Search Results (4,248)

The past decade has witnessed an unprecedented transformation in breast cancer management, driven by parallel advances in targeted therapies, immunomodulation, drug-delivery technologies, and molecular diagnostic tools. This review summarizes the key achievements of 2015–2025, encompassing all major biological subtypes of breast cancer as well as technological innovations with substantial clinical relevance. In hormone receptor-positive (HR+)/HER2− disease, the integration of CDK4/6 inhibitors, modulators of the PI3K/AKT/mTOR pathway, oral Selective Estrogen Receptor Degraders (SERDs), and real-time monitoring of Estrogen Receptor 1 (ESR1) mutations has enabled clinicians to overcome endocrine resistance and dynamically tailor treatment based on evolving molecular alterations detected in circulating biomarkers. In HER2-positive breast cancer, treatment paradigms have been revolutionized by next-generation antibody–drug conjugates, advanced antibody formats, and technologies facilitating drug penetration across the blood–brain barrier, collectively improving systemic and central nervous system disease control. The most rapid progress has occurred in triple-negative breast cancer (TNBC), where synergistic strategies combining selective cytotoxicity via Antibody-Drug Conjugates (ADCs), DNA damage response inhibitors, immunotherapy, epigenetic modulation, and therapies targeting immunometabolic pathways have markedly expanded therapeutic opportunities for this historically challenging subtype. In parallel, photodynamic therapy has emerged as an investigational and predominantly local phototheranostic approach, incorporating nanocarriers, next-generation photosensitizers, and photoimmunotherapy capable of inducing immunogenic cell death and modulating antitumor immune responses. A defining feature of the past decade has been the surge in patent-driven innovation, encompassing multispecific antibodies, optimized ADC architectures, novel linker–payload designs, and advanced nanotechnological and photoactive delivery systems. By integrating data from clinical trials, molecular analyses, and patent landscapes, this review illustrates how multimechanistic, biomarker-guided therapies supported by advanced drug-delivery technologies are redefining contemporary precision oncology in breast cancer. The emerging therapeutic paradigm underscores the convergence of targeted therapy, immunomodulation, synthetic lethality, and localized immune-activating approaches, charting a path toward further personalization of treatment in the years ahead. Full article

This study investigates the demographic transformation of Spain’s settlement system from 2000 to the present, driven by intersecting forces of rural depopulation, metropolitan concentration, immigration, and welfare-state dynamics. Building on an integrated theoretical framework that combines Maslow’s hierarchy of needs, demographic accounting, territorial carrying capacity, and spatial centrality, the research aims to (1) identify the mechanisms governing population redistribution across Spanish municipalities, and (2) simulate future demographic trajectories under current policy regimes. Key findings reveal that all net population growth since 2000 stems exclusively from immigration and its demographic sequelae, while the native Spanish cohort has experienced a net decline of 5.5 million due to negative natural change. The analysis further uncovers a self-reinforcing “demographic trap,” wherein welfare eligibility tied to household size incentivizes higher fertility among economically vulnerable immigrant groups, even as native families delay childbearing due to economic precarity. These dynamics are accelerating a process of “territorial transmutation,” projected to culminate in a shift in de facto governance by 2045. The study concludes that immigration alone cannot reverse rural depopulation or ensure fiscal sustainability without structural reforms to welfare design, territorial incentives, and demographic foresight. Full article

