Search Results (34,909)

Plant-derived peptides have become one of the most promising classes of compounds in cancer research due to their specificity, safety, and different therapeutic actions. Generally, plant peptides have a size of 2 to 100 amino acids, and they can be extracted from different parts of the plant including leaves, seeds, stems, and roots. The present review brings together more than 300 prominent plant peptides, their sources, structural classes, extraction methods, anticancer effects, and mechanisms of action. We show the cytotoxicity of plant peptides against a wide range of human cancer cell lines (such as MCF-7, A549, HL-60, and HCT-116), as well as their effectiveness in preclinical animal models of cancer, where they resulted in lesser tumor growth and metastasis. Moreover, we go into the anticancer activity of plant peptides and reveal the interconnectedness of apoptosis, cell cycle arrest, angiogenesis inhibition, metastasis suppression, and the modulation of signaling pathways as some of the mechanisms through which plant peptides perform. In addition to their therapeutic potential, many of these peptides are derived from edible plant sources and can be delivered through functional foods or dietary supplements, offering a promising avenue for cancer prevention and adjunctive nutritional support. The review also touches upon the major hurdles in peptide drug development at present, such as stability, oral bioavailability, and large-scale production, while at the same time giving future perspectives that include bioengineering, nanotechnology-based delivery systems, and combination therapies for translating these natural products into clinical oncotherapeutics and health-promoting foods Full article

Uncertain fractional differential equations model complex systems that exhibit memory effects and are influenced by human-based uncertainty. These equations provide a flexible and accurate framework for representing real-world phenomena, particularly in situations where traditional probabilistic methods are inadequate, such as modeling financial market systems where uncertainty and memory effects play a significant role. This research first presents an existence and uniqueness result for the uncertain fractional system with the $\phi$-Hilfer fractional derivative, obtained via the successive approximation approach. Then the analytical solution is derived using the Mittag–Leffler function, and sample continuity is demonstrated under Lipschitz and linear growth conditions. To illustrate the applicability of the theory, we consider an interest rate model and provide two numerical examples to support the theoretical results on existence and uniqueness. All results are developed using the $\phi$-Hilfer fractional derivative, which generalizes the Caputo, Hadamard, and Katugampola fractional derivatives. Consequently, the results are presented in a generalized form. Full article

The implementation of the national emergency industry demonstration bases’ policies is a new way to achieve safety production governance and a key factor in improving the effectiveness of national safety production governance. This study regards China’s national emergency industry demonstration bases’ policies as a quasi-natural experiment. Based on panel data from 31 provinces in China from 2010 to 2022, a multi-period difference in differences (DID) model is conducted to systematically evaluate the impact and mechanism of this policy on China’s safety production governance. The results show that this policy significantly reduced the death rate of safety production accidents with a GDP of 100 million yuan and has a significant governance improvement effect. Further analysis of the mediating effect shows that policies mainly exert governance effects by increasing public safety financial investment and promoting innovation output. The heterogeneity analysis results indicate that policy effects are more significant in regions with weaker energy-resource industrial bases and lower levels of digital development, suggesting that the marginal governance benefits of policies are mainly concentrated in areas with relatively weak supporting conditions for safety governance. This study makes three primary contributions to the literature. Theoretically, it expands the safety governance paradigm by shifting the focus from traditional administrative “command and control” regulations to market-driven industrial agglomeration. Methodologically, by utilizing a multi-period DID model, it overcomes endogeneity issues prevalent in prior correlation-based studies to rigorously identify causal effects. Empirically, it opens the “black box” of policy transmission by validating dual pathways—fiscal resource allocation and technological innovation—while highlighting a critical “filling the gap” marginal utility effect in resource-constrained regions. This study empirically reveals the mechanism and context-dependent characteristics of industrial policies in safety governance, providing empirical evidence for understanding the inherent logic between industrial policies, public safety governance, and regional sustainable development. It offers practical insights for optimizing the precise implementation and resource allocation of emergency industrial policies to foster socially sustainable and resilient industrial growth. Full article

