Search Results (437)

Background: Acute myeloid leukemia (AML) is a common and aggressive adults hematological malignancies. This study explored megakaryocyte–erythroid progenitors (MEPs) signature genes and constructed a prognostic model. Methods: Uniform manifold approximation and projection (UMAP) identified distinct cell types, with differential analysis between AML-MEP and normal MEP groups. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression selected biomarkers to build a risk model and nomogram for 1-, 3-, and 5-year survival prediction. Results: Ten differentially expressed genes (DEGs) related to overall survival (OS), six (AHSP, MYB, VCL, PIM1, CDK6, as well as SNHG3) were retained post-LASSO. The model exhibited excellent efficiency (the area under the curve values: 0.788, 0.77, and 0.847). Pseudotime analysis of UMAP-defined subpopulations revealed that MYB and CDK6 exert stage-specific regulatory effects during MEP differentiation, with MYB involved in early commitment and CDK6 in terminal maturation. Finally, although VCL, PIM1, CDK6, and SNHG3 showed significant associations with AML survival and prognosis, they failed to exhibit pathological differential expression in quantitative real-time polymerase chain reaction (qRT-PCR) experimental validations. In contrast, the downregulation of AHSP and upregulation of MYB in AML samples were consistently validated by both qRT-PCR and Western blotting, showing the consistency between the transcriptional level changes and protein expression of these two genes (p < 0.05). Conclusions: In summary, the integration of single-cell/transcriptome analysis with targeted expression validation using clinical samples reveals that the combined AHSP-MYB signature effectively identifies high-risk MEP-AML patients, who may benefit from early intensive therapy or targeted interventions. Full article

Clinical and animal studies suggest that multiple brain systems are involved in mediating reward-motivated and related emotional behavior including the consumption of commonly used drugs and palatable food, and there is evidence that the repeated ingestion of or exposure to these rewarding substances may in turn stimulate these brain systems to produce an overconsumption of these substances along with co-occurring emotional disturbances. To understand this positive feedback loop, this review focuses on a specific population of hypothalamic peptide neurons expressing melanin-concentrating hormone (MCH), which are positively related to dopamine reward and project to forebrain areas that mediate this behavior. It also examines neurons expressing the peptide hypocretin/orexin (HCRT) that are anatomically and functionally linked to MCH neurons and the molecular systems within these peptide neurons that stimulate their development and ultimately affect behavior. This report first describes evidence in animals that exposure in adults and during adolescence to rewarding substances, such as the drugs alcohol, nicotine and cocaine and palatable fat-rich food, stimulates the expression of MCH as well as HCRT and their intracellular molecular systems. It also increases reward-seeking and emotional behavior, leading to excess consumption and abuse of these substances and neurological conditions, completing this positive feedback loop. Next, this review focuses on the model involving embryonic exposure to these rewarding substances. In addition to revealing a similar positive feedback circuit, this model greatly advances our understanding of the diverse changes that occur in these neuropeptide/molecular systems in the embryo and how they relate, perhaps causally, to the disturbances in behavior early in life that predict a later increased risk of developing substance use disorders. Studies using this model demonstrate in animals that embryonic exposure to these rewarding substances, in addition to stimulating the expression of peptide neurons, increases the intracellular molecular systems in neuroprogenitor cells that promote their development. It also alters the morphology, migration, location and neurochemical profile of the peptide neurons and causes them to develop aberrant neuronal projections to forebrain structures. Moreover, it produces disturbances in behavior at a young age, which are sex-dependent and occur in females more than in males, that can be directly linked to the neuropeptide/molecular changes in the embryo and predict the development of behavioral disorders later in life. These results supporting the close relationship between the brain and behavior are consistent with clinical studies, showing females to be more vulnerable than males to developing substance use disorders with co-occurring emotional conditions and female offspring to respond more adversely than male offspring to prenatal exposure to rewarding substances. It is concluded that the continued consumption of or exposure to rewarding substances at any stage of life can, through such peptide brain systems, significantly increase an individual’s vulnerability to developing neurological disorders such as substance use disorders, anxiety, depression, or cognitive impairments. Full article

With the rapid advancement of UAV-based remote sensing and image recognition techniques, identifying environmental risk factors from aerial imagery has emerged as a focal point in intelligent inspection during the power transmission and distribution projects construction phase. The uneven spatial distribution of risk factors on construction sites, their weak texture signatures, and the inherently multi-scale nature of UAV imagery pose significant detection challenges. To address these issues, we propose a one-stage SRW-YOLO algorithm built upon the YOLOv11 framework. First, a P2-scale shallow feature detection layer is added to capture high-resolution fine details of small targets. Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. Finally, a dynamic non-monotonic focusing mechanism WIoU v3 loss function is employed to reweigh low-quality annotations, thereby improving small-object localization accuracy. Experimental results demonstrate that SRW-YOLO achieves an overall precision of 80.6% and mAP of 79.1% on the State Grid dataset, and exhibits similarly superior performance on the VisDrone2019 dataset. Compared with other one-stage detectors, SRW-YOLO delivers markedly higher detection accuracy, offering critical technical support for multi-scale, heterogeneous environmental risk monitoring during the power transmission and distribution projects construction phase, and establishes the theoretical foundation for rapid and accurate inspection using UAV-based intelligent imaging. Full article

On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article

Through the energy conversion of building skins, building-integrated photovoltaic (BIPV) technology, the core carrier of the smart city energy system, encourages the conversion of buildings into energy-generating units. However, the decommissioning of the module faces the challenge of physical dismantling and financial environmental damage because of the close coupling with the building itself. As the first tranche of BIPV projects will enter the end of their life cycle, it is urgent to establish a multi-dimensional collaborative recycling mechanism that meets the characteristics of building pv systems. Based on the theory of reverse logistics network, the research focuses on optimizing the reverse logistics network during the decommissioning stage of BIPV modules, and proposes a dual-objective optimization model that considers both cost and carbon emissions for BIPV. Meanwhile, the multi-level recycling network which covers “building points-regional transfer stations-specialized distribution centers” is designed in the research, the Pareto solution set is solved by the improved NSGA-II algorithm, a “1 + 1” du-al-core construction model of distribution center and transfer station is developed, so as to minimize the total cost and life cycle carbon footprint of the logistics network. At the same time, the research also reveals the driving effect of government reward and punishment policies on the collaborative behavior of enterprise recycling, and provides methodological support for the construction of a closed-loop supply chain of “PV-building-environment” symbiosis. The study concludes that in the process of constructing smart city energy system, the systematic control of resource circulation and environmental risks through the optimization of reverse logistics network can provide technical support for the sustainable development of smart city. Full article

Rectangular box jacking is widely used in densely developed urban areas. However, when conducted with limited clear distance near existing metro tunnels, it introduces considerable structural safety risks. This study investigates a large-section rectangular box jacking project in Suzhou that crosses a double-line metro tunnel with minimal vertical clear distance. Integrated field monitoring and finite element simulations were conducted to analyze the tunnel’s deformation behavior during various jacking phases. The results show that the upline tunnel experienced greater uplift than the downline tunnel, with maximum vertical displacement occurring directly beneath the jacking axis. The affected zone extended approximately 20 m beyond the pipe gallery boundaries. Both the tunnel vault and ballast bed exhibited vertical uplift, while the hance displaced laterally toward the launching shaft. These deformations showed clear stage-dependent patterns strongly influenced by the relative position of the jacking machine. Numerical simulations demonstrated that doubling the pipe–tunnel clearance reduced the vault displacement by 58.87% (upline) and 51.95% (downline). Increasing the pipe–slurry friction coefficient from 0.1 to 0.3 caused the hance displacement difference to rise from 0.12 mm to 0.36 mm. Further sensitivity analysis reveals that when the jacking machine is positioned directly above the tunnel, grouting pressure is the greatest influence on the structural response and must be carefully controlled. The proposed methodology and findings offer valuable insights for future applications in similar tunnelling projects. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains among the most lethal malignancies, with a five-year survival rate below 12%, largely attributable to its asymptomatic onset, late-stage diagnosis, and limited curative treatment options. Although PDAC accounts for approximately 3% of all cancers, it is projected to become the second leading cause of cancer-related mortality in the United States by 2030. A major contributor to its dismal prognosis is the lack of validated early detection strategies for asymptomatic individuals. In this review, we present a comprehensive synthesis of current advances in the early detection of PDAC, with a focus on the identification of high-risk populations, novel biomarker platforms, advanced imaging modalities, and artificial intelligence (AI)-driven tools. We highlight high-risk groups—such as those with new-onset diabetes after age 50, pancreatic steatosis, chronic pancreatitis, cystic precursor lesions, and hereditary cancer syndromes—as priority populations for targeted surveillance. Novel biomarker panels, including circulating tumor DNA (ctDNA), miRNAs, and exosomes, have demonstrated improved diagnostic accuracy in early-stage disease. Recent developments in imaging, such as multiparametric MRI, contrast-enhanced endoscopic ultrasound, and molecular imaging, offer improved sensitivity in detecting small or precursor lesions. AI-enhanced radiomics and machine learning models applied to prediagnostic CT scans and electronic health records are emerging as valuable tools for risk prediction prior to clinical presentation. We further refine the Define–Enrich–Find (DEF) framework to propose a clinically actionable strategy that integrates these innovations. Collectively, these advances pave the way for personalized, multimodal surveillance strategies with the potential to improve outcomes in this historically challenging malignancy. Full article

