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Vascular endothelial functional dysregulation and barrier disruption are involved the initiation and development of sepsis. Growth arrest-specific protein 6 (Gas6), one of the endogenous ligands of TAM receptors (Tyro3, Axl, and Mertk), is confirmed to have beneficial functions in hemostasis, inflammation, and cancer growth. Here, we demonstrated the protective effects of Gas6 on multi-organ dysfunction syndrome (MODS) in sepsis and the underlying mechanisms. We investigated Gas6-ameliorated MODS by inhibiting vascular endothelial hyperpermeability in a mouse model of sepsis. Additionally, , under lipopolysaccharide (LPS) stimulation in vascular endothelial cells, Gas6 attenuated vascular endothelial hyperpermeability by reinforcing the tight junction proteins occludin, zonula occludens-1 (ZO-1), and claudin5. Furthermore, Gas6 substantially suppressed NF-κB p65 activation. In addition, blocking the Gas6 receptor, Axl, partially reduced the protective effect of Gas6 on the vascular endothelial barrier and diminished the inhibitive effect of Gas6 on NF-κB p65 activation. Taken together, this study suggests that Gas6 has a protective effect on MODS in sepsis by inhibiting the vascular endothelial hyperpermeability and alteration of tight junction and that the Axl/NF-κB signaling pathway underlies these effects.

In order to study the coupling mechanism of the process parameters during the double-glow discharge process, and thus to enhance the theoretical study of double-glow plasma surface metallurgical technology, in this paper, a two-dimensional fluid model is established using COMSOL simulation software. The effects of key processing factors on the distribution of electrons and excited argon ions, potential and electron temperature in the coupling process of double-glow discharge were investigated. The results indicated that the electron density between the two electrode plates increases as the voltage difference increases. The optimal working pressure was kept between 0.14 Torr and 0.29 Torr. The optimal electrode spacing was between 15 mm and 30 mm and decreased with the increase in pressure. Compared with the actual plasma surface alloying process experiment, the simulation results were consistent with the experiments. The research can guide experiments by combining simulation and theory, and the predictability and accuracy of double-glow surface metallurgy technology have been improved.

The practical deployment of Neural Radiance Fields (NeRF) in rendering applications faces several challenges, with the most critical one being low rendering speed on even high-end graphic processing units (GPUs). In this paper, we present ICARUS, a specialized accelerator architecture tailored for NeRF rendering. Unlike GPUs using general purpose computing and memory architectures for NeRF, ICARUS executes the complete NeRF pipeline using dedicated plenoptic cores (PLCore) consisting of a positional encoding unit (PEU), a multi-layer perceptron (MLP) engine, and a volume rendering unit (VRU). A PLCore takes in positions \\& directions and renders the corresponding pixel colors without any intermediate data going off-chip for temporary storage and exchange, which can be time and power consuming. To implement the most expensive component of NeRF, i.e., the MLP, we transform the fully connected operations to approximated reconfigurable multiple constant multiplications (MCMs), where common subexpressions are shared across different multiplications to improve the computation efficiency. We build a prototype ICARUS using Synopsys HAPS-80 S104, a field programmable gate array (FPGA)-based prototyping system for large-scale integrated circuits and systems design. We evaluate the power-performance-area (PPA) of a PLCore using 40nm LP CMOS technology. Working at 400 MHz, a single PLCore occupies 16.5 \\(mm^2\\) and consumes 282.8 mW, translating to 0.105 uJ/sample. The results are compared with those of GPU and tensor processing unit (TPU) implementations.

Unknown quantum pure states of arbitrary but definite S-level of a particle can be transferred onto a group of remote two-level particles through two-level EPRs as many as the number of those particles in this group. We construct such a kind of teleportation, the realization of which need a nonlocal unitary transformation to the quantum system that is made up of the s-level particle and all the two-level particles at one end of the EPRs, and measurements to all the single particles in this system. The unitary transformation to more than two particles is also written into the product form of two-body unitary transformations.

A theoretical scheme for controlled quantum teleportation is presented, using the entanglement property of GHZ state.

To address the diverse needs of enterprise users and the cold-start issue of recommendation system, this paper proposes a quality-service demand classification method—1D-CNN-CrossEntorpyLoss, based on cross-entropy loss and one-dimensional convolutional neural network (1D-CNN) with the comprehensive enterprise quality portrait labels. The main idea of 1D-CNN-CrossEntorpyLoss is to use cross-entropy to minimize the loss of 1D-CNN model and enhance the performance of the enterprise quality-service demand classification. The transaction data of the enterprise quality-service platform are selected as the data source. Finally, the performance of 1D-CNN-CrossEntorpyLoss is compared with XGBoost, SVM, and logistic regression models. From the experimental results, it can be found that 1D-CNN-CrossEntorpyLoss has the best classification results with an accuracy of 72.44%. In addition, compared to the results without the enterprise-quality portrait, the enterprise-quality portrait improves the accuracy and recall of 1D-CNN-CrossEntorpyLoss model. It is also verified that the enterprise-quality portrait can further improve the classification ability of enterprise quality-service demand, and 1D-CNN-CrossEntorpyLoss is better than other classification methods, which can improve the precision service of the comprehensive quality service platform for MSMEs.

