Search Results (18,428)

Wide-field infrared imaging systems are often constrained by detector size, cooling requirements, and payload limitations, leading to the need for multi-FOV detector sharing. However, conventional geometric multiplexing introduces severe spatial aliasing, which significantly degrades target localization performance. This paper proposes a polarization-encoded field-of-view multiplexing method for recovering spatial information from aliased detector measurements. The imaging plane is divided into multiple FOV regions, each assigned a distinct polarization state. After optical folding, the modulated sub-images are superimposed onto a common detector region. Six-channel polarization measurements are used to reconstruct pixel-wise Stokes vectors, and the spatial origin of each pixel is identified through polarization-domain similarity matching and target-level voting. MATLAB-based simulations were conducted using a nine-region multiplexing configuration. The proposed method achieves 97.3% pixel-level classification accuracy under ideal conditions and maintains over 95% accuracy at a noise level of σ = 0.02. The normalized Stokes reconstruction error is below 0.02, and stable performance is observed under polarization modulation deviations within ±10°. By introducing polarization as an additional encoding dimension, the proposed framework enables efficient separation of multiplexed spatial information without increasing detector resources, demonstrating its potential for compact wide-field infrared sensing applications. Full article

Injection molding process optimization has predominantly relied on simulation-generated data, which cannot capture machine-specific variability and stochastic process noise inherent in real manufacturing environments. This paper presents an experiment-driven machine learning framework for multi-objective optimization of injection molding process parameters targeting volumetric shrinkage, warpage, cycle time, and part weight. Physical experiments were conducted on an industrial injection molding machine using high-density polyethylene with a face-centered central composite design. Systematic benchmarking of four machine learning algorithms under identical cross-validation protocols identified Gaussian process regression as the best-performing surrogate model for the majority of quality metrics, while warpage prediction remained challenging across all algorithms due to its complex thermo-mechanical origins. Permutation-based feature importance analysis established a clear parameter hierarchy, identifying holding time as the dominant factor governing multiple quality responses. Constrained Bayesian optimization with progressive constraint tightening was employed to identify optimal parameter sets and fundamental process capability boundaries. The resulting parameter configurations were validated against a held-out test set. This work demonstrates that rigorous, data-driven optimization using exclusively experimental data provides a viable and practically achievable alternative to simulation-based approaches, contributing to experiment-centric smart manufacturing in polymer processing. Full article

Cannabinoid receptors occupy strategic control nodes within motor circuitry, making them potential targets for modulating different motor manifestations. They are positioned both within basal ganglia circuits that regulate movement and within spinal circuits that control skeletal muscle tone. Consequently, cannabinoids have been studied across diverse motor disorders, most notably in movement disorders and tone disorders, particularly those resulting in spasticity. Because motor control spans multiple anatomically and functionally distinct levels, relating cannabinoid signaling to effects on motor function is not straightforward. Limited understanding of cannabinoid receptor distribution has led to cannabinoids being tested even in disorders where receptor localization would predict little or no benefit. Mapping receptor distribution within individual motor circuits and integrating them with their pharmacological effects can help anticipate how cannabinoid signaling shapes motor output. Combined with characteristic motor manifestations, one can identify motor disorders in which cannabinoids may have therapeutic value. In this review, we integrate existing evidence to place cannabinoid receptors within key motor pathways, ranging from basal ganglia circuits controlling movement to peripheral mechanisms governing muscle tone. We consider both cannabinoid 1 receptor (CB₁R) and cannabinoid 2 receptor (CB₂R), with CB₂R gaining attention only recently for its potential relevance within the central nervous system. Building on this framework, we infer how cannabinoids acting at these sites may modulate motor control, and consequently, influence motor manifestations across major motor disorders. Finally, we examine how these distribution-based expectations align with available clinical observations. Full article

