High photo-excited carrier multiplication by charged InAs dots in AlAs/GaAs/AlAs resonant tunneling diode

We present an approach for the highly sensitive photon detection based on the quantum dots (QDs) operating at temperature of 77K. The detection structure is based on an AlAs/GaAs/AlAs double barrier resonant tunneling diode combined with a layer of self-assembled InAs QDs (QD-RTD). A photon rate of 115 photons per second had induced 10nA photocurrent in this structure, corresponding to the photo-excited carrier multiplication factor of 10^7. This high multiplication factor is achieved by the quantum dot induced memory effect and the resonant tunneling tuning effect of QD-RTD structure.Comment: 10 pages,5 figures. Submitted to Applied Physics Letter

Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares

A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods

TMSB4 Overexpression Enhances the Potency of Marrow Mesenchymal Stromal Cells for Myocardial Repair

ObjectiveThe actin-sequestering proteins, thymosin beta-4 (Tβ4) and hypoxia-inducible factor (HIF)-1α, are known to be associated with angiogenesis after myocardial infarction (MI). Herein, we aimed to identify the mechanism of HIF-1α induction by Tβ4 and investigate the effects of bone marrow mesenchymal stromal cells (BMMSCs) transfected with the Tβ4 gene (TMSB4) in a rat model of MI.MethodsRat BMMSCs were isolated, cultured, and transfected with the TMSB4 gene by using the lentivirus-mediated method. Rats with surgically induced MI were randomly divided into three groups (n = 9/group); after 1 week, the rats were injected at the heart infarcted border zone with TMSB4-overexpressed BMMSCs (BMMSC-TMSB4OE), wild-type BMMSCs that expressed normal levels of TMSB4 (BMMSC-TMSB4WT), or medium (MI). The fourth group of animals (n = 9) underwent all surgical procedures necessary for MI induction except for the ligation step (Sham). Four weeks after the injection, heart function was measured using transthoracic echocardiography. Infarct size was calculated by TTC staining, and collagen volume was measured by Masson staining. Angiogenesis in the infarcted heart area was evaluated by CD31 immunofluorescence histochemistry. In vitro experiments were carried out to observe the effect of exogenous Tβ4 on HIF-1α and explore the various possible mechanism(s).ResultsIn vivo experiments showed that vascular density 4 weeks after treatment was about twofold higher in BMMSC-TMSB4OE-treated animals than in BMMSC-TMSB4WT-treated animals (p < 0.05). The cardiac function and infarct size significantly improved in both cell-treatment groups compared to controls. Notably, the cardiac function and infarct size were most prominent in BMMSC-TMSB4OE-treated animals (both p < 0.05). HIF-1α and phosphorylated HIF-1α (p-HIF-1α) in vitro were significantly enhanced by exogenous Tβ4, which was nonetheless blocked by the factor-inhibiting HIF (FIH) promoter (YC-1). The expression of prolyl hydroxylase domain proteins (PHD) was decreased upon treatment with Tβ4 and further decreased with the combined treatment of Tβ4 and FG-4497 (a specific PHD inhibitor).ConclusionTMSB4-transfected BMMSCs might significantly improve recovery from myocardial ischemia and promote the generation of HIF-1α and p-HIF-1α via the AKT pathway, and inhibit the degradation of HIF-1α via the PHD and FIH pathways

Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image

Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods

The evolution of arch filament systems and moving magnetic features around a sunspot

Context. Arch filament systems (AFSs) are usually considered as the chromospheric manifestations of the emerging flux regions (EFRs) seen in Hα observations. Moving magnetic features (MMFs) look similar to EFRs in magnetograms, but often appear in the decaying phase of an active region (AR) and behave differently from EFRs. A possible relation between AFS and MMF would be important for revealing a common mechanism for building up basic structures on the Sun. Aims. Based on Hα and magnetic field observations with high spatial resolution, we study the evolution of MMFs around a sunspot, as well as their related AFSs from birth to death. Methods. The multiwavelength observations from the New Vacuum Solar Telescope (NVST) and the Solar Dynamic Observatories (SDO) are co-aligned in the spatial and the temporal sense. MMFs appeared near the northern end of a light bridge (LB). Their related AFSs were carefully identified and traced from their appearance to disappearance based on Hα, EUV data, and magnetograms. Results. In the main sunspot of AR NOAA 11711 during April 1−4, 2013, many slow-speed MMFs with a polarity opposite to that of the sunspot appeared from the close vicinity of the northern end of a LB. Different from other smaller MMFs around the sunspot, these MMFs were always related to arch filaments and eventually formed AFSs with three twisting branches. The total flux involved in the AFSs was estimated to be about 2.7 × 1021 Mx. The largest MMF “M1” evolved into a small pore that led to an intensity reduction in the continuum intensity images. The appearance and evolution of the AFSs near the sunspot seems to be controlled by MMFs emanating from the penumbra. Owing to continual magnetic cancellation between the MMFs and their surrounding opposite flux, the AFSs gradually disintegrated and finally disappeared. Conclusions. The appearance and evolution of the AFSs near the sunspot seem to be controlled by these MMFs emanating from the penumbra

