Search Results (1,243)

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

As sustainable cultural heritage tourism increasingly demonstrates its unique value and appeal, effectively stimulating tourists’ emotional experiences and value co-creation behaviors has become a focal issue. This study investigates how multiple tourist motivations (self-enhancement, escapism, and social interaction) shape value co-creation through emotional mediators—namely aesthetic, nostalgic, and flow experiences. Data were collected from 470 valid responses from visitors to the UNESCO-listed Suzhou Classical Gardens in China and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that these emotional experiences significantly drive value co-creation behavior: self-enhancement motivation enhances all three experiences, escapism mainly promotes nostalgic and flow experiences, and social interaction primarily affects aesthetic experience. These findings clarify the psychological mechanisms through which tourists’ motivations and emotional experiences influence value co-creation behavior in cultural heritage tourism. This research advances our understanding of the motivation–experience–behavior framework and emphasizes that enhancing emotional engagement is key to fostering sustainable cultural heritage tourism practices. The study provides practical implications for designing experiences and strategies that balance visitor satisfaction with the long-term vitality of cultural heritage sites and local communities, thereby contributing to broader sustainable development goals. Full article

Background/Objectives: Brain metastasis (BM) is a common and often early manifestation in lung adenocarcinoma (LUAD), yet its tumor microenvironment remains poorly defined at the time of initial diagnosis. This study aims to characterize early immune microenvironmental alterations in synchronous BM using spatial proteomic profiling. Methods: We performed digital spatial proteomic profiling using the NanoString GeoMx platform on formalin-fixed paraffin-embedded tissues from five treatment-naïve LUAD patients in whom BM was the initial presenting lesion. Paired primary lung and brain metastatic samples were analyzed across tumor and stromal compartments using 68 immune- and tumor-related protein markers. Results: Spatial profiling revealed distinct expression patterns between primary tumors and brain metastases. Immune regulatory proteins—including IDO-1, PD-1, PD-L1, STAT3, PTEN, and CD44—were significantly reduced in brain metastases (p < 0.01), whereas pS6, a marker of activation-induced T-cell death, was significantly upregulated (p < 0.01). These alterations were observed in both tumor and stromal regions, suggesting a more immunosuppressive and apoptotic microenvironment in brain lesions. Conclusions: This study provides one of the first spatially resolved proteomic characterizations of synchronous BM at initial LUAD diagnosis. Our findings highlight early immune escape mechanisms and suggest the need for site-specific immunotherapeutic strategies in patients with brain metastasis. Full article

The Tree–Seed Algorithm (TSA) is a symmetry-driven metaheuristic algorithm that shows potential for complex optimization problems, but it suffers from local optimum entrapment and slow convergence. To address these limitations, we propose the ADTSA algorithm. First, ADTSA adopts a symmetry-driven dual-layer framework for seed generation, which promotes effective information exchange between subpopulations and accelerates convergence speed. In later iterations, ADTSA enhances the population’s exploitation ability through a population fusion mechanism, further improving the convergence speed. Moreover, we propose a historical optimal solution archiving and replacement mechanism, along with a t-distribution perturbation mechanism, to enhance the algorithm’s ability to escape local optima. ADTSA also strengthens population diversity and avoids local optima through convex lens symmetric reverse generation based on the optimal solution. With these mechanisms, ADTSA converges more effectively to the global optimum during the evolutionary process. Tests on the IEEE CEC 2014 benchmark functions showed that ADTSA outperformed several top-performing algorithms, such as LSHADE, JADE, LSHADE-RSP, and the latest TSA variants, and it also excelled in comparison with other optimization algorithms, including GWO, PSO, BOA, GA, and RSA, underscoring its robust performance across diverse testing scenarios. The proposed ADTSA’s applicability in solving complex constrained problems was also validated, with the results showing that ADTSA achieved the best solutions for these complex problems. Full article

