Search Results (4,367)

Advances in the molecular characterization of acute myeloid leukemia (AML) in recent decades have driven the development and clinical introduction of several targeted therapies, and menin inhibitors are the most recent additions to the therapeutic arsenal of AML treatments. The KMT2A and NPM1 loci have been the focus of intense investigation over the past two decades because of their well-established roles leukemogenesis, particularly in pre-clinical models of AML involving human xenografts. The ability of KMT2A and NPM1 aberrancies to drive and sustain leukemogenesis has provided a strong rationale for the development of therapeutic strategies to disrupt these molecular pathways. Menin inhibitors preferentially eliminate clones harboring KMT2A rearrangements or NPM1 mutations due to the dependence of these leukemia cells on the menin–KMT2A complex and its downstream HOXA9/MEIS signaling to sustain leukemic self-renewal and disease maintenance. In this review, we discuss the biological rationale for menin–MLL disruption in patients with acute leukemia with KMT2A rearrangement or NPM1 mutation. We explore data from the landmark AUGMENT-101 and KOMET-001 trials, which have led to regulatory approval of revumenib and ziftomenib for select patients. We also explore cutting-edge studies using menin inhibitors in combination strategies, including data from KOMET-007. The evidence supporting combination approaches remains based on early-phase, single-arm trials with relatively short follow-up, and longer-term and comparative data are needed to define their clinical benefit. We highlight resistance mechanisms that have emerged from the use of contemporary menin inhibitors and efforts aimed at addressing this resistance, with a focus on emerging clinical data on enzomenib and bleximenib. Finally, we discuss prospects on the putative role of menin inhibitors in the measurable residual disease (MRD)-positive and maintenance settings in AML. Menin inhibitors have successfully transitioned from biological proof-of-concept to approved targeted therapy, and future advances will depend on rational front line combination strategies, MRD-guided treatment, post-transplant maintenance strategies, and therapeutic sequencing informed by mechanisms of resistance. Full article

Simulation of a human neurological disease on an animal model has the advantage of allowing control and manipulation of most of the regulating factors and producing real biological replicas while, beyond several common traits, every human is dynamic and unique. Moreover, one can explore novel therapeutic strategies on animals before asking permission to apply them to humans. Nevertheless, experimental outcomes depend on species, strain, sex, age, hormonal status, diet, exposure to hypoxia, toxins, radiation, external stimuli, stress, and housing conditions. Further complications stem from tissue hetero-cellularity, technological constraints, computational complexity and difficulties integrating the experimental results into a coherent biological picture. Moreover, most diseases are multi-factorial and associated with altered structure and/or expression of several genes. A major problem with genetically engineered animals is that together with the targeted gene(s), numerous other genes are mutated and/or regulated, owing to their interlinkage in functional pathways. This experience-based methodological commentary presents the challenges, relevance and limitations of the mouse, rat and rabbit models we used to decipher the transcriptomic alterations associated with several neurological disorders. Links to publicly accessible databases presenting experimental protocols and expression profiles are provided for readers interested in reanalyzing our data and comparing them with others’ results. Full article

Adaptive Reinforced Gray Langur Optimization (ARGLO), an enhanced variant of the Gray Langurs Optimizer, is developed for high-dimensional, multimodal, and nonlinear search landscapes susceptible to local trapping. Although the original GLO performs multi-population cooperative search by simulating the social structures of gray langurs, it still suffers from uneven random initialization, insufficient adaptive population partitioning, weak local perturbation, and premature convergence. ARGLO incorporates three strategies: good point set-based oppositional and quasi-oppositional learning initialization, hierarchical equilibrium adaptive population partitioning, and elite-guided hybrid mutation. Collectively, these mechanisms generate a higher-quality starting population, coordinate global search with local refinement, and reduce the risk of entrapment in suboptimal regions. Evidence from component-wise experiments together with the CEC test suite indicates that ARGLO delivers higher solution precision, steadier convergence, as well as more consistent performance, especially as dimensionality increases. Moreover, ARGLO is applied to near-infrared spectral feature selection and SVR parameter optimization for polysaccharide content prediction in Dendrobium huoshanense. Compared with unoptimized SVR, ARGLO-SVR reduces RMSE by 35.35% and improves R² by 21.92%; compared with GLO-SVR, it reduces RMSE by 6.05% and improves R² by 2.30%. These results demonstrate the effectiveness and application potential of ARGLO in complex optimization and rapid nondestructive quality detection of traditional Chinese medicinal materials. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article

