Search Results (3,191)

Telomere maintenance enables unlimited cell proliferation by counteracting telomere erosion. While the majority of tumors activate telomerase, a significant subset—approximately 10–15%—utilizes alternative lengthening of telomeres (ALT), a recombination-based mechanism. ALT-positive cancers are classically associated with genomic instability, anaphase bridges, chromosomal rearrangements, and resistance to DNA-damaging therapies. This process is closely associated with genetic instability, which contributes to chromosomal rearrangements and tumor evolution. Consequently, ALT has traditionally been considered an adverse prognostic marker in aggressive malignancies such as osteosarcoma, pancreatic neuroendocrine tumors, and high-grade sarcomas. Paradoxically, recent evidence demonstrates that ALT positivity correlates with improved survival in glioblastoma (GBM) and chondrosarcoma, two tumor types that have historically been regarded as immune-cold and therapeutically intractable. This favorable outcome likely reflects a convergence of factors, including replication stress and DNA damage that impose a fitness cost in slow-growing or metabolically constrained tumors. Loss of ATRX/DAXX, while enabling ALT, further amplifies chromatin fragility, and ALT-mediated instability may paradoxically enhance immunogenicity within immune-quiescent microenvironments. Moreover, ALT-positive cells exhibit unique therapeutic vulnerabilities, particularly to ATR and PARP inhibitors. Together, these observations support a context-dependent model in which ALT functions as a double-edged sword, acting as a driver of malignant aggressiveness in rapidly proliferating cancers while serving as a relative liability in slower-growing, immune-cold tumors. Understanding this duality not only refines prognostic stratification but also opens opportunities for precision oncology. By integrating ALT-specific biomarkers into clinical workflows and exploiting ALT-related DNA repair dependencies, clinicians may transform a once uniformly negative prognostic factor into an actionable therapeutic target. Full article

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation. Full article

Besiege and Conquer Algorithm (BCA) is a swarm intelligence algorithm proposed in 2025 based on tactical concepts. However, its besiege and conquer strategies have some problems, such as insufficient diversity and local stagnation. To solve the above problems, this paper proposes an Enhanced Besiege and Conquer Algorithm with Three-Three Tactics and Collision Theory, referred to as TTBCA⁺. TTBCA⁺ has four innovations. Firstly, a three-three allocation mechanism of battlefield and roles is proposed to enhance its besiege capability; secondly, a three-three deployment mechanism of soldiers is proposed to enhance its conquer capability; thirdly, the balance factor between exploration and exploitation is modified for three-three tactics implementation; finally, the collision mechanism from collision theory is introduced to deal with soldiers beyond the search space. The performance of the proposed TTBCA⁺ is verified at the IEEE CEC 2017 and IEEE CEC 2022 benchmark functions, compared with 13 swarm intelligence algorithms, including classical algorithms, well-known algorithms such as JADE, lshade_rsp, AGWO, and recent 5 years algorithms, such as BCA, HOA, PLO, CFOA, HLOA, DBO, BOA. Meanwhile, the proposed algorithms are applied to two practical complex optimization problems. The results show that TTBCA⁺ effectively solved the limitations in BCA, and it is superior to other compared algorithms. Full article

Flexible pipes have significant application potential in deep-sea mineral resource exploitation. As the innermost barrier of flexible pipes, the liner directly withstands abrasive wear from mineral particles. The extrusion quality of the liner is a decisive factor for the service life of the pipe and requires optimization of process parameters for improvement. However, the extrusion process of wear-resistant liners made of ultra-high molecular weight polyethylene (UHMWPE) involves complex thermo-mechanical coupling behavior, which creates major challenges in developing accurate numerical models that represent the entire process. To precisely simulate the extrusion process and guide process parameter optimization, this paper establishes a numerical simulation model for flexible pipe liner extrusion based on the Eulerian–Lagrangian coupling method. Simulations under various outlet temperature and screw speed conditions were carried out to reveal the evolution of mechanical behavior during extrusion and clarify the influence of key process parameters. The main conclusions can be summarized as follows. An increase in extrusion temperature reduces the maximum stress and promotes better molecular orientation and crystallinity in UHMWPE material, while the maximum heat flux remains essentially unchanged. An increase in screw speed has little effect on maximum material stress but leads to a significant increase in maximum heat flux. In addition, significant stress appears in the UHMWPE material at the extrusion die exit and is mainly concentrated in the unextruded material section. The numerical model effectively addresses the challenges of simulating material phase transition, large deformation and long-distance flow, which are difficult to capture with traditional methods. The findings offer a theoretical basis and technical guidance for optimizing extrusion process parameters and strengthening quality control in flexible pipe liner extrusion. Full article

