Search Results (8,526)

Ergogenic aids have long attracted scientific interest for their potential to enhance neuromuscular performance, with caffeine being among the most extensively studied. While traditionally attributed to peripheral actions on skeletal muscle, accumulating evidence indicates that, at physiological doses, caffeine’s ergogenic effects are predominantly mediated by antagonism of central adenosine receptors. This antagonism leads to increased arousal, reduced inhibitory neuromodulation, enhanced corticospinal excitability, and altered motor unit recruitment and firing behavior. Importantly, the concentrations required to elicit direct effects on excitation–contraction coupling via ryanodine receptors exceed those compatible with human safety, rendering such mechanisms unlikely in vivo. This narrative review synthesizes contemporary neurophysiological evidence to propose that caffeine acts primarily by “tuning” motor system gain through central neurotransmitter modulation, rather than by directly augmenting muscle contractile properties. Additionally, we highlight unresolved questions regarding persistent inward currents, sex-dependent neuromodulatory influences—including the potential role of estrogen in regulating adenosine receptor expression—and the implications of repeated caffeine use during training for neural adaptation and motor control. Finally, we outline key methodological and conceptual directions for future research aimed at refining our understanding of caffeine’s neuromuscular effects in both acute and chronic contexts. Full article

High-rise building areas (HRBs), a key urban land-cover type defined by distinct morphological and functional characteristics, play a critical role in urban development. Their spatial distribution and temporal dynamics serve as essential indicators for quantifying urbanization and analyzing the evolution of urban spatial structure. This study addresses the dynamic monitoring needs of HRBs by developing a temporal change detection model, TCA-Unet (Temporal Change-Aware U-Net), based on a temporal change-aware attention module. The model adopts a dual-path design, combining a temporal attention encoder and a change-aware encoder. By explicitly modeling temporal difference features, it captures change information in temporal remote sensing images. It incorporates a multi-level weight generation mechanism that dynamically balances temporal features and change-aware features through an adaptive fusion strategy. This mechanism effectively integrates temporal context and enhances the model’s ability to capture long-term temporal dependencies. Using the Xiong’an New Area and its surrounding regions as the study area, experiments were conducted using Sentinel-2 time-series imagery from 2017 to 2024. The results demonstrate that the proposed model outperforms existing approaches, achieving an overall accuracy (OA) of 90.98%, an F1 score of 82.63%, and a mean intersection over union (mIoU) of 72.22%. Overall, this study provides an effective tool for extracting HRBs for dynamic monitoring and offers valuable guidance for urban development and regulation. Full article

Lentiviral transduction remains the gold standard in adoptive modified cellular therapy, such as CAR-T; however, genome integration is not always desirable, such as when treating non-fatal autoimmune disease or for additional editing steps using CRISPR to produce allogeneic CAR-modified cells. Delivering in vitro-transcribed (IVT) mRNA represents an alternative solution but the labile nature of mRNA has led to efforts to improve half-life and translation efficiencies using a range of approaches including chemical and structural modifications. In this study, we explore the role of N⁶–methyladenosine (m6A) in a CD19-CAR sequence when delivered to T cells as an IVT mRNA. In silico analysis predicted the presence of four m6A consensus (DRACH) motifs in the CAR coding sequence and treating T cells with an inhibitor of the m6A methyltransferase (METTL3) resulted in a significant reduction in CAR protein expression. RNA analysis confirmed m6A bases at three of the predicted sites, indicating that the modification occurs independently of nuclear transcription. Synonymous mutation of the DRACH sites reduced the levels of CAR protein from 15 to >50% depending on the T cell donor. We also tested a panel of CAR transcripts with different UTRs, some containing m6A consensus motifs, and identified those which further improved protein expression. Furthermore, we found that the methylation of consensus m6A sites seems to be somewhat sequence-context-dependent. These findings demonstrate the importance of the m6A modification in stabilising and enhancing expression from IVT-derived mRNA and that this occurs within the cell, meaning targeted in vitro chemical modification during mRNA manufacturing may not be necessary. Full article

