Search Results (8,295)

Obtaining multiple MRI contrasts for each patient prolongs scan acquisition time, increases healthcare costs, and may not always be feasible due to patient specific constraints. Deep learning-based MRI contrast synthesis offers a potential solution, yet most existing approaches are evaluated on preprocessed public benchmarks that do not reflect real-world clinical variability. In this study, we propose a fusion U-Net transformer framework for bidirectional T1-weighted ↔ T2-weighted brain MRI synthesis trained and evaluated exclusively on retrospectively acquired clinical data. The proposed architecture integrates multiscale convolutional feature extraction with axial attention mechanisms and a transformer bottleneck for efficient global context modeling. A fusion refinement block is incorporated to mitigate skip connection artifacts. An adversarial training strategy with the least squares GAN objective and a hybrid loss combining L1 reconstruction and structural similarity (SSIM) is employed to promote both pixel-level accuracy and perceptual fidelity. The model is evaluated using SSIM and PSNR metrics alongside qualitative expert assessment conducted by two board-certified radiologists. For both synthesis directions, the framework achieves competitive quantitative performance against baseline models under the challenging conditions of clinical data. Expert evaluation confirms high anatomical fidelity and clinically acceptable image quality across both synthesis directions. These results indicate that the proposed framework represents a promising approach for multi-contrast MRI synthesis in clinically heterogeneous data environments. Full article

Irreversible covalent enzyme inhibitors, including targeted covalent inhibitors (TCIs) and mechanism-based enzyme inhibitors (MBEIs), play an increasingly important role in drug discovery. Their pharmacological behavior is governed by intrinsic inactivation parameters, typically described by the inactivation constant K_I, the maximal inactivation rate constant k_inact, and their ratio k_inact/K_I. However, no consensus exists regarding how these parameters should be experimentally determined and interpreted, particularly in high-throughput screening environments where IC₅₀ values are often used as primary descriptors. This article presents a critical survey of the kinetic methodologies employed to characterize irreversible enzyme inhibition. Continuous progress-curve analysis, discontinuous end-point assays, IC₅₀-based estimation strategies, direct mass-spectrometric monitoring of covalent modification, and numerical approaches required by pre-incubation protocols are examined and compared. Attention is given to the statistical robustness of parameter estimation under realistic experimental error, including bootstrap-based uncertainty analysis. For mechanism-based enzyme inhibitors, the kinetic consequences of branching between productive turnover and irreversible inactivation are analyzed, and limitations of classical half-life-based linearization methods are discussed. Intrinsic inactivation parameters are distinguished from protocol-dependent observables, and experimental conditions that may compromise reliable parameter extraction are identified. The objective is to clarify how irreversible inhibitors should be kinetically characterized when the goal is mechanistic understanding and rational drug design. By bridging classical enzymology with current discovery practices, this review provides practical guidance on what experimental data can legitimately support and where caution is required. Full article

Introduction: Paget’s disease of bone (PDB), the second most common bone disease after osteoporosis, is still of unknown etiology and thus deserves attention. PDB and chronic kidney disease (CKD) are chronic diseases characterized by alterations in bone turnover, mineralization, volume, and strength. Both conditions carry an increased cardiovascular risk, as well as increased morbidity and mortality. Both are common in the Western world and primarily affect men over 50 years of age. Despite these similarities, little data exists on their coexistence. Purpose and Methodology: By evaluating the available literature, we found extensive documentation on individual diseases, which has led to consolidated guidelines. The coexistence of the two diseases has provided sporadic studies describing individual cases or small case series. More limited information is available on patients who have received or are eligible for kidney transplants and also have PDB. This narrative (non-systematic) review aims to examine the topic with a particular focus on the relationship between PDB and CKD, especially concerning issues around kidney transplantation. The overlapping factors of the two diseases, and their impact on PDB diagnosis and treatment are discussed. Additionally, examining CKD patients may offer valuable insights for the design of prospective longitudinal or cross-sectional studies aimed at expanding our understanding of PDB. Limitations: The different points of discussion that emerged from the examination of this topic may be useful in the management of PDB-CKD patients but, at the moment, there are not enough data available to draw definitive conclusions to support clinical practice. Conclusions and Future Directions: The coexistence of PDB and CKD is not a rare phenomenon; studying patients with both diseases could provide insights into new research avenues. Above all, and more immediately, attention to the coexistence of the two diseases could improve patient management with personalized choices based on their renal function. Full article

