Gastric Mammalian Target of Rapamycin Signaling Contributes to Inhibition of Ghrelin Expression Induced by Roux-En-Y Gastric Bypass

Background/Aims: Roux-en-Y Gastric Bypass, RYGB, is the most effective strategy to control body weight in morbid obesity. RYGB leads to rapid improvement of glycemic status and weight loss, which are largely attributed to the alteration of gastrointestinal hormones including ghrelin. The current study examined potential mechanisms of altered ghrelin synthesis after RYGB. Methods: Gastric mammalian target of rapamycin (mTOR) signaling, ghrelin synthesis and secretion were determined in lean or obese male mice with or without RYGB operation, as well as in obese patients pre- and post-RYGB surgery. Ghrelin expression and mTOR signaling were investigated by western blotting and immunohistochemistry. Ghrelin mRNA levels were detected by real-time PCR. Plasma ghrelin was measured by enzyme immunoassay. Results: mTOR activity in the gastric fundus was significantly lower than in the forestomachs. Both of them were decreased after 24h fasting. A significant negative correlation was found between gastric levels of phospho-S6 (phospho-S6 ribosomal protein) and proghrelin during changes of energy status. mTOR activity was activated, whereas ghrelin expression was inhibited by Roux-en-Y Gastric Bypass in both rodents and human beings. Increment of ghrelin synthesis and decline of mTOR signaling induced by rapamycin were significantly reversed by RYGB in both lean and obese mice. Administration of Ad-S6K1 (adenovirus-mediated p70 ribosomal protein subunit 6 kinase 1) from tail vein suppressed the expression of ghrelin in RYGB-operated mice relative to control animals. Conclusion: mTOR is therefore a gastric fuel sensor whose activity is linked to the regulation of ghrelin after Roux-en-Y Gastric Bypass

Increased CD14+HLA-DR−/low Myeloid-Derived Suppressor Cells Correlate With Disease Severity in Systemic Lupus Erythematosus Patients in an iNOS-Dependent Manner

Myeloid-derived suppressor cells (MDSCs) comprise of a population of cells, which suppress the innate and adaptive immune system via different mechanisms. MDSCs are accumulated under pathological conditions. The present study aimed to clarify the pathological role of MDSCs in systemic lupus erythematosus (SLE) patients. Consequently, the level of circulating M-MDSCs was significantly increased in newly diagnosed SLE patients as compared to healthy controls. An elevated level of M-MDSCs was positively correlated with the disease severity in SLE patients and an immunosuppressive role was exerted in an iNOS-dependent manner. The decrease in the number of M-MDSCs after therapy rendered them as an indicator for the efficacy of treatment. These results demonstrated that M-MDSCs participated in the pathological progress in SLE patients. Thus, MDSCs are attractive biomarkers and therapeutic targets for SLE patients

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

A New GPT2w Model Improved by PSO-LSSVM for GNSS High-Precision Positioning

Tropospheric delay is an important error affecting GNSS high-precision navigation and positioning, which will decrease the precision of navigation and positioning if it is not well corrected. Actually, tropospheric delay, especially in the zenith direction, is related to a series of meteorological parameters, such as temperature and pressure. To estimate the zenith tropospheric delay (ZTD) as accurately as possible, the paper proposes a new fused model using the least squares support vector machines (LSSVM) and the particle swarm optimization (PSO) to improve the precision and temporal resolution of meteorological parameters in global pressure and temperature 2 wet (GPT2w). The proposed model uses the time series of meteorological parameters from the GPT2w model as the initial value, and thus, the time series of the residuals can be obtained between the meteorological parameters from meteorological sensors (MS) and the GPT2w model. The long time series of meteorological parameters is the evident periodic signal. The GPT2w model describes its dominant frequency (harmonics), and the residuals thus can be seen as the short-period signal (nonharmonics). The combined PSO and LSSVM model (PSO-LSSVM) is used to predict the specific value of the short-period signal. The new GPT2w model, in which the meteorological parameter value is obtained by combining the estimated meteorological parameters residuals and the GPT2w-derived meteorological parameters, can be acquired. The GNSS network stations in Hong Kong throughout 2017-2018 are processed by the GNSS Processing and Analysis Software (GPAS), which is developed by the Chinese Academy of Surveying & Mapping, to estimate the zenith tropospheric delay and station coordinates using the new GPT2w model. Statistical results reveal that the accuracy of the new GPT2w model-derived ZTD was improved by 60% or more compared with that of the GPT2w-derived ZTD. In addition, the positioning accuracy of the GNSS station has been effectively improved up to 44.89%. Such results reveal that the new GPT2w model can greatly reduce the influence of nonharmonic components (short-period terms) of the meteorological parameter time series and achieve better accuracy than the GPT2w model