Perforation, a common well completion method in oil and gas exploitation, introduces structural defects in casings that alter their mechanical properties. Based on engineering specifications, this study calculates critical loads (i.e., collapse pressure and yield pressure) and the triaxial equivalent stress for casings. Four load cases were selected for analysis: uniform external pressure, uniform internal pressure, external pressure with axial compression, and internal pressure with axial tension. The equivalent stresses around circular, elliptical, pentagonal, and hexagonal perforation defects were computed. A self-defined perforation influence coefficient was used to evaluate changes in mechanical performance. Results show that circular defects have the least effect on the mechanical properties of the casing. Maximum equivalent stress occurs along the hole centerline parallel to the casing axis and increases with greater disparity between ellipse axes or smaller polygon angles. High shot density (>24 holes/m) and large phase angle (60°) generally enhance safety, but an optimal combination exists. Under tensile loads near cracked defects, crack propagation may lead to fracture. For elliptical defects with cracks, the Mode I stress intensity factor grows faster with greater axis disparity, accelerating crack tip stress and deformation, and raising fracture risk. Cracks perpendicular to tensile stress influence the stress intensity factor more significantly than parallel ones. Full article

Ferroptosis, an iron-dependent form of regulated cell death marked by lipid peroxidation, has emerged as a promising therapeutic target in breast cancer, particularly in aggressive subtypes such as triple-negative breast cancer (TNBC). This systematic review explores the molecular mechanisms underlying ferroptosis sensitivity and resistance, focusing on the interplay between iron metabolism, antioxidant defenses, and tumor microenvironmental factors. Literature retrieved from PubMed and Scopus up to May was analyzed in accordance with PRISMA guidelines, including mechanistic studies, preclinical experiments, and ongoing clinical trials. Findings reveal that breast cancer cells evade ferroptosis through enhanced glutathione synthesis, upregulation of GPX4 and system Xc- and adaptive metabolic reprogramming; yet these same mechanisms create exploitable vulnerabilities, including dependence on cystine, polyunsaturated lipids, and dysregulated iron handling. Therapeutic strategies that target key ferroptosis regulators, such as GPX4, ACSL4, and SLC7A11, or that harness agents like statins, sulfasalazine, and nanoparticle-based iron complexes demonstrate strong potential to overcome chemoresistance and selectively eliminate therapy-resistant cancer cell populations. Taken together, the evidence highlights ferroptosis as a critical Achilles’ heel of breast cancer biology and supports further clinical translation of ferroptosis-inducing therapies to improve outcomes in otherwise refractory breast cancer subtypes. Full article

To address the issue of hysteresis nonlinearity adversely affecting the tracking accuracy of piezoelectric actuators, an improved particle swarm optimization (PSO) algorithm is proposed to improve the accuracy of hysteresis model parameter identification. Additionally, a discrete-time linear quadratic optimal tracking (DLQT) control strategy incorporating hysteresis compensation is developed to improve tracking performance. This study employs the Hammerstein model to characterize the nonlinear hysteresis behavior of piezoelectric actuators. Regarding parameter identification, the conventional PSO algorithm tends to suffer from premature convergence and being trapped in local optima. To address this, a cross-variation mechanism is introduced to enhance population diversity and improve global search ability. Furthermore, adaptive and dynamically adjustable inertia weights are designed based on evolutionary factors to balance exploration and exploitation, thereby enhancing convergence and identification accuracy. The inertia weights and learning factors are adaptively adjusted based on the evolutionary factor to balance local and global search capabilities and accelerate convergence. Benchmark function tests and model identification experiments demonstrate the improved algorithm’s superior convergence speed and accuracy. In terms of control strategy, a hysteresis compensator based on an asymmetric hysteresis model is designed to improve system linearity. To address the issues of incomplete hysteresis compensation and low tracking accuracy, a DLQT controller is developed based on hysteresis compensation. Hardware-in-the-loop tracking control experiments using single and composite frequency reference signals show that the relative error is below 3.3% in the no-load case and below 4.5% in the loaded case. Compared with the baseline method, the proposed control strategy achieves lower root-mean-square error and maximum steady-state error, demonstrating its effectiveness. Full article