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

With rapid advancements in sub-meter satellite and aerial imaging technologies, high-resolution remote sensing imagery has become a pivotal source for geospatial information acquisition. However, current semantic segmentation models encounter two primary challenges: (1) the inherent trade-off between capturing long-range global context and preserving precise local structural details—where excessive reliance on downsampled deep semantics often results in blurred boundaries and the loss of small objects and (2) the difficulty in modeling complex scenes with extreme scale variations, where objects of the same category exhibit drastically different morphological features. To address these issues, this paper introduces MAFMamba, a multi-scale adaptive fusion visual Mamba network tailored for high-resolution remote sensing images. To mitigate scale variation, we design a lightweight hybrid encoder incorporating an Adaptive Multi-scale Mamba Block (AMMB) in each stage. Driven by a Multi-scale Adaptive Fusion (MSAF) mechanism, the AMMB dynamically generates pixel-level weights to recalibrate cross-level features, establishing a robust multi-scale representation. Simultaneously, to strictly balance local details and global semantics, we introduce a Global–Local Feature Enhancement Mamba (GLMamba) in the decoder. This module synergistically integrates local fine-grained features extracted by convolutions with global long-range dependencies modeled by the Visual State Space (VSS) layer. Furthermore, we propose a Multi-Scale Cross-Attention Fusion (MSCAF) module to bridge the semantic gap between the encoder’s shallow details and the decoder’s high-level semantics via an efficient cross-attention mechanism. Extensive experiments on the ISPRS Potsdam and Vaihingen datasets demonstrate that MAFMamba surpasses state-of-the-art Convolutional Neural Network (CNN), Transformer, and Mamba-based methods in terms of mIoU and mF1 scores. Notably, it achieves superior accuracy while maintaining linear computational complexity and low memory usage, underscoring its efficiency in complex remote sensing scenarios. Full article

Future agriculture will depend on smart systems and digital technologies to improve food production and sustainability. Data-driven methods, such as artificial intelligence, will become integral to agricultural research and development, transforming how decisions are made and how sustainability goals are achieved. Reliable, high-quality data is essential to ensure that research users can trust their conclusions and decisions. To achieve this, a standard for assessing and reporting data quality is required to realise the full potential of data-driven agriculture. Two practical and empirical data quality assessment tools are proposed—a trial data quality test (primarily for data contributors) and a trial data quality statement (for data users). These tools provide information on data qualities assessed for contributors to the submitted trial data and those seeking to use the data for decision support purposes. An action case study using the Online Farm Trials platform illustrates their application. The proposed data quality framework provides a consistent approach for evaluating trial quality and determining fitness for purpose. Flexible and adaptable, the DQF and its tools can be tailored to different agricultural contexts, strengthening confidence in data-driven decision-making and advancing sustainable agriculture. Full article

Lean implementations often deploy tools in isolation, leaving gaps in how abnormalities are exposed, resolved at the root cause, escalated when needed, and converted into organisational learning. This study proposes a five-stage closed-loop routine for daily factory management that integrates problem visibility, standardised shop-floor cadence, disciplined problem-solving, and tiered escalation within a single operating logic. The novelty lies not in the individual Lean tools, but in the specification of cadence, triggers, accountable roles, and verification steps that connect them into a replicable end-to-end routine. The model was evaluated through a 19-month longitudinal, single-site field intervention (quasi-experimental before–and–after) on the bottleneck production line of a pharmaceutical plant in Hengoed, Wales (UK). Line OEE increased by over 50% in relative terms. At factory level, total output increased by 20% year-on-year in 2024 (context indicator), alongside qualitative field observations of shorter time-to-resolution and improved cross-functional coordination. As a single-site study, external validity is context-dependent; nevertheless, the paper provides a specified closed-loop routine and field evidence on the operational effects of embedding an integrated Lean cycle into daily management. Practically, the study provides a specified routine that practitioners can replicate and adapt; academically, it contributes to Lean implementation research by showing how tool bundles can be operationalised as an end-to-end daily management routine with observable performance effects. Full article

Background/Objectives: Within Paediatric Palliative Care (PPC), motherhood in the context of severe cognitive impairment is shaped by unique emotional, relational, and identity-related challenges. Traditional understandings of maternal identity are strained when verbal communication and typical developmental milestones are absent. Although caregiving in PPC has been widely studied, the subjective and symbolic dimensions of motherhood in this setting have received far less attention. This study sought to explore how mothers construct, interpret, and make sense of their maternal identity while caring for a child with severe cognitive impairment in a PPC context, and to underscore the clinical relevance of these identity-related processes. Methods: A qualitative study was conducted involving nine mothers of children receiving paediatric palliative care services at a regional centre in Italy. Participants engaged in three online focus groups, totalling 270 min. Reflexive thematic analysis was employed to interpret the transcribed data, using ATLAS.ti software, version 25.0.1 ATLAS.ti Scientific Software Development GmbH, Berlin, Germany, for support. Member reflections were incorporated to validate the findings. Results: Three interconnected themes emerged from the reflexive thematic analysis. First, mothers described the development of a fusion-like, enmeshed mother–child relationship, characterised by embodied attunement, specialised interpretive expertise, and lifelong care dependency. Second, mothers detailed the construction of their maternal role, shaped by emotional labour, identity negotiation, sacrifice, loneliness, and peer support, alongside the construction of the child’s role, in which children were perceived as unique, symbolically meaningful beings whose social presence and limited reciprocity shaped maternal identity. Third, mothers articulated a search for meaning that sustained them throughout the caregiving journey, reframing their experience within a broader existential and relational perspective. Conclusions: Maternal caregiving in PPC encompasses distinct emotional, relational, and symbolic dimensions that extend beyond conventional understandings of motherhood. Grasping these identity-related dynamics has direct clinical relevance: it enables more attuned communication, strengthens the therapeutic alliance, and supports personalised, meaning-oriented care. These insights highlight the need for tailored interventions and further qualitative research to inform health care professionals and interdisciplinary practice. Full article

