Search Results (1,398)

The JAK2 46/1 (“GGCC”) haplotype is an inherited genetic variation within the Jak2 gene locus that has become a focal point in research related to oncogenesis, particularly in myeloproliferative neoplasms (MPNs). We conducted a narrative review of landmark discoveries in hematological malignancies and Jak2, focusing on its role in oncogenesis, risk stratification, and drug resistance in MPNs. This haplotype spans several polymorphisms within the Jak2 gene. It has been found to increase susceptibility to a variety of hematologic cancers, especially when linked with the somatic JAK2 V617F mutation, which results in the alteration of the JAK/STAT pathway, which is particularly essential for hematopoiesis. The “GGCC” part is characterized by four SNPs, with the G allele of the rs10974944 SNP in this haplotype correlated with MPNs progressing to myelofibrosis. Moreover, the G allele seems to be crucial for the predisposition to onco-drug resistance onset. To conclude, identifying the 46/1 haplotype in patients may not only enhance risk stratification for JAK2-driven cancers but also guide more effective, personalized therapeutic strategies to overcome resistance. Thus, this review aims to describe current knowledge about the JAK2 46/1 haplotype as a marker for diagnosis and the prediction of disease outcome. Full article

The largest and endemic bivalve of the Mediterranean Sea, Pinna nobilis, is on the brink of extinction after a mass mortality event (MME) that has affected its populations since autumn 2016. Since then, different actions have been performed to improve the conservation status of P. nobilis. The monitoring of survivors in open coastal systems along the Spanish Mediterranean coast showed, after an 8-year period since the start of the MME (2017–2024), that the geographical distribution of the survivors in open sea is currently concentrated in a few regions, with focal points of specimen density in Cap de Creus (Catalonia) and Menorca (Balearic Islands). During the exhaustive monitoring of individuals of P. nobilis, the active participation of citizen science became decisive, locating almost half of the survivors. Most individuals were found in marine protected areas, mainly in Posidonia oceanica meadows in the upper 15 m. As a safety measure, several survivors were translocated to safer areas, while evaluation of the impact of the translocation showed no demonstrable effects. The knowledge acquired during these years has highlighted the necessity for collaborative monitoring, specifically to understand the current critical situation of P. nobilis and to implement effective conservation measures for this emblematic species. Full article

Glucagon-like peptide-1 receptor (GLP-1R) agonists are injectable peptide-based therapies that have become a focal point in the medical community due to their significant therapeutic efficacy in type 2 diabetes and obesity treatment. Recent advancements in medicinal chemistry have enabled the development of small-molecule GLP-1R agonists, presenting advantages such as oral administration, improved patient adherence, and cost-effectiveness. These compounds demonstrate promising efficacy in enhancing insulin secretion and promoting weight loss, in a similar way to peptide agonists. This narrative review focuses on the pharmacodynamic profiles and the current progress in clinical and preclinical research on small-molecule GLP-1R agonists. As this class of agents continues to evolve, it represents a compelling therapeutic alternative with the potential to reshape the treatment for metabolic disorders. Full article

The sediment yield resulting from storm erosion has become a focal point of research and a significant area of interest in the upper reaches of the Yangtze River amid changing environmental conditions. The issue of numerous types of erosivity factors (R) in storm erosion sediment yield models, with unclear applicability. This study examines two classical types of erosivity factors: the rainfall erosivity factor (EI₃₀, Zhang Wenbo empirical formula, etc.) and runoff erosivity power. Four combinatorial forms of erosion dynamic factors, encompassing rainfall and runoff elements, were developed. Based on the rainfall, runoff and sediment data of four stations along the Fu River basin–Pingwu station, Jiangyou station, Shehong station and Xiaoheba station from 2008 to 2018, the correlation between different R factors and sediment transport in different watershed areas was studied, and the semi-monthly sediment transport model of heavy rainfall in the Fu River basin was constructed and verified. The results revealed a weak correlation between the rainfall erosivity factor and the sediment transport modulus, making it unsuitable for developing a sediment transport model. In smaller basin areas, the correlation between the combined erosivity factor and sediment transport modulus was strongest; conversely, in larger basins, the relationship between runoff erosivity power and the sediment transport model was most pronounced. The power function relationship between the erosivity factor and sediment transport modulus yielded a more accurate simulation of sediment transport during the verification period, particularly during rainstorms, surpassing that of SWAT. These findings provide a scientific basis for predicting sediment transport during storms and floods in small mountainous basins. Full article