Early diagnosis of lung cancer is crucial for improving patient prognosis. In this study, we developed a diagnostic model for lung cancer based on serum proteomic data from the GSE168198 dataset using four machine learning algorithms (nnet, glmnet, svm, and XGBoost). The model’s performance was validated on datasets that included normal controls, disease controls, and lung cancer data containing both. Furthermore, the model’s diagnostic capability was further validated on an independent external dataset. Our analysis identified SLC16A4 as a key protein in the model, which was significantly downregulated in lung cancer serum samples compared to normal controls. The expression of SLC16A4 was closely associated with clinical pathological features such as gender, tumor stage, lymph node metastasis, and smoking history. Functional assays revealed that overexpression of SLC16A4 significantly inhibited lung cancer cell proliferation and induced cellular senescence, suggesting its potential role in lung cancer development. Additionally, correlation analyses showed that SLC16A4 expression was linked to immune cell infiltration and the expression of immune checkpoint genes, indicating its potential involvement in immune escape mechanisms. Based on multi-omics data from the TCGA database, we further discovered that the low expression of SLC16A4 in lung cancer may be regulated by DNA copy number variations and DNA methylation. In conclusion, this study not only established an efficient diagnostic model for lung cancer but also identified SLC16A4 as a promising biomarker with potential applications in early diagnosis and immunotherapy. Full article

The red imported fire ant (Solenopsis invicta) is a dangerous invasive insect. These ants rely on releasing an alarm pheromone, mainly composed of 2-ethyl-3,6-dimethylptrazine (EDMP), to warn nestmates of danger and trigger group defense or escape behaviors. This study found two NPC2 proteins in the ant antennae: SinvNPC2a and SinvNPC2b. SinvNPC2a was highly expressed in the antennae; phylogenetic analysis also suggests that SinvNPC2 likely possesses conserved olfactory recognition functions. By knocking down the SinvNPC2a gene, we found that the electrophysiological response of ant antennae to EDMP became weaker. More importantly, ants lacking SinvNPC2a showed significantly reduced movement range and speed when exposed to EDMP, compared to normal ants not treated with RNAi. These ants did not spread out quickly. Furthermore, tests showed that the purified SinvNPC2a protein could directly bind to EDMP molecules. Computer modeling also showed that they fit together tightly. These findings provide direct evidence that the SinvNPC2a protein plays a key role in helping fire ants detect the EDMP alarm pheromone. It enables the ants to sense this chemical signal, allowing ant colonies to respond quickly. Understanding this mechanism improves our knowledge of how insects smell things. It also suggests a potential molecular target for developing new methods to control fire ants, such as using RNAi to block its function. Full article

The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling, which combined molecular simulations with the ensemble-based mutational scanning of the binding interfaces and binding free energy computations. A central theme emerging from this work is that the unique binding strength and resilience to immune escape of the BD55-1205 antibody are determined by leveraging a broad epitope footprint and distributed hotspot architecture, additionally supported by backbone-mediated specific interactions, which are less sensitive to amino acid substitutions and together enable exceptional tolerance to mutational escape. In contrast, BD-604 and OMI-42 exhibit localized binding modes with strong dependence on side-chain interactions, rendering them particularly vulnerable to escape mutations at K417N, L455M, F456L and A475V. Similarly, P5S-1H1 and P5S-2B10 display intermediate behavior—effective in some contexts but increasingly susceptible to antigenic drift due to narrower epitope coverage and concentrated hotspots. Our computational predictions show strong agreement with experimental deep mutational scanning data, validating the accuracy of the models and reinforcing the value of binding hotspot mapping in predicting antibody vulnerability. This work highlights that neutralization breadth and durability are not solely dictated by epitope location, but also by how binding energy is distributed across the interface. The results provide atomistic insight into mechanisms driving resilience to immune escape for broadly neutralizing antibodies targeting the ACE2 binding interface—which stems from cumulative effects of structural diversity in binding contacts, redundancy in interaction patterns and reduced vulnerability to mutation-prone positions. Full article