With the rapid advancement of urban underground space development, shield tunnel construction has seen a significant increase. However, at the initial launching stage of shield tunnels in shallow-buried weak strata, engineering risks such as face instability and sudden surface settlement frequently occur. At present, there are relatively few studies on the reinforcement technology of the initial section of shield tunnel in shallow soft ground and the evolution law of ground disturbance. This study takes the launching section of the Guanggang New City depot access tunnel on Guangzhou Metro Line 10 as the engineering background. By applying MIDAS/GTS numerical simulation, settlement monitoring, and theoretical analysis, the reinforcement technology at the tunnel face, the spatiotemporal evolution of ground settlement, and the mechanism of soil disturbance transmission during the launching process in muddy soil layer are revealed. The results show that: (1) the reinforcement scheme combining replacement filling, high-pressure jet grouting piles, and soil overburden counterpressure significantly improves surface settlement control. The primary influence zone is concentrated directly above the shield machine and in the forward excavation area. (2) When the shield machine reaches the junction between the reinforced and unreinforced zones, a large settlement area forms, with the maximum ground settlement reaching −26.94 mm. During excavation in the unreinforced zone, ground deformation mainly occurs beneath the rear reinforced section, with subsidence at the crown and uplift at the invert. (3) The transverse settlement trough exhibits a typical Gaussian distribution and the discrepancy between the measured maximum settlement and the numerical and theoretical values is only 3.33% and 1.76%, respectively. (4) The longitudinal settlement follows a trend of initial increase, subsequent decrease, and gradual stabilization, reaching a maximum when the excavation passes directly beneath the monitoring point. The findings can provide theoretical reference and engineering guidance for similar projects. Full article

This article focuses on forecasting morphological changes in small rivers, using the Ulken Almaty River, located on the northern slope of the Ile Alatau range in the Tien Shan mountain system, as a case study. One of the key components of river morphology is the dynamics of channel processes, including erosion, accretion, and the shifting of channel forms. Understanding these processes in rivers flowing through urbanized areas is essential for mitigating environmental and infrastructural risks. Despite their importance, studies of this nature in Kazakhstan remain at a formative stage and are largely fragmentary, underscoring the need for modern approaches to river morphology analysis. Three representative sections of the Ulken Almaty River (upstream, midstream, and downstream) were selected for analysis. Satellite imagery from 2012 to 2021 was used for manual digitisation of river channel outlines. Annual erosion and accretion areas were calculated based on these data. The DSAS 5.1 module, integrated into ArcGIS 10.8.1, was applied to determine the rates of erosion and accretion over the ten-year period. To forecast future channel changes, the Kalman filter model was employed, enabling projections for 10 and 20 years into the future. A comparative analysis of the intensity of the erosion and accretion processes was conducted for each river section. Spatial and temporal variations in bank dynamics were identified, with the most significant changes occurring in the middle and lower reaches. Forecasted scenarios indicate the possible deformation pathways of the river channel influenced by both natural and anthropogenic factors. The results provide valuable insights into the spatiotemporal dynamics of fluvial processes in small mountain rivers under the pressure of urban development and climatic variability. The methodology employed in this study offers practical applications for urban planning, river management, and the mitigation of geomorphological hazards. Full article