Accurate yield estimation is vital for precision wheat management and breeding. Traditional methods based on single growth stages or single-source data cannot capture cumulative growth effects, limiting prediction accuracy. UAV remote sensing provides high-resolution, multi-source, and multi-temporal data, enabling improved non-destructive yield estimation. In this study, UAV-based multispectral and RGB imagery were collected at six key growth stages, and vegetation indices, texture, and color features were extracted to develop yield prediction models using RF, XGBoost, and KNN under single- and multi-temporal scenarios. The results showed that red-edge-based vegetation indices were highly sensitive to wheat yield and outperformed texture- and color-based features. Multi-feature fusion further improved prediction accuracy at key growth stages, particularly during booting and flowering (R² = 0.53–0.67). Compared with single-temporal models, multi-temporal data fusion significantly enhanced yield estimation accuracy, achieving a maximum R² of 0.72 by integrating data from the late-jointing, booting and flowering stages. Among the algorithms, XGBoost and KNN exhibited superior accuracy and stability across most growth stages. Overall, these results demonstrate that integrating UAV-based multi-source and multi-temporal remote sensing data effectively improves the accuracy and robustness of wheat yield estimation, providing valuable technical support for precision agriculture and phenotyping-assisted breeding. Full article

Environmental economics and policy research has paid limited attention to interfirm spillover effects on firm-level performance. This study addresses this gap by distinguishing between the direct and spillover effects of environmental regulation and firm-specific resources on firm performance. Using panel data for Korean manufacturing firms, we estimate a dynamic spatial Durbin model (SDM) that accounts for both temporal persistence and spatial dependence. The empirical results provide clear evidence. First, environmental regulation and firm-specific factors—including intellectual capital, physical capital, and organizational slack—exert statistically significant positive direct effects on firms’ sustainable growth rate (SGR). Second, interaction effects are crucial: environmental regulation significantly enhances SGR when combined with organizational slack, highlighting the importance of internal resource conditions. Third, spatial spillover effects are identified only under specific configurations. Environmental regulation generates positive spillover effects when interacting jointly with intellectual capital, physical capital, and organizational slack, rather than as an independent driver. Similarly, physical capital produces spillover effects through its interactions with other firm resources. Importantly, these effects vary across firms. Spillover effects are more pronounced in firms with high absorptive capacity, whereas they are weaker or insignificant in firms with low absorptive capacity. Overall, the findings indicate that environmental regulation affects firm performance primarily through resource complementarities and conditional spatial interactions, offering policy implications for more targeted regulatory design Full article

Cruise ships and superyachts have experienced significant global expansion throughout the 21st century. Although the growth in cruise passenger numbers was temporarily disrupted by the COVID-19 pandemic, occupancy rates have since rebounded and even exceeded pre-pandemic levels. This study highlights the significant environmental impact of cruise ships and luxury yachts, particularly in terms of air emissions and marine pollution. Emission levels associated with different fuel types and marine engines are analysed, including the average emissions generated by the Norwegian Cruise Line fleet while docked in ports, as well as the estimated emission reductions achievable through the implementation of onshore power supply systems. To identify environmentally preferable fuel options, a hybrid ANN/MCDM framework is applied. The weighting coefficients of eight evaluation criteria are determined using the Artificial Neural Network/Extreme Learning Machine (ANN/ELM) model, ensuring an objective and data-driven assessment of their relative importance. The ANN/ELM model was trained using emission and fuel-related data collected from the literature and industry reports, and its performance was validated using standard validation procedures, achieving satisfactory predictive accuracy for determining the weighting coefficients. The final ranking of eight fuel alternatives is subsequently performed using the Ranking Alternatives by Weighting of Evaluated Criteria (RAWEC) method. The considered alternatives include conventional and emerging marine fuels currently used in practice or under technological development (A₁–A₈), while the optimization criteria (C₁–C₈) encompass major air pollutants (CO₂, NOx, SOx, CO, PM, CH₄), the fuel cost-to-consumption ratio, and the potential impact on water pollution. The water pollution criterion is assessed qualitatively using the Saaty scale. The integrated ANN/ELM–RAWEC approach enables a systematic comparison of marine fuels and supports the identification of options with the lowest overall environmental impact. Full article

Green Total Factor Productivity (GTFP) serves as a pivotal indicator for balancing high-quality economic growth with increasingly stringent environmental regulations. However, empirical evidence regarding whether and how firm-level GTFP is associated with enhanced Environmental, Social, and Governance (ESG) performance in emerging markets remains limited. This study addresses this gap by examining the GTFP–ESG nexus within the macro-context of China’s “Dual-Carbon” goals (aiming for peak carbon emissions by 2030 and carbon neutrality by 2060). Utilizing an unbalanced panel dataset of Chinese A-share listed companies strictly covering the period from 2011 to 2022 (with 2010 data exclusively used for one-period lagged variables), we construct firm-level GTFP metrics using a non-radial SBM-DDF global Malmquist–Luenberger index—incorporating both desirable economic outputs and undesirable environmental emissions—and link them with Huazheng ESG ratings. To ensure robust empirical identification, we employ two-way fixed-effects models with lagged variables, propensity score matching (PSM), and an instrumental variable two-stage least squares (IV-2SLS) approach utilizing the leave-one-out provincial average GTFP as an instrument. The results indicate a significant positive association between GTFP and overall ESG performance, as well as its three sub-pillars. Specifically, a one-standard-deviation increase in GTFP corresponds to a 0.15-standard-deviation increase in the ESG score, a marginal effect of profound economic significance, providing robust associational insights via the IV estimates. Mechanism analyses reframe traditional mediation as descriptive associational pathways, revealing that digital transformation, green innovation, and information transparency serve as significant channels, theoretically demonstrating how resource efficiency translates into social legitimacy. Heterogeneity tests show that this association is more pronounced for non-state-owned enterprises, firms in eastern China, and those with lower financing constraints. These findings unpack the “black box” between technical efficiency and sustainability, providing empirical support for policymakers to align corporate productivity with international disclosure standards (such as the EU’s CSRD). Full article

