Search Results (43,325)

Allosteric modulation has emerged as a powerful strategy for achieving superior selectivity and safety in drug discovery and protein function regulation. Unlike highly conserved orthosteric sites, allosteric pockets are structurally diverse and less evolutionarily constrained, making them particularly suitable for modulation by designed miniproteins. Miniproteins can provide extended binding interfaces and high affinity for shallow, dynamic, or cryptic regulatory surfaces that are often inaccessible to small molecules. Recent advances in artificial intelligence (AI) are transforming this field through deep learning-based structure prediction and generative modeling. These AI-driven approaches enable the identification of allosteric hotspots, characterization of conformational ensembles, and de novo design of structured miniprotein binders. They are rapidly expanding the landscape for designing selective modulators across diverse allosteric targets, including GPCRs, receptor tyrosine kinases, nuclear receptors, ion channels, and other protein–protein interaction systems. This review summarizes state-of-the-art AI-driven computational methodologies for designing miniproteins as potential allosteric modulators and discusses their current challenges and future opportunities in allosteric drug discovery. Full article

Accurate crack segmentation is critical for automated infrastructure inspection but remains challenging due to the inherent conflict between preserving fine-grained geometric details and modeling global semantic context. Existing deep learning approaches typically encode both requirements within a single hierarchical representation, leading to irreversible boundary degradation or fragmented predictions under complex backgrounds. To address this limitation, we propose DCDRNet, a detail–context decoupled network that explicitly separates geometry-sensitive and context-aware representations into parallel encoding streams. The Detail Encoder maintains high-resolution features to preserve thin crack boundaries, while the Context Encoder performs adaptive global reasoning to reinforce structural continuity. Their controlled interaction enables effective integration of local precision and long-range context without representational interference. Extensive experiments on three public crack segmentation benchmarks demonstrate that DCDRNet consistently outperforms state-of-the-art methods in accuracy and robustness, achieving superior performance especially on challenging datasets with thin and fragmented cracks. Moreover, DCDRNet delivers a favorable accuracy–efficiency trade-off, combining compact model size with near real-time inference speed, making it well-suited for practical deployment in real-world inspection scenarios. Full article

Unmanned Aerial Vehicle imagery often suffers from severe spatial detail degradation due to sensor limitations and motion blur, hindering downstream vision tasks. To address this, we propose a lightweight super-resolution framework leveraging a Tiny-Transformer backbone enhanced by a multi-domain feature fusion strategy. Specifically, we jointly model spatial structural semantics and frequency domain texture priors via a cross-domain fusion attention mechanism, enabling coordinated restoration of global consistency and local details. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on standard benchmarks, achieving significant gains in Peak Signal-to-Noise Ratio and structural similarity index while maintaining low computational cost. Notably, the model exhibits superior robustness in reconstructing high-frequency textures common in aerial scenes. This work provides an efficient, deployable solution for enhancing visual fidelity in resource-constrained applications such as urban planning and precision agriculture. Full article

This study evaluates the effectiveness of laser cleaning techniques for the non-contact removal of unwanted deposits from the surface of contemporary urban mural paintings. Two sets of mock-up samples, painted with popular graffiti spray paints on lime-based plaster, and artificially contaminated, were subjected to various cleaning procedures using Nd:YAG lasers operated in Q-switched (QS), long Q-switched (LQS) or short free-running mode (SFR). A multi-analytical approach—including X-ray fluorescence spectroscopy (XRF), Fourier-transform infrared spectroscopy (FTIR), colorimetry, and hyperspectral imaging (HSI)—was used to identify pigments and binders, and to evaluate cleaning efficiency and pigment preservation. XRF and FTIR were useful in understanding the composition of the sprays, while colorimetric ΔE values quantified cleaning efficiency and potential damage, and hyperspectral reflectance and LSU (linear spectral unmixing) abundance maps provided spatial distribution insights into contaminant removal and pigment preservation. The results demonstrate that laser cleaning effectiveness and selectivity are strongly dependent on the operational regime and fluence. In particular, long Q-switched laser irradiation at moderate fluence levels achieved effective contaminant removal with minimal chromatic and chemical alteration of the original paint layers. These findings support the development of tailored, sustainable, and non-contact laser cleaning protocols for the conservation of contemporary urban murals and contribute to the establishment of objective, multi-parameter criteria for evaluating cleaning outcomes in street art conservation. Full article