Background: The clinical efficacy of the BCL-2 inhibitor venetoclax in acute myeloid leukemia (AML) is significantly undermined by the frequent emergence of drug resistance, which precipitates disease progression and poor patient outcomes. However, the molecular landscape of this resistance remains insufficiently understood. Methods: To address this, we developed venetoclax-resistant AML cell models and utilized transcriptomic profiling integrated with comprehensive in vitro and in vivo functional assays. Results: Resistant cells demonstrated sustained proliferation even under the suppression of BCL-2, MCL-1, and key intrinsic apoptotic markers, including cleaved PARP and caspase-9, indicating a bypass mechanism independent of classical BCL-2 signaling. Compared to their sensitive counterparts, resistant Kasumi-1 (VENK) and MV4-11 (VENM) cells exhibit aggressive growth phenotypes in vitro and in vivo, characterized by larger, more numerous spheroids and colonies, alongside heightened tumorigenicity in murine models. Transcriptomic profiling and KEGG analysis identified the neuroactive ligand–receptor interaction (NLRI) pathway as a significant signaling node shared between these resistant lines. While multiple NLRI-associated genes were altered, CHRNB4 was consistently and significantly downregulated in both VENK and VENM cells and tumors. Re-expression of CHRNB4 in resistant cells, a primary gain-of-function approach, significantly impaired colony formation, and tumor growth in vivo. Clinically, CHRNB4 downregulation correlates with shortened overall survival and diminished response to venetoclax. Conclusions: Our findings implicate the NLRI pathway in venetoclax resistance and identify CHRNB4 as a robust prognostic indicator and a promising therapeutic target for developing next-generation AML strategies. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Zr alloy-shaped charge liners (SCLs) offer broad application prospects due to their multiple post-penetration damage effects. However, research on these liners is still in its early stages. The mechanisms of jet formation and penetration for Zr alloys SCL remain unclear, and the specific contribution of different liner regions to the penetration process is not yet understood. This gap in knowledge has limited their structural design to a black-box correlation between global structural parameters and macroscopic penetration efficiency. To address this gap, a region-tracing Smoothed Particle Hydrodynamics (SPH) simulation was employed. Following a strategy of “wall thickness layering + axial segmentation,” the Zr alloy liner was partitioned into ten characteristic regions. This methodology facilitated the tracking of material transport from each region during jet formation and penetration into an AISI 1045 steel target. The contribution of each region to the penetration depth was then quantitatively assessed via post-processing. For the first time, the “critical region” contributing most to penetration depth was identified, and the influence of the liner’s cone angle and wall thickness on the contribution of each region was revealed. This study enhances the theoretical framework for understanding the damage effects of Zr alloy shaped charge liners. It not only advances the fundamental understanding of jet penetration mechanisms but also provides a theoretical basis for the refined design and performance optimization of these liners. Full article

Recent evidence links lens epithelial cell (LEC) dysfunction and cellular senescence—an irreversible cell cycle arrest with a pro-inflammatory secretory phenotype—to age-related cataract (ARC) progression. This systematic review synthesizes current knowledge on LEC senescence, its molecular features, and laboratory methods for senescence assessment in the ARC. Following PRISMA guidelines, a comprehensive search of PubMed, Scopus and Cochrane databases retrieved 3417 records from inception to 9 February 2025, with 14 studies ultimately included (821 patients and multiple in vitro LEC models). The following multiple senescence expression pathways were identified: SA-β-gal activity, p53/p21 and p16INK4A pathway activation, mitochondrial dysfunction, oxidative stress, and secretion of senescence-associated secretory phenotype (SASP) factors. Notably, cortical cataract demonstrated direct association with local senescent cell accumulation, while nuclear cataract reflected cumulative oxidative damage from impaired LEC-mediated antioxidant defense. Senescence markers correlated positively with cataract severity across multiple studies. Several potential therapeutic targets emerged, including metformin (AMPK activation/autophagic restoration), circMRE11A silencing, NLRP3 inflammasome inhibition, and modulation of FYCO1/PAK1 and MMP2 pathways. This review establishes LEC senescence as a central process in ARC pathogenesis and highlights promising senotherapeutic approaches. Future research should prioritize human surgical samples, develop standardized senescence detection panels (SA-β-gal + p21/p16 + SASP factors), and conduct longitudinal studies to establish causal relationships between senescence accumulation and cataract progression. Full article