Hydrodynamic efficiency in wetland systems is governed by the complex interaction between fluid flow and vegetation density. This study quantifies the impact of seasonal emergent vegetation growth on solute transport in a V-shaped flume. Using high-resolution tracer data from high-density (January) and low-density (November) conditions, we characterized hydraulic parameters, longitudinal velocity (v), and dispersion (D), across an upstream conduit (Reach 1) and a downstream retention zone (Reach 2). Results revealed that in January, Reach 2 exhibited massive hydraulic retardation (v ≈ 1.8 cm s⁻¹) and extensive non-Fickian tailing (variance > 30,000 s²), maintaining an idealized retentive state (Pe ≈ 20). Conversely, seasonal biomass reduction in November resulted in lower variance (≈16,500 s²) and drastically increased the risk of extreme advective bypass (Pe > 500). These findings provide critical empirical validation for macro-scale models like the Dynamic Model for Stormwater Treatment Areas (DMSTAs). Specifically, the massive temporal variance observed during the retentive state yielded an empirical Tanks-in-Series value of N ≈ 5.7, directly validating standard DMSTA defaults for dense emergent marshes. Furthermore, the Transient Storage Model (TSM) storage ratio (As/A) offers a quantitative mechanism to penalize modeled void fractions, accounting for vegetative “dead zones.” By integrating these flume-derived metrics, wetland managers can optimize hydraulic designs and improve the prediction of treatment efficiency across seasonal variations. Full article

Afghanistan is highly vulnerable to the effects of climate change, which poses significant challenges to food security and environmental systems. To mitigate these challenges and promote sustainable development, it is important to adopt an integrated method that promotes food production and climate resilience for environmental sustainability. This manuscript aims to estimate the decoupling impact of green energy on CO₂ emissions and food crop production in Afghanistan, with a focus on promoting Sustainable food production. In this research article, the Nonlinear Auto Regressive Distributed Lag (NARDL) model was used to estimate data from 1996 to 2021 in Afghanistan. The NARDL bounds test confirms a stable long-run equilibrium relationship between climatic factors and food crop production. The long-run results reveal an asymmetric decoupling impact of green energy on CO₂ emission and food crop production. Specifically, a 1% positive or negative shock in the interaction between green energy and CO₂ emissions produces different outcomes for food crop production. Increasing temperature tends to decrease food production, while precipitation increases food production over the long term. Furthermore, raising CO₂ emissions negatively affects long-term food production, while greater use of green energy contributes to food production in the future. These findings underscore the need to adopt climate-resilient technologies, including climate-smart agriculture, to help farmers withstand the adverse effects of climate change. In addition, to ensure long-term stability in food production, Afghanistan should prioritize the development of green technologies. This approach would reduce agriculture’s dependence on fossil fuels and foster the growth of sustainable agricultural industries. Full article

Background: Acute erythroid leukemia (AEL) or AML-M6 predominantly affects older adults and is rare in childhood. Compared with other AML subtypes, AEL remains relatively understudied because of its rarity. We established LS-CHM, a novel AEL cell line derived from the ascitic fluid of a patient with congenital leukemia. Interestingly, leukemic cells persisted in the ascitic fluid even after successful eradication from the bone marrow and extramedullary sites. Method: Leukemia cells from the ascites fluid exhibited robust proliferation in culture independent of cytokine requirement and were further characterized by flow cytometric immunophenotyping, cytogenetics, cell cycle and doubling time analysis, colony formation, genome and RNA sequencing, myeloid gene next generation sequencing, and cytotoxicity analysis. Results: LS-CHM displayed CD36, partial CD235a, CD31, CD43, and CD71 expression and demonstrated in vitro robust growth and high sensitivity to chemotherapeutic agents. A PDX mouse model showed development of leukemia. Genomic analysis revealed a frameshift BCOR mutation in the absence of additional mutations and downregulated TP53 expression with an exonic non-deleterious mutation. RNA sequencing of LS-CHM cells revealed upregulation of two cohesin complex genes, RAD21 and SMC3, whose high levels are associated with hematopoietic stem cell differentiation into erythroid lineage. Conclusions: LS-CHM represents the first congenital AEL-derived cell line, in contrast to the predominantly adult-origin and often secondary erythroid leukemia cell lines available currently. Thus, LS-CHM provides a unique pediatric and extramedullary AEL model, expanding the existing spectrum of AEL cell lines and offering valuable opportunities for biologic and therapeutic investigations. Full article