Retrieval-Augmented Generation (RAG) is widely used for long-text summarization due to its efficiency and scalability. However, standard RAG methods flatten documents into independent chunks, disrupting sequential flow and thematic structure, resulting in significant loss of contextual information. This paper presents MOEGAT, a novel graph-enhanced retrieval framework that addresses this limitation by explicitly modeling document structure. MOEGAT constructs an Orthogonal Context Graph to capture sequential discourse and global semantic relationships—long-range dependencies between non-adjacent text spans that reflect topical similarity and logical associations beyond local context. It then employs a query-aware Mixture-of-Experts Graph Attention Network to dynamically activate specialized reasoning pathways. Experiments conducted on three public long-text summarization datasets demonstrate that MOEGAT achieves state-of-the-art performance. Notably, on the WCEP dataset, it outperforms the previous state-of-the-art Graph of Records (GOR) baseline by 14.9%, 18.1%, and 18.4% on ROUGE-L, ROUGE-1, and ROUGE-2, respectively. These substantial gains, especially the 14.9% improvement in ROUGE-L, reflect significantly better capture of long-range coherence and thematic integrity in summaries. Ablation studies confirm the effectiveness of the orthogonal graph and Mixture-of-Experts components. Overall, this work introduces a novel structure-aware approach to RAG that explicitly models and leverages document structure through an orthogonal graph representation and query-aware Mixture-of-Experts reasoning. Full article

Background: Psoriasis involving difficult-to-treat anatomical areas, such as the scalp, genitalia, fingernails, and palmoplantar regions, carries a disproportionate disease burden and often requires systemic therapy. In this context, real-life data comparing the long-term effectiveness of tildrakizumab 100 mg versus 200 mg in patients with difficult-to-treat psoriasis remain limited. Methods: This multicenter retrospective observational study included adult patients in three Italian dermatology centers. Global efficacy endpoints included PASI75, PASI90, PASI100, and absolute PASI ≤ 2 at weeks 16, 32, 52, and 104. Site-specific effectiveness was assessed as complete clearance (PGA = 0) in patients with baseline involvement (PGA ≥ 2) of difficult-to-treat areas. Outcomes were described by dose. Results: 183 patients were included (100 mg: n = 89; 200 mg: n = 94). Patients receiving 200 mg had higher baseline BMI and were more frequently biologic-experienced. At week 104, PASI75 was achieved by 94.2% of patients receiving 100 mg and 94.7% receiving 200 mg, while PASI90 and PASI100 were achieved by 82.7% vs. 57.9% and 48.1% vs. 47.4%, respectively. Clearance of difficult-to-treat areas improved progressively across all sites. Scalp and genital psoriasis showed higher and earlier clearance rates, whereas nail and palmoplantar psoriasis showed slower and more heterogeneous responses. No consistent dose-dependent advantage emerged, despite less favorable baseline characteristics in the 200 mg group. Conclusions: Over 104 weeks, tildrakizumab showed sustained long-term effectiveness in both global disease control and difficult-to-treat areas. The 200 mg dose, used in a more difficult-to-treat population, achieved comparable long-term outcomes, supporting dose optimization in clinical practice. Full article

This paper examines the application of Artificial Intelligence (AI) to protect satellite communication networks, focusing on the identification and prevention of cyber threats. With the rapid development of the commercial space sector, the importance of effective cyber defense has grown due to the increasing dependence of global infrastructure on satellite technologies. The study applies a structured comparative analysis of AI methods across three main satellite architectures: geostationary (GEO), low Earth orbit (LEO), and hybrid systems. The methodology is based on guiding research question and evaluates representative AI algorithms in the context of specific threat scenarios, including jamming, spoofing, DDoS attacks, and signal interception. Real-world cases such as the KA-SAT AcidRain attack and reported Starlink jamming in Ukraine, as well as experimental demonstrations of RL-based anti-jamming and GNN/DQN routing, are used to provide evidence of practical applicability. The results highlight both the potential and limitations of AI solutions, showing measurable improvements in detection accuracy, throughput, latency reduction, and resilience under interference. Architectural approaches for integrating AI into satellite security are presented, and their effectiveness, trade-offs, and deployment feasibility are discussed. Full article