Road infrastructure is the foundation of the predominant modes of transport, and its effective management is crucial to meet mobility needs. Although necessary for reconstruction, maintenance, and expansion projects, roadworks produce negative impacts, resulting in further risk for workers and drivers and failing to ensure sustainable development. The objective of this paper is twofold: Firstly, investigate the contributing factors to the occurrence of crashes in roadworks. Secondly, develop a model to estimate crash numbers in these areas. The results, which could support municipalities at the planning stage and implement policies for safe and sustainable development, are achieved by examining 121 sites, where 549 crashes occurred, and 25 contributing factors. The variables are divided into three categories: technical characteristics of the site, infrastructural, and environmental. Besides the conventional variables, a risk-increasing factor is calibrated. It assesses the impact of roadworks according to the manoeuvres imposed and the number of lanes. Consistent with previous findings, several variables related to the work zone layout, traffic conditions, infrastructure, and surrounding environment are correlated with the crash number. After performing a further statistical analysis, a multiple linear regression model, statistically significant (0.000) and suitable for accurately estimating the possible number of crashes (R²_adj = 0.41), is determined. Full article

In the context of green development and digital transformation, the technological innovation cooperation in megaprojects requires a spanning from policy guidance, technological breakthroughs, and localized pilot projects to driven demand, integrated innovation (i.e., collaborative innovation across sectors, stages, and stakeholders), and comprehensive promotion. Despite the potential benefits, many megaprojects face challenges related to complex relationships, behavioral uncertainties, low performance, and technological innovation risks. A question of practical and theoretical significance is how to facilitate technological innovation cooperation in megaprojects. This study proposes the 3C Theory, which integrates cooperative relationships, behaviors, and performance, and investigates how technological innovation risks moderate these interactions. Using data from 19 megaprojects, we employ a mixed-methods approach involving hypothesis testing through regression analysis. The findings reveal that strong cooperative relationships positively influence cooperative performance through cooperative behaviors and that technological innovation risks play a significant moderating role. This study offers several practical recommendations for megaproject managers, including enhancing cooperative relationships, promoting effective behaviors, managing innovation risks, and developing cooperative innovation platforms. This study introduces the 3C Theory to megaprojects and provides novel insights into how collaboration and risk management can drive sustainable innovation. Full article

Background/Objectives: Accurately estimating survival times is critical for clinical decision-making, treatment evaluation, resource allocation, and other purposes. Yet data from relatively recent diagnosis cohorts is strongly affected by right censoring that biases average survival times downward. For example, 5-, 10-, or 20-year survival time averages are not available until 5, 10, or 20 years later, which may be in the future, thus presenting a challenge to obtain in the present. An approach to addressing this problem is described in this report. Here it is demonstrated for kidney cancer survival but could also be applied to survival questions for other types of cancer, other diseases, stage progression times, and similar problems in medicine and other fields in which there is a need for up-to-date analyses of survival improvement trends. Methods: This study introduces STETI, an approach to survival estimation that integrates information about survival times of diagnosis year cohorts with information about survival times of death year cohorts. By leveraging data from death year cohorts in addition to the more familiar diagnosis year cohorts, STETI incorporates recent survival data often excluded by traditional approaches due to right censoring, caused when the post-diagnosis time period of interest has not yet elapsed. Using data from SEER, we explain how the proposed approach integrates diagnosis year cohorts with the death year cohorts of recent years. We demonstrate that incorporating death year cohorts addresses an important source of right censorship that is inherent in diagnosis year cohorts from relatively recent years. This permits survival time trend analysis that accounts for recent improvements in survival time that would be difficult to account for using diagnosis year cohorts alone. We tested linear and exponential models to demonstrate the method’s ability to derive survival time trends using valuable data that would otherwise risk being left unused. Conclusions: Improved survival estimation can better support personalized treatment planning, healthcare benchmarking, and research into cancer subtypes as well as other domains. To this end, we introduce a hybrid analytical approach that addresses an important source of right censorship. Demonstrating it within the domain of kidney cancer is expected to help pave the way to other applications in oncology and beyond, and offers a case study of STETI, an approach to quantifying and projecting trends in survival time associated with therapeutic advancements. Full article