To address the problems of enhanced non-stationarity, significant feature distribution shift, and insufficient cross-condition generalization capability of traditional fault diagnosis methods under variable operating conditions such as varying speed and load, a rolling bearing fault diagnosis method integrating group sparse reconstruction and a multi-strategy improved quantum particle swarm optimization-based relevance vector machine (RVM) is proposed. First, group sparse representation learning is employed to reconstruct the original vibration signals, thereby suppressing background noise and enhancing fault-related impulsive components to improve signal separability and stability. Subsequently, a modal component selection criterion combining kurtosis and correlation coefficients is introduced to optimize and reconstruct the decomposed modal components, enabling the reconstructed signals to retain more fault-sensitive information. On this basis, multiple information entropy features are extracted from the reconstructed signals to construct high-dimensional state feature vectors for comprehensively characterizing the dynamic operating states of rolling bearings. To further enhance the parameter optimization capability, Chebyshev chaotic mapping is incorporated into the quantum particle swarm optimization (QPSO) algorithm to improve the uniformity of population initialization. Meanwhile, a Cauchy mutation strategy is introduced to strengthen the global search capability and avoid premature convergence, thereby forming a multi-strategy improved QPSO algorithm. Finally, the improved optimization algorithm is utilized to adaptively optimize the key hyperparameters of the RVM, resulting in a fault diagnosis model with high accuracy, strong generalization capability, and sparse characteristics. Experimental validation on the HUST and XJTU-SY bearing datasets demonstrates that the proposed MIQPSO-RVM framework achieves diagnostic accuracies of 96.70% and 94.83%, respectively. Compared with several representative intelligent diagnosis methods and deep learning models, the proposed method exhibits superior diagnostic performance, robustness, and generalization capability under complex operating conditions. Full article

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

Population immune pressure from vaccination and prior infection continues to drive the evolution of SARS-CoV-2. Systematic characterization of RBD mutations under complex immune backgrounds is essential for understanding viral adaptation and evolutionary trajectories. Here, we applied a deep mutational scanning (DMS) to comprehensively map the neutralization escape landscape of the Omicron variant JN.1 and its descendant lineage XEC, under immune pressure from individuals who experienced Omicron breakthrough infections following three doses of inactivated vaccines. A neutralization escape map for the single amino acid substitutions in the RBD of JN.1 or XEC was generated, and the escape efficiency of each mutation was determined. The results show that RBD escape mutations are hierarchically organized: low-intensity signals are widespread, whereas high-intensity escape is confined to a few key sites. These escape mutations are not confined solely to the receptor-binding motif (RBM) but are broadly distributed across the entire RBD. Many escape sites could accommodate multiple amino acid substitutions. Integration of DMS data with genomic surveillance of circulating variants from 2024 to 2025 revealed significant overlap between experimentally identified escape sites and mutations observed in natural isolates. This overlap increased substantially in 2025, with site concordance rising from 27.17% and 26.81% to 45.09% and 47.10% for JN.1 and XEC, respectively. The natural prevalence of these escape mutations is further shaped by factors such as receptor-binding affinity, protein stability, and epistatic interactions. Overall, our findings suggest that SARS-CoV-2 antigenic evolution follows the pattern of multiple pathways within a constrained space, providing new insights into the adaptive mechanisms of Omicron-derived variants under hybrid immune pressure. Full article