Mitochondrial dysfunction plays a critical role in the pathogenesis of diabetic cardiomyopathy. Translocase of mitochondrial outer membrane 70 (Tom70) primarily facilitates the import of mitochondrial preproteins that may be involved in the regulation of oxidative stress and mitochondrial function. This study aimed to investigate the role of Tom70 in the development of myocardial injury in leptin receptor-deficient (db/db) diabetic mice. Tom70 siRNA or an overexpressing lentivirus was intramuscularly injected into mouse hearts or used to treat cultured neonatal cardiomyocytes. We found that Tom70 was downregulated in the diabetic hearts compared with the level in the wild-type hearts and that knocking down Tom70 exacerbated cardiac hypertrophy, fibrosis, and ventricular dysfunction in the db/db mice. Similarly, the in vitro data demonstrated that silencing Tom70 enhanced high-glucose and high-fat (HGHF) medium treatment-induced mitochondrial superoxide production, decreased ATP production and the mitochondrial membrane potential, and enhanced cell apoptosis in neonatal cardiomyocytes. Importantly, overexpression of Tom70 alleviated HGHF medium-induced oxidative stress, mitochondrial dysfunction, and cell apoptosis. Furthermore, in vivo data confirmed that reconstitution of Tom70 ameliorated cardiac hypertrophy, interstitial fibrosis, and ventricular dysfunction in the db/db mice. In addition, Tom70 overexpression mitigated mitochondrial fragmentation and dysfunction in the hearts of the db/db mice. Taken together, these findings suggest that downregulation of Tom70 contributes to the development of diabetic cardiomyopathy and that reconstitution of Tom70 may be a new therapeutic strategy for the prevention and treatment of diabetic cardiomyopathy.

Azimuth ambiguity caused by spectral aliasing severely degrades the quality of Synthetic Aperture Radar (SAR) images. To suppress azimuth ambiguity while preserving image details as much as possible, this paper proposes an azimuth ambiguity suppression method for SAR based on time-frequency joint analysis. By exploiting the distribution differences of ambiguous signals across different sub-spectra, the method locates azimuth ambiguity in the time domain through multi-sub-spectrum change detection and fusion, followed by ambiguity suppression in the azimuth time-frequency domain. Experimental results demonstrate that the proposed method effectively suppresses azimuth ambiguity while maintaining superior performance in preserving genuine targets.

Rapid and accurate earthquake magnitude estimations are essential for earthquake early warning (EEW) systems. The distance information between the seismometers and the earthquake hypocenter can be important to the magnitude estimation. We designed a deep-learning, multiple-seismometer-based magnitude estimation method using three heterogeneous multimodalities: three-component acceleration seismograms, differential P-arrivals, and differential seismometer locations, with a specific transformer architecture to introduce the implicit distance information. Using a data-augmentation strategy, we trained and selected the model using 5365 and 728 earthquakes. To evaluate the magnitude estimation performance, we use the root mean square error (RMSE), mean absolute error (MAE), and standard deviation error ( ϭ ) between the catalog and the predicted magnitude using the 2051 earthquakes. The model could achieve RMSE, MAE, and ϭ less than 0.38, 0.29, and 0.38 when the passing time of the earliest P-arrival is 3 s and stabilize to the final values of 0.20, 0.15, and 0.20 after 14 s. The comparison between the proposed model and model ii, which is retrained without the specific architecture, indicates that the architecture contributes to the magnitude estimation. The P-arrivals picking error testing indicates the model could provide robust magnitude estimation on EEW with an absolute error of less than 0.2 s. Graphical Abstract

The morbidity of frontotemporal dementia (FTD), one of the most prevalent dementias praccox, is second to Alzheimer disease (AD). It is different with AD that FTD has a rapider course and a higher mortality. FTD has not yet been fully understood in terms of etiology or pathogenesis, but genetic factors are believed to be involved. In this paper, we were committed to providing a comprehensive overview to FTD in aspects of the neuropathology features and the relevant molecular genetics advances, so that there would be insights to those researchers in search of novel approaches in FTD diagnosis and treatment.