In complex wireless communication environments, automatic modulation classification (AMC) faces two critical challenges: the lack of robustness under low-signal-to-noise ratio (SNR) conditions and the inefficiency of integrating multi-scale feature representations. To address these issues, this paper proposes CAIC-Net, a robust modulation classification network that integrates a dynamic cross-attention mechanism with a cross-SNR contrastive learning strategy. CAIC-Net employs a dual-stream feature extractor composed of ConvLSTM2D and Transformer blocks to capture local temporal dependencies and global contextual relationships, respectively. To enhance fusion effectiveness, we design a Dynamic Cross-Attention Unit (CAU) that enables deep bidirectional interaction between the two branches while incorporating an SNR-aware mechanism to adaptively adjust the fusion strategy under varying channel conditions. In addition, a Cross-SNR Contrastive Learning (CSCL) module is introduced as an auxiliary task, where positive and negative sample pairs are constructed across different SNR levels and optimized using InfoNCE loss. This design significantly strengthens the intrinsic noise-invariant properties of the learned representations. Extensive experiments conducted on two standard datasets demonstrate that CAIC-Net achieves competitive classification performance at moderate-to-high SNRs and exhibits clear advantages in extremely low-SNR scenarios, validating the effectiveness and strong generalization capability of the proposed approach. Full article

Cadmium (Cd) contamination in agricultural systems poses significant ecotoxicological risks through bioaccumulation in food chains. While lime-based amendments are widely applied for Cd immobilization, mechanistic understanding of bioavailability control pathways remains limited. This study employed a meta-analysis methodology based on 260 datasets from 55 publications to systematically investigate the mechanisms and differences in the effectiveness of calcium hydroxide, calcium carbonate, and calcium oxide in regulating Cd migration in acidic soil–plant systems. The study revealed that lime-based materials synergistically regulated Cd migration through two processes: chemical fixation and ionic competition. Results showed lime application reduced soil available Cd by 33.0%, decreased grain Cd by 44.8%, increased soil pH by 15.6%, and enhanced exchangeable Ca by 35.2%. Chemical fixation was evidenced by Cd transformation from labile to stable forms (residual Cd: +29.5%, acid-soluble Cd: −17.5%). Ionic competition was quantitatively confirmed through strong negative correlation between exchangeable Ca and grain Cd (R² = 0.704). Among the materials, Ca(OH)₂ exhibits the highest efficiency in rapid pedogenic passivation (58.7% reduction in available Cd), whereas CaCO₃ demonstrates superior long-term grain Cd attenuation (65.7% inhibition) via sustained Ca²⁺ release and rhizosphere-regulated dissolution. This study advances mechanistic understanding of Cd bioavailability control and establishes quantitative frameworks for predicting ecotoxicological outcomes, providing scientific basis for optimizing remediation strategies to minimize Cd transfer through agricultural food chains. Full article

The resummation of Stieltjes series remains a key challenge in mathematical physics, especially when Padé approximants fail, as in the case of superfactorially divergent series. Weniger’s $\delta$-transformation, which incorporates a priori structural information on Stieltjes series, offers a superior framework with respect to Padé. In the present work, the following fundamental question is addressed: Is the $\delta$-transformation, once it is applied to a typical Stieltjes series, capable of correctly simulating the branch cut structure of the corresponding Stieltjes function? Here, it is proved that the intrinsic log-convexity of the Stieltjes moment sequence (guaranteed via the positivity of Hankel’s determinants) allows the necessary condition for $\delta$ to have all real poles to be satisfied. The same condition, however, is not sufficient to guarantee this. In attempting to bridge such a gap, we propose a mechanism rooted in the iterative action of a specific linear differential operator acting on a class of suitable auxiliary log-concave polynomials. To this end, we show that the denominator of the $\delta$-approximants can always be recast as a high-order derivative of a log-concave polynomial. Then, on invoking the Gauss–Lucas theorem, a consistent geometrical justification of the $\delta$ pole positioning is proposed. Through such an approach, the pole alignment along the negative real axis can be viewed as the result of the progressive restriction of the convex hull under differentiation. Since a fully rigorous proof of this conjecture remains an open challenge, in order to substantiate it, a comprehensive numerical investigation across an extensive catalog of Stieltjes series is proposed. Our results provide systematic evidence of the potential $\delta$-transformation ability to mimic the singularity structure of several target functions, including those involving superfactorial divergences. Full article

