Interferons control viral infection by inducing the expression of antiviral effector proteins encoded by interferon-stimulated genes (ISGs). The field has mostly focused on identifying individual antiviral ISG effectors and defining their mechanisms of action. However, fundamental gaps in knowledge about the interferon response remain. For example, it is not known how many ISGs are required to protect cells from a particular virus, though it is theorized that numerous ISGs act in concert to achieve viral inhibition. Here, we used CRISPR-based loss-of-function screens to identify a markedly limited set of ISGs that confer interferon-mediated suppression of a model alphavirus, Venezuelan equine encephalitis virus (VEEV). We show via combinatorial gene targeting that three antiviral effectors-ZAP, IFIT3, and IFIT1-together constitute the majority of interferon-mediated restriction of VEEV, while accounting for < 0.5% of the interferon-induced transcriptome. Together, our data suggest a refined model of the antiviral interferon response in which a small subset of "dominant" ISGs may confer the bulk of the inhibition of a given virus.
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http://dx.doi.org/10.15252/embr.202356901 | DOI Listing |
Med Biol Eng Comput
January 2025
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.
View Article and Find Full Text PDFJ Microsc
January 2025
Department of Mechanical, Materials and Aerospace Engineering, University of Liverpool, Liverpool, UK.
Electron backscatter diffraction (EBSD) has developed over the last few decades into a valuable crystallographic characterisation method for a wide range of sample types. Despite these advances, issues such as the complexity of sample preparation, relatively slow acquisition, and damage in beam-sensitive samples, still limit the quantity and quality of interpretable data that can be obtained. To mitigate these issues, here we propose a method based on the subsampling of probe positions and subsequent reconstruction of an incomplete data set.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Integrated Health Sciences, Department of Kinesiology and Nutrition Sciences, University of Nevada, Las Vegas, NV 89154, USA.
Introduction: As wearable technology becomes increasingly popular and sophisticated, independent validation is needed to determine its accuracy and potential applications. Therefore, the purpose of this study was to evaluate the accuracy (validity) of VO2max estimates and blood oxygen saturation measured via pulse oximetry using the Garmin fēnix 6 with a general population participant pool.
Methods: We recruited apparently healthy individuals (both active and sedentary) for VO2max (n = 19) and pulse oximetry testing (n = 22).
Sensors (Basel)
December 2024
Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China.
Ground-Penetrating Radar (GPR) has demonstrated significant advantages in the non-destructive detection of road structural defects due to its speed, safety, and efficiency. This paper proposes a three-dimensional (3D) reconstruction method for GPR images, integrating the back-projection (BP) imaging algorithm to accurately determine the size, location, and other parameters of road structural defects. Initially, GPR detection images were preprocessed, including direct wave removal and wavelet denoising, followed by the application of the BP algorithm to effectively restore the defect's location and size.
View Article and Find Full Text PDFSensors (Basel)
December 2024
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
IoT (Internet of Things) networks are vulnerable to network viruses and botnets, while facing serious network security issues. The prediction of payload states in IoT networks can detect network attacks and achieve early warning and rapid response to prevent potential threats. Due to the instability and packet loss of communications between victim network nodes, the constructed protocol state machines of existing state prediction schemes are inaccurate.
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