Indoor environmental quality (IEQ) significantly impacts people’s comfort, health, and productivity in buildings, and modern green rating systems are primarily focused on energy efficiency rather than the direct user experience. This paper analyses the relationship between IEQ and the perceived quality of life (QoL) of certified and conventional office buildings in Malaysia using a mixed-methods design. The questionnaires were completed by 162 employees working in four open-plan offices: two were certified under the Green Building Index (GBI) established in Malaysia, and two were traditional. This was supplemented by 14 semi-structured interviews and 2 focus groups. The factors of IEQ were divided into ambient, designed, and behavioral environments. It was statistically determined that behavioral factors, such as visual privacy, personalization, ergonomics, and control, exhibited the strongest correlations with overall QoL, compared to ambient factors such as air quality or thermal comfort. Green buildings performed better in terms of daylighting and esthetics than conventional buildings, though they did not always deliver higher occupant satisfaction. The results indicate that current green certification frameworks pay insufficient attention to occupant-centered aspects. The proposed research adds a validated IEQ-QoL framework that predicts the incorporation of subjective user experience into building performance indicators, which can be important for certification reform, post-occupancy evaluation (POE), and human-centered sustainable design approaches. Full article

With the intensification of the energy crisis and environmental issues, thermoelectric conversion technology has become a research focus due to its ability to directly convert thermal and electrical energy. β-Cu₂Se thermoelectric materials have garnered considerable attention owing to their distinctive physical and chemical characteristics. However, their practical implementation is hindered by the inherent imbalance between electrical and thermal transport properties. In this work, β-Cu₂Se/SnSe composite thermoelectric materials were successfully synthesized via a facile and scalable in situ compositing strategy by introducing SnSe micro-flakes as the secondary phase. The results demonstrate that the introduced SnSe secondary phase effectively modulates the carrier concentration and enhances the density-of-states effective mass through the energy filtering effect and resonant energy level regulation, thereby significantly optimizing the electrical transport properties. Meanwhile, the abundant heterointerfaces formed between the β-Cu₂Se matrix and introduced SnSe secondary phase induce intense phonon scattering, which efficiently suppresses the lattice thermal conductivity of the β-Cu₂Se/SnSe composites. Benefiting from the synergistic optimization of electrical and thermal transport behaviors, the β-Cu₂Se/5 mol% SnSe composite sample achieves a maximum figure of merit (ZT) value of ~0.51 at 750 K, which represents a 70% enhancement compared with the pristine β-Cu₂Se and a 60% improvement compared with the direct composite sample. This study provides a simple and effective in situ composite strategy for designing and synthesizing high-performance thermoelectric materials. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer accounting for the majority of cases and exhibiting persistent challenges related to therapy resistance and metastatic progression. Increasing evidence indicates that dysregulated G protein-coupled receptor signaling and ion channel activity function cooperatively as master regulators of tumor cell proliferation, migration, survival, and therapeutic response. Cannabinoids, including phytocannabinoids such as delta-9-tetrahydrocannabinol and cannabidiol, as well as endogenous endocannabinoids, are uniquely positioned to modulate both G protein-coupled receptors and ion channels, thereby influencing key oncogenic signaling networks. This review synthesizes current knowledge on the role of major ion channel families, including transient receptor potential channels, potassium channels, and sodium channels, and principal G protein-coupled receptor pathways involved in lung cancer progression. We further discuss how cannabinoids reprogram these interconnected signaling systems through canonical cannabinoid receptors, non-classical targets such as G protein-coupled receptor 55 and adenosine receptors, and direct modulation of ion channel activity. Special attention is given to G protein-coupled receptor–ion channel coupling within membrane microdomains and to the capacity of cannabinoids to act as biased ligands, redirecting downstream pathways, such as the phosphoinositide 3-kinase–protein kinase B–mechanistic target of rapamycin and epidermal growth factor receptor signaling, toward apoptosis and reduced metastatic potential. Emerging strategies, including cannabinoid-based combination therapies, selective receptor biasing, and targeted delivery systems, are also highlighted. Altogether, cannabinoid-driven rewiring of G protein-coupled receptor and ion channel signaling represents a promising mechanistic framework for developing innovative therapeutic approaches against lung cancer. Full article