With the increasing global demand for energy, the development of unconventional resources has become a focal point of research. Among these, shale gas has drawn considerable attention due to its abundant reserves. However, its low permeability and complex fracture networks present substantial challenges. This study investigates the composite fracturing technology combining supercritical CO₂ and slickwater for shale gas extraction, elucidating the mechanisms by which it influences shale fracture roughness and conductivity through an integrated approach of theory, experiments, and numerical modeling. Experimental results demonstrate that the surface roughness of shale fractures increases markedly after supercritical CO₂–slickwater treatment. Moreover, the dynamic evolution of permeability and porosity is governed by roughness strain, adsorption expansion, and corrosion compression strain. Based on fluid–solid coupling theory, a mathematical model was developed and validated via numerical simulations. Sensitivity analysis reveals that fracture density and permeability have a pronounced impact on shale gas field productivity, whereas fracture dip angle exerts a comparatively minor effect. The findings provide a theoretical basis for optimizing composite fracturing technology, thereby enhancing shale gas extraction efficiency and promoting effective resource utilization. Full article

In light of the global energy shortage, the development of renewable energy has become increasingly vital. With China’s commitment to achieving “carbon peak and carbon neutrality,” photovoltaic power generation has emerged as a focal point in new energy development. However, DC arc faults in photovoltaic systems pose significant safety hazards, potentially leading to electrical fires. While new detection technologies for DC arc faults in photovoltaic power generation systems have advanced rapidly, the diversity of international standards—such as UL 1699 B, GB/T 39750, IEC 63027, and CGC/GF 175—limits both the construction of experimental platforms and the universality of detection technologies. Current research often relies on a single standard to establish experimental platforms, resulting in detection methods with limited applicability and an inability to validate technological effectiveness fully. To address this issue, this paper conducts an in-depth study of four international and national standards (IEC 63027; UL 1699 B, GB/T 39750, and CGC/GF 175), focusing on the discrepancies in decoupling methods, impedance parameter settings, and experimental circuit topologies, including series and parallel arc scenarios. Through comprehensive comparative analysis of multiple standards, this study integrates major international and domestic specifications to develop a multi-standard compatible experimental platform. The platform is designed to accommodate diverse topologies and parameter requirements, enabling efficient collection of arc test data and performance evaluation of arc fault detection devices. It also provides a standardized foundation for the performance testing and classification of DC arc circuit breakers in photovoltaic power generation systems. Through a comprehensive multi-standard comparative analysis, we systematically analyze the technical differences in photovoltaic DC arc detection. We construct a multi-standard compatible experimental platform by integrating mainstream international and domestic standards. This platform is designed to accommodate various topological structures and parameter requirements, facilitating the collection of arcing experimental data and assessment of the performance of arc fault detection devices. The findings from this research provide both theoretical and experimental foundations for developing unified technical guidelines for photovoltaic DC arc protection. This will aid in standardizing the development of detection devices and enhancing the electrical safety of photovoltaic systems. Ultimately, this work is significant for promoting the safe utilization of new energy within the framework of the dual carbon goals. Moving forward, it is crucial to enhance the generalization abilities of detection algorithms further and foster the integration of standards and industrial applications. Full article