This paper presents a parametric modeling and aerodynamic matching optimization methodology for the second-stage stator of a multi-stage centrifugal compressor. Firstly, based on the geometric configuration of the two-stage components, a flexible parametric template is established for the second-stage stator. Secondly, numerical simulations are conducted to analyze the internal flow field and evaluate the performance of the initial design of this compressor, revealing performance deficits such as significant vortex-induced losses and a large outlet circumferential flow angle (−12.138°). Thirdly, an aerodynamic optimization framework integrating a Kriging surrogate model and a Genetic Algorithm (GA) is applied to the second-stage stator, targeting at the aerodynamic matching optimization under multiple operating conditions. The optimization objectives include maximizing the overall polytropic efficiency of compressor and the static pressure ratio of second-stage stator, as well as minimizing the total pressure loss coefficient and the outlet circumferential flow angle of second-stage stator. The results demonstrate that the optimized design achieves a 2.17% improvement in the overall polytropic efficiency and a 12.01% improvement in the static pressure recovery coefficient at the design condition, along with a notable reduction in the outlet circumferential flow angle to 0.663°. Under multi-condition operation, the optimized stator exhibits enhanced performance stability. The overall polytropic efficiency is improved by 2.06% and the static pressure recovery coefficient is improved by 23.31% at the low-flow condition, confirming the effectiveness of the employed parametric modeling and sequential optimization approach. Full article

Alpha-mangostin (AM), a xanthone from Garcinia mangostana, has shown promising nephroprotective properties, but its mechanisms in acute kidney injury (AKI) remain incompletely defined. In this study, we applied an integrative network pharmacology pipeline combined with molecular docking to clarify AM’s multi-target mechanisms in AKI. We identified 128 predicted AM targets and intersected them with AKI-related genes, yielding 122 shared targets. Protein–protein interaction analysis identified ten hub genes—TNF, AKT1, IL6, SRC, CTNNB1, HSP90AA1, NFKB1, HIF1A, PPARG, and PTGS2—implicating inflammatory, hypoxia, and cell-survival pathways. KEGG enrichment highlighted HIF-1 signaling, PI3K–Akt signaling, chemokine signaling, AGE–RAGE signaling, and pathways related to cellular senescence and oxidative stress, while GO terms emphasized responses to chemical/oxygen-containing compounds, kinase activity, signal transduction, and apoptosis. Molecular docking against the ten hub proteins showed favorable binding energies across multiple targets. The strongest predicted affinities were observed for PTGS2 (−11.13 kcal/mol), TNF (−9.74 kcal/mol), and AKT1 (−9.48 kcal/mol). Docking positioned AM within the COX-2 catalytic pocket, engaging key catalytic and hydrophobic residues similar to known inhibitors. MD simulation interaction analysis confirmed that AM maintained stable contacts with key human PTGS2 residues, characterized by dominant hydrogen bonds and water-bridge interactions with SER353, TYR355, ARG513, and SER530, along with consistent hydrophobic contacts, and persistent interactions sustained throughout the 200 ns trajectory. Collectively, these results suggest that AM modulates interconnected inflammatory, hypoxic, and survival pathways relevant to AKI, acting as a multi-target ligand with notable interaction involving COX-2, TNF, and AKT1. Further experimental validation and formulation strategies to improve bioavailability are recommended for the advancement of AM toward therapeutic evaluation in AKI. Full article

Cyclodextrins (CDs) have gained increasing attention as versatile platforms for enhancing drug delivery to the central nervous system, particularly in overcoming the restrictive properties of the blood–brain barrier (BBB). Owing to their unique cyclic oligosaccharide structure, CDs are capable of forming inclusion complexes with a wide range of therapeutic agents, thereby improving their solubility, stability, and bioavailability. In addition to their role as excipients, growing evidence indicates that CDs can actively modulate biological processes, including membrane fluidity and cholesterol homeostasis, which are critical factors in neurological disorders. This review explores the application of CDs in facilitating drug transport across the BBB through multiple mechanisms, including carrier-mediated transport, receptor-mediated transcytosis, and nanoparticle-based delivery systems. Special emphasis is placed on their use in the treatment of neurodegenerative and neurological diseases, such as Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, Niemann–Pick type C disease, and other central nervous system disorders. In these contexts, CD-based formulations have demonstrated the ability to enhance brain targeting, reduce pathological protein aggregation, and improve therapeutic outcomes in preclinical models. This review uniquely integrates cyclodextrin’s physicochemical properties with specific blood–brain barrier transport mechanisms, proposing a structure–transport–therapy framework that enables a more predictive understanding of brain-targeted drug delivery. Full article

The pathways governing the transformation of crop residues into humic acid (HA) remain incompletely understood because multiple biochemical routes may operate simultaneously during composting-like humification. In this study, a 30-day solid-state humification experiment was conducted by integrating physicochemical pretreatments, including steam explosion (SE) and ammonification coupled with steam explosion (SE-N), with a multi-fungal inoculation strategy involving Aspergillus niger, Candida spp., and Phanerochaete chrysosporium. Across three representative substrate–pretreatment systems and 81 experimental groups, the contents of lignocellulosic fractions, reducing sugars (RS), a UV-280-based soluble nitrogen-containing precursor index (operationally denoted as SNP), fulvic acid (FA), and HA were compared. The results showed that neither physicochemical pretreatment alone nor single-strain inoculation was sufficient to achieve substantial HA formation. SE mainly improved substrate accessibility and promoted carbon release, whereas ammonification provided essential nitrogen preloading for subsequent precursor coupling. In the saccharification-dominant treatment, RS reached 27.5%, but HA remained negligible. In the Candida-only treatment, the soluble nitrogen-containing precursor index increased markedly, yet HA formation was still minimal. By contrast, the highest HA yield (13.7%) was obtained under multi-fungal co-inoculation, particularly when nitrogen preloading by ammonification was combined with concurrent accumulation of carbon and aromatic precursors. The data suggest that lignin-targeting activity by P. chrysosporium was associated with the likely generation of phenolic and quinone-like intermediates that bridged the condensation of sugar- and nitrogen-derived compounds. Overall, the findings support a synergistic humification framework in which polysaccharide depolymerization, microbial nitrogen transformation, and lignin-derived aromatic precursor formation jointly contribute to HA accumulation, rather than a single linear pathway dominating the process. Full article

Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation–maximization (EM) framework is proposed in which data association variables and motion model variables are jointly modeled as latent variables. These variables are estimated through iterative updates based on the loopy belief propagation (LBP) algorithm and the interacting multiple model (IMM) filtering and smoothing algorithms to generate high-confidence tracklets. Then, a delayed decision-making strategy based on the multi-hypothesis approach is employed to associate these tracklets into complete target trajectories. The resulting algorithm is named IMM-TrackletMHT. The performance of the IMM-TrackletMHT algorithm is evaluated and compared with several baseline algorithms in simulated scenarios under different clutter rates and detection probabilities. The simulation results demonstrate that the proposed algorithm consistently outperforms the baseline methods in terms of tracking accuracy, exhibits strong robustness to variations in the operating environment, and achieves higher computational efficiency in multi-scan measurement processing, thereby demonstrating the effectiveness and superiority of the proposed tracklet generation approach for maneuvering MTT. Full article

Eigenvector-based centrality captures recursive notions of importance in networks. While the direct problem computes centrality from given edge weights, the inverse eigenvector centrality problem seeks edge weights that reproduce a prescribed centrality profile; for directed multigraphs, this inverse task is typically non-unique and depends on the admissible arc structure. We study the direct and inverse problems on directed multigraphs and derive an explicit linear characterization of the set of admissible edge-weight vectors that are compatible with a given centrality target. On this feasible set, we formulate a generalized Nash equilibrium problem with shared centrality constraints, in which multiple agents select edge weights to maximize economically interpretable payoffs that incorporate arc-level competition effects. We provide conditions under which the induced game admits a concave potential function, yielding equilibrium existence and, under standard strict concavity assumptions, uniqueness. Finally, we illustrate the model on an airport network where nodes represent airports and parallel arcs represent airline-specific routes, showing that equilibrium selection produces a feasible and interpretable weight configuration that preserves the prescribed centrality. Full article

The invasive brown macroalga Rugulopteryx okamurae has rapidly expanded across the Mediterranean–Atlantic region, generating severe ecological impacts. Nevertheless, the considerable amount of biomass available creates opportunities for valorisation within circular bioeconomy frameworks. This study provides an integrated characterization of the chemical profile and bioactivities of freshly collected floating biomass of R. okamurae from southern Portugal. Proximate composition was determined, and lipophilic (hexane) and hydrophilic (water) extracts were analyzed by GC–MS and spectrophotometric methods. Antioxidant activity was assessed using complementary radical-scavenging, reducing power, and metal-chelation assays, and enzyme inhibition was evaluated against targets associated with neurodegenerative, metabolic, and dermatological disorders. The lipophilic fraction was dominated by long-chain alkanes (≈101 mg/g extract) and sterols, particularly fucosterol (≈43 mg/g extract), but exhibited low radical-scavenging capacity (no EC₅₀ reached in DPPH or ABTS assays), and no relevant enzyme inhibition. In contrast, the water extract contained measurable phlorotannins (6.61 mg PGE/g extract) and showed moderate antioxidant (ABTS: EC₅₀ = 5.17 mg/mL; FRAP: EC₅₀ = 0.78 mg/mL) and enzyme inhibition activities (BChE: IC₅₀ = 5.17 mg/mL; tyrosinase: IC₅₀ = 0.78 mg/mL). Compared with previous studies on R. okamurae, this work applies a systematic fractionation of biomass from southern Portugal into polar and non-polar fractions and, for the first time, correlates the resulting detailed chemical profiles with multiple bioactivities. This approach revealed a clear functional differentiation between fractions, with bioactivity being mainly associated with polar metabolites. Overall, these findings highlight the value of structured extraction strategies for biomass valorisation and support the sustainable management of R. okamurae. Full article

Breast cancer remains a major global health challenge, requiring novel therapeutic strategies that can overcome drug resistance and improve treatment efficacy. This study investigates the synergistic antitumor effects of etoposide, a conventional chemotherapeutic agent, and resveratrol, a natural polyphenol with anticancer properties, in human breast cancer cell lines, with particular focus on their ability to activate the parthanatos cell death pathway. Using MCF-7 (estrogen receptor-positive) and MDA-MB-231 (triple-negative) breast cancer cells, we assessed cell viability via MTT assays and evaluated parthanatos activation through multiple complementary approaches including AIF translocation determined by subcellular fractionation, NAD+ depletion measurement, and gene expression analysis. Synergy was quantified using the Chou–Talalay method across multiple effect levels (ED50, ED75, ED90). To establish causality, Olaparib PARP inhibitor experiments were performed to confirm that PARP-1 hyperactivation is essential for the observed cytotoxic effects. The results demonstrated that the etoposide–resveratrol combination significantly enhanced cell death and inhibited proliferation compared to single-agent treatments, with combination index (CI) values indicating strong synergism (CI = 0.62–0.75 for MCF-7; CI = 0.58–0.71 for MDA-MB-231). This synergy was associated with robust parthanatos activation, evidenced by increased PARP-1 expression, AIF nuclear translocation confirmed by subcellular fractionation, and significant NAD+ depletion. Critically, Olaparib pre-treatment (3 µM) significantly rescued cells from combination-induced death, restored NAD+ levels to near-control values, and prevented AIF translocation, establishing a causal link between PARP-1 hyperactivation and parthanatos-mediated cytotoxicity. The combination also induced significant DNA fragmentation, elevated oxidative stress, and cell death with morphological features consistent with parthanatos, while caspase activity remained low, confirming caspase-independent cell death. These findings suggest that targeting parthanatos with etoposide and resveratrol could offer a promising therapeutic strategy for breast cancer, potentially overcoming resistance and improving efficacy. Further in vivo studies and clinical investigations are needed to validate these results and explore translational applications. Full article