The West Liaohe River Basin, a core arid region in Northeast China, faces a significant evaporation–precipitation imbalance and exhibits fragmented land systems, epitomized by the Horqin Sandy Land. Integrating three decades of land use/land cover (LULC) data with meteorological, ecological, and socioeconomic variables, we employed obstacle diagnosis and structural equation modeling (SEM) to elucidate the spatiotemporal dynamics and drivers of LULC transformations. The results demonstrate the following: (1) Land use exhibited a spatially heterogeneous pattern, with forests, shrubs, and grasslands predominantly concentrated in the northwest and southwest. (2) Vegetation coverage significantly increased from 53.15% in 1990 to 61.32% in 2020, whereas cropland and sandy land areas declined. While the overall basin landscape underwent a marked increase in fragmentation. (3) Human activities were the dominant contributor of LULC changes, particularly for cropland conversion, with key determinants such as population and GDP showing negative path coefficients of −0.59 and −0.77, respectively. Climate change was a secondary contributor, with precipitation exerting a strong positive path coefficient (0.63) that was particularly pronounced during the conversion of grassland to forest. These findings offer a scientific basis for land management, ecological restoration strategies, and water resource utilization in the basin. Full article

Value factorization methods have become a standard tool for cooperative multi-agent reinforcement learning (MARL) in the centralized-training, decentralized-execution (CTDE) setting. QMIX (a monotonic mixing network for value factorization), in particular, constrains the joint action–value function to be a monotonic mixing of per-agent utilities, which guarantees consistency with individual greedy policies but can severely limit expressiveness on tasks with non-monotonic agent interactions. This work revisits this design choice and proposes Relaxed Monotonic QMIX (R-QMIX), a simple regularized variant of QMIX that encourages but does not strictly enforce the monotonicity constraint. R-QMIX removes the sign constraints on the mixing network weights and introduces a differentiable penalty on negative partial derivatives of the joint value with respect to each agent’s utility. This preserves the computational benefits of value factorization while allowing the joint value to deviate from strict monotonicity when beneficial. R-QMIX is implemented in a standard PyMARL (an open-source MARL codebase) and evaluated on the StarCraft Multi-Agent Challenge (SMAC). On a simple map (3m), R-QMIX matches the asymptotic performance of QMIX while learning substantially faster. On more challenging maps (MMM2, 6h vs. 8z, and 27m vs. 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for example increasing the final-quarter mean win rate from $42.3\%$ to $97.1\%$ on MMM2, from $0.0\%$ to $57.5\%$ on 6h vs. 8z, and from $58.0\%$ to $96.6\%$ on 27m vs. 30m. These results suggest that soft monotonicity regularization is a practical way to bridge the gap between strictly monotonic value factorization and fully unconstrained joint value functions. A further comparison against QTRAN (Q-value transformation), a more expressive value factorization method, shows that R-QMIX achieves higher and more reliably convergent win rates on the challenging SMAC maps considered. Full article

This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia. Full article

The emergence of artificial intelligence as a transformative force in the field of information technology has exerted a significant impact on the development of renewable energy. In-depth analysis of the impact of AI on renewable energy development is crucial for promoting energy transition and facilitating sustainable development. This research utilizes a dataset comprising 30 provincial panels spanning from 2010 to 2023. This study found that AI technology can promote renewable energy development, a conclusion that still holds after robustness and endogeneity tests. An examination of the mechanism reveals that AI technology facilitates the advancement of renewable energy through the enhancement of trade openness and the concentration of manufacturing activities. The analysis of the moderating effect indicates that environmental regulation and environmental protection expenditures positively moderated the relationship between AI technology and renewable energy development and climate policy uncertainty negatively moderated the relationship between AI technology and renewable energy development. Further analysis revealed that AI technology has the potential to substantially improve the development of local renewable energy resources while also facilitating the advancement of renewable energy in adjacent areas, exhibiting spatial spillover effects. This study verifies the positive effects of AI technology on renewable energy development and enriches existing research perspectives in the field of energy economics. Full article