This study aims to mitigate slope-collapse hazards that threaten life and property at the Lujiawan resettlement site in Wanbi Town, Dayao County, Yunnan Province, within the Guanyinyan hydropower reservoir. It integrates centimeter-level point-cloud data collected by a DJI Matrice 350 RTK equipped with a Zenmuse L2 airborne LiDAR (Light Detection And Ranging) sensor with detailed structural-joint survey data. First, qualitative structural interpretation is conducted with stereographic projection. Next, safety factors are quantified using the limit-equilibrium method, establishing a dual qualitative–quantitative diagnostic framework. This framework delineates six hazardous rock zones (WY1–WY6), dominated by toppling and free-fall failure modes, and evaluates their stability under combined rainfall infiltration, seismic loading, and ambient conditions. Subsequently, six-degree-of-freedom Monte Carlo simulations incorporating realistic three-dimensional terrain and block geometry are performed in RAMMS::ROCKFALL (Rapid Mass Movements Simulation—Rockfall). The resulting spatial patterns of rockfall velocity, kinetic energy, and rebound height elucidate their evolution coupled with slope height, surface morphology, and block shape. Results show peak velocities ranging from 20 to 42 m s⁻¹ and maximum kinetic energies between 0.16 and 1.4 MJ. Most rockfall trajectories terminate within 0–80 m of the cliff base. All six identified hazardous rock masses pose varying levels of threat to residential structures at the slope foot, highlighting substantial spatial variability in hazard distribution. Drawing on the preceding diagnostic results and dynamic simulations, we recommend a three-tier “zonal defense with in situ energy dissipation” scheme: (i) install 500–2000 kJ flexible barriers along the crest and upper slope to rapidly attenuate rockfall energy; (ii) place guiding or deflection structures at mid-slope to steer blocks and dissipate momentum; and (iii) deploy high-capacity flexible nets combined with a catchment basin at the slope foot to intercept residual blocks. This staged arrangement maximizes energy attenuation and overall risk reduction. This study shows that integrating high-resolution 3D point clouds with rigid-body contact dynamics overcomes the spatial discontinuities of conventional surveys. The approach substantially improves the accuracy and efficiency of hazardous rock stability assessments and rockfall trajectory predictions, offering a quantifiable, reproducible mitigation framework for long slopes, large rock volumes, and densely fractured cliff faces. Full article

Aiming to mitigate engineering risks such as tunnel face collapse and equipment jamming caused by poor geological conditions during the construction of tunnel boring machines (TBMs), this study proposes a TBM surrounding rock grade prediction method based on multi-source feature fusion. Firstly, a multi-source dataset is established by systematically integrating TBM tunnelling parameters, horizontal acoustic profile (HSP) detection data and three-dimensional geological spatial information. In the data preprocessing stage, the TBM data is cleaned and divided according to the mileage section, the statistical characteristics of key tunnelling parameters (thrust, torque, penetration, etc.) are extracted, and the rock fragmentation index (TPI, FPI, WR) is fused to construct a composite feature vector. The Direct-LiNGAM causal discovery algorithm is innovatively introduced to analyse the nonlinear correlation mechanism between multi-source features, and then a hybrid model, TRNet, which combines the local feature extraction ability of convolutional neural networks and the nonlinear approximation advantages of Kolmogorov–Arnold networks, is constructed. Verified by a real tunnel project in western Sichuan, China, the prediction accuracy of TRNet for surrounding rock grade on the test set reaches an average of 92.15%, which is higher than other data-driven methods. The results show that the prediction method proposed in this paper can effectively predict the surrounding rock grade of the tunnel face during TBM tunnelling, and provide decision support for the dynamic regulation of tunnelling parameters. Full article

Serving as critical sustainable transportation infrastructure, inland waterways provide dual socioeconomic and ecological value by (1) facilitating high-efficiency freight logistics through cost-effective bulk cargo transport while stimulating regional economic growth, and (2) delivering essential ecosystem services including flood regulation, water resource preservation, and biodiversity conservation. This study establishes a stakeholder-centered risk assessment framework to enhance decision-making of waterway engineering projects and promote the sustainable development of Inland Waterway Transport. We propose a three-layer approach: (1) identifying key stakeholders in the decision-making stage of waterway engineering projects through multi-dimensional criteria; (2) listing and classifying decision-making risks from the perspectives of managers, users, and other stakeholders; (3) applying the Decision-Making Trial and Evaluation Laboratory (DEMATEL) to prioritize key risks and proposing a risk assessment model based on fuzzy reasoning theory to evaluate decision-making risks under uncertain conditions. This framework was applied to the Yangtze River Trunk Line Wuhan–Anqing Waterway Regulation Project. The results show that the risk ranking is managers, users, and other stakeholders, among which the risk of engineering freight demand is particularly prominent. This suggests that we need to pay attention to optimizing material transportation and operational organization, promote the development of large-scale ships, and realize the diversification of ship types and transportation organizations. This study combines fuzzy reasoning with stakeholder theory, providing a replicable tool for the Waterway Management Authority to address the complex sustainability challenges in global waterway development projects. Full article