ResGEM: Multi-scale Graph Embedding Network for Residual Mesh Denoising

Mesh denoising is a crucial technology that aims to recover a high-fidelity 3D mesh from a noise-corrupted one. Deep learning methods, particularly graph convolutional networks (GCNs) based mesh denoisers, have demonstrated their effectiveness in removing various complex real-world noises while preserving authentic geometry. However, it is still a quite challenging work to faithfully regress uncontaminated normals and vertices on meshes with irregular topology. In this paper, we propose a novel pipeline that incorporates two parallel normal-aware and vertex-aware branches to achieve a balance between smoothness and geometric details while maintaining the flexibility of surface topology. We introduce ResGEM, a new GCN, with multi-scale embedding modules and residual decoding structures to facilitate normal regression and vertex modification for mesh denoising. To effectively extract multi-scale surface features while avoiding the loss of topological information caused by graph pooling or coarsening operations, we encode the noisy normal and vertex graphs using four edge-conditioned embedding modules (EEMs) at different scales. This allows us to obtain favorable feature representations with multiple receptive field sizes. Formulating the denoising problem into a residual learning problem, the decoder incorporates residual blocks to accurately predict true normals and vertex offsets from the embedded feature space. Moreover, we propose novel regularization terms in the loss function that enhance the smoothing and generalization ability of our network by imposing constraints on normal consistency. Comprehensive experiments have been conducted to demonstrate the superiority of our method over the state-of-the-art on both synthetic and real-scanned datasets.

FFCM-MRF: An accurate and generalizable cerebrovascular segmentation pipeline for humans and rhesus monkeys based on TOF-MRA

PURPOSE

Cerebrovascular segmentation and quantification of vascular morphological features in humans and rhesus monkeys are essential for prevention, diagnosis, and treatment of brain diseases. However, current automated whole-brain vessel segmentation methods are often not generalizable to independent datasets, limiting their usefulness in real-world environments with their heterogeneity in participants, scanners, and species.

MATERIALS AND METHODS

In this study, we proposed an automated, accurate and generalizable segmentation method for magnetic resonance angiography images called FFCM-MRF. This method integrated fast fuzzy c-means clustering and Markov random field optimization by vessel shape priors and spatial constraints. We used a total of 123 human and 44 macaque MRA images scanned at 1.5 T, 3 T, and 7 T MRI from 9 datasets to develop and validate the method.

RESULTS

FFCM-MRF achieved average Dice similarity coefficients ranging from 69.16 % to 89.63 % across multiple independent datasets, with improvements ranging from 3.24 % to 7.3 % compared to state-of-the-art methods. Quantitative analysis showed that FFCM-MRF can accurately segment major arteries in the Circle of Willis at the base of the brain and small distal pial arteries while effectively reducing noise. Test-retest analysis showed that the model yielded high vascular volume and diameter reliability.

CONCLUSIONS

Our results have demonstrated that FFCM-MRF is highly accurate and reliable and largely independent of variations in field strength, scanner platforms, acquisition parameters, and species. The macaque MRA data and user-friendly open-source toolbox are freely available at OpenNeuro and GitHub to facilitate studies of imaging biomarkers for cerebrovascular and neurodegenerative diseases.

Spectral Descriptors for 3D Deformable Shape Matching: A Comparative Survey

A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustness to a set of nuisances. It is, however, unclear which descriptors are more suitable for a particular application. This paper fills the gap by comprehensively evaluating nine state-of-the-art spectral descriptors on ten popular deformable shape datasets as well as perturbations such as mesh discretization, geometric noise, scale transformation, non-isometric setting, partiality, and topological noise. Our evaluated terms for a spectral descriptor cover four major concerns, i.e., distinctiveness, robustness, compactness, and computational efficiency. In the end, we present a summary of the overall performance and several interesting findings that can serve as guidance for the following researchers to construct a new spectral descriptor and choose an appropriate spectral feature in a particular application.

LARNeXt: End-to-End Lie Algebra Residual Network for Face Recognition

Face recognition has always been courted in computer vision and is especially amenable to situations with significant variations between frontal and profile faces. Traditional techniques make great strides either by synthesizing frontal faces from sizable datasets or by empirical pose invariant learning. In this paper, we propose a completely integrated embedded end-to-end Lie algebra residual architecture (LARNeXt) to achieve pose robust face recognition. First, we explore how the face rotation in the 3D space affects the deep feature generation process of convolutional neural networks (CNNs), and prove that face rotation in the image space is equivalent to an additive residual component in the feature space of CNNs, which is determined solely by the rotation. Second, on the basis of this theoretical finding, we further design three critical subnets to leverage a soft regression subnet with novel multi-fusion attention feature aggregation for efficient pose estimation, a residual subnet for decoding rotation information from input face images, and a gating subnet to learn rotation magnitude for controlling the strength of the residual component that contributes to the feature learning process. Finally, we conduct a large number of ablation experiments, and our quantitative and visualization results both corroborate the credibility of our theory and corresponding network designs. Our comprehensive experimental evaluations on frontal-profile face datasets, general unconstrained face recognition datasets, and industrial-grade tasks demonstrate that our method consistently outperforms the state-of-the-art ones.

[Impact of different diagnostic criteria for assessing mild micro-hepatic encephalopathy in liver cirrhosis: an analysis based on a prospective, multicenter, real-world study]

Objective: To compare the differences in the prevalence of mild micro-hepatic encephalopathy (MHE) among patients with cirrhosis by using the psychometric hepatic encephalopathy score (PHES) and the Stroop smartphone application (Encephal App) test. Methods: This prospective, multi-center, real-world study was initiated by the National Clinical Medical Research Center for Infectious Diseases and the Portal Hypertension Alliance and registered with International ClinicalTrials.gov (NCT05140837). 354 cases of cirrhosis were enrolled in 19 hospitals across the country. PHES (including digital connection tests A and B, digital symbol tests, trajectory drawing tests, and serial management tests) and the Stroop test were conducted in all of them. PHES was differentiated using standard diagnostic criteria established by the two studies in China and South Korea. The Stroop test was evaluated based on the criteria of the research and development team. The impact of different diagnostic standards or methods on the incidence of MHE in patients with cirrhosis was analyzed. Data between groups were differentiated using the t-test, Mann-Whitney U test, and χ (2) test. A kappa test was used to compare the consistency between groups. Results: After PHES, the prevalence of MHE among 354 cases of cirrhosis was 78.53% and 15.25%, respectively, based on Chinese research standards and Korean research normal value standards. However, the prevalence of MHE was 56.78% based on the Stroop test, and the differences in pairwise comparisons among the three groups were statistically significant (kappa = -0.064, P < 0.001). Stratified analysis revealed that the MHE prevalence in three groups of patients with Child-Pugh classes A, B, and C was 74.14%, 83.33%, and 88.24%, respectively, according to the normal value standards of Chinese researchers, while the MHE prevalence rates in three groups of patients with Child-Pugh classes A, B, and C were 8.29%, 23.53%, and 38.24%, respectively, according to the normal value standards of Korean researchers. Furthermore, the prevalence rates of MHE in the three groups of patients with Child-Pugh grades A, B, and C were 52.68%, 58.82%, and 73.53%, respectively, according to the Stroop test standard. However, among the results of each diagnostic standard, the prevalence of MHE showed an increasing trend with an increasing Child-Pugh grade. Further comparison demonstrated that the