Reconstruction of fluorescence molecular tomography with a cosinoidal level set method

Abstract Background Implicit shape-based reconstruction method in fluorescence molecular tomography (FMT) is capable of achieving higher image clarity than image-based reconstruction method. However, the implicit shape method suffers from a low convergence speed and performs unstably due to the utilization of gradient-based optimization methods. Moreover, the implicit shape method requires priori information about the number of targets. Methods A shape-based reconstruction scheme of FMT with a cosinoidal level set method is proposed in this paper. The Heaviside function in the classical implicit shape method is replaced with a cosine function, and then the reconstruction can be accomplished with the Levenberg–Marquardt method rather than gradient-based methods. As a result, the priori information about the number of targets is not required anymore and the choice of step length is avoided. Results Numerical simulations and phantom experiments were carried out to validate the proposed method. Results of the proposed method show higher contrast to noise ratios and Pearson correlations than the implicit shape method and image-based reconstruction method. Moreover, the number of iterations required in the proposed method is much less than the implicit shape method. Conclusions The proposed method performs more stably, provides a faster convergence speed than the implicit shape method, and achieves higher image clarity than the image-based reconstruction method

Image Restoration for Fluorescence Planar Imaging with Diffusion Model

Fluorescence planar imaging (FPI) is failure to capture high resolution images of deep fluorochromes due to photon diffusion. This paper presents an image restoration method to deal with this kind of blurring. The scheme of this method is conceived based on a reconstruction method in fluorescence molecular tomography (FMT) with diffusion model. A new unknown parameter is defined through introducing the first mean value theorem for definite integrals. System matrix converting this unknown parameter to the blurry image is constructed with the elements of depth conversion matrices related to a chosen plane named focal plane. Results of phantom and mouse experiments show that the proposed method is capable of reducing the blurring of FPI image caused by photon diffusion when the depth of focal plane is chosen within a proper interval around the true depth of fluorochrome. This method will be helpful to the estimation of the size of deep fluorochrome

Ordering for Reduced Transmission Energy Detection in Sensor Networks Testing a Shift in the Mean of a Gaussian Graphical Model

Detection of a shift in the mean of a vector following a decomposable Gaussian graphical model (DGGM) is considered, where each component of the vector is measured at a different sensor in a network. We provide a new method eliminating transmissions normally needed in an optimum clustering approach by using ordered transmissions while achieving the same Bayes risk as the optimum clustering approach. In the new approach, the Bayes optimum test statistic is represented as a sum of local test statistics, where each local test statistic depends only on the observations made at one clique in a generalization of the ordered transmission approach previously suggested for statistically independent observations. Hence, we propose to organize the sensors into clusters based on the clique of the DGGM they belong to, and each cluster selects one sensor to be its cluster head (CH). After collecting and summarizing the observed data at each cluster, the ordered transmission approach is employed over the CHs in an attempt to reduce the number of communications from the CHs to the fusion center where the decision is made. It is shown that the developed approach can guarantee a lower bound on the average number of transmissions saved from the ordered transmission approach for any given DGGM which approaches approximately half the number of cliques when the norm of the mean-shift vector in each clique becomes sufficiently large. In all the cases considered, numerical results imply that a significant portion of the transmissions can be saved, and the developed lower bound is obeyed. The development of the appropriate local processing and the proof of savings are highly nontrivial generalizations of ordering for statistically independent observations and this paper represents the first justification of an ordering approach for cases with statistically dependent observations

SiO_2@Fe_3O_4@C胶体纳米颗粒的制备及其光学性质

Highly dispersed SiO_2@Fe_3O_4@C core-shell nanoparticles with controllable particle sizes were prepared.Under electric field,the optical properties of SiO_2@Fe_3O_4@C nanoparticle suspensions of different size,different concentrations,different the solvent was studied,and the results show that the reflection spectrum of 150 nm SiO_2@Fe_3O_4@C nanoparticles at a concentration of 10%(mass fraction) obtain the widest tunable range from 735 nm to 540 nm.With increasing concentration of nanoparticle suspension,the overall reflectance spectra blue shift.The solvent dependence of the electric fieldresponsive photonic crystals by dispersing 150 nm particles into different solvents was investigated,and the results show that the greater refractive index,the greater the reflection wavelength.The response time of the suspensions is 150 ms,and the suspensions have recovery after electric field disappeared