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AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization
Abstract Quantitative ultrasound (QUS) methods can derive insightful biomarkers from raw radiofrequency (RF) signals for tissue characterization and monitoring, but their clinical adoption is limited by the inaccessibility and storage burden of RF data. This study is the first to investigate the potential of deep generative models in synthesizing RF data from standard B-mode images and evaluate their efficacy in downstream QUS analysis. Three conditional generative adversarial networks (cGAN), namely Pix2Pix, a shallow ViT‐based cGAN, and a deep ViT‐based cGAN, were adapted and trained on a large paired dataset of RF/B‐mode frames (21,174 training, 3,456 validation, 8,919 test frames) collected from 152 patients (98 patients in the training, 16 in validation, and 38 in the test set) with suspicious breast lesions. The synthesized RF data were assessed using sample-level evaluation metrics, and via a benign-malignant lesion classification task based on the corresponding QUS features. The generative models achieved a structural similarity index measure (SSIM) of 0.82 ± 0.05 on the synthetic RF data and an average peak signal-to-noise ratio (PSNR) of about 33 dB on the corresponding B-mode images, confirming strong reconstruction fidelity. In the lesion classification experiments, a classifier trained on a selected subset of six QUS features derived from the original RF data achieved a test accuracy of 82 ± 6%. In training and testing the classifier with the same subset of QUS features derived from the synthetic RF data, the deep ViT cGAN matched the original model’s performance (accuracy = 82 ± 6%), outperforming the Pix2Pix and shallow ViT cGANs. When the feature selection and classifier training and testing were exclusively performed on the synthetic QUS parameters, the Deep ViT cGAN (accuracy = 81 ± 7%) and Pix2Pix cGAN (accuracy = 81 ± 6%) demonstrated competitive performance, while the Shallow ViT remained slightly lower (accuracy = 79 ± 6%). The promising results obtained in this study demonstrate the feasibility of RF data synthesis from B‐mode images, and therefore, is a step forward towards QUS‐based tissue characterization without the necessity of direct access to RF data.
Evaluating long-read metagenomics for bloodstream infection diagnostics: a pilot study from a Thai Tertiary Hospital
A CAM bioimaging model reveals the connection between VEGFA vascular remodeling and enhanced sarcoma progression via tumor secretome
Abstract We developed a sensitive bioimaging system for sarcoma by creating a cell line that stably expresses both Katushka2S fluorophore and NanoLuc luciferase, enabling robust dual tracking of tumor growth and metastasis in the chick chorioallantoic membrane (CAM) model. NanoLuc luciferase was notably more effective than Katushka2S for identifying tumor cell metastases in embryo tissues. Pretreating CAM with tumor cell-conditioned medium (TCM) significantly increased neovascularization, Ki67 expression, tumor volumes, and metastasis of the most difficult-to-establish low-tumorigenic and low-metastatic U2OS cells, indicating that tumor cell secretome actively alter the CAM vascular environment to aid tumor progression. The bead-based multiplex profiling of the TCM demonstrated a notable increase in pro-angiogenic factors. Neutralizing VEGFA, the most abundant factor in the TCM, effectively counteracted vascular and pro-metastatic effects of TCM. In contrast, elevating VEGFA levels brought back the pro-tumorigenic effects of TCM. This study reveals the importance of tumor cell secretomes in creating the vascular niche in CAM and points to VEGFA as a target to prevent secretome-induced angiogenesis and sarcoma development. Furthermore, our optimized CAM model permits continuous tumor growth and metastasis monitoring in embryonic development, providing a reliable platform for prognostic studies of sarcoma treatments in ongoing anti-angiogenic and multikinase inhibitors trials.
Comparative biology and morphometrics of pink bollworm, Pectinophora gossypiella (Saunders) on Bt cotton and alternate malvaceous hosts
Bayesian neural network-based policy effect prediction for green transformation of power business environment
Abstract Predicting how green policies reshape power business environments remains notoriously difficult. The underlying dynamics are nonlinear, the uncertainties substantial, and conventional models often fall short. This study develops a Bayesian neural network framework designed specifically for forecasting and optimizing green policy outcomes within the Fujian power system, placing particular weight on quantifying prediction uncertainty to support sound decision-making. Our methodology weaves together stochastic variational inference and multi-objective optimization, thereby capturing the channels through which policies transmit their effects to environmental outcomes. Drawing on empirical data spanning 2018–2024, we find that this approach outperforms standard machine learning techniques by roughly 4–5% points in prediction accuracy while delivering markedly better uncertainty calibration. Scenario analyses reveal that moderate-to-high policy intensity tends to achieve favorable cost-effectiveness, with renewable energy incentives, carbon pricing, and regulatory enforcement standing out as especially potent drivers of transformation. Perhaps more importantly for practitioners, the framework demonstrates that well-designed moderate-intensity strategies can surpass maximum-intensity approaches once diminishing returns enter the picture. By enabling joint assessment of environmental gains, economic efficiency, and operational stability under uncertainty, this work offers a practical foundation for evidence-based policy design—though readers should bear in mind that our validation remains grounded in the Fujian regional context.
