Study on the Role of Vitamin D in Systemic Lupus Erythematosus

Vitamin D is a hormone precursor with multiple biological effects. It binds to vitamin D receptors on target cells. It is an important participant in the metabolism of calcium and phosphorus in vivo. It is closely related to cell cycle, cell proliferation, differentiation, apoptosis, signal transduction and immune regulation. Its role in the treatment of infection, tumor and even immune diseases has been gradually recognized and studied. Patients with systemic lupus erythematosus generally have decreased levels of active vitamin D, and low levels of vitamin D are associated with disease occurrence, disease activity and complications. In the past ten years, a large number of studies have been carried out on it globally to explore the role of vitamin D in the occurrence and development of systemic lupus erythematosus. This paper summarizes its recent research progress

Optical sensors using chaotic correlation fiber loop ring down

We have proposed a novel optical sensor scheme based on chaotic correlation fiber loop ring down (CCFLRD). In contrast to the well-known FLRD spectroscopy, where pulsed laser is injected to fiber loop and ring down time is measured, the proposed CCFLRD uses a chaotic laser to drive a fiber loop and measures autocorrelation coefficient ring down time of chaotic laser. The fundamental difference enables us to avoid using long fiber loop as required in pulsed FLRD, and thus generates higher sensitivity. A strain sensor has been developed to validate the CCFLRD concept. Theoretical and experiment results demonstrate that the proposed method is able to enhance sensitivity by more than two orders of magnitude comparing to the existing FLRD method. We believe the proposed method could find great potential applications for chemical, medical, and physical sensing

Energy-Storage Optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

Wind power penetration ratios of power grids have increased in recent years; thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation

Deep Hash Remote-Sensing Image Retrieval Assisted by Semantic Cues

With the significant and rapid growth in the number of remote-sensing images, deep hash methods have become a research topic. The main work of deep hash method is to build a discriminate embedding space through the similarity relation between sample pairs and then map the feature vector into Hamming space for hashing retrieval. We demonstrate that adding a binary classification label as a kind of semantic cue could further improve the retrieval performance. In this work, we propose a new method, which we called deep hashing, based on classification label (DHCL). First, we propose a network architecture, which can classify and retrieve remote-sensing images under a unified framework, and the classification labels are further utilized as the semantic cues to assist in network training. Second, we propose a hash code structure, which can integrate the classification results into the hash-retrieval process to improve accuracy. Finally, we validate the performance of the proposed method on several remote-sensing image datasets and show the superiority of our method

Using Conditional Generative Adversarial 3-D Convolutional Neural Network for Precise Radar Extrapolation

Radar echo extrapolation is a basic but essential task in meteorological services. It could provide radar echo prediction results with high spatiotemporal resolution in a computationally efficient way, and effectively enhance the operational system's forecasting capability for meteorological hazards. Traditional methods perform extrapolation by estimating echo motions between contiguous radar data. This strategy is difficult to characterize complex nonlinear meteorological processes effectively, and it is difficult to benefit from large historical data. Recently, machine learning (ML) models have been used for radar echo extrapolation. These methods have effectively improved extrapolation quality in a data-driven way and from the statistical perspective. Although the ML-based methods show excellent performance, they usually produce blurry extrapolations. This leads to underestimating radar echo intensity and making echo lack small-scale details. Moreover, it makes models difficult to predict severe convective hazards. To solve this problem, a two-stage extrapolation model based on 3-D convolutional neural network and conditional generative adversarial network is proposed. These two models form the “pre-extrapolation” and “postprocessing” paradigm. The pre-extrapolation model is trained in the traditional way and performs rough extrapolation. The postprocessing model uses the pre-extrapolation result as input and is trained with the adversarial strategy. It could correct the echo intensity and increase the echo's details. In the experiment, our model could provide more precise radar echo extrapolations than other methods, especially for intense echoes and convective systems, in the data of North China from 2015 to 2016

HMGB1/autophagy pathway mediates the atrophic effect of TGF-β1 in denervated skeletal muscle

Abstract Background Transforming growth factor beta 1 (TGF-β1) is a classical modulator of skeletal muscle and regulates several processes, such as myogenesis, regeneration and muscle function in skeletal muscle diseases. Skeletal muscle atrophy, characterized by the loss of muscle strength and mass, is one of the pathological conditions regulated by TGF-β1, but the underlying mechanism involved in the atrophic effects of TGF-β1 is not fully understood. Methods Mice sciatic nerve transection model was created and gastrocnemius were analysed by western blot, immunofluorescence staining and fibre diameter quantification after 2 weeks. Exogenous TGF-β1 was administrated and high-mobility group box-1 (HMGB1), autophagy were blocked by siRNA and chloroquine (CQ) respectively to explore the mechanism of the atrophic effect of TGF-β1 in denervated muscle. Similar methods were performed in C2C12 cells. Results We found that TGF-β1 was induced in denervated muscle and it could promote atrophy of skeletal muscle both in vivo and in vitro, up-regulated HMGB1 and increased autophagy activity were also detected in denervated muscle and were further promoted by exogenous TGF-β1. The atrophic effect of TGF-β1 could be inhibited when HMGB1/autophagy pathway was blocked. Conclusions Thus, our data revealed that TGF-β1 is a vital regulatory factor in denervated skeletal muscle in which HMGB1/ autophagy pathway mediates the atrophic effect of TGF-β1. Our findings confirmed a new pathway in denervation-induced skeletal muscle atrophy and it may be a novel therapeutic target for patients with muscle atrophy after peripheral nerve injury

Secrecy Enhancing Scheme for Spatial Modulation Using Antenna Selection and Artificial Noise

In this paper, we present a new secrecy-enhancing scheme for the spatial modulation (SM) system, by considering imperfect channel state information (CSI). In the proposed scheme, two antennas are activated at the same time. One of the activated antennas transmits information symbols along with artificial noise (AN) optimized under the imperfect CSI condition. On the other hand, the other activated antenna transmits another AN sequence. Because the AN are generated by exploiting the imperfect CSI of the legitimate channel, they can only be canceled at the legitimate receiver, while the passive eavesdropper will suffer from interference. We derive the secrecy rate of the proposed scheme in order to estimate the performance. The numerical results demonstrated in this paper verify that the proposed scheme can achieve a better secrecy rate compared to the conventional scheme at the same effective data rate

Fermentative production of Vitamin E tocotrienols in Saccharomyces cerevisiae under cold-shock-triggered temperature control

Tocotrienols are valuable supplementations to α-tocopherol-dominated Vitamin E products. Here, the authors engineer baker’s yeast by combining the heterologous genes from photosynthetic organisms with the endogenous pathway for the production of tocotrienols under cold-shock-triggered temperature control