The accelerating global urbanization process has posed new challenges to urban planning. With the rapid advancement of artificial intelligence (AI) technology, the application of AI in urban planning has gradually emerged as a prominent research focus. This study systematically reviews the current state, development trends, and challenges of AI applications in urban planning through a combination of bibliometric analysis using Citespace, AI-assisted reading based on generative models, and predictive analysis via support vector machine (SVM) algorithms. The findings reveal the following: (1) The application of AI in urban planning has undergone three stages—namely, the budding stage (January 1984 to January 2017), the rapid development stage (January 2017 to January 2023), and the explosive growth stage (January 2023 to January 2025). (2) Research hotspots have shifted from early-stage basic data integration and fundamental technology exploration to a continuous fusion and iteration of foundational and emerging technologies. (3) Globally, China, the United States, and India are the leading contributors to research in this field, with inter-country collaborations demonstrating regional clustering. (4) High-frequency keywords such as “deep learning,” “machine learning,” and “smart city” are prevalent in the literature, reflecting the application of AI technologies across both macro and micro urban planning scenarios. (5) Based on current research and predictive analysis, the application scenarios of technologies like deep learning and machine learning are expected to continue expanding. At the same time, emerging technologies, including generative AI and explainable AI, are also projected to become focal points of future research. This study offers a technical application guide for urban planning, promotes the scientific integration of AI technologies within the field, and provides both theoretical support and practical guidance for achieving efficient and sustainable urban development. Full article

Mercury, a global pollutant with both persistence and high toxicity, has remained a focal point in environmental science research over the past half-century. As a key pathway in the terrestrial mercury cycle, plants actively assimilate gaseous elemental mercury (Hg⁰) through leaf stomata, constituting a critical pathway for terrestrial mercury cycling. The litterfall mercury concentration serves as a biological indicator to quantify vegetation’s mercury interception capacity, providing essential data for global mercury cycle modelling. To investigate this, 15 sampling sites throughout the country were selected, and litterfall was collected monthly for 12 consecutive months to determine the litterfall amount, composition, and leaf mercury dynamics. The results revealed that annual litterfall production ranged from 1.10–8.56 t·hm⁻², with leaf components dominating (45.58%–89.11%). Furthermore, three seasonal litterfall patterns emerged: unimodal, bimodal, and irregular. Regarding mercury, the mercury concentration in leaf litter exhibited a certain seasonal variation trend, with the mercury content in leaves in most areas being higher in autumn and winter. Specifically, the mercury concentration in litterfall showed a significant negative correlation with latitude and a significant positive correlation with air temperature, precipitation, and litterfall amount (p < 0.05). Additionally, the concentration of Hg in dying leaves exhibited some geographical variations. Full article

Introducing deep learning methods into the quality inspection of production lines can reduce labor and improve efficiency, with great potential for the development of manufacturing systems. However, in specific closed production-line environments, robust and high-quality 3D fixed-area quality inspection is a common and challenging problem due to improper assembly, high data resolution, pose perturbation, and other reasons. In this article, we propose a robust 3D fixed-area quality inspection framework for production lines consisting of two steps: recursive segmentation and one-class classification. First, a Focal Segmentation Module (FSM) is proposed to gradually focus on the areas to be inspected by recursively segmenting the downsampled low-resolution point cloud, thereby ensuring efficient high-resolution segmentation. Moreover, Local Reference Frame (LRF)-based rotation-invariant local feature extraction is introduced to improve the robustness of the proposed method to pose variations. Second, a uniquely designed Semi-Nested Point Cloud Autoencoder (SN-PAE) is proposed to improve data imbalance and hard-to-classify samples. Particularly, we first introduce rotation-invariant feature extraction to a point cloud autoencoder to learn descriptive latent variables, then measure the latent variables using a semi-nested Latent Autoencoding Module (LAM). This avoids unreliable chamfer distance measurement and makes SN-PAE a more robust measurement method. In addition, we implement a set of experiments using solder joints as an example. Compared with PointNet++, the memory usage of recursive segmentation is reduced by 92%, and the time cost is reduced by 97.5%. The recall of SN-PAE on unaligned samples exceeds that of competitors by nearly 30% in the classification stage. The results demonstrate the feasibility and effectiveness of the proposed framework. Full article