Although greenhouse gas emissions have significantly reduced, the European Union still faces a major challenge in meeting its 2050 net-zero goal set under the European Green Deal. Focusing on the impacts of population, economic output, and carbon intensity of economy, this study employs Index Decomposition Analysis to estimate the reductions in carbon intensity needed to reach this target. The findings show that the extent of the technical effort required for decarbonization is much influenced by economic expansion. Under a 3% annual Gross Domestic Product growth scenario, the EU’s carbon intensity of economy must decline by 11.8% per year, which is a particularly demanding rate given the already low baseline. The decomposition also quantifies the technological challenge: under high growth, up to 5867 MtCO₂ in reductions would be needed by 2050 (compared with 1990), with Carbon Capture and Storage (CCS) contributing only 10–15%. In contrast, in zero- or negative-growth scenarios, required reductions fall to 4923–4594 MtCO₂, with CCS accounting for up to 50–90%. These results show that decarbonization in EU industrial sectors requires systemic transformations and strategic CCS deployment. A balanced approach, limiting economic growth and increasing innovation, appears essential to achieve the climate neutrality target. Full article

Background: Poly(ADP-ribose) polymerase inhibitors (PARPi) have transformed cancer therapy for patients harbouring homologous recombination repair (HRR) deficiencies, notably BRCA1/2 mutations. However, resistance to PARPi remains a clinical challenge, with restoration of BRCA1 function via hypomorphic variants representing an understudied scenario. Methods: Here, we engineered a doxycycline-inducible BRCA1 expression system in the BRCA1-mutant, triple-negative breast cancer cell line MDAMB436, permitting controlled analysis of functionally distinct BRCA1 hypomorphs in vitro and in vivo. Results: Among multiple BRCA1 variants generated—including RING, coiled-coil, and BRCT domain mutants—only overexpression of the ∆exon11 hypomorph robustly conferred resistance to olaparib and carboplatin, with drug sensitivity correlating to ∆exon11 expression levels. While ∆exon11 BRCA1 mediated HRR restoration, its efficiency was consistently lower than full-length BRCA1, as measured by RAD51 foci formation and interaction with repair partners such as PALB2. In vivo, tumours expressing Δexon11 BRCA1 exhibited only partial resistance to olaparib compared to those expressing full-length BRCA1. Importantly, the combination of olaparib and the ATR inhibitor, ceralasertib, overcame ∆exon11-mediated resistance, impairing RAD51 foci formation in ∆exon11-expressing cells. Conclusions: Our findings identify a dose-dependent, hypomorphic HRR restoration by ∆exon11 BRCA1, help explain the variable resistance observed in BRCA1-mutant pre-clinical models expressing this hypomorph, and propose ATR inhibition in combination with PARPi as a clinical strategy to counteract therapeutic resistance mediated by ∆exon11 BRCA1 hypomorphs. Full article

Globalization, climate change and the energy crisis are having a negative impact on the agricultural sector. Moreover, in Greece, producers need to invest in modernizing their farms, introducing innovations, achieve digital transformation and increase productivity. Incentives are available for green investments, thus facilitating the adoption of innovations. This paper examines the attitudes and perceptions of winegrowers in Western Macedonia regarding environmental protection and organic farming. Specifically, with the help of the k-means algorithm, respondents are divided into two groups. The first group consists of producers who have a negative attitude towards environmental issues and have a lower level of education, while the second group consists of those who have a positive attitude. In the first group in particular, most respondents do not seem to have a good knowledge of environmental protection issues, believe that cross-compliance is very stringent and do not have a high level of education. This means that policy measures must be taken to adequately inform and educate farmers belonging to this group to achieve the goal of environmental protection. The results of the second group include producers with a positive attitude, who are expected to play an important role in the development of sustainable viticulture by creating conditions for a better future at an individual, regional and social level. Full article