The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogue ligands against wild-type and mutant MERS-CoV Mpro. A library of 15,889 Nirmatrelvir derivatives generated through systematic scaffold modification was docked against the wild-type and five variants of the Mpro, producing a total of 95,334 structural and docking score datasets of these protein–ligand complexes. During the ML model development phase, ligand effects were learned from RDKit molecular descriptors and graph-based representations, and the mutation-induced effects were captured through delta-encoded physicochemical properties (hydrophobicity, charge, aromaticity, and polarity) of the active-site residues. Among the evaluated models, the CatBoost regressor tree-based algorithm achieved the lowest mean absolute error (MAE) value of 0.23 Kcal/mol and an R² of 0.87. Further improvement was achieved by creating a weighted ensemble model combining the CatBoost regressor, XGBoost and LightGBM regressor, resulting in a prediction accuracy with a MAE of 0.19 Kcal/mol and an R² of 0.90 relative to docking scores. Model robustness was further evaluated through random-, ligand group- and scaffold group- K-fold cross-validation along with their Y-randomization. Moreover, the models were also tested with a new set of 1000 structurally diverse compounds. SHAP analysis was conducted, which identified 20 molecular descriptors critical for accurate predictions. The ensemble model accurately predicted the binding affinities of Nirmatrelvir and its four analogues (E1–E4), reproducing the experimental pIC50 trend and correctly identifying the most potent inhibitors. The ensemble model also showed consistent performance across all MERS-CoV Mpro variants, S147Y, S142G, L144A, S142G/S147Y, and S142G/L144A/S147Y, demonstrating its potential for rapidly discovering mutation-resistant antiviral drugs. Full article

Alternative pre-mRNA splicing is a central layer of gene regulation that enables a limited number of genes to generate a far larger and more context-dependent transcriptome and proteome. In cancer, splicing is disrupted by mutations in cis-regulatory sequences, recurrent lesions in spliceosome components, altered abundance or activity of RNA-binding proteins, and changes in transcription, chromatin, RNA modification, metabolism and stress signalling. These alterations are not merely by-products of malignant transformation. They can create oncogenic protein isoforms, eliminate tumour-suppressive products, remodel cellular identity, promote metastasis and drug resistance, and generate tumour-restricted peptides that are visible to the immune system. Large pan-cancer datasets, long-read sequencing, single-cell isoform profiling, proteogenomics and functional perturbation screens are now resolving this complexity at unprecedented scale. In parallel, multiple therapeutic strategies are advancing, including modulators of the SF3B complex, molecular glues that degrade RBM39, inhibitors of protein arginine methyltransferases and splicing kinases, splice-switching oligonucleotides, programmable RNA-targeting systems, and vaccines or T-cell receptors directed against splicing-derived neoantigens. This review integrates the molecular logic of splice-site selection with the cancer-specific mechanisms that perturb it, summarizes representative isoform switches across the hallmarks of cancer, evaluates emerging technologies and clinical biomarkers, and discusses the opportunities and constraints of translating splicing biology into precision oncology. Particular emphasis is placed on tumour specificity, intratumoural heterogeneity, proteomic validation, therapeutic windows and rational combination strategies. Full article

Melanoma, a skin cancer with the highest mortality rate, poses a significant medical challenge. Despite the revolution in the treatment of this cancer brought about by the development of immunotherapy and targeted therapies using BRAF/MEK inhibitors, the complex mutation profile and the development of drug resistance compel researchers to seek new solutions. Cyclin-dependent kinases (CDKs), a group of enzymes regulating fundamental processes in every eukaryotic cell, are generating significant interest in the context of potential targeted therapies for melanoma. The best-studied CDKs, responsible for controlling specific phases of the cell-cycle, have been extensively described in the literature, and their inhibition is increasingly used as a treatment for various cancers. However, in addition to the classic cell-cycle CDKs, CDKs regulating transcription can also be distinguished. Other family members responsible for tissue-specific processes are commonly referred to as atypical or untypical CDKs. These include CDK5, which plays a critical role in the nervous system. In recent years, a growing body of research has focused on the role of transcriptional and atypical CDKs in the progression of cancers, including melanoma. However, their precise function remains unclear. This paper will provide an overview of the role of CDKs, other than cell cycle CDKs, in melanoma development and provide a comprehensive understanding of their potential use in future targeted therapies. The advantages and disadvantages of inhibiting these kinases in melanoma therapy will be discussed, as well as the synergies with various molecular pathways analyzed to date. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article