Background: Personalized anti-tumor therapy that activates the immune response has demonstrated clinical benefits in various cancers. However, its efficacy against testicular cancer (TC) remains uncertain. This study aims to identify suitable patients for anti-tumor immunotherapy and to uncover potential therapeutic targets in TC for the development of tailored anti-tumor immunotherapy. Methods: Consensus clustering analysis was conducted to delineate immune subtypes, while weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, and support vector machine (SVM) algorithms were employed to evaluate the potential efficacy of anti-tumor immunotherapy. Candidate immunotherapy targets were systematically identified through multi-gene panel analyses and subsequently validated using molecular biology assays. A prioritized target emerging from cellular screening was further evaluated for its capacity to potentiate anti-tumor immunity. The therapeutic efficacy of this candidate was rigorously confirmed through a comprehensive suite of immunological experiments. Results: Following systematic screening of five candidate genes (WNT11, FAM181B, GRK5, FSCN1, and ECHS1), GRK5 emerged as a promising therapeutic target for immunotherapy based on its distinct functional and molecular associations with immune evasion mechanisms. Cellular functional assays revealed that GRK5 knockdown significantly attenuated the malignant phenotype of testicular cancer cells, as evidenced by reduced proliferative capacity and invasive potential. Complementary immunological validation established that specific targeting of GRK5 with the selective antagonist GRK5-IN-2 disrupts immune evasion pathways in testicular cancer, as quantified by T-cell-mediated cytotoxicity. Conclusions: These findings position GRK5 as a critical modulator of tumor-immune escape, warranting further preclinical exploration of GRK5-IN-2 as a candidate immunotherapeutic agent. Full article

Cloud–edge collaboration industrial control systems (ICSs) face critical security and privacy challenges that existing dynamic heterogeneous redundancy (DHR) architectures inadequately address due to two fundamental limitations: event-triggered scheduling approaches that amplify common-mode escape impacts in resource-constrained environments, and insufficient privacy-preserving arbitration mechanisms for sensitive industrial data processing. In contrast to existing work that treats scheduling and privacy as separate concerns, this paper proposes a unified polymorphic heterogeneous security architecture that integrates hybrid event–time triggered scheduling with adaptive privacy-preserving arbitration, specifically designed to address the unique challenges of cloud–edge collaboration ICSs where both security resilience and privacy preservation are paramount requirements. The architecture introduces three key innovations: (1) a hybrid event–time triggered scheduling algorithm with credibility assessment and heterogeneity metrics to mitigate common-mode escape scenarios, (2) an adaptive privacy budget allocation mechanism that balances privacy protection effectiveness with system availability based on attack activity levels, and (3) a unified framework that organically integrates privacy-preserving arbitration with heterogeneous redundancy management. Comprehensive evaluations using natural gas pipeline pressure control and smart grid voltage control systems demonstrate superior performance: the proposed method achieves 100% system availability compared to 62.57% for static redundancy and 86.53% for moving target defense, maintains 99.98% availability even under common-mode attacks (${10}^{-2}$ probability), and consistently outperforms moving target defense methods integrated with state-of-the-art detection mechanisms (99.7790% and 99.6735% average availability when false data deviations from true values are 5% and 3%, respectively) across different attack detection scenarios, validating its effectiveness in defending against availability attacks and privacy leakage threats in cloud–edge collaboration environments. Full article

The Adaptive Cross Ivy (ACIVY) algorithm is a novel bio-inspired metaheuristic that emulates ivy plant growth behaviors for complex optimization problems. While the original Ivy Optimization Algorithm (IVYA) demonstrates a competitive performance, it suffers from limited inter-individual information exchange, inadequate directional guidance for local optima escape, and abrupt exploration–exploitation transitions. To address these limitations, ACIVY integrates three strategic enhancements: the crisscross strategy, enabling horizontal and vertical crossover operations for improved population diversity; the LightTrack strategy, incorporating positional memory and repulsion mechanisms for effective local optima escape; and the Top-Guided Adaptive Mutation strategy, implementing ranking-based mutation with dynamic selection pools for smooth exploration–exploitation balance. Comprehensive evaluations on the CEC2017 and CEC2022 benchmark suites demonstrate ACIVY’s superior performance against state-of-the-art algorithms across unimodal, multimodal, hybrid, and composite functions. ACIVY achieved outstanding average rankings of 1.25 (CEC2022) and 1.41 (CEC2017 50D), with statistical significance confirmed through Wilcoxon tests. Practical applications in engineering design optimization and UAV path planning further validate ACIVY’s robust performance, consistently delivering optimal solutions across diverse real-world scenarios. The algorithm’s exceptional convergence precision, solution reliability, and computational efficiency establish it as a powerful tool for challenging optimization problems requiring both accuracy and consistency. Full article