Objective: We analyzed nanoparticle regulation research to examine the evolution of regulatory frameworks, identify major thematic structures, and evaluate current challenges in the governance of rapidly advancing nanotechnologies. By drawing parallels with the historical development of radiation regulation, the study aimed to contextualize emerging regulatory strategies and derive lessons for future governance. Methods: A total of 9095 PubMed-indexed articles published between January 2015 and October 2025 were analyzed using text mining, keyword frequency analysis, and topic modeling. Preprocessed titles and abstracts were transformed into a TF-IDF (Term Frequency–Inverse Document Frequency) document–term matrix, and NMF (Non-negative Matrix Factorization) was applied to extract semantically coherent topics. Candidate topic numbers (K = 1–12) were evaluated using UMass coherence scores and qualitative interpretability criteria to determine the optimal topic structure. Results: Six major research topics were identified, spanning energy and sensor applications, metal oxide toxicity, antibacterial silver nanoparticles, cancer nano-therapy, and nanoparticle-enabled drug and mRNA delivery. Publication output increased markedly after 2019 with interdisciplinary journals driving much of the growth. Regulatory considerations were increasingly embedded within experimental and biomedical research, particularly in safety assessment and environmental impact analyses. Conclusions: Nanoparticle regulation matured into a dynamic multidisciplinary field. Regulatory efforts should prioritize adaptive, data-informed, and internationally harmonized frameworks that support innovation while ensuring human and environmental safety. These findings provide a data-driven overview of how regulatory thinking was evolved alongside scientific development and highlight areas where future governance efforts were most urgently needed. Full article

Although many long-distance migratory birds choose stable wintering sites and staging posts, irruptive migrants may exhibit considerable interannual variability in their migratory patterns, often depending on food availability. The Franklin’s gull (Leucophaeus pipixcan) is a common long-distance migrant along Chile’s coast during the austral summer. Using census data from three estuaries in central Chile (2006–2023), we analyzed variation in summer populations in relation to chlorophyll-a (chl-a) concentration along the migration route, used as a proxy for food availability. The best model predicting the number of gulls reaching Chile included a negative effect of chl-a concentration on the Peruvian coast (0–10° S) during winter (June–July). Considering the time lag associated with the transformation of phytoplankton into seagull food, this result suggests that primary productivity along the route may influence how far south these birds migrate in search of food. We also found a negative correlation between the summer abundance of Franklin’s gulls in Chile and an eBird index for the species in Peru during the same period, suggesting redistribution of individuals between the two countries in response to resource availability. Models such as ours provide a useful tool for understanding and managing populations of migratory waterbirds. Full article

As Thailand’s financial sector accelerates its digital transformation, cybersecurity has transitioned from a mere technical support function to a strategic imperative that governs operational risk and financial stability. This study empirically examines the efficacy of cyber risk controls and their correlation with perceived organizational readiness. Utilizing a quantitative survey of 53 specialized practitioners (N = 53), we assessed maturity across the six dimensions of the Bank of Thailand’s Cyber Resilience Assessment regulatory framework: Governance, Identification, Protection, Detection, Response, and Third-Party Risk Management. While descriptive statistics indicate high overall maturity ($\overline{\mathrm{x}}$ = 4.19, S.D. = 0.37), multiple regression analysis uncovers a critical “Protection Paradox”. Specifically, the “Protection” dimension exhibits a statistically significant negative impact on readiness (β = −0.432, p = 0.01), suggesting that over-engineered technical controls induce operational friction. In contrast, “Identification” emerged as the primary positive driver of readiness (β = 0.627, p < 0.01), highlighting visibility as a superior strategic lever. Furthermore, a structural disconnect was identified between strategic “Governance” and “Third-Party Risk Management” (r = 0.46), highlighting a “Silo Effect” where board-level policy fails to effectively mitigate supply chain risks. These findings suggest that financial institutions must pivot from volume-based compliance to risk-optimized integration to bridge these strategic and operational gaps. Full article