Background and Objectives: Male infertility has emerged as a growing global health concern, contributing to 20–30% of all infertility cases. It is a multifactorial condition, arising from genetic, endocrine, structural, environmental and lifestyle factors. This narrative review synthesizes current evidence on epidemiology, diagnostic advances and therapeutic strategies while highlighting emerging trends and research priorities. Materials and Methods: This review adheres to SANRA guidelines. Literature was sourced from PubMed, Saudi Digital Library, Google Scholar, and PsycINFO using MeSH terms including “Male Infertility,” “Diagnosis,” “Treatment,” and “Epidemiology.” Results: Diagnostic evaluation of male infertility includes clinical assessment, advanced semen analysis, imaging techniques, hormonal assays and molecular testing. Despite significant advances in the evaluation of male infertility, idiopathic causes (30–40%) remain challenging. Management strategies include lifestyle modifications, medical therapies including hormones and drugs, surgical interventions, and assisted reproductive technologies (ARTs). However, outcomes remain suboptimal in idiopathic and severe cases, particularly regarding sperm DNA fragmentation and environmental exposures. Conclusions: Substantial knowledge gaps exist in male infertility, particularly in idiopathic cases, molecular mechanisms of environmental pollutants, and long-term ART offspring outcomes. Future research priorities include: (1) molecular and epigenetic biomarkers for improved diagnosis and prognosis; (2) environmental exposure assessment and mitigation strategies; (3) metabolomics-guided personalized therapies; (4) regenerative medicine approaches including spermatogonial stem cell therapy; and (5) multidisciplinary integrative care models. Addressing these gaps through coordinated research and clinical innovation is essential for improving male reproductive health globally. Full article

Reliable prediction of asphalt pavement performance is essential for scientific maintenance decision-making. However, current methodologies have two primary challenges that represent significant research gaps: a heavy reliance on high-dimensional multi-source data—which is often inaccessible in resource-constrained remote regions—and the inability of traditional deep learning models to adequately capture nonlinear bidirectional temporal correlations within short-time-series pavement data. To address these limitations, this study proposes a hybrid CNN–BiLSTM–Attention architecture. The model was trained using a four-year dataset (2067 records from Xinjiang) of Pavement Condition Index (PCI) and Riding Quality Index (RQI) scores to predict fifth-year performance. Benchmarked against four state-of-the-art models, the proposed method demonstrated superior accuracy: PCI predictions achieved an R² of 0.837 (a 1.7% improvement) and a Mean Absolute Error (MAE) of 5.31 (a 0.57% reduction) compared to the second-best model. Similarly, RQI predictions yielded an R² of 0.855 and an MAE of 1.84, representing a 1.1% increase in accuracy and a 5.6% reduction in error, respectively. By obviating the dependency on multi-source data, this approach reduces the data acquisition and processing overhead by over 80%. Consequently, this research fills a critical gap in single-source, short-time-series prediction and provides a robust, data-driven solution for infrastructure maintenance in remote areas. Full article

Cellular survival and adaptability depend on the dynamic regulation of proteins—the central actors of biological systems. Through mechanisms such as post-translational modifications, protein turnover, and the formation of membraneless organelles, cells can sense and respond to a variety of stressors. Recent advances in artificial intelligence and chemical biology have provided powerful tools to study and manipulate these processes, paving the way for novel therapeutic strategies in cancer. This review explores how cells “tame” their proteome in response to stress by coordinating protein synthesis, modification, degradation, and structural organization to maintain functional resilience. Full article

Water electrolysis is a key technology for sustainable hydrogen production and a cornerstone of future low-carbon energy systems. However, large-scale deployment is constrained not only by efficiency and cost, but increasingly by the sustainability and availability of materials used in electrocatalysts and membranes. This review provides a materials-centric assessment of state-of-the-art and emerging electrocatalysts for alkaline (AEL), proton exchange membrane (PEM), and solid oxide electrolysis (SOEC) technologies, emphasizing the interdependence of performance, durability, cost, and sustainability. Electrocatalyst activity and stability are linked to cell- and stack-level efficiency, energy demand, and the levelized cost of hydrogen. Life cycle assessment (LCA) and resource criticality analyses are integrated to quantify environmental impacts, supply risks, and recycling potential of key materials, including platinum group metals, nickel, rare earth elements, and ceramic oxides. Particular attention is given to recycling and circularity strategies, which are essential for mitigating material scarcity and reducing upstream emissions, especially in PEM electrolyzers. Emerging catalyst concepts such as single-atom catalysts, high-entropy alloys, and noble-metal-free systems are discussed as promising pathways to reduce critical material dependence. The review concludes by highlighting the need for integrated material–technology–system approaches to enable efficient, scalable, and truly sustainable hydrogen production. Full article

The inherent bottleneck of the von Neumann architecture and the limited power budget of edge devices necessitate energy-efficient hardware solutions for artificial intelligence. Memristor-based In-Memory Computing (IMC) has emerged as a promising candidate; however, the high-power consumption of peripheral circuits, particularly Analog-to-Digital Converters (ADCs), and the reliability issues of memristive devices remain significant challenges. In this paper, we propose a hybrid Convolutional Spiking Neural Network (CSNN) architecture designed for resource-constrained edge computing. Our approach integrates digital Non-Leaky Integrate-and-Fire (NLIF) neurons with Knowm Self-Directed Channel (SDC) memristor-based synapses in a 1T1R crossbar array. To maximize power efficiency, we replace conventional high-resolution ADCs with a streamlined readout circuit utilizing a Current Sense Amplifier (CSA) and a 1-bit comparator. Furthermore, we employ an intensity-to-latency temporal coding scheme to minimize spike activity and mitigate device endurance degradation. We validated the proposed system using the MNIST dataset, achieving a classification accuracy of 97.8%, which is comparable to state-of-the-art floating-point SNNs using supervised learning methods. Power analysis confirms that our 1-bit readout method consumes only 18.4% of the energy required by an 8-bit ADC-based approach while maintaining negligible accuracy loss. Additionally, the deterministic single-spike nature of our temporal coding significantly reduces write stress on memristors compared to rate coding. These results demonstrate that the proposed hybrid CSNN offers a robust and energy-efficient solution for neuromorphic edge intelligence. Full article

Background: Despite improvements in life expectancy, people living with HIV (PWH) continue displaying immune activation and high rates of comorbid conditions. No comparative studies concerning activation markers exist between simplification strategies to either oral or long-acting (LA) dual ART. Methods: We prospectively collected plasma samples from PWH on successful ART, simplifying treatment from triple oral to either oral or LA dual ART based on integrase inhibitors. We measured changes in soluble CD14 (sCD14), soluble CD163 (sCD163), monocyte chemoattractant protein-1, and interleukin-6. Background measurements and markers of microbial translocation and gut integrity (I-FABP, LBP) were also collected. Results: From 2019 to 2023, 38 PWH were analyzed (mean age 52, 87% male, 21 years HIV diagnosis, CD4 730 cells/mm³, nadir CD4 317 cells/mm³, AIDS 13%). After 7.2 months, sCD14 trajectories differed according to regimen (+0.43 ng/mL, p = 0.033 for LA ART, −0.62 ng/mL, p < 0.001 for oral ART) but were not related to I-FABP or to LBP values. In case of CD4 nadir < 200 cc/mm³, AIDS, or very-low-level viremia, sCD163 values significantly increased when switching to oral but not to LA dual ART. Conclusion: We found different trends in immune activation markers and risk factors associated with PWH switching to either oral or LA ART, requiring larger studies. Full article

Concept Bottleneck Models facilitate interpretable image classification by predicting human-understandable concepts prior to class labels. However, when constructed upon CLIP, they exhibit unreliable concept scores stemming from CLIP’s global representation bias and insufficient region-level sensitivity, which severely constrain their effectiveness in sensor-driven applications like remote sensing and medical imaging where localized visual evidence is critical. To mitigate this, we propose the Local–Global Aware Concept Bottleneck Model (LGA-CBM), which improves concept prediction through a training-free refinement pipeline. Building on initial CLIP-derived concept scores, LGA-CBM incorporates three key components: a Dual Masking Guided Concept Score Refinement (DMCSR) module that exploits attention weights to strengthen region–concept alignment; a Local-to-Global Concept Reidentification (L2GCR) strategy to harmonize local and global activations; and a Similar Concepts Correction Mechanism (SCCM) integrating Grounding DINO for fine-grained disambiguation. A sparse linear layer then maps the refined concepts to class labels, enabling highly interpretable classification with minimal concept usage. Experiments across six benchmark datasets demonstrate that LGA-CBM consistently achieves state-of-the-art performance in both accuracy and interpretability, producing explanations that align closely with human cognition. Full article