Rhinogenic contact point headache (RCPH) represents a diagnostic challenge due to different anatomical presentations and unstandardized imaging markers. This prospective multicenter study involving 120 patients aimed to develop and validate a CT-based imaging framework for RCPH diagnosis. High-resolution CT scans were systematically assessed for seven parameters: contact point (CP) type, contact intensity (CI), septal deviation, concha bullosa (CB) morphology, mucosal edema (ME), turbinate hypertrophy (TH), and associated anatomical variants. Results revealed CP-I (37.5%) and CP-II (22.5%) as predominant patterns, with moderate CI (45.8%) and septal deviation > 15° (71.7%) commonly observed. CB was found in 54.2% of patients, primarily bulbous type (26.7%). Interestingly, focal ME at CP was independently associated with greater pain severity in the multivariate model (p = 0.003). The framework demonstrated substantial to excellent interobserver reliability (κ = 0.76–0.91), with multivariate analysis identifying moderate–severe CI, focal ME, and specific septal deviation patterns as independent predictors of higher pain scores. Our imaging classification system highlights key radiological biomarkers associated with symptom severity and may facilitate future applications in quantitative imaging, automated phenotyping, and personalized treatment approaches. Full article

Background and Clinical Significance: Scapular stress fractures are exceptionally rare in athletes and are notoriously difficult to diagnose due to their subtle presentation and poor sensitivity on initial radiographs. This case report describes the diagnostic challenge of a scapular body stress fracture in an elite boxer who initially presented with wrist pain. Case Presentation: A 19-year-old right-hand-dominant female elite boxer presented with a three-month history of bilateral wrist pain. Initial examination and MRI were consistent with a triangular fibrocartilage complex (TFCC) injury. Despite conservative management, her symptoms persisted, and she subsequently developed mechanical right shoulder pain and a sensation of instability. Physical examination revealed scapular dyskinesis, with a positive push-up test and weakness on punch protraction. Plain radiographs of the scapula were unremarkable. Point-of-care musculoskeletal ultrasound (MSK US) identified a cortical irregularity at the medial scapular border. A subsequent computed tomography (CT) scan obtained at three-month follow-up definitively confirmed a stress fracture at this site. Treatment focused on scapular stabilization via prolotherapy and activity modification, leading to symptomatic resolution and a successful return to sport. Conclusions: This case underscores the importance of evaluating the entire kinetic chain in athletes presenting with focal complaints. It demonstrates the utility of MSK US as an effective initial screening tool for cortical stress fractures and highlights the necessity of CT for definitive confirmation. Clinicians should maintain a high index of suspicion for scapular stress injuries in overhead athletes with unexplained shoulder dysfunction. Full article

The continuous increase in demand for polyethylene terephthalate (PET) has drawn global attention to the significant environmental pollution caused by the degradation of PET plastics. Exploring new PET-degrading enzymes is essential for enhancing the degradation efficiency of PET, and esterases and lipases with plastic degradation capabilities have become a focal point of research. In this study, we utilized the ultra-efficient mutant FASTase of the PET-degrading enzyme IsPETase, derived from Ideonella sakaiensis, as a positive control, based on the similarity in enzyme activity and substrate. We investigated the PET model substrate degradation activities of the esterase Gs and lipase GI, both derived from Bacillus spp., as well as the lipase CAI derived from Pseudomonas spp. The results indicated that Gs exhibited excellent bis(2-hydroxyethyl) terephthalate (BHET) degradation activity; however, Gs demonstrated a lack of thermal stability when hydrolyzing BHET. Molecular docking analyses were conducted to identify the key amino acids involved in the degradation of BHET by Gs from a structural perspective. At the same time, GI and CAI showed no BHET degradation activity. The combination of Gs and the mono-2-hydroxyethyl terephthalate (MHET) hydrolase, MHETase, can completely hydrolyze BHET, and Gs also exhibited degradation activity against the PET model substrate bis(benzyloxyethyl) terephthalate and PET nanoparticles. Given the structural similarity between PET hydrolase LCC-ICCG and Gs, this study provides new enzyme resources for advancing the efficient biological enzymatic degradation of PET plastics. Full article