Developmental disorders often arise from complex interactions between genetic variation and environmental factors, yet the molecular mechanisms underlying gene-gene (G × G) and gene-environment (G × E) interactions remain poorly understood. Mouse embryonic eyelid closure provides a genetically tractable in vivo model for investigating these mechanisms. Eyelid closure requires coordinated epithelial migration and cytoskeletal remodeling orchestrated by interconnected signaling pathways. Among these pathways, MAP3K1 functions as a critical signaling hub that integrates inputs from S1PR-RHOA-ROCK and other upstream regulators to activate JNK and promote eyelid closure. Genetic studies show that multiple components within the GPCR-RHOA-ROCK-MAP3K1-JNK network cooperate to maintain developmental robustness. Reducing the activity of pathway components dose-dependently impairs eyelid closure and produces the characteristic eye-open-at-birth (EOB) phenotype. Environmental factors also converge on this network. Although exposure to dioxin does not impair eyelid closure in wild-type embryos, it induces EOB in embryos harboring otherwise phenotypically silent mutations in the MAP3K1 network, such as Map3k1^+/−, Jnk1^−/− and S1pr2^−/−. These findings identify the MAP3K1 pathway as a point of convergence of genetic and environmental signals and establish embryonic eyelid closure as a powerful model for elucidating molecular mechanisms underlying developmental robustness, susceptibility and resilience. Full article

Climate change and human activities are significantly altering hydrological systems, intensifying extreme events such as hydrological drought patterns. These changes pose critical challenges for water sustainability, requiring a deeper understanding of prolonged hydrological droughts. Under the complex changing environment, hydrological drought in the Upper Yangtze River is becoming increasingly severe. Using monthly scale meteorological and hydrological data, this study quantitatively conducted an attribution analysis of hydrological drought in the Upper Yangtze River through a multi-method framework combining mutation analysis, a hydrological model, and machine learning. The results showed that: (1) the coupled model had higher precision and better applicability in a basin with more complex changing environments. (2) Climate change was the dominant factor for most months of monthly scale hydrological drought in the Wujiang, Jialing, and Upper Yangtze River Basins, while human activities were the main cause of hydrological drought in most months in the Tongtian River. (3) The dominant factor for seasonal hydrological drought in the Wujiang and Upper Yangtze River Basins was climate change. The seasonal hydrological drought in the Tongtian River and Jialing River basins was jointly affected by climate change and human activities. Full article

Philadelphia-negative (Ph-negative) myeloproliferative neoplasms (MPNs) include polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF), which are clonal hematopoietic disorders caused by somatic gene mutations in the JAK2, CALR, or MPL genes. Mutations activate the JAK–STAT pathway and disrupt NF-κB signaling, leading to a chronic inflammatory state caused by pro-inflammatory cytokines and reactive oxygen species (ROS). This altered microenvironment causes serious clinical features of the disease, such as bone marrow fibrosis, splenomegaly, vascular niche remodeling, and a greater probability of thrombosis or secondary leukemic transformation. Concurrently, MPNs cause both severe immune dysregulation and tumor evasion, as evidenced by progressive lymphopenia, T and B cell exhaustion, Natural Killer cell maturation arrest, and the accumulation of myeloid-derived suppressor cells. Although FDA-approved JAK1/JAK2 inhibitors ruxolitinib, fedratinib pacritinib and momelotinib effectively reduce splenomegaly and symptom burden and have demonstrated survival benefits in clinical trials, their ability to eliminate malignant clones or induce durable disease modification remains limited, and disease progression continues to occur in most patients. Finally, this review assesses the complex immunological dysfunction and chronic inflammatory dysregulation that characterize Ph-negative MPNs, as well as emerging therapeutic strategies, emphasizing the importance of fully understanding these intricate microenvironmental mechanisms for the identification and development of novel precision treatment targets. Full article

Airport surface scheduling at multi-runway airports is a complex system engineering task that balances efficiency, safety, and sustainability, making it a key research focus in the field of air traffic management. This study proposes a cosine curve-based pushback rate control strategy and a collaborative pushback and taxiing decision-making method for departure aircraft pushback and taxiing. Additionally, the Markov decision process under dynamic pushback control at dual-runway airports is analyzed. An adaptive departure operation optimization model is established. This model considers path conflicts, fuel consumption, taxiway queuing, and runway occupancy during an aircraft departure process, enhancing the operational efficiency in the temporal and spatial dimensions. In the aircraft departure process, a genetic simulated annealing algorithm with nested Markov state transitions is proposed as the optimization algorithm, utilizing a Q-learning algorithm to adaptively adjust crossover and mutation parameters. The effectiveness of the proposed model and algorithm is validated through simulation experiments at Beijing Capital International Airport. Results indicate that this approach can significantly reduce the estimated taxiing fuel-related operating cost by 17.53% in the simulated case, shorten average taxiway waiting time by 56.24%, and decrease average taxi completion time by 20.81%, thereby improving overall airport operational efficiency. Full article