Cerebral Arterial Stiffness as Measured Based on the Pulse Wave Velocity Is Associated With Intracranial Artery Calcification in Patients With Acute Stroke

Background@#and Purpose By measuring a newly defined parameter, the carotid–cerebral pulse wave velocity (ccPWV), this study aimed to determine the association of intracranial artery calcification (IAC) with arterial stiffness as reflected by the pulse wave velocity between the carotid and middle cerebral arteries using transcranial Doppler sonography in patients with acute stroke. @*Methods@#We recruited 146 patients with ischemic stroke from our stroke center. Computed tomography of the head was used to assess the presence and severity of IAC. Arterial stiffness was evaluated using ccPWV. Data are presented as quartiles of ccPWV. A multivariable logistic regression model was used to assess the independent relationship between ccPWV and IAC. @*Results@#The IAC prevalence increased with the ccPWV quartile, being 54%, 76%, 83%, and 89% for quartiles 1, 2, 3, and 4, respectively (p<0.001) as did IAC scores, with median [interquartile range] values of 0 [0–2], 3 [2–4], 4 [2–5], and 5 [4–6], respectively (p<0.001). After additionally adjusting for age and hypertension, a significant correlation was only found between quartiles 3 and 4 of ccPWV and IAC scores. The odds ratio (95% confidence interval) for the IAC scores was 1.78 (1.28–2.50) (p=0.001) in quartile 4 of ccPWV and 1.45 (1.07–1.95) (p=0.015) in quartile 3 compared with quartile 1. @*Conclusions@#We found that in patients with acute ischemic stroke, ccPWV was positively related to the degree of IAC. Future longitudinal cohort studies may help to identify the potential role of IAC in the progression of cerebral arterial stiffness.

Rapid colorimetric detection of COVID-19 coronavirus using a reverse tran-scriptional loop-mediated isothermal amplification (RT-LAMP) diagnostic plat-form: iLACO

The recent outbreak of a novel coronavirus SARS-CoV-2 (also known as 2019-nCoV) threatens global health, given serious cause for concern. SARS-CoV-2 is a human-to-human pathogen that caused fever, severe respiratory disease and pneumonia (known as COVID-19). By press time, more than 70,000 infected people had been confirmed worldwide. SARS-CoV-2 is very similar to the severe acute respiratory syndrome (SARS) coronavirus broke out 17 years ago. However, it has increased transmissibility as compared with the SARS-CoV, e.g. very often infected individuals without any symptoms could still transfer the virus to others. It is thus urgent to develop a rapid, accurate and onsite diagnosis methods in order to effectively identify these early infects, treat them on time and control the disease spreading. Here we developed an isothermal LAMP based method-iLACO (isothermal LAMP based method for COVID-19) to amplify a fragment of the ORF1ab gene using 6 primers. We assured the species-specificity of iLACO by comparing the sequences of 11 related viruses by BLAST (including 7 similar coronaviruses, 2 influenza viruses and 2 normal coronaviruses). The sensitivity is comparable to Taqman based qPCR detection method, detecting synthesized RNA equivalent to 10 copies of 2019-nCoV virus. Reaction time varied from 15-40 minutes, depending on the loading of virus in the collected samples. The accuracy, simplicity and versatility of the new developed method suggests that iLACO assays can be conveniently applied with for 2019-nCoV threat control, even in those cases where specialized molecular biology equipment is not available.

Optimization of a fluorescent qPCR detection for RNA of SARS-CoV-2 / 生物工程学报

We optimized a fluorescent quantitative polymerase chain reaction (qPCR) assay system for rapid and real time detection of SARS-CoV-2 RNA. The results show that the lowest dilution of RNA samples used for the detection of SARS-CoV-2 RNA could reach 1/10 000 (the initial value is set as 10 ng/μL). Moreover, the cycle threshold (Ct) for samples of clinically diagnosed COVID-19 was lower than 35 or 40. The sensitivity of this method was satisfactory. The results were consistent with those of the COVID-19 detection kit on the market under the same conditions, but the number of cycles required was shortened by about 2. Therefore, the optimized assay developed in this study can be used in screening and early clinical diagnosis. Our work provides a tool to facilitate rapid clinical diagnosis of COVID-19.

CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion.

Salient object detection from RGB-D images aims to utilize both the depth view and RGB view to automatically localize objects of human interest in the scene. Although a few earlier efforts have been devoted to the study of this paper in recent years, two major challenges still remain: 1) how to leverage the depth view effectively to model the depth-induced saliency and 2) how to implement an optimal combination of the RGB view and depth view, which can make full use of complementary information among them. To address these two challenges, this paper proposes a novel framework based on convolutional neural networks (CNNs), which transfers the structure of the RGB-based deep neural network to be applicable for depth view and fuses the deep representations of both views automatically to obtain the final saliency map. In the proposed framework, the first challenge is modeled as a cross-view transfer problem and addressed by using the task-relevant initialization and adding deep supervision in hidden layer. The second challenge is addressed by a multiview CNN fusion model through a combination layer connecting the representation layers of RGB view and depth view. Comprehensive experiments on four benchmark datasets demonstrate the significant and consistent improvements of the proposed approach over other state-of-the-art methods.

Local Regression and Global Information-Embedded Dimension Reduction.

A large family of algorithms for unsupervised dimension reduction is based on both the local and global structures of the data. A fundamental step in these methods is to model the local geometrical structure of the data. However, the previous methods mainly ignore two facts in this step: 1) the dimensionality of the data is usually far larger than the number of local data, which is a typical ill-posed problem and 2) the data might be polluted by noise. These facts normally may lead to an inaccurate learned local structure and may degrade the final performance. In this paper, we propose a novel unsupervised dimension reduction method with the ability to address these problems effectively while also preserving the global information of the input data. Specifically, we first denoise the local data by preserving their principal components and we then apply a regularization term to the local modeling function to solve the illposed problem. Then, we use a linear regression model to capture the local geometrical structure, which is demonstrated to be insensitive to the parameters. Finally, we propose two criteria to simultaneously model both the local and the global information. Theoretical analyses for the relations between the proposed methods and some classical dimension-reduction methods are presented. The experimental results from various databases demonstrate the effectiveness of our methods.

Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification.

Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve promising performance. Generally, in the field of multi-view learning, these algorithms construct informative graph for each view, on which the following clustering or classification procedure are based. However, in many real-world data sets, original data always contain noises and outlying entries that result in unreliable and inaccurate graphs, which cannot be ameliorated in the previous methods. In this paper, we propose a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously. The obtained optimal graph can be partitioned into specific clusters directly. Moreover, our model can allocate ideal weight for each view automatically without explicit weight definition and penalty parameters. An efficient algorithm is proposed to optimize this model. Extensive experimental results on different real-world data sets show that the proposed model outperforms other state-of-the-art multi-view algorithms.

Correlation of iron metabolism with some stress hormones and insulin resistance in patients with gestational diabetes mellitus / 中华内分泌代谢杂志

Objective@#To investigate the association of iron with stress hormones and insulin resistance in patients with gestational diabetes mellitus (GDM).@*Methods@#Seventy-five pregnant women diagnosed as GDM during 24-28 weeks were collected from January to November 2015 in Yantai Yuhuangding Hospital, and 75 normal pregnant women were used as control group. Blood glucose, insulin, stress hormones and iron metabolism related indexes were detected. Homeostasis model assessment for insulin resistance (HOMA-IR) was used to evaluate insulin resistance, and the correlation of iron metabolism with stress hormones and insulin resistance was analyzed.@*Results@#Compared with control group, norepinephrine (NE) and epinephrine (E) levels were higher in GDM group (both P<0.05), but there was no significant difference in cortisol level between two groups (P>0.05). Serum ferritin (SF), serum iron, and transferrin saturation levels were higher in GDM group than those in control group(all P<0.01). Multivariate logistic analysis showed that cortisol, E, and NE levels were positively correlated with SF level in GDM group (all P<0.05). Cortisol and E levels were positively correlated with transferrin saturation (both P<0.05). SF and transferrin saturation were positively correlated with HOMA-IR in two groups (P<0.05 or P<0.01).@*Conclusion@#Iron overload may involve in dysfunction of stress adaptation and insulin resistance in patients with GDM. (Chin J Endocrinol Metab, 2018, 34: 563-566)

Convex Multiview Semi-Supervised Classification.

In many practical applications, there are a great number of unlabeled samples available, while labeling them is a costly and tedious process. Therefore, how to utilize unlabeled samples to assist digging out potential information about the problem is very important. In this paper, we study a multiclass semi-supervised classification task in the context of multiview data. First, an optimization method named Parametric multiview semi-supervised classification (PMSSC) is proposed, where the built classifier for each individual view is explicitly combined with a weight factor. By analyzing the weakness of it, a new adapted weight learning strategy is further formulated, and we come to the convex multiview semi-supervised classification (CMSSC) method. Comparing with the PMSSC, this method has two significant properties. First, without too much loss in performance, the newly used weight learning technique achieves eliminating a hyperparameter, and thus it becomes more compact in form and practical to use. Second, as its name implies, the CMSSC models a convex problem, which avoids the local-minimum problem. Experimental results on several multiview data sets demonstrate that the proposed methods achieve better performances than recent representative methods and the CMSSC is preferred due to its good traits.

Weakly Supervised Multimodal Kernel for Categorizing Aerial Photographs.

Accurately distinguishing aerial photographs from different categories is a promising technique in computer vision. It can facilitate a series of applications, such as video surveillance and vehicle navigation. In this paper, a new image kernel is proposed for effectively recognizing aerial photographs. The key is to encode high-level semantic cues into local image patches in a weakly supervised way, and integrate multimodal visual features using a newly developed hashing algorithm. The flowchart can be elaborated as follows. Given an aerial photo, we first extract a number of graphlets to describe its topological structure. For each graphlet, we utilize color and texture to capture its appearance, and a weakly supervised algorithm to capture its semantics. Thereafter, aerial photo categorization can be naturally formulated as graphlet-to-graphlet matching. As the number of graphlets from each aerial photo is huge, to accelerate matching, we present a hashing algorithm to seamlessly fuze the multiple visual features into binary codes. Finally, an image kernel is calculated by fast matching the binary codes corresponding to each graphlet. And a multi-class SVM is learned for aerial photo categorization. We demonstrate the advantage of our proposed model by comparing it with state-of-the-art image descriptors. Moreover, an in-depth study of the descriptiveness of the hash-based graphlet is presented.

Template Deformation-Based 3-D Reconstruction of Full Human Body Scans From Low-Cost Depth Cameras.

Full human body shape scans provide valuable data for a variety of applications including anthropometric surveying, clothing design, human-factors engineering, health, and entertainment. However, the high price, large volume, and difficulty of operating professional 3-D scanners preclude their use in home entertainment. Recently, portable low-cost red green blue-depth cameras such as the Kinect have become popular for computer vision tasks. However, the infrared mechanism of this type of camera leads to noisy and incomplete depth images. We construct a stereo full-body scanning environment composed of multiple depth cameras and propose a novel registration algorithm. Our algorithm determines a segment constrained correspondence for two neighboring views, integrating them using rigid transformation. Furthermore, it aligns all of the views based on uniform error distribution. The generated 3-D mesh model is typically sparse, noisy, and even with holes, which makes it lose surface details. To address this, we introduce a geometric and topological fitting prior in the form of a professionally designed high-resolution template model. We formulate a template deformation optimization problem to fit the high-resolution model to the low-quality scan. Its solution overcomes the obstacles posed by different poses, varying body details, and surface noise. The entire process is free of body and template markers, fully automatic, and achieves satisfactory reconstruction results.

Latent Semantic Minimal Hashing for Image Retrieval.

Hashing-based similarity search is an important technique for large-scale query-by-example image retrieval system, since it provides fast search with computation and memory efficiency. However, it is a challenge work to design compact codes to represent original features with good performance. Recently, a lot of unsupervised hashing methods have been proposed to focus on preserving geometric structure similarity of the data in the original feature space, but they have not yet fully refined image features and explored the latent semantic feature embedding in the data simultaneously. To address the problem, in this paper, a novel joint binary codes learning method is proposed to combine image feature to latent semantic feature with minimum encoding loss, which is referred as latent semantic minimal hashing. The latent semantic feature is learned based on matrix decomposition to refine original feature, thereby it makes the learned feature more discriminative. Moreover, a minimum encoding loss is combined with latent semantic feature learning process simultaneously, so as to guarantee the obtained binary codes are discriminative as well. Extensive experiments on several well-known large databases demonstrate that the proposed method outperforms most state-of-the-art hashing methods.

Spatiotemporal Statistics for Video Quality Assessment.

It is an important task to design models for universal no-reference video quality assessment (NR-VQA) in multiple video processing and computer vision applications. However, most existing NR-VQA metrics are designed for specific distortion types, which are not often aware in practical applications. A further deficiency is that the spatial and temporal information of videos is hardly considered simultaneously. In this paper, we propose a new NR-VQA metric based on the spatiotemporal natural video statistics in 3D discrete cosine transform (3D-DCT) domain. In the proposed method, a set of features are first extracted based on the statistical analysis of 3D-DCT coefficients to characterize the spatiotemporal statistics of videos in different views. These features are used to predict the perceived video quality via the efficient linear support vector regression model afterward. The contributions of this paper are: 1) we explore the spatiotemporal statistics of videos in the 3D-DCT domain that has the inherent spatiotemporal encoding advantage over other widely used 2D transformations; 2) we extract a small set of simple but effective statistical features for video visual quality prediction; and 3) the proposed method is universal for multiple types of distortions and robust to different databases. The proposed method is tested on four widely used video databases. Extensive experimental results demonstrate that the proposed method is competitive with the state-of-art NR-VQA metrics and the top-performing full-reference VQA and reduced-reference VQA metrics.

Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine.

Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely circle convolutional restricted Boltzmann machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models. Specifically, by introducing the novel circle convolution, CCRBM holds a novel ring-like multi-layer structure to learn 3D local features in a structure preserving manner. Circle convolution convolves across 3D local regions via rotating a novel circular sector convolution window in a consistent circular direction. In the process of circle convolution, extra points are sampled in each 3D local region and projected onto the tangent plane of the center of the region. In this way, the projection distances in each sector window are employed to constitute a novel local raw 3D representation called projection distance distribution (PDD). In addition, to eliminate the initial location ambiguity of a sector window, the Fourier transform modulus is used to transform the PDD into the Fourier domain, which is then conveyed to CCRBM. Experiments using the learned local features are conducted on three aspects: global shape retrieval, partial shape retrieval, and shape correspondence. The experimental results show that the learned local features outperform other state-of-the-art 3D shape descriptors.

Joint Multilabel Classification With Community-Aware Label Graph Learning.

As an important and challenging problem in machine learning and computer vision, multilabel classification is typically implemented in a max-margin multilabel learning framework, where the inter-label separability is characterized by the sample-specific classification margins between labels. However, the conventional multilabel classification approaches are usually incapable of effectively exploring the intrinsic inter-label correlations as well as jointly modeling the interactions between inter-label correlations and multilabel classification. To address this issue, we propose a multilabel classification framework based on a joint learning approach called label graph learning (LGL) driven weighted Support Vector Machine (SVM). In principle, the joint learning approach explicitly models the inter-label correlations by LGL, which is jointly optimized with multilabel classification in a unified learning scheme. As a result, the learned label correlation graph well fits the multilabel classification task while effectively reflecting the underlying topological structures among labels. Moreover, the inter-label interactions are also influenced by label-specific sample communities (each community for the samples sharing a common label). Namely, if two labels have similar label-specific sample communities, they are likely to be correlated. Based on this observation, LGL is further regularized by the label Hypergraph Laplacian. Experimental results have demonstrated the effectiveness of our approach over several benchmark data sets.

Ranking graph embedding for learning to rerank.

Dimensionality reduction is a key step to improving the generalization ability of reranking in image search. However, existing dimensionality reduction methods are typically designed for classification, clustering, and visualization, rather than for the task of learning to rank. Without using of ranking information such as relevance degree labels, direct utilization of conventional dimensionality reduction methods in ranking tasks generally cannot achieve the best performance. In this paper, we show that introducing ranking information into dimensionality reduction significantly increases the performance of image search reranking. The proposed method transforms graph embedding, a general framework of dimensionality reduction, into ranking graph embedding (RANGE) by modeling the global structure and the local relationships in and between different relevance degree sets, respectively. The proposed method also defines three types of edge weight assignment between two nodes: binary, reconstruction, and global. In addition, a novel principal components analysis based similarity calculation method is presented in the stage of global graph construction. Extensive experimental results on the MSRA-MM database demonstrate the effectiveness and superiority of the proposed RANGE method and the image search reranking framework.

Enhanced Biologically Inspired Model for Object Recognition.

The biologically inspired model (BIM) proposed by Serre presents a promising solution to object categorization. It emulates the process of object recognition in primates' visual cortex by constructing a set of scale- and position-tolerant features whose properties are similar to those of the cells along the ventral stream of visual cortex. However, BIM has potential to be further improved in two aspects: mismatch by dense input and randomly feature selection due to the feedforward framework. To solve or alleviate these limitations, we develop an enhanced BIM (EBIM) in terms of the following two aspects: 1) removing uninformative inputs by imposing sparsity constraints, 2) apply a feedback loop to middle level feature selection. Each aspect is motivated by relevant psychophysical research findings. To show the effectiveness of the EBIM, we apply it to object categorization and conduct empirical studies on four computer vision data sets. Experimental results demonstrate that the EBIM outperforms the BIM and is comparable to state-of-the-art approaches in terms of accuracy. Moreover, the new system is about 20 times faster than the BIM.

Exploiting Local Coherent Patterns for Unsupervised Feature Ranking.

Prior to pattern recognition, feature selection is often used to identify relevant features and discard irrelevant ones for obtaining improved analysis results. In this paper, we aim to develop an unsupervised feature ranking algorithm that evaluates features using discovered local coherent patterns, which are known as biclusters. The biclusters (viewed as submatrices) are discovered from a data matrix. These submatrices are used for scoring relevant features from two aspects, i.e., the interdependence of features and the separability of instances. The features are thereby ranked with respect to their accumulated scores from the total discovered biclusters before the pattern classification. Experimental results show that this proposed method can yield comparable or even better performance in comparison with the well-known Fisher score, Laplacian score, and variance score using three UCI data sets, well improve the results of gene expression data analysis using gene ontology annotation, and finally demonstrate its advantage of unsupervised feature ranking for high-dimensional data.

The Experimental Rearch of Silkworm Cocoon/ Portulaca Oleracea on the Glucose and Lipid Metabolism to type 2 Diabetic Rats / 国际中医中药杂志

Objective To approach the effect of Silkworm Cocoon,Portulaca oleracea on the glucose and lipid metabolism to type 2 Diabetic rats. Methods type 2 diabetic rat models induced by STZ were randomly divided into diabetes model group,Silkworm Cocoon group,Portulaca oleracea group,Silkworm Cocoon+Portulaca oleracea group,and normal contrast group,after eight weeks' intervention,We analyze blood glucose(GLU), triglyeeride(TG), SOD, GSH, MDA, calculate, the indexes of insulin resistance (HOMA-1R). Results compared with diabetes model group, the GLU in Silkworm Cocoon+Portulaca oleracea group decrease 53.3%(P<0.01) : The TG decrease 44.1%(P<0.05). the Homa-IR decrease groups has no statistic difference. Conclusion The mixture group can obviously improve the glucose and lipid metabolism、 relieve insulin resistance、 significantly increase antioxidatian、 decrease the products oflipid peroxide.