Background/Objectives: While human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) is pivotal for breast cancer management, its reliance on additional tissue processing beyond routine H&E staining remains a clinical burden. Although virtual staining offers a potential solution, current methods often fail to explicitly account for HER2 score-specific expression patterns. To address this gap, we developed a score-aware framework designed for the precise generation of virtual HER2 IHC images. Methods: We introduce the non-contrastive multi-task (NCMT) framework, which integrates negative-free patch alignment, style–content constraints, and auxiliary HER2 score supervision for high-fidelity H&E-to-IHC translation. For rigorous evaluation, the model was validated on the BCI dataset, utilizing an official split of 3896 training and 977 independent test images derived from 51 whole-slide images. Results: NCMT demonstrated superior virtual staining performance, achieving a Fréchet Inception Distance (FID) of 38.8, a Kernel Inception Distance (KID) of 5.6, and an average Perceptual Hash Value (PHV) of 0.439. In downstream HER2 scoring tasks, while virtual IHC images alone yielded an accuracy of 83.01%, the fusion of H&E and virtual IHC further elevated performance to 97.85% accuracy and a 98.23% F1 score. These findings suggest that our framework effectively preserves diagnostic features while providing complementary information to H&E-based morphological analysis. Conclusions: NCMT enables HER2 score-aware virtual IHC generation from H&E and can serve as a complementary tool for HER2 assessment in digital pathology. Full article

Objectives: This study aimed to investigate the biological role and molecular mechanism of the RNA m⁵C methyltransferase NSUN4 in cervical cancer progression, with a focus on its involvement in ferroptosis regulation. Methods: Differential expression and survival analyses were performed using TCGA and GEPIA datasets. Functional enrichment and GSEA identified pathways associated with NSUN4 dysregulation. NSUN4 expression was validated in clinical tissues by qRT-PCR, Western blot, and immunohistochemistry. Gain- and loss-of-function assays, including CCK-8, colony formation, and Transwell assays, were conducted to assess cell proliferation and invasion. Furthermore, a nude mouse subcutaneous xenograft model was established to validate the oncogenic role of NSUN4 in vivo. Ferroptosis was evaluated using specific inhibitors and measurement of GSH and ferroptosis-related proteins. RIP, m⁵C-RIP, RNA stability, and dual-luciferase assays were performed to explore the underlying mechanism. Results: NSUN4 was markedly upregulated in cervical cancer tissues and correlated with poor prognosis. Functionally, NSUN4 enhanced tumor cell growth, migration, and invasion while inhibiting ferroptosis. Mechanistically, NSUN4 bound to and stabilized C-MYC mRNA via m⁵C methylation, activating the PI3K/Akt signaling pathway and promoting ferroptosis resistance. Conclusions: NSUN4 promotes cervical cancer progression by stabilizing C-MYC mRNA through m⁵C modification, leading to PI3K/Akt activation and suppression of ferroptosis. These findings identify NSUN4 as a novel oncogenic regulator and potential therapeutic target in cervical cancer. Full article

Background: Cytokine-like receptor family 1 (CRLF1) has been implicated in tumor progression, yet its prognostic function and mechanistic actions in prostate cancer (PCa) remain elusive. Objective: This investigation sought to clarify the functional role, molecular mechanisms, and clinical relevance of CRLF1 in the progression of PCa. Methods: We conducted extensive bioinformatics analyses utilizing the protein interaction networks and the TCGA-PRAD dataset. CRLF1 and cartilage oligomeric matrix protein (COMP) expression were validated in clinical samples by qRT-PCR and Western blot (WB). Functional assessments, including Transwell invasion, flow cytometry, CCK-8, and wound healing, were conducted in vitro. An in vivo xenograft tumor model was used for further validation. Mechanistic investigations involved genetic perturbation (overexpression and inhibition) of CRLF1 and COMP. Results: Compared to benign tissues, the levels of CRLF1 and COMP were markedly elevated in PCa tissues. Bioinformatics assessments illustrated a robust positive relationship between CRLF1 and COMP, suggesting COMP may function as a downstream mediator. In vitro and in vivo investigations illustrated that silencing CRLF1 significantly suppressed PCa cell growth, invasion, and tumor progression, while enhancing apoptosis. Importantly, suppressing COMP counteracted the cancer-promoting effects triggered by CRLF1 overexpression. At the mechanistic level, CRLF1 facilitates tumor progression by modulating COMP to activate the FAK/PI3K/AKT signaling cascade. Conclusions: Our outcomes demonstrate that CRLF1 promotes PCa progression by targeting COMP to stimulate the FAK/PI3K/AKT signaling axis. This newly identified CRLF1/COMP/FAK/PI3K/AKT pathway underscores CRLF1 as a potential biomarker and therapeutic target for PCa. Full article

In the context of China’s “Dual Carbon” goals, the composite policy mechanism combining carbon trading and carbon taxation is widely considered a key pathway to achieve emission reductions. Although households are a major source of carbon emissions, their consumption behaviour has long remained outside the mainstream carbon reduction system, as existing policies focus primarily on enterprises and lack sufficient household-level participation and incentive mechanisms. Because China has not yet implemented an actual carbon tax, this study uses household high-carbon consumption dependency (HCD) as a proxy variable to capture the hypothetical administrative pressure that a carbon tax would impose on high-carbon consumption. Based on the concept of “Carbon Inclusion”, we construct an analytical framework for a composite mechanism that combines the carbon trading pilot policy (ETS) with this carbon-tax proxy. Using data from the China Family Panel Studies (CFPS) and a two-way fixed-effects panel model, we empirically test the impact of this composite mechanism on household carbon emissions (total volume) and carbon intensity. The findings show that, while the composite mechanism does not lead to a statistically significant reduction in total household carbon emissions, it effectively lowers household carbon intensity by restraining high-carbon consumption and optimizing the consumption structure. This decoupling of intensity from total volume occurs because the mechanism reduces the share of high-carbon consumption (a compositional effect) but does not suppress total consumption growth (a scale effect). This result remains robust across multiple tests, confirming the policy effectiveness of the composite mechanism at the micro-individual level. By reducing carbon intensity without suppressing total consumption, this mechanism contributes directly to sustainable development, aligning with UN Sustainable Development Goals 12 (Responsible Consumption and Production) and 13 (Climate Action). The main contributions of this paper are threefold: (1) it moves beyond traditional single-policy or single-agent studies by linking a carbon-trading-and-proxy-carbon-tax composite mechanism with household carbon consumption; (2) it explores a Carbon Inclusion pathway that connects households, enterprises and the nation; and (3) it provides empirical support and a theoretical reference for improving household-level emission reduction policies and promoting public participation in achieving the “Dual Carbon” goals. Full article

Pig weighing is an essential procedure for monitoring growth and animal health; however, conventional methods are often labor-intensive, costly, and potentially stressful. In this context, this study proposes a non-invasive approach for estimating the body weight of pigs during the growing stage based on computer vision and the YOLOv11 algorithm, enabling automatic segmentation and individual identification in multi-animal environments. The study used RGB images of 10 group-housed pigs captured throughout the growing phase, in which automatic dorsal segmentation was combined with individual identification through numerical markings. From the generated binary masks, the segmented dorsal area was extracted and used as a predictor variable in Linear Regression and a Multilayer Perceptron (MLP) Artificial Neural Network. The YOLOv11 model showed consistent performance in the segmentation task, achieving test-set metrics of Precision = 0.849, Recall = 0.886, mAP@0.50 = 0.936, and mAP@0.50–0.95 = 0.819, demonstrating good generalization capability in scenarios with intense animal interaction. In the weight prediction stage, Linear Regression and the MLP achieved high coefficients of determination (R² = 0.96 and 0.95, respectively) with low errors (RMSE = 1.52 kg and 1.63 kg; MAE = 1.20 kg and 1.25 kg), indicating a strong correlation between segmented dorsal area and actual body weight. Class-wise analysis revealed superior performance for classes 7 and 9, with R² values up to 0.98 and RMSE below 1.1 kg, whereas class 8 showed greater error dispersion, associated with higher morphological variability and a smaller number of available samples. These results demonstrate that the direct use of morphometric information extracted from segmented masks in 2D images constitutes a robust, accurate, and low-cost approach for automatic pig body-weight estimation. Moreover, this study is among the few addressing this task specifically during the growing stage, highlighting its potential for future deployment in embedded systems and intelligent monitoring platforms for precision pig farming, although further evaluation of computational efficiency and real-time performance is still required. Full article