Major social, cultural, and sociopolitical events routinely disrupt daily life, yet their effects on sleep are rarely conceptualized at the population level beyond anecdotal sharing. The purpose of this Opinion piece is to initiate a preliminary discussion of “public sleep” as a novel construct describing systematic, event-related changes in sleep timing, duration, and quality that emerge coherently within communities in response to shared social experiences. Drawing on similarities with the well-established concept of public mood, we posit that sleep can be shaped by social environments in which shared attention, emotional climate, and coordinated schedules exert systematic influence. In support of this claim, we describe preliminary evidence from diverse domains demonstrating population-level sleep disruption following major events, including the transition to Daylight Saving Time, national elections, prolonged crises such as the COVID-19 pandemic and armed conflicts, and highly salient cultural activities such as major sporting events. These reports from disparate fields provide an initial indication that public sleep disruptions can be acute or prolonged, geographically localized or global, and may be shaped by the duration, emotional intensity, and perceived importance of the associated event. We further highlight the potential public health, safety, social, and economic consequences of collective sleep loss, underscoring its relevance beyond individual well-being. Finally, we outline key directions for future research, emphasizing the need for systematic reviews, mechanistic studies, longitudinal designs, and policy-relevant recommendations. Recognizing public sleep as a measurable population phenomenon would provide a foundation for anticipating, monitoring, and mitigating sleep disruption during periods of collective strain, with implications for both individual health and societal resilience. Full article

In the context of the digital economy and quality engineering construction, micro-innovation has gained increasing attention in construction projects. This study incorporates knowledge and character management theory into micro-innovation research and explores how intellectual capital and team character shape micro-innovation in construction projects within the digital economy. A questionnaire design was used to collect data from 315 projects, and the structural equation modeling was applied to test the conceptual model. The results reveal three main findings: (1) intellectual capital exerts a direct positive effect on micro-innovation in construction projects, whereas team character does not; (2) both intellectual capital and team character indirectly enhance micro-innovation through policy perception and market sensing; and (3) the mediating effect of market sensing is stronger in the relationship between intellectual capital and micro-innovation, while the mediating effect of policy perception is more prominent in the relationship between team character and micro-innovation. By theorizing the mechanisms of micro-innovation in construction projects, this study provides important implications for improving micro-innovation practices in the digital era. Full article

As public demand for health and well-being continues to rise, rural landscapes are increasingly valued as settings for stress reduction and psycho-physiological restoration. Drawing on five “Beautiful Villages” in Jiangning District, Nanjing (China), this study categorizes rural landscapes into three types—farmland production landscapes, rural settlement landscapes, and rural mountain–water landscapes—based on the proportional dominance of key landscape elements. Audio-visual stimuli were developed from on-site photography and field recordings to construct controlled rural audio-visual environments. Using a combination of physiological indicators and self-reported psychological assessments, we systematically compare restorative responses across modalities (visual, auditory, and audio-visual) and across landscape types, and examine how specific landscape elements relate to restorative outcomes. Results show that (1) auditory stimuli generally produce stronger restorative responses than visual stimuli, and audio-visual interactions are evident; (2) restorative benefits vary significantly across the three rural landscape types; and (3) visually natural and structurally rich elements are associated with greater restoration, while auditory cues can direct visual attention and natural sounds are positively linked to restorative outcomes. These findings advance understanding of multi-sensory restorative processes in rural landscapes and provide evidence for sustainable rural landscape planning and design by supporting healthier, more restorative, and more human-centered rural environments. Full article

With the continuous development of the petroleum industry, bottomhole pressure prediction technology, which exerts a significant impact on oil production and recovery, has become a key research direction in the current oil and gas field. To enhance the accuracy and robustness of bottomhole pressure prediction under transient and variable operating conditions, a method based on data augmentation strategies and hyperparameter optimization was proposed in this paper. Addressing challenges such as limited data volume and significant disturbances in actual oilfield production, a data augmentation strategy incorporating noise perturbation and sliding windows was introduced to expand training samples and improve model generalization. In terms of model architecture, a deep network integrating CNN, BiGRU, and Multi-Head Attention mechanisms was proposed in this paper, which is referred to as the CNN-BiGRU-Multi-Head Attention model. By introducing Bayesian optimization for automatic hyperparameter search, the performance of the temporal model was further enhanced, achieving efficient extraction and dynamic focusing of wellbore pressure temporal features. Prediction results demonstrated that the proposed method outperforms existing mainstream forecasting models in metrics such as Mean Absolute Error (MAE) and Coefficient of Determination (R²), with R² reaching 0.9831, which confirms its strong generalization capability and engineering applicability. Practical guidance for intelligent oilfield production management and bottomhole pressure forecasting, along with a novel prediction method, is provided by this study, which holds significant importance for extending well life and stabilizing hydrocarbon production. Full article

Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven methods for bearing fault prognosis and remaining useful life (RUL) prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyses how these approaches have been individually and implicitly combined to cope with non-stationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

The global shift toward carbon-neutral energy systems has renewed interest in biorefineries as integrated platforms for the sustainable production of fuels, chemicals, and materials. In this context, lignin, the second most abundant natural polymer and the only renewable source of aromatic carbon, has gained attention as a promising feedstock for high-value applications. Despite its high energy density and chemically complex structure, lignin is primarily used as a low-value fuel through combustion, a practice that fails to capitalize on its molecular potential and offers minimal energetic and economic benefits to the industry. Unlocking its value requires a fundamental shift toward energy-efficient valorization strategies that minimize external energy input while retaining carbon in marketable products. To enable a comprehensive evaluation of this shift, this perspective introduces a three-criterion framework—operating below 250 °C and 50 bar, achieving a fossil energy ratio above one across all process steps, and retaining more than 40% of lignin carbon in recoverable products—and applies it to critically evaluate four lignin valorization pathways: catalytic depolymerization, solvent-assisted fractionation, biological and electrochemical conversion, and material-based applications. Across all pathways, system-level integration, namely, separation, solvent recycling, and catalyst generation, constantly influences the overall energy balance and represents the field’s unresolved challenge. To address these barriers, this perspective discusses several future research directions spanning advanced catalyst design, biotechnology, computational tools, and process intensification, alongside the policy and economic measures needed to enable the commercial deployment of integrating lignin valorization with existing biorefinery operations. Collectively, these insights aim to elevate lignin from an underutilized by-product to a foundational resource for circular, low-carbon bioeconomy. Full article

Median overall survival remains a central endpoint in oncology, but it can obscure a clinically meaningful long tail of patients with advanced solid tumors who survive well beyond the median. One biological context in which this pattern may be relevant is tumor microenvironment (TME) acidosis. Driven by aerobic glycolysis, hypoxia, impaired perfusion, and proton-export programs, acidic TME is increasingly implicated in invasion, therapeutic resistance, and immune suppression. This narrative review examines TME acidosis as the primary biological framework and considers long-tail survival as a clinical lens through which its implications may be interpreted. We summarize the biological basis and heterogeneity of acidic TME, review current approaches to clinical and translational assessment of tumor acidity, including acidoCEST magnetic resonance imaging (MRI) and positron emission tomography (PET)-based approaches, and discuss the potential and limitations of alkalization-oriented interventions such as buffering and diet-based strategies. Particular attention is given to the distinction between direct measurements of tumor acidity and clinically feasible but indirect markers such as urinary pH, which should not be interpreted as a direct surrogate for local tumor extracellular pH. From a science-based medicine perspective, long-tail survival is treated here as a hypothesis-generating clinical signal rather than proof of causality. Overall, alkalization-oriented interventions appear biologically plausible and clinically testable, but current clinical evidence remains limited and context-dependent. Future progress will require mechanistically informed biomarkers, careful safety evaluation, and trial designs capable of detecting delayed separation of survival curves and tail-oriented patterns of benefit. Full article

The increasing penetration of renewable energy sources, distributed generation, and advanced control technologies has transformed microgrids into complex, dynamic systems that pose significant challenges for protection coordination. This paper presents a comprehensive bibliometric analysis of the scientific literature on protection strategies in modern microgrids. Using a curated dataset from the Scopus database, four types of analyses were conducted: trend topic analysis, dendrogram clustering, co-occurrence network mapping, and thematic map analysis. The trend topic analysis highlights the temporal evolution of specific topics. The dendrogram analysis reveals thematic groupings and highlights concepts that have received limited attention. The co-occurrence network analysis reveals interactions between terms, and the thematic map analysis identifies basic, niche, and motor themes, as well as emerging or declining themes. These insights provide a structured overview of current knowledge and potential future research directions in microgrid protection. This study serves as a valuable reference for researchers and practitioners aiming to understand and address the evolving challenges associated with protection coordination in modern microgrids. Full article