Search Results (1,568,155)

Background: Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, characterized by high genomic instability, metabolic stress, and limited therapeutic options. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a promising vulnerability in TNBC, yet its subtype-specific regulatory landscape remains insufficiently defined. Methods: Using transcriptomic (METABRIC, TCGA, GEO) and proteomic (CPTAC) datasets, ferroptosis-related genes were profiled across PAM50 breast cancer subtypes. Differential expression, univariate Cox regression, LASSO modeling, survival analyses, GSEA, and dimensionality reduction (PCA, t-SNE) were applied. A Ferroptosis Index (FI) was calculated using β-coefficients from the Cox/LASSO regression model. Single-cell RNA-seq data was used to map ferroptosis-associated signature across tumor and microenvironmental compartments. Results: Basal-like tumors exhibited the strongest ferroptosis-associated transcriptional shift, characterized by upregulation of ACSL4 and EZH2 and downregulation of AR, GPX4, and CIRBP. Sixteen ferroptosis-related genes were associated with overall survival, forming a ferroptosis-associated signature. The FI was significantly higher in Basal-like tumors, indicating elevated ferroptosis-associated transcriptional state. GSEA revealed enrichment of cell cycle, mitotic, cytoskeletal, and metabolic stress pathways. Single-cell analysis demonstrated expression of ferroptosis markers across cancer epithelial, stromal, and myeloid populations. Conclusions: Basal-like tumors harbor a distinct ferroptosis-associated transcriptional state linked to tumor aggressiveness and poor prognosis. These findings provide a biologically grounded framework for ferroptosis-related stratification and support future functional and translational studies targeting ferroptosis vulnerabilities in aggressive breast cancer. Full article

This paper presents a superpixel segmentation algorithm that integrates anisotropic total variation regularization within a fuzzy clustering framework. While isotropic total variation is well-known for its edge-preserving properties, its non-adaptive nature often leads to over-regularization. In contrast, the anisotropic model formulates superpixel regularity in relation to image contours, thereby preventing the loss of image details in areas of high contour density during optimization. Compared to classical segmentation algorithms that employ non-adaptive regularization, the proposed content-adaptive approach enhances superpixel regularity while maintaining boundary adherence to image contours. Furthermore, to optimize the functional effectively, an alternating direction method of multipliers along with the enhanced Chambolle’s fast duality projection algorithm are employed. Competitive experiments against existing regular segmentation algorithms demonstrate that our proposed methodology achieves superior performance in terms of boundary recall, compactness, and shape regularity criteria, outperforming these methods by an average of at least 3%, 5%, and 3%, respectively. Furthermore, when compared with irregular segmentation algorithms, our approach achieves the best results in terms of compactness, contour density, and shape regularity criteria, with average improvements of at least 56%, 22%, and 45%, respectively. Full article

Today, machine learning (ML) is generally considered a potent and efficient tool for addressing studies in various diverse domains, including image processing and event prediction on a timescale. ML represents complex relations between features, and these mappings between such features may be applied in simulations of time-dependent events, such as the behavior of animals. Still, ML inherently strongly depends on extensive and consistent datasets, a fact that reveals both the benefits and drawbacks of ML. In the use of ML, insufficient or skewed data can limit the ability of algorithms to accurately predict or generalize possible states. To overcome this limitation, this work proposes an integrated hybrid approach that combines machine learning with methods from cognitive science, here especially inspired by the ACT-R model to approach cases of missing or unbalanced data. By incorporating cognitive processes such as memory, perception, and attention, the model accounts for the internal mechanisms of decision-making and environmental interaction where traditional ML methods fall short. This approach is particularly useful in representing states that are not directly observable or are underrepresented in the data, such as rare behavioral responses for animals, or adaptive strategies. Experimental results show that the combination of machine learning for data-driven analysis and cognitive ‘rule-based’ frameworks for filling in gaps provides a more comprehensive model of animal behavior. The findings suggest that this hybrid approach to simulation models can offer a more robust and consistent way to study complex, real-world phenomena, especially when data is inherently incomplete or unbalanced. Full article