Arundo donax L. has been introduced in markets worldwide due to its economic value. However, it is listed in the world’s 100 worst alien invasive species because it easily escapes from cultivation, and forms dense monospecific stands in riparian areas, agricultural areas, and grassland areas along roadsides, including in protected areas. This species grows rapidly and produces large amounts of biomass due to its high photosynthetic ability. It spreads asexually through ramets, in addition to stem and rhizome fragments. Wildfires, flooding, and human activity promote its distribution and domination. It can adapt to various habitats and tolerate various adverse environmental conditions, such as cold temperatures, drought, flooding, and high salinity. A. donax exhibits defense mechanisms against biotic stressors, including herbivores and pathogens. It produces indole alkaloids, such as bufotenidine and gramine, as well as other alkaloids that are toxic to herbivorous mammals, insects, parasitic nematodes, and pathogenic fungi and oomycetes. A. donax accumulates high concentrations of phytoliths, which also protect against pathogen infection and herbivory. Only a few herbivores and pathogens have been reported to significantly damage A. donax growth and populations. Additionally, A. donax exhibits allelopathic activity against competing plant species, though the allelochemicals involved have yet to be identified. These characteristics may contribute to its infestation, survival, and population expansion in new habitats as an invasive plant species. Dense monospecific stands of A. donax alter ecosystem structures and functions. These stands impact abiotic processes in ecosystems by reducing water availability, and increasing the risk of erosion, flooding, and intense fires. The stands also negatively affect biotic processes by reducing plant diversity and richness, as well as the fitness of habitats for invertebrates and vertebrates. Eradicating A. donax from a habitat requires an ongoing, long-term integrated management approach based on an understanding of its invasive mechanisms. Human activity has also contributed to the spread of A. donax populations. There is an urgent need to address its invasive traits. This is the first review focusing on the invasive mechanisms of this plant in terms of adaptation to abiotic and biotic stressors, particularly physiological adaptation. Full article

Liver cancer has high incidence and mortality rates worldwide, with hepatocellular carcinoma (HCC) being the main histological subtype, accounting for 90% of primary liver cancers. The high mutation rate of viruses combined with endoplasmic reticulum stress may lead to the occurrence of cancer. Hepatitis B virus (HBV) infection is one of the most important pathogenic factors of HCC. The carcinogenic mechanisms of HBV have been widely studied. Among these mechanisms, immune escape and vaccine escape caused by mutations in the HBV S gene have been reported in numerous studies of patients with chronic hepatitis B. In addition, pre-S1/S2 mutations and surface protein truncation mutations may activate multiple signaling pathways. This activation leads to the abnormal proliferation and differentiation of hepatocytes, thereby contributing to the development of HCC. This review aims to integrate the existing literature, summarize the common mutations in the HBV S gene region, and explore the related pathogenic mechanisms. Full article

Background/Objectives: Cardiac aging involves the progressive structural and functional decline of the myocardium. Endurance training is a well-recognized non-pharmacological intervention that counteracts this decline, yet the molecular mechanisms driving exercise-induced cardiac rejuvenation remain inadequately elucidated. This study aimed to identify key effector genes and regulatory pathways by integrating human cardiac aging transcriptomic data with multi-omic exercise response datasets. Methods: A systems biology framework was developed to integrate age-downregulated genes (n = 243) from the GTEx human heart dataset and endurance-exercise-responsive genes (n = 634) from the MoTrPAC mouse dataset. Thirty-seven overlapping genes were identified and subjected to Enrichr for pathway enrichment, KEA3 for kinase analysis, and ChEA3 for transcription factor prediction. Candidate effector genes were ranked using ToppGene and ToppNet, with integrated prioritization via the FLAMES linear scoring algorithm. Results: Pathway enrichment revealed complementary patterns: aging-associated genes were enriched in mitochondrial dysfunction and sarcomere disassembly, while exercise-responsive genes were linked to protein synthesis and lipid metabolism. TTN, PDK family kinases, and EGFR emerged as major upstream regulators. NKX2-5, MYOG, and YBX3 were identified as shared transcription factors. SMPX ranked highest in integrated scoring, showing both functional relevance and network centrality, implying a pivotal role in mechano-metabolic coupling and cardiac stress adaptation. Conclusions: By integrating cardiac aging and exercise-responsive transcriptomes, 37 effector genes were identified as molecular bridges between aging decline and exercise-induced rejuvenation. Aging involved mitochondrial and sarcomeric deterioration, while exercise promoted metabolic and structural remodeling. SMPX ranked highest for its roles in mechano-metabolic coupling and redox balance, with X-inactivation escape suggesting sex-specific relevance. Other top genes (e.g., KLHL31, MYPN, RYR2) form a regulatory network supporting exercise-mediated cardiac protection, offering targets for future validation and therapy. Full article