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous malignancy characterized by the proliferation of skin-homing CD4⁺ T cells and profound immune dysregulation within the tumor microenvironment (TME). This review synthesizes evidence on chemokine–receptor networks that govern malignant T-cell trafficking among blood, skin, and lymph nodes, the formation of immunosuppressive niches, and clinically actionable biomarker candidates. Among the best-supported axes, CCL17/CCL22–CCR4 and CCL27/CCL28–CCR10 mediate skin tropism, CCL19/CCL21–CCR7 contributes to lymph node homing, and CXCL12–CXCR4 supports skin trafficking and is associated with disease progression. In contrast, CCR2/CCR5/CCR6/CCR8-centered circuits and CXCR3/CXCR5 pathways are emerging regulators of myeloid recruitment, regulatory T-cell accumulation, and context-dependent immune activation. Therapeutically, agents targeting chemokine pathways, most notably the CCR4 monoclonal antibody Mogamulizumab, have demonstrated clinical efficacy, while emerging inhibitors of CCR6, CCR5, and CXCR4 offer promising avenues for intervention. We further highlight how recent single-cell and other high-dimensional omics studies refine cell-type–specific chemokine sources and receptor expression, enabling more precise mapping of chemokine-driven intercellular communication programs in CTCL TME remodeling and better prioritization of therapeutic targets and biomarkers. Full article

Obesity is among the top contributing factors for non-communicable chronic disease development and has attained menacing global proportions, affecting approximately one of eight adults. Phytochemicals that support energy metabolism and prevent obesity development have been the subject of intense research endeavors over the past several decades. Cycloastragenol is a natural triterpenoid compound and aglycon of astragaloside IV, known for activating telomerase and mitigating cellular aging. Here, we aim to characterize the effect of cycloastragenol on lipid metabolism in a glucose-induced obesity model in Caenorhabditis elegans. We assessed the changes in the body length, width, and area in C. elegans maintained under elevated glucose through automated WormLab system. Lipid accumulation in the presence of either cycloastragenol (100 μM) or orlistat (12 μM), used as a positive anti-obesity control drug, was quantified through Nile Red fluorescent staining. Furthermore, we evaluated the changes in key energy metabolism molecular players in GFP-reporter transgenic strains. Our results revealed that cycloastragenol treatment decreased mean body area and reduced lipid accumulation in the C. elegans glucose-induced model. The mechanistic data indicated that cycloastragenol suppresses the nuclear hormone receptor family member NHR-49 and the delta(9)-fatty-acid desaturase 7 (FAT-7) enzyme, and activates the 5′-AMP-activated protein kinase catalytic subunit alpha-2 (AAK-2) and the protein skinhead 1 (SKN-1) signaling. Collectively, our findings highlight that cycloastragenol reprograms lipid metabolism by down-regulating the insulin-like receptor (daf-2)/phosphatidylinositol 3-kinase (age-1)/NHR-49 signaling while simultaneously enhancing the activity of the AAK-2/NAD-dependent protein deacetylase (SIR-2.1) pathway. The anti-obesogenic potential of cycloastragenol rationalizes further validation in the context of metabolic diseases and obesity management. Full article

Signal transducer and activator of transcription 2 (STAT2) is a key component of the type I interferon (IFN-I/III) signaling pathway, which is pivotal in host defense against cancer and viral infections and in shaping immune responses. Building on our previously reported conditional Stat2 knockout (KO) mouse, we expand its utility by validating additional tissue-specific models and exploring novel functional contexts. Mice carrying loxP-flanked Stat2 alleles were crossed with CMV-Cre, Cdx2-Cre or CD11c-Cre mice. Deletion of STAT2 was validated by PCR genotyping and western blotting in the relevant tissues. To confirm defective IFN-I signaling with STAT2 deletion, IFN-β stimulation of splenocytes from CMV-Cre Stat2 KO mice showed a lack of induction of canonical IFN-I target genes, confirming functional disruption of the pathway. In vivo, global Stat2 deletion significantly impaired the antitumor efficacy of IFN-β treatment. Similarly, lung fibroblasts isolated from globally deleted Stat2 KO mice showed defective antiviral responses to IFN-β. Tissue-specific Cre models demonstrated selective ablation of STAT2 in target compartments without affecting its expression in non-target tissues. Together, these studies expand our published conditional Stat2 KO findings and highlight the value of this model as a versatile platform for dissecting STAT2-dependent signaling pathways in a tissue- and disease-specific manner. Full article

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article