Internal gelation-based alginate hydrogel films incorporating Quercus infectoria gall extract for multifunctional wound dressing applications
Chronic high-altitude exposure and cognitive health in Chinese college students: a 4-year longitudinal neuroimaging study
Molecular identification of bat fly species and associated Bartonella bacteria from Lopburi and Sa Kaeo Provinces in Thailand
Integrating circulating microRNAs with epidemiological factors enhances breast cancer detection across subtypes: the MCC-Spain study
Huperzine A improves neurological function in mice with intracerebral hemorrhage by alleviating neuroinflammation and ferroptosis
The small-cage induced sedentariness in male young rats: evidence from energy expenditure and glucose uptake
Response of sediment delivery ratio to water-sediment and riverbed boundary conditions during flood events in the lower yellow river since 2000
Abstract The sediment delivery ratio is greatly affected by the water-sediment and the riverbed boundary, which represent the river’s capacity to transport sediment under specified conditions. This study examines the response of the sediment delivery ratio to water-sediment and riverbed boundary conditions in the Lower Yellow River (LYR) since the operation of the Xiaolangdi Reservoir began. It evaluates the spatial-temporal variations of water-sediment and riverbed boundaries based on hydrological data and topographic data from 2000 to 2023. Based on the sediment transport rate equation, a theoretical equation for the sediment delivery ratio during flood events has been developed, thoroughly considering the effects of riverbed boundary conditions, including median particle size of bed sediment, river gradient, and width-to-depth ratio. The results show that the sediment delivery ratio negatively correlates with the incoming sediment coefficient, the median particle size of bed sediment, and the width-to-depth ratio. In contrast, it positively correlates with the water load variation coefficient and river gradient. In comparison to solely accounting for water and sediment conditions, incorporating the riverbed boundary into the theoretical equation results in a more precise alignment with the measured sediment delivery ratio data. This indicates that the riverbed boundary is a crucial factor influencing the sediment delivery ratio. Under the current boundary conditions, the Aishan to Lijin reach has the highest sediment transport capacity. To improve the sediment transport capacity of the Tiexie to Lijin reach, it is recommended to narrow the river width upstream of Gaocun and increase the width-to-depth ratio. The findings of this study provide essential scientific insights into the sediment transport capacity of alluvial rivers subjected to variations in water-sediment and riverbed boundary conditions, thereby offering important references for hydrological engineering and river management practices.
ACSL4 mediates ferroptosis to promote immunoglobulin A nephropathy progression: scRNA-seq analysis
Skeletal and dentoalveolar effects of different hyrax maxillary expansion protocols compared with novel magnetic expansion: a CBCT-based study
Short-term effects of meteorological factors on hand, foot, and mouth disease in Zhengzhou, China
Adaptive machine learning models for predictive maintenance in industrial internet of things (IIoT) systems
Gaze dynamics toward familiar and unfamiliar faces in prosopagnosia
Simulation-based optimization analysis of passenger flow organization in metro interchange stations using AnyLogic
Abstract In large-scale metro interchange stations, significant passenger flow volumes during peak hours are prone to induce pedestrian congestion phenomena, presenting operational challenges. Taking Metro S transfer station as the research object, this study constructs an AnyLogic pedestrian simulation platform based on the social force model to simulate the current passenger flow conditions in the station concourse area, identifying key bottleneck zones. By employing an optimization method that integrates passenger flow guidance with coordinated allocation of equipment resources, a dual-path passenger flow diversion mechanism is designed to alleviate congestion caused by intersecting passenger flow lines. The optimization results demonstrate that this approach can effectively mitigate peak-hour congestion while reducing passenger walking time and improving throughput efficiency. This offers decision support for passenger flow management in large metro transfer stations.
The deubiquitinating enzyme Otu1 releases substrates from the conserved initiation complex of the Cdc48/p97 ATPase for proteasomal degradation
Abstract Many eukaryotic proteins are modified with a polyubiquitin chain and then recruited to either the Cdc48 ATPase (p97 or VCP in mammals) or the 26S proteasome by conserved cofactors. They can then shuttle between the Cdc48 ATPase and the 26S proteasome before being degraded. How substrates avoid being trapped on the Cdc48 ATPase complex is incompletely understood, as they can undergo repeated cycles of translocation through the ATPase pore. Here, we show that the deubiquitinating enzyme (DUB) Otu1 (Yod1 in mammals) can break this futile cycle. Otu1 trims the ubiquitin chain of the substrate before its translocation through the Cdc48 pore is initiated, allowing transfer to the proteasome and subsequent degradation. A cryo-EM structure shows that the mammalian homolog Yod1 binds to p97 simultaneously with other Cdc48/p97 cofactors. As in the yeast system, polypeptide translocation through the ATPase pore is initiated by the unfolding of a ubiquitin molecule, suggesting that the mechanism of substrate processing is conserved in all eukaryotes.