynamically balancing operational efficiency and public safety. By employing the Delphi Technique with a panel of 17 experts, a specialized cost function was derived that incorporates twelve critical parameters, including traffic fluidity, population density, and chemical dispersion metrics modeled via Areal Location of Hazardous Atmosphere (ALOHA) This research applied the proposed model to a high-stakes Hazardous Material (HAZMAT) emergency response scenario to benchmark its performance against established baselines, specifically Dijkstra’s algorithm and Ant Colony Optimization (ACO). Simulation results demonstrate that the Delphi-weighted A* algorithm achieves an approximately 3.8% reduction in travel time relative to Dijkstra’s algorithm while enhancing expert-validated safety scores (a weighted metric of risk factors including population density and chemical dispersion) by approximately 8.6%. These findings provide a robust framework for algorithmic decision-support in time-critical logistics and infrastructure management. While numerically modest, these improvements are critical in HAZMAT scenarios, where even marginal time savings directly support the ‘Golden Hour’ principle and minor route adjustments can prevent catastrophic secondary exposure. Full article

Background/Objectives: Bitter taste perception is important for pig feeding behavior and survival. The type 2 taste receptors (T2Rs) are G protein-coupled receptors responsible for the sense of bitter taste perception in mammals. T2Rs are expressed in taste receptor cells located in the taste buds of the papillae of the tongue and in other tissues such as the gastrointestinal tract, respiratory epithelia, and immune system. In pigs, twelve T2R genes have previously been experimentally identified, although only a limited number of studies have investigated this gene family. We hypothesized that the full T2R gene repertoire in pigs has yet to be uncovered. Methods: Circumvallate papillae (CVP) were collected from 12 pigs, and a combination of bioinformatics analysis and experimental validation was used to identify and annotate T2R transcripts in the pig transcriptome. The CVP transcriptome was explored using reference-guided assembly to identify potential novel transcripts, and newly identified protein-coding transcripts were confirmed by PCR and Sanger sequencing. Results: The results confirmed significant expression of 10 of the 12 known T2Rs (3, 4, 7, 9, 10, 16, 20, 39, 41, and 60). Two novel T2R transcripts (ENSSSCT00000089410.2 and ENSSSCT00000091318.1) were discovered and referred to as T2R61 and T2R62. T2R62 contained larger exons than those annotated in the reference genome. The results also showed that porcine T2R20 is a member of the porcine T2R family highly similar to several human TAS2Rs, including TAS2R20 (TAS2R49). In total, the porcine T2R repository contains 14 transcripts supported by strong evidence. Conclusions: This study expands knowledge of the porcine T2R repertoire and provides insight into the genetic basis of taste perception, food selection, nutrition, and adaptation biology in pigs. Full article

High-quality maritime infrared images are crucial for accurate object detection, classification, and segmentation in maritime environments. However, maritime infrared images are often degraded by various types of noise, including non-uniform noise and detector non-uniformity-induced fixed-pattern noise (e.g., vertical stripe noise), which pose significant challenges for the aforementioned high-level vision tasks. A novel network, termed SWDNet (Spatial–Wavelet Joint Denoising Network), is proposed to jointly model spatial- and wavelet-domain features, enabling the effective enhancement of maritime infrared image quality while preserving fine image details. Two parallel sub-networks with distinct architectures are employed to extract complementary information for maritime infrared image denoising. In the upper branch, hierarchical spatial attention aggregation (HSAA) modules are employed at multiple scales to extract spatial features and adaptively assign importance weights to different spatial locations. The lower branch employs a Haar-based DWT for sub-band decomposition, a pixel-grouped self-attention module for boundary refinement, and parallel multi-scale horizontal convolutions to suppress vertical stripe noise in the HL sub-band. Finally, the directional edge enhancement (DEE) module employs learnable Sobel operators in conjunction with multi-layer convolutions to effectively extract and enhance directional edge features. Experimental results demonstrate that, compared with state-of-the-art methods, the proposed SWDNet achieves superior denoising performance on both synthetic and real maritime infrared datasets. Full article

During pandemic emergencies, demand for relief supplies in affected areas surges abruptly and evolves randomly and dynamically, resulting in highly asymmetric supply and demand. Ensuring timely and reliable supply requires robust decision-making under risk. This study addresses a stochastic multi-objective location-routing problem (LRP) that simultaneously considers demand uncertainty and travel time variability. A multi-scenario stochastic programming model is developed with three objectives: minimizing total system cost, minimizing total waiting time, and minimizing the composite conditional value at risk (CVaR–Rcomp) to capture tail risks under extreme scenarios. A novel regret-based risk mechanism is introduced to unify temporal and cost dimensions, enabling joint evaluation of uncertainties within a single framework. To solve this challenging high-dimensional problem, a reinforcement learning-enhanced NSGA-III (RL-NSGAIII) is proposed. Specifically, Q-learning generates high-quality initial solutions, which accelerate convergence and improve population diversity for NSGA-III. Case studies demonstrate that the proposed method outperforms traditional evolutionary algorithms in convergence efficiency and Pareto solution quality, while effectively revealing potential risk blind spots. The results provide quantitative decision support and robust optimization insights for emergency logistics networks operating under uncertain conditions. Full article

This research proposes a novel hardware-optimized design to accelerate Convolutional Neural Networks (CNNs) using Verilog HDL. The design is specifically developed for the DARKNET-19 system model, which serves as the backbone of the YOLOv3-tiny algorithm, a widely used framework for real-time object detection in dynamic environments. The CNN architecture was implemented in Verilog HDL and synthesized using Synopsys Design Compiler, with a focus on improving both object detection accuracy and hardware resource efficiency. The proposed design efficiently performs key CNN operations, including convolution, pooling, and activation, enabling faster real-time object detection compared to many existing methods. To improve performance, the hardware design incorporates parallel processing techniques, allowing multiple computations to be executed simultaneously. This significantly reduces the system latency and power consumption. The convolutional layers of the DARKNET-19 architecture are efficiently mapped onto the hardware platform, ensuring optimized data storage and fast memory access, which further enhances processing speed and detection accuracy. An innovative feature of the design is a 2-dimensional image preprocessing module that prepares input images before they are fed into the CNN. This preprocessing stage includes image resizing, brightness normalization, and color adjustment, which helps the CNN process visual data more effectively. After preprocessing, the images pass through several CNN layers. The convolutional layers extract key features from the images, while the pooling and activation layers refine these features to improve detection performance. Finally, the processed data is analyzed by the YOLOv3-tiny algorithm, which identifies and locates objects in the images with high precision. Experimental results demonstrate that the proposed high-speed and resource-efficient hardware architecture is well-suited for real-time object detection applications, particularly in highly dynamic and unpredictable environments. Full article