Hydrometallurgical pretreatment of pyrite-bearing concentrates and tailings by hydrothermal interaction with Cu(II) solutions is a promising route for chemical beneficiation and mitigation of acid mine drainage but is limited by passivation caused by elemental sulfur and secondary copper sulfides. Here, the effect of sodium lignosulfonate (SLS) on the hydrothermal reaction between natural pyrite and CuSO₄ in H₂SO₄ media at 180–220 °C was studied at [H₂SO₄]₀ = 10–30 g/dm³, [Cu]₀ = 6–24 g/dm³, and [SLS]₀ = 0–1.0 g/dm³. Process efficiency was evaluated by Fe extraction into solution and Cu precipitation on the solid phase, and products were characterized by XRD and SEM/EDS. SLS markedly intensified pyrite conversion: at 200 °C and 120 min, Fe extraction increased from 14 to 26% and Cu precipitation from 5 to 23%, while at 220 °C, Fe extraction reached 33.4% and Cu precipitation 26.8%. XRD confirmed the sequential transformation CuS → Cu_1.8S. SEM/EDS showed that SLS converts localized nucleation of Cu_xS on defect sites into the formation of a fine, loosely packed, and well-dispersed copper sulfide phase. The results demonstrate that lignosulfonate surfactants efficiently suppress passivation and enhance mass transfer, providing a basis for intensifying hydrothermal pretreatment of pyrite-bearing industrial materials. Full article

Helicobacter pylori is a prevalent gastric pathogen that establishes chronic infection and contributes to gastritis, peptic ulcer disease, and gastric cancer. Its persistence depends on immune evasion strategies that promote sustained low-grade inflammation in the gastric mucosa. Nucleotide-binding oligomerization domain-like receptors (NLRs) are cytosolic pattern recognition receptors that play key roles in innate immune responses against H. pylori. Nod1 and Nod2 detect bacterial peptidoglycan delivered via the type IV secretion system or outer membrane vesicles, activating NF-κB, MAPK, and interferon signaling pathways that regulate inflammatory cytokine production, epithelial barrier function, autophagy, and antimicrobial defense. The NLRP3 inflammasome mediates the maturation of IL-1β and IL-18 primarily in myeloid cells, thereby shaping inflammatory and immunoregulatory responses during infection. In contrast, NLRC4 functions in a context-dependent manner in epithelial cells and is largely dispensable for myeloid IL-1β production. Emerging evidence also implicates noncanonical NLRs, including NLRP6, NLRP9, NLRP12, NLRX1, and NLRC5, in regulating inflammation, epithelial homeostasis, and gastric tumorigenesis. In addition, genetic polymorphisms in NLR genes influence host susceptibility to H. pylori-associated diseases. This review highlights the interplay between NLR signaling, bacterial virulence, and host immunity and identifies potential therapeutic targets. Full article

This study established an HPLC-MS/MS method to quantify the enantiomers of spirotetramat in tubifex. To assess the accuracy and precision of the approach, recovery tests were conducted for insecticide. For all enantiomers, the limits of detection were 0.003 mg/kg. The quantization limits were 0.01 mg/kg. Spirotetramat enantiomers recovery rates in tubifex were found to be between 81 and 114%, with relative standard deviations being less than 7%. The half-lives of spirotetramat enantiomers in tubifex were 3.81–10.58 d, respectively. The 22.4% spirotetramat suspension was sprayed on tubifex three times at a low dosage (high dosage advised). After 14 days after harvesting, the terminal residues of spirotetramat enantiomers in the tubifex were less than 0.03 mg/kg. The findings offer a quantitative foundation for setting China’s maximum residue limits as well as a recommendation for the safe and responsible usage of spirotetramat enantiomers in tubifex. Full article

Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host–pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies—particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics—has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice. Full article

With the transformation of energy structure to low carbon, the clean and efficient utilization of coal has received extensive attention. Among them, the technology of preparing super-clean coal by solvothermal extraction has become a research hotspot because of its good product characteristics. In this paper, FGZ coal was used as a raw material to explore its synergistic extraction effect with wood charcoal at different mass ratios, and the extracted products were systematically characterized by various analytical methods. The results show that the addition of biomass can effectively improve the extraction yield of this coal. When the biomass addition ratio was 35%, the extraction yield reached the highest value of 69.47%, which was about 28.3% higher than that without biomass. Hypercoal has a smooth surface with significantly fewer impurities than raw coal. At the same time, Raman spectroscopy and X-ray diffraction analysis showed that when the biomass addition ratio was 35%, the I_D/I_G reached a minimum value of 0.8354, and the structural order of the extracted product was the highest. When the biomass addition ratio was 35%, the L_c reached a maximum value of 2.0569 nm, indicating the highest degree of carbon layer stacking and structural order among the samples. Full article