We demonstrate coating-free optoacoustic generation and focusing of ultrasound using a mechanically Q-switched (MQS) erbium-doped yttrium aluminum garnet (Er:YAG) source (~100 ns, ≤20 mJ) combined with a concave water interface that simultaneously serves as converter and acoustic lens. Axial, lateral, and focal-point measurements mapped the pressure field while varying beam diameter (2w = 5–15 mm) and pulse energy (E = 10–20 mJ). The maximum focal positive pressure (P_max = 7 MPa) occurs at an intermediate diameter (~10 mm), whereas the tightest lateral/axial confinement and strongest spectral enhancement arise at larger diameters (14–15 mm) with f_c = ~5 MHz and −6 dB bandwidth up to 7 MHz. Pressure increases nearly monotonically with energy. For equal fluence, larger diameters yield higher focal pressures due to greater focusing gain. Small beams (2w ≈ 5–7 mm) show shorter apparent time-of-flight (TOF) and waveform broadening, consistent with early shock-like emission from locally vaporizing region. These results provide practical rules for tuning amplitude, spectrum, and confinement, enabling sub-millimeter focusing for contamination-sensitive and therapeutic applications. Full article

Against the backdrop of the global integration of green transformation and the digital economy, how manufacturing enterprises leverage digitalisation to drive green innovation has become a focal point for both academic and industrial sectors. Based on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), this study constructs a moderated mediation model to explore the internal mechanism through which digital transformation influences green innovation via green dynamic capabilities and examines the boundary role of environmental munificence. Questionnaire data, collected in two stages from 312 Chinese manufacturing enterprises using SPSS 27.0 and AMOS 24.0, was analysed, and the empirical results indicate that digital transformation not only directly promotes green innovation but also exerts an indirect influence through the three dimensions of green dynamic capabilities: insights into the capability of green opportunities, green resource integration, and green resource reconstruction. Furthermore, environmental munificence significantly and positively moderates the relationship between green dynamic capabilities and green innovation, suggesting that this relationship is strengthened in resource- and opportunity-rich environments. Path analysis of the three green dynamic capability dimensions reveals that back-end capabilities (resource integration and reconfiguration) have a more pronounced impact on green innovation than front-end capabilities (opportunity insights). From the dual perspectives of capability building and contextual fit, this study elucidates the mechanism and boundary conditions of digital transformation driving green innovation, enriches green innovation theory, and offers practical insights into the digital-green transformation of manufacturing enterprises. Full article

(1) Background: The extracellular matrix (ECM) is a natural scaffold for cells, creating a three-dimensional architecture composed of fibrous proteins (mainly collagen) and proteoglycans, which are synthesized by resident cells. In this study, a physiological hypoxic environment was utilized to enhance ECM production by human mesenchymal stem cells (hMSCs), a process relevant to tissue engineering and regenerative medicine. (2) Methods: hMSCs were treated with deferoxamine (DFO), a pharmaceutical hypoxia-mimetic agent that induces cellular responses similar to low-oxygen conditions through stabilization of hypoxia inducible factor-1α (HIF-1α). The time points 0 h 24 h, 3 h 24 h, and 24 h 24 h refer to DFO being added immediately after cell seeding (before cells adhesion), 3 h after cell seeding (during initial cells attachment), and 24 h after cell seeding (after focal adhesions formation and actin organization), respectively, to evaluate the influence of cell adhesion on ECM deposition. hMSCs incubated in culture media were subsequently exposed to DFO for 24 h. Samples were then subjected to cell viability tests, scanning electron microscopy (SEM), atomic force microscopy (AFM) and laser scanning confocal microscopy (CLSM) assessments. (3) Results: Viability tests indicated that DFO concentrations in the range of 0–300 µM were non-toxic over 24 h. The presence of collagen fibers in the DFO-derived ECM was confirmed with anti-collagen antibodies under CLSM. Increased ECM secretion was observed under the following conditions: 3 μM DFO (24 h 24 h), 100 μM DFO (0 h 24 h) and 300 μM DFO (3 h 24 h). SEM and AFM images revealed the morphology of various stages of collagen formation with both collagen fibrils and fibers identified. (4) Conclusions: Our preliminary study demonstrated enhanced ECM secretion by hMSC treated with DFO at concentrations of 3, 100, and 300 µM within a short cultivation period of 24–48 h without significant affecting cell viability. By mimicking physiological processes, it may be possible to stimulate endogenous tissue regeneration, for example, at an injury site. Full article