Symmetry is an essential characteristic in both solution spaces and cloud task scheduling loads, as it reflects a structural balance that can be exploited to enhance algorithmic efficiency and robustness. In recent years, with the rapid development of 6G networks, the number of tasks requiring computation in the cloud has surged, prompting an increasing number of researchers to focus on how to efficiently schedule these tasks to idle computing nodes at low cost to enhance system resource utilization. However, developing reliable and cost-effective scheduling schemes for cloud computing tasks in real-world environments remains a significant challenge. This paper proposes a method for cloud computing task scheduling in real-world environments using an improved dhole optimization algorithm (IDOA). First, we enhance the quality of the initial population by employing a uniform distribution initialization method based on the Sobol sequence. Subsequently, we further improve the algorithm’s search capabilities using a sine elite population search method based on adaptive factors, enabling it to more effectively explore promising solution spaces. Additionally, we propose a random mirror perturbation boundary control method to better address individual boundary violations and enhance the algorithm’s robustness. By explicitly leveraging symmetry characteristics, the proposed algorithm maintains balanced exploration and exploitation, thereby improving convergence stability and scheduling fairness. To evaluate the effectiveness of the proposed algorithm, we compare it with nine other algorithms using the IEEE CEC2017 test set and assess the differences through statistical analysis. Experimental results demonstrate that the IDOA exhibits significant advantages. Finally, to verify its applicability in real-world scenarios, we applied IDOA to cloud computing task scheduling problems in actual environments, achieving excellent results and successfully completing cloud computing task scheduling planning. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

Climatic factors have always played a key role in the construction of food-related architecture: mitigation of outdoor temperatures or winds, adoption of raining waters in the productive processes, etc. However, sometimes, climate change has impacted the profitability of those structures and eventually caused their abandonment. Today, historical food-related architectures are significant elements of local rural heritage, and they are also tangible symbols of all the values connected to the corresponding typical food productions. When the cultural value of rural cultural assets and the historical management of climatic factors coexist, this potential can be investigated, and the results can ultimately be included in the corresponding enhancement processes. To exploit this potential, the paper introduces the theoretical concept of food-related architecture as climatic indicators, with reference to the changes in the climate that have occurred during their construction, as well as their abandonment. According to the thesis of the research, the adoption of the concept of climatic indicators can implement the value of selected minor cultural assets, support sustainable rural regeneration plans and integrate missing historical climate series and data. In the Materials and Methods section, two theoretical charts have been introduced, and the pyramid of the Mediterranean diet was analyzed to allow for the selection of some food-related architectures to test the theoretical approach developed. Then, three Italian case studies have been analyzed: the concept of climate indicators was tested, and some potential focus points of actions connected to this aspect were elucidated. The case studies are the Pietragalla wine district in the Basilicata Region, the Apulian rock-cut oil mills and Mills’s Valley in the Campania Region. Full article

Intestinal microbiota populations are constantly shaped by both intrinsic and extrinsic factors, including diet, environment, and host genetics. As a result, understanding how to assess, monitor, and exploit microbiome–host interplay remains an active area of investigation, especially in aquaculture. In this study, we analyzed the taxonomic structure and functional potential of the intestinal microbiota of European sea bass and rainbow trout, incorporating gilthead sea bream as a final reference. The results showed that the identified core microbiota (40 taxa for sea bass and 20 for trout) held a central role in community organization, despite taxonomic variability, and exhibited a predominant number of positive connections (>60% for both species) with the rest of the microbial community in a Bayesian network. From a functional perspective, core-associated bacterial clusters (75% for sea bass and 81% for sea bream) accounted for the majority of predicted metabolic pathways (core contribution: >75% in sea bass and >87% in trout), particularly those involved in carbohydrate, amino acid, and vitamin metabolism. Comparative analysis across ecological phenotypes highlighted distinct microbial biomarkers, with genera such as Vibrio, Pseudoalteromonas, and Paracoccus enriched in saltwater species (Dicentrarchus labrax and Sparus aurata) and Mycoplasma and Clostridium in freshwater (Oncorhynchus mykiss). Overall, this study underscores the value of integrating taxonomic, functional, and network-based approaches as practical tools to monitor intestinal health status, assess welfare, and guide the development of more sustainable production strategies in aquaculture. Full article