Comparative Study Of Complex Network Community Structure Algorithms In network Pharmacology Analysis

Community structure is an extremely important characteristic of complex networks composed of network pharmacology. The mining of network community structure is of great importance in many fields such as biology, computer science and sociology. In recent years, for different types of large-scale complex networks, researchers had proposed many algorithms for finding community structures. This paper reviewed some of the most representative algorithms in the field of network pharmacology, and focused on the analysis of the improved algorithms based on the modularity index and the new algorithms that could reflect the level and overlap of the community. Finally, a benchmark was established to measure the quality of the community classification algorithm

Study On The Remote Monitoring System For Medical Insurance Prescription Of Chinese Patent Medicine

Chinese patent medicine was the preparation and integration of traditional Chinese medicine in traditional Chinese medicine. It was a continuation of traditional Chinese medicine in modern society. China's hospital information construction was at a stage of development. The implementation of the medical insurance prescription monitoring system had significantly improved the level of medical insurance service management in hospitals. Based on the Eclipse platform and MYSQL database, this paper constructed a prescription insurance monitoring system for Chinese patent medicines. A visual graph of drug monitoring and medication recording was formed to determine whether there is any unreasonable phenomenon. At the same time, auxiliary diagnosis needed to input common symptoms of disease into database. According to the search, it could respond quickly, assist doctors to diagnose and improve the speed of seeking medical treatment. Prescription management recorded the prescribed prescriptions to achieve the visualization of prescription management, and achieved maximum maintainability and operability

Comparative Study Of Complex Network Community Structure Algorithms In network Pharmacology Analysis

Community structure is an extremely important characteristic of complex networks composed of network pharmacology. The mining of network community structure is of great importance in many fields such as biology, computer science and sociology. In recent years, for different types of large-scale complex networks, researchers had proposed many algorithms for finding community structures. This paper reviewed some of the most representative algorithms in the field of network pharmacology, and focused on the analysis of the improved algorithms based on the modularity index and the new algorithms that could reflect the level and overlap of the community. Finally, a benchmark was established to measure the quality of the community classification algorithm

Perceptual Metric Guided Deep Attention Network for Single Image Super-Resolution

Deep learning has been widely applied to image super-resolution (SR) tasks and has achieved superior performance over traditional methods due to its excellent feature learning capabilities. However, most of these deep learning-based methods require training image sets to pre-train SR network parameters. In this paper, we propose a new single image SR network without the need of any pre-training. The proposed network is optimized to achieve the SR reconstruction only from a low resolution observation rather than training image sets, and it focuses on improving the visual quality of reconstructed images. Specifically, we designed an attention-based decoder-encoder network for predicting the SR reconstruction, in which a residual spatial attention (RSA) unit is deployed in each layer of decoder to capture key information. Moreover, we adopt the perceptual metric consisting of L1 metric and multi-scale structural similarity (MSSSIM) metric to learn the network parameters. Different than the conventional MSE (mean squared error) metric, the perceptual metric coincides well with perceptual characteristics of the human visual system. Under the guidance of the perceptual metric, the RSA units are capable of predicting the visually sensitive areas at different scales. The proposed network can thus pay more attention to these areas for preserving visual informative structures at multiple scales. The experimental results on the Set5 and Set14 image set demonstrate that the combination of Perceptual metric and RSA units can significantly improve the reconstruction quality. In terms of PSNR and structural similarity (SSIM) values, the proposed method achieves better reconstruction results than the related works, and it is even comparable to some pre-trained networks

Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image

Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods