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A textured urban 3D mesh is an important part of 3D real scene technology. Semantically segmenting an urban 3D mesh is a key task in the photogrammetry and remote sensing field. However, due to the irregular structure of a 3D mesh and redundant texture information, it is a challenging issue to obtain high and robust semantic segmentation results for an urban 3D mesh. To address this issue, we propose a semantic urban 3D mesh segmentation network (MeshNet) with sparse prior (SP), named MeshNet-SP. MeshNet-SP consists of a differentiable sparse coding (DSC) subnetwork and a semantic feature extraction (SFE) subnetwork. The DSC subnetwork learns low-intrinsic-dimensional features from raw texture information, which increases the effectiveness and robustness of semantic urban 3D mesh segmentation. The SFE subnetwork produces high-level semantic features from the combination of features containing the geometric features of a mesh and the low-intrinsic-dimensional features of texture information. The proposed method is evaluated on the SUM dataset. The results of ablation experiments demonstrate that the low-intrinsic-dimensional feature is the key to achieving high and robust semantic segmentation results. The comparison results show that the proposed method can achieve competitive accuracies, and the maximum increase can reach 34.5%, 35.4%, and 31.8% in mR, mF1, and mIoU, respectively. [ABSTRACT FROM AUTHOR]

The active components of ginseng, such as ginsenosides and polysaccharides, have high therapeutic value in treating cancer, decreasing obesity, and enhancing immunity. However, simple primary ginseng treatment cannot maximize this medicinal potential. Therefore, in this study, Panax ginseng was co-fermented with multi-enzyme-coupling probiotics to obtain a fermentation broth with higher levels of ginsenosides, polysaccharides, and probiotics. When compared to other treatment methods for cyclophosphamide-induced immunosuppression in mice, the results reveal that the P. ginseng fermentation broth treated with multi-enzyme-coupling probiotics could significantly improve the immune function of immunosuppressive mice and restore intestinal flora stability. Overall, this processing method will provide a novel strategy for promoting the application of ginseng and the relief of immunosuppression. [ABSTRACT FROM AUTHOR]

Automatic registration of unordered point clouds is the prerequisite for urban reconstruction. However, most of the existing technologies still suffer from some limitations. On one hand, most of them are sensitive to noise and repetitive structures, which makes them infeasible for the registration of large-scale point clouds. On the other hand, most of them dealing with point clouds with limited overlaps and unpredictable location. All these problems make it difficult for registration technology to obtain qualified results in outdoor point cloud. To overcome these limitations, this paper presents a grid graph-based point cloud registration (GGR) algorithm to align pairwise scans. First, point cloud is divided into a set of 3D grids. We propose a voting strategy to measure the similarity between two grids based on feature descriptor, transforming the superficial correspondence into 3D grid expression. Next, a graph matching is proposed to capture the spatial consistency from putative correspondences, and graph matching hierarchically refines the corresponding grids until obtaining point-to-point correspondences. Comprehensive experiments demonstrated that the proposed algorithm obtains good performance in terms of successful registration rate, rotation error, translation error, and outperformed the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The rumen of livestock grazing on the Qinghai‐Tibetan Plateau (QTP) acts as a transfer station for the circulation of soil, grass, faecal mineral elements and nutrients. Whether the microorganisms from the soil and grass could circulate through livestock rumen and excreted faeces. We studied the structural composition and interactive networks of microbiomes (bacteria and fungi) in soil, grass, and grazing yaks (rumen and faeces) on the QTP by using 16S rRNA gene and internally transcribed spacer (ITS) sequencing technology and to calculate the contribution rate of microorganisms from one habitat to another habitat using SourceTracker analysis. The meta‐co‐occurrence network revealed that soil, grass, rumen, and faeces comprise four independent habitats. The bacterial and fungal composition was significantly different in these four habitats. Soil microbiota showed the highest alpha diversity and microbial network complexity. Rumen microbiota demonstrated the highest microbial network stability and synergy, while grass endophytes showed the lowest microbial network complexity, stability, and synergy. According to the SourceTracker model, grass contributes 0.02% to the rumen microbes of yaks, while soil microorganisms do not circulate in the rumen. The soil and grass microbiota originating from faeces were 4.5% and 1.2%, respectively. The contribution of soil to grass was found to be 1.1%. Overall, the rumen microbiota of yaks is relatively stable and is only minimally influenced by the microbiota inhabiting the environment under natural grazing conditions. However, the contribution of yaks to soil and grass microbiota is relatively high when compared with the contribution of soil and grass to yaks microbiota. [ABSTRACT FROM AUTHOR]

Background: Myelin oligodendrocyte glycoprotein immunoglobulin G (MOG-IgG) has been considered a diagnostic marker for patients with demyelinating disease, termed "MOG-IgG associated disorder" (MOGAD). Recently, the coexistence of MOG-IgG and other neuronal or glial antibodies has attracted extensive attention from clinicians. In this article, we systematically review the characteristics of MOG-IgG-related antibody coexistence syndrome. Methods: Two authors independently searched PubMed for relevant studies published before October 2021. We also manually searched the references of each related article. The appropriateness of the included studies was assessed by reading the titles, abstracts, and full texts if necessary. Results: Thirty-five relevant publications that met our inclusion criteria were finally included, of which fourteen were retrospective studies and twenty-one were case reports. A total of 113 patients were reported to show the coexistence of MOG-IgG and neuronal or glial antibodies. Additionally, 68.14% of patients were double positive for MOG-IgG and N-Methyl-D-Aspartate Receptor-IgG (NMDAR-IgG), followed by 23.01% of patients who were double positive for MOG-IgG and aquaporin4-IgG (AQP4-IgG). Encephalitis was the predominant phenotype when MOG-IgG coexisted with NMDAR-IgG, probably accompanied by imaging features of demyelination. Patients with dual positivity for MOG-IgG and AQP4-IgG experienced more severe disease and more frequent relapses. The coexistence of MOG-IgG and antibodies other than NMDAR-IgG and AQP4-IgG was extremely rare, and the clinical presentations were diverse and atypical. Except for patients who were double positive for MOG-IgG and AQP4-IgG, most patients with multiple antibodies had a good prognosis. Conclusions: MOG-IgG may coexist with neuronal or glial antibodies. Expanded screening for neuronal or glial antibodies should be performed in patients with atypical clinical and radiological features. [ABSTRACT FROM AUTHOR]

In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data. [ABSTRACT FROM AUTHOR]

Background and Objective: Myasthenia gravis (MG) is an autoimmune neuromuscular disease. Nearly 10–30% of patients with MG are refractory to conventional therapy. Rituximab (RTX), a monoclonal antibody targeting CD20, is increasingly used in autoimmune disorders. We performed a systematic review and meta-analysis to evaluate the effectiveness and safety of RTX for refractory MG. Methods: Studies published between January 1, 2000 and January 17, 2021 were searched in PubMed, EMBASE, Cochrane Library, and ClincalTrails.gov. Primary outcomes included proportion of patients achieving minimal manifestation status (MMS) or better and quantitative MG (QMG) score change from baseline. Secondary outcomes were glucocorticoids (GC) doses change from baseline and proportion of patients discontinuing oral immunosuppressants. Results: A total of 24 studies involving 417 patients were included in the meta-analysis. An overall 64% (95% confidence interval, 49–77%) of patients achieved MMS or better. The estimated reduction of QMG score was 1.55 (95% confidence interval, 0.88–2.22). The mean reduction of GC doses was 1.46 (95% confidence interval, 1.10–1.82). The proportion of patients discontinuing oral immunosuppressants was 81% (95% confidence interval, 66–93%). Subgroup analyses showed that the proportion of patients achieving MMS or better and discontinuing oral immunosuppressants was higher in MuSK-MG group than those in AChR-MG group. Improvement was more pronounced in patients with mild to moderate MG compared to those with severe MG. Moreover, the efficacy appeared to be independent of the dose of RTX. 19.6% of patients experienced adverse events, most of which were mild to moderate. Only one patient developed progressive multifocal leukoencephalopathy. Conclusions: RTX can alleviate the symptom of weakness, decrease QMG score and reduce the doses of steroids and non-steroid immunosuppressive agents in refractory MG. It is well-tolerated with few severe adverse events. Randomized controlled trials are urgently needed to study the efficacy of RTX in treating refractory MG and to identify the characteristics of patients who might respond well to RTX. [ABSTRACT FROM AUTHOR]

Traditional Light Detection and Rangings (LiDARs) can quickly collect high-accuracy of three-dimensional (3D) point cloud data at a designated wavelength (i.e., cannot obtain hyperspectral data), while the passive hyperspectral imager can collect rich spectral data of ground objects, but are lack of 3D spatial data. This paper presents one innovative study on the design of airborne-oriented supercontinuum laser hyperspectral (SCLaHS) LiDAR with 50 bands covering 400 nm to 900 nm at a spectral resolution of 10 nm and ground sampling distance (GSD) of 0.5 m. The major innovations include (1) development of the high-power narrow-pulse supercontinuum laser source covering 400 nm to 900 nm with 50 bands using multi-core microstructure fibre, all-polarization maintaining fibre and ultra-long cavity structure, (2) a miniaturized aberration correction holographic concave grating spectroscopic and streak tube technique are developed for 50 bands laser echoes detection at high spectral-spatial-temporal resolution and dynamic airborne platform, and (3) the algorithm theoretic basis for SCLaHS LiDAR point cloud data 3D geodetic coordination calculation, including in-flight airborne calibration algorithm. The initial experimental results demonstrated that the designed SCLaHS LiDAR is doable, and a prototype of the (SCLaHS) LiDAR intends to be implemented. [ABSTRACT FROM AUTHOR]

Abstract Background Both severe stenosis and completed occlusion in internal carotid artery or its distal branches have been considered the main reasons of cerebral hypoperfusion, which contributes to the washout disturbances of embolism in low perfusion territories distal to stenosis. An aggravated hypoperfusion state in certain brain region may induce ischemic stroke and further cognitive decline. However, the effective medication for cerebral hypoperfusion is largely unsettled. Methods/design By using computed tomography perfusion (CTP) imaging, the trial will evaluate the effectiveness, safety and tolerability of hydroxyethyl starch (HES) 130/0.4 for patients with extra-/intra-cranial artery stenosis and cerebral hypoperfusion. From 5 neurological inpatient wards, 300 patients will be randomly recruited for administered routine medications plus intravascular volume therapies using the equal volume of HES 130/0.4 or 0.9% sodium chloride solution. Cerebral hypoperfusion state after 7-day intervention is the primary outcome measure. The secondary outcome measures includes, impaired renal function, abnormal heart function, hematological changes, neurological dysfunctions and cerebrovascular events in peri-intervention period and/or 3-month follow-up. The sample size will allow the detection of a two-sided 5% significance level between groups in the endpoint with a power of 80%. Discussion The trial would provide important efficacy and safety data on the intravascular administration of HES 130/0.4 in patients with unilateral cerebral hypoperfusion. The effects on kidney function, heart function, coagulation, neurological function and cerebralvascular events will be assessed. Trial registration ClinicalTrials.gov (Identifier: NCT01192581)

This paper first focuses on the study of the relationship between the urban heat island (UHI) and the selected physical variables (percentage of urban surface covers, Normalized Difference Vegetation Index (NDVI)) and social variables (population density (PDEN)), and then concentrates on the study of the relationship between UHI and the landscape spatial geometric patterns. The researched results discover that urban Land Surface Temperature (LST) is not only impacted by land cover composition, i.e. land use/cover, which is expressed in this paper as the PURB (commerce/industry/transportation), but also its spatial geometric configuration, i.e. various landscape geometric pattern metrics, which in this paper are expressed by compositional percentage of landscape area (PLAND), configurational edge density (ED), patch density (PD), landscape shape index (LSI), clumpiness index (CI), and Shannon's diversity index (SHDI). The results show that the proportion of vegetation coverage out of a tract impacts its contribution to an entire UHI in Washington District of Columbia (DC), in particular, interspersing vegetation within a tract is capable of making a stronger mitigation effect to UHI than its concentrated form. Thus, a scatter spatial arrangement and distribution of vegetation is proposed to mitigate UHI effect. [ABSTRACT FROM AUTHOR]

To meet the high frame rate requirements of correct point correspondences with a sub-pixel precision, this paper first proposes a Field Programmable Gate Array (FPGA) architecture that consists of corner detection, corner matching, outlier rejection, and sub-pixel precision localisation. In the architecture, a combined Features from Accelerated Segment Test (FAST)+ Binary Robust Independent Elementary Features (BRIEF) algorithm is adopted for detection and matching with pixel precision, a combined algorithm of Slope-based Rejection (SR) and Correlation-Coefficient-based Rejection (CCR) is used to reject the outliers, and a gradient centroid-based algorithm is used for sub-pixel precision localisation. The whole FPGA architecture is implemented on a single FPGA platform (Xilinx XC72K325T). Five image datasets with different spatial resolutions, textures, lights, rotate angles, and viewpoints are used to evaluate the performance of the FPGA-based implementation. The experimental results show that (1) the SR and CCR algorithms are effective for outlier rejection; (2) a higher correct matching rate is achieved for the image pairs that cover artificial textures than for those that cover natural textures; (3) the proposed architecture is also suitable for image pairs with small change of lights, rotate angles, and viewpoints; (4) the speed of the FPGA-based implementation can reach 280 Frames Per Second (FPS), which is 35 times faster than the Personal Computer (PC)-based implementation; and (5) the usage of FPGA resources is acceptable for the selected FPGA platform. The speed and usage of the FPGA resources can be improved when the whole FPGA-based implementation is further optimised. [ABSTRACT FROM AUTHOR]

Recently, very high resolution (VHR) remote sensing (RS) images have become widely available. While a VHR RS image provides more detailed information of the imaged scene, inter-class variation is enhanced as well, which makes it a challenging task to accurately classify the image at pixel level. It has been found that spatial features of a VHR image derived from the spectral measurements can improve the accuracy of image classification. Recent studies have shown that deep convolutional neural networks (CNNs) are powerful to extract high-level spatial features for object recognition and image retrieval. However, few CNNs have been developed for the pixel-wise classification of RS image. Moreover, in order to obtain large-scale spatial features, CNNs can be unmanageably complicated. In this paper, we propose a novel deep CNNs based method for VHR image classification. Firstly, pixel level CNNs are applied to multiscale images generated from the original VHR RS image using the Gaussian pyramid method. The hierarchical multiscale spatial features are then resampled to the size of the raw image. Finally, feature fusion is conducted with the original spectral bands for spectral-spatial based classification, where a support vector machine (SVM) classifier can be employed. Experiments were conducted on the image of VHR aerial image in Vaihingen, Germany. Compared to using spectral features only or with extended morphological profiles (EMPs) for image classification, the proposed method performs better, and the best improvement of classification accuracy is as high as 13%. [ABSTRACT FROM AUTHOR]

Some dynamics of a new 4D chaotic system describing the dynamical behavior of the finance are considered. Ultimate boundedness and global attraction domain are obtained according to Lyapunov stability theory. These results are useful in estimating the Lyapunov dimension of attractors, Hausdorff dimension of attractors, chaos control, and chaos synchronization. We will also present some simulation results. Furthermore, the volumes of the ultimate bound set and the global exponential attractive set are obtained. [ABSTRACT FROM AUTHOR]

In this paper, the global attractive sets of the general Lorenz system are studied based on the Lyapunov stability theory and optimization theory. The innovation of this paper lies in that the method of constructing Lyapunov functions applied to many other dynamical systems is not applicable to this general Lorenz system. Comparing with the best results in the current literature, our new results fill the gap of the estimate for the case of , , [ABSTRACT FROM AUTHOR]

In this paper, the ultimate bound and globally exponentially attractive sets of a modified Lorenz-Stenflo system are studied based on the Lyapunov stability theory and optimization theory. Comparing with the best results in the current literature, our new results include the existing results as special cases. Furthermore, the new results offer a theoretical support to studying the Hausdorff dimension of attractor of this modified Lorenz-Stenflo system. These theoretical results are also important and useful for chaos control and chaos synchronization. [ABSTRACT FROM AUTHOR]

In this paper, the ultimate bound for a new chaotic system is derived based on stability theory of dynamical systems. The meaningful contribution of this article is that the results presented in this paper contain the existing results as special cases. Finally, numerical simulations are given to verify the effectiveness and correctness of the obtained results. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this article, the bounds of the Lorenz-like chaotic system describing the flow between two concentric rotating spheres have been studied. Based on Lagrange multiplier method, the function extremum theory and the generalized positive definite and radially unbound Lyapunov functions with respect to the parameters of the system, we derive the ultimate bound and the globally exponentially attractive set for this system. The results that obtained in this article provides theory basis for chaotic synchronization, chaotic control, Hausdorff dimension and the Lyapunov dimension of chaotic attractors. © 2016 Wiley Periodicals, Inc. Complexity 21: 67-72, 2016 [ABSTRACT FROM AUTHOR]

In this article, some dynamics of Bloch chaotic system have been studied. Based on Lagrange multiplier method, optimization theory, and the generalized positively definite and radially unbound Lyapunov functions with respect to the parameters of the system, we derive the ultimate bound and a family of mathematical expressions of globally exponentially attractive sets for this system with respect to the parameters of system. The results obtained in this article provides theory basis for chaotic synchronization, chaotic control, Hausdorff dimension, and Lyapunov dimension of chaotic attractors of Bloch chaotic system. © 2016 Wiley Periodicals, Inc. Complexity 21: 201-206, 2016 [ABSTRACT FROM AUTHOR]

In this article, the dynamical behavior of a generalized Lorenz system is derived based on stability theory of dynamical systems. The meaningful contribution of this article is that the domain of attraction of the new chaotic system is studied in detailed. Finally, numerical simulations are given to verify the effectiveness and correctness of the obtained results. © 2015 Wiley Periodicals, Inc. Complexity 21: 99-105, 2016 [ABSTRACT FROM AUTHOR]

This article deals with the ultimate bound on the trajectories of the hyperchaos Lorenz system based on Lyapunov stability theory. The innovation of this article lies in that the method of constructing Lyapunov functions applied to the former chaotic systems is not applicable to this hyperchaos system, and moreover, one Lyapunov function can not estimate the bounds of this hyperchaos Lorenz system. We successfully estimate the bounds of this hyperchaos system by constructing three generalized Lyapunov functions step by step. Some computer simulations are also given to show the effectiveness of the proposed scheme. © 2016 Wiley Periodicals, Inc. Complexity 21: 440-445, 2016 [ABSTRACT FROM AUTHOR]

Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested. [ABSTRACT FROM PUBLISHER]