In complex maritime environments, the safety of path planning for Unmanned Surface Vehicles (USVs) remains a significant challenge. Existing methods for handling dynamic obstacles often suffer from inadequate predictability and generate non-smooth trajectories. To address these issues, this paper proposes a reliable hybrid path planning approach that integrates a reinforcement learning-enhanced A* algorithm with an improved Dynamic Window Approach (DWA). Specifically, the A* algorithm is augmented by incorporating a dynamic five-neighborhood search mechanism, a reinforcement learning-based adaptive weighting strategy, and a path post-optimization procedure. These enhancements collectively shorten the path length and significantly improve trajectory smoothness. While ensuring that the global path avoids dynamic obstacles smoothly, a Kalman Filter (KF) is integrated into the Long Short-Term Memory (LSTM) network to preprocess historical data. This mechanism suppresses transient outliers and stabilizes the trajectory prediction of dynamic obstacles. Moreover, the evaluation function of the DWA is refined by incorporating the International Regulations for Preventing Collisions at Sea (COLREGs) constraints, enabling compliant navigation behaviors. Simulation results in MATLAB demonstrate that the enhanced A* algorithm better conforms to the kinematic model of the USVs. The improved DWA significantly reduces collision risks, thereby ensuring safer navigation in dynamic marine environments. Full article

Wildfires are one of the main natural hazards around the world, and are becoming increasingly important in the current context of climate change. To limit the impacts of fires, policies are implemented following various phases of risk management. These concern prevention (risk communication and information, forest monitoring, fuel management, the installation of firewalls, etc.) and suppression (firefighting interventions) measures. This article presents a systematic literature review analyzed through the prism of climate change and policy. It is carried out using a textometric approach. The corpus is composed of 720 articles published from 1997. A marked increase is evident from 2021. The analysis enables the clustering of the main issues. Six main themes were revealed by Reinert Clustering: Health issues, Disaster risk management, Natural environment, Management of the natural environment, Fire characteristics, and Fire modeling. These themes are composed of 36 sub-themes. In addition, the article shows that some issues (anthropogenic health and management/governance issues, and natural environment issues around fire and natural environment characterization) remain constant over time while others increase/decrease in importance (air quality, carbon storage and CO₂ emissions, ecosystems and biodiversity, and the effects of fires on the natural environment at the expense of anthropogenic issues). Full article

China’s roof-cutting and pressure relief gob-side entry retaining (RCPR-GER) technology provides an efficient non-pillar mining solution that significantly enhances coal recovery. This paper presents a systematic review of the technological progress in Chinese coal mines from 2011 to 2023, based on an analysis of 1038 publications from CNKI, EI, and Web of Science using VOS viewer and Origin software. Four main technical approaches are examined: gob-side entry retaining without roadside filling, with roadside filling, with roof-cutting and pressure relief, and hybrid methods. Five key roof-cutting techniques are evaluated: dense drilling, high-pressure water-jet slotting, hydraulic fracturing, blasting, presplitting, and roof water injection softening. Successful applications have been documented in coal seams with thicknesses of 1.6–6.15 m and burial depths of 92–1037 m, demonstrating wide adaptability. The roof-cutting short-beam theory underpins the mechanism, which reduces roadway deformation, shortens the cantilever beam length, and alters stress transfer paths. Compared to previous reviews on general gob-side entry retaining, this study offers a dedicated synthesis and comparative analysis of RCPR-GER technologies, establishing a selection framework grounded in geological compatibility and engineering practice. Future research should focus on adaptive parameter design for deep hard composite roofs, quantitative modeling of passive roof-cutting effects, optimization of cutting timing and orientation, and floor-heave control technologies to extend applications under complex geological conditions. Full article

Polyethylene film (PE) mulching produces substantial “white pollution,” prompting the use of biodegradable film (BF) alternatives, yet their performance in rice systems on Northeast black soils is still uncertain. We compared three BFs with different induction periods (45 d, BF₄₅; 60 d, BF₆₀; 80 d, BF₈₀), PE and a no-film control (CK) to quantify their effects on soil hydrothermal conditions, rice growth, yield, grain quality, irrigation water use efficiency (IWUE) and soil C, N. Results showed that mulching increased soil temperature and soil moisture. Across the growing season, the mean soil temperature at the 0–5 cm depth under PE was 5.5% and 2.2–5.5% higher than that under CK and BFs, respectively. Specifically, compared with CK, PE increased grain yield by 31–77% and IWUE by 75–123%, while BFs improved yield by 25–73% and IWUE by 48–101%. PE only slightly outperformed BF₈₀ in yield (by 2.3% in 2023 and 2.1% in 2024) but achieved higher IWUE (11.0–11.7%). Grain chalkiness and sensory scores under BFs were comparable to PE and better than CK. At 0–20 cm, PE increased SOC (2.3–6.8%) and the C/N ratio (0–0.8%) but reduced total nitrogen (TN) (2.7–3.9%) and total carbon (TC) (2.5–3.1%), whereas BFs increased Org-N by 0.4–4.2%, SOC by 2.9–7.1%, and TN by 0.2–0.7%, with BF₈₀ showing the greatest stimulatory effect. Overall, BFs—particularly BF₈₀—are promising substitutes for PE in black soil rice systems, supporting sustainable rice production with strong application potential. Full article