This manuscript assesses the critical role of exosomal PD-L1 (ExoPD-L1) in immune suppression, tumor progression, and resistance to therapy. ExoPD-L1 has been identified as a key mediator of tumor immune evasion, contributing to systemic immunosuppression beyond the tumor microenvironment (TME) due to its capacity to travel to distant anatomical sites. In this context, the review aims to elaborate on the mechanisms by which exosomal PD-L1 interacts with T cell receptors and modulates both the tumor microenvironment and immune responses, impacting patient outcomes. We further explore emerging therapeutic strategies that target ExoPD-L1 to enhance the effectiveness of immunotherapy. Blocking ExoPD-L1 offers a novel approach to counteracting immune escape in cancer. Promising strategies include inhibiting exosome biogenesis with GW4869 or Rab inhibitors, neutralizing ExoPD-L1 with targeted antibodies, and silencing PD-L1 expression through RNA interference (RNAi) or CRISPR-based methods. While each approach presents certain limitations, their integration into combination therapies holds significant potential to improve the efficacy of immune checkpoint inhibitors. Future research should focus on optimizing these strategies for clinical application, with particular attention to improving delivery specificity and minimizing off-target effects. Full article

As a classical statistical method, multiple regression is widely used for forecasting tasks in power, medicine, finance, and other fields. The rise of machine learning has led to the adoption of neural networks, particularly Long Short-Term Memory (LSTM) models, for handling complex forecasting problems, owing to their strong ability to capture temporal dependencies in sequential data. Nevertheless, the performance of LSTM models is highly sensitive to hyperparameter configuration. Traditional manual tuning methods suffer from inefficiency, excessive reliance on expert experience, and poor generalization. Aiming to address the challenges of complex hyperparameter spaces and the limitations of manual adjustment, an enhanced sparrow search algorithm (ISSA) with adaptive parameter configuration was developed for LSTM-based multivariate regression frameworks, where systematic optimization of hidden layer dimensionality, learning rate scheduling, and iterative training thresholds enhances its model generalization capability. In terms of SSA improvement, first, the population is initialized by the reverse learning strategy to increase the diversity of the population. Second, the mechanism for updating the positions of producer sparrows is improved, and different update formulas are selected based on the sizes of random numbers to avoid convergence to the origin and improve search flexibility. Then, the step factor is dynamically adjusted to improve the accuracy of the solution. To improve the algorithm’s global search capability and escape local optima, the sparrow search algorithm’s position update mechanism integrates Lévy flight for detection and early warning. Experimental evaluations using benchmark functions from the CEC2005 test set demonstrated that the ISSA outperforms PSO, the SSA, and other algorithms in optimization performance. Further validation with power load and real estate datasets revealed that the ISSA-LSTM model achieves superior prediction accuracy compared to existing approaches, achieving an RMSE of 83.102 and an $R^2$ of 0.550 during electric load forecasting and an RMSE of 18.822 and an $R^2$ of 0.522 during real estate price prediction. Future research will explore the integration of the ISSA with alternative neural architectures such as GRUs and Transformers to assess its flexibility and effectiveness across different sequence modeling paradigms. Full article