Aiming at the problems of high-dimensional nonlinear constraints, multi-objective conflicts, and low solution efficiency in microgrid optimal scheduling, this paper proposes a multi-objective Harris Hawk–Grey Wolf hybrid intelligent algorithm (IMOHHOGWO). The problem of balancing the global exploration and local exploitation of the algorithm is solved by introducing an adaptive energy factor and a nonlinear convergence factor; in terms of the algorithm’s exploration scope, the stochastic raid strategy of Harris Hawk optimization (HHO) is used to generate diversified solutions to expand the search scope, and constraints such as the energy storage SOC and DG outflow are finely tuned through the α/β/δ wolf bootstrapping of the Grey Wolf Optimizer (GWO). It is combined with a simulated annealing perturbation strategy to enhance the adaptability of complex constraints and local search accuracy, at the same time considering various constraints such as power generation, energy storage, power sales, and power purchase. We establish the microgrid multi-objective operation cost and carbon emission cost objective function, and through the simulation examples, we verify and determine that the IMOHHOGWO hybrid intelligent algorithm is better than the other three algorithms in terms of both convergence speed and convergence accuracy. According to the results of the multi-objective test function analysis, its performance is superior to the other four algorithms. The IMOHHOGWO hybrid intelligent algorithm reduces the grid operation cost and carbon emissions in the microgrid optimal scheduling model and is more suitable for the microgrid multi-objective model, which provides a feasible reference for future integrated microgrid optimal scheduling. Full article

The Shizhuyuan polymetallic deposit in Hunan Province, China, is a world-class ore field rich in tungsten (W), tin (Sn), molybdenum (Mo), and bismuth (Bi), now facing resource depletion due to prolonged exploitation. This study addresses the limitations of traditional geological prediction methods in complex terrain by integrating multi-source datasets—including γ-ray spectrometry, high-precision magnetometry, induced polarization (IP), and soil radon measurements—across 5049 samples. Unsupervised factor analysis was employed to extract five key ore-indicating factors, explaining 82.78% of data variance. Based on these geological features, predictive models including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were constructed and compared. SHAP values were employed to quantify the contribution of each geological feature to the prediction outcomes, thereby transforming the machine learning “black-box models” into an interpretable geological decision-making basis. The results demonstrate that machine learning, particularly when integrated with multi-source data, provides a powerful and interpretable approach for deep mineral prospectivity mapping in concealed terrains. Full article

Road traffic crashes (RTCs) have emerged as a major global cause of fatalities, with the number of accident-related deaths rising rapidly each day. To mitigate this issue, it is essential to develop early prediction methods that help drivers and riders understand accident statistics relevant to their region. These methods should consider key factors such as speed limits, compliance with traffic signs and signals, pedestrian crossings, right-of-way rules, weather conditions, driver negligence, fatigue, and the impact of excessive speed on RTC occurrences. Raising awareness of these factors enables individuals to exercise greater caution, thereby contributing to accident prevention. A promising approach to improving road traffic accident severity classification is the stacking ensemble method, which leverages multiple machine learning models. This technique addresses challenges such as imbalanced datasets and high-dimensional features by combining predictions from various base models into a meta-model, ultimately enhancing classification accuracy. The ensemble approach exploits the diverse strengths of different models, capturing multiple aspects of the data to improve predictive performance. The effectiveness of stacking depends on the careful selection of base models with complementary strengths, ensuring robust and reliable predictions. Additionally, advanced feature engineering and selection techniques can further optimize the model’s performance. Within the field of artificial intelligence, various machine learning (ML) techniques have been explored to support decision making in tackling RTC-related issues. These methods aim to generate precise reports and insights. However, the stacking method has demonstrated significantly superior performance compared to existing approaches, making it a valuable tool for improving road safety. Full article

In karst regions (KRs), unique surface morphology and irrational human exploitation have led to increasingly prominent issues such as land fragmentation and rocky desertification. Understanding the spatiotemporal evolution of cultivated land (CL) in these areas is of great significance for supporting regional socioeconomic development, food security, and ecological sustainability. This study focuses on Guilin, combining GIS spatial analysis with methods including kernel density analysis, dynamic degree, spatial transfer matrix, and a Geodetector to examine the spatiotemporal distribution characteristics, evolution trends, and driving factors of land use based on five-phase of land use data from 2000 to 2020. The results show that: (1) over the past two decades, land use in Guilin has been dominated by CL and forest land, with CL exhibiting a spatial pattern of more in the east and south, and less in the west and north; (2) the CL transfer-out rate exceeded the transfer-in rate, mainly shifting to construction land and forest land; (3) the overall density of CL showed a declining trend, with a relatively stable spatial pattern; and (4) driving factor analysis indicates that the spatiotemporal changes in CL are jointly influenced by multiple factors, with natural factors exerting a stronger influence than socio-economic factors. Among them, the interaction between elevation and temperature had the greatest impact and served as the dominant factor. Although GDP and population were not dominant individually, their explanatory power and sensitivity increased significantly when interacting with other factors, making them key sensitive factors. The results can provide a scientific reference for the protection and rational utilization of CL resources in KR. Full article