Bipartite network community detection is of significant importance for understanding the underlying structure and functional organization of real-world complex systems. Although many mature community detection algorithms exist for unipartite networks, they cannot be directly applied to bipartite networks due to their unique topological structure, characterized by heterogeneous node types and cross-layer connections. Furthermore, some existing bipartite network community detection methods still rely heavily on manual experience to set key parameters, which limits their applicability and scalability in practical scenarios. To address these issues, this paper proposes an enhanced framework—the Adaptive Network Reconstruction Framework (ANRF)—by introducing an adaptive parameter optimization mechanism based on the existing Network Reconstruction Framework (NRF). This framework can be effectively integrated with traditional unipartite network community detection algorithms to achieve automatic community detection with reduced dependence on manual parameter tuning. The core procedure of the method consists of four main steps. First, we calculate the interaction forces between node pairs. Second, through comprehensive analysis of the network topological features, we adaptively determine the threshold parameter $\theta$ and related parameters for the interaction forces. Third, based on these thresholds and parameters, we perform edge filtering on the bipartite network to construct a reconstructed network. Finally, we apply unipartite community detection algorithms directly to the reconstructed network to obtain the community structure. To validate the effectiveness of ANRF, we combined it with the Louvain method and the Greedy modularity method, and conducted experimental evaluations on multiple synthetic and real-world network datasets. A systematic comparison with current state-of-the-art algorithms was made. The experimental results on multiple synthetic and real-world datasets within our evaluated scope demonstrate that ANRF achieves competitive performance in terms of community modularity and community density compared to state-of-the-art algorithms, while significantly reducing reliance on manual parameter tuning and enhancing robustness under the tested conditions. Full article

High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article

In this study, we explore how to balance the preservation of the original appearance of ancient villages with their development within the framework of World Heritage protection. We applied resilience theory and constructed a simple checklist, taking cultural ecosystem services into consideration, and selected the Zuojiang Huashan Rock Art Heritage Area in China for field investigation, as well as conducted in-depth interviews, the distribution of short questionnaires, and two rounds of Delphi surveys. This comprehensive approach enabled us to discover the key cultural ecosystem services that villagers rely on for their livelihoods. Then, we tracked how these services enhanced buffering capacity, helped people self-organize, and promoted adaptive learning. The results show that cultural ecosystem services constitute the core framework of the social–ecological resilience of the villages. The quantity and combination of the services directly determine the resilience score, and the resilience of villages within the heritage area shows significant spatial differentiation. High-resilience villages have diverse and mutually reinforcing cultural ecosystem services and local community rules, while low-resilience villages face service loss, weakened social connections, and single development options. Through this study, we aim to further enrich the cultural connotation of resilience theory, provide a practical assessment tool for practitioners of the method, and offer practical guidance and suggestions for transforming heritage protection from static protection to a dynamic, vibrant system that promotes vitality and resilience in practice. Full article

With applications in psychology, security, and human–computer interaction, facial expression recognition (FER) has become an essential tool for non-verbal communication. Current research often categorizes expressions into micro- and macro-types, yet existing datasets suffer from inconsistent labelling for classes, limited diversity of the databases, and insufficient scale for the currently available datasets. To address these gaps, this work proposes a novel framework combining the diffusion model with pre-trained CNNs. Leveraging original images from established datasets, CASME II, we generate synthetic facial expressions to augment training data, mitigating bias and inconsistency. The synthetic dataset is evaluated using ResNet 50, VGG16 and Inception V3 architectures. Inception V3 trained on the proposed AI-generated dataset and tested using CASME II, VGG-16 with data augmentation applied is trained on CASME II and tested on the proposed AI-generated dataset, and Inception V3 with 30% freezing layers method is trained on the proposed AI-generated dataset and tested using CASME II. These all successfully achieved state-of-the-art performance. The data augmentation and freezing layers approaches significantly improved the performance of the models. Our proposed approaches achieved state-of-the-art performance and outperformed most of the existing state-of-the-art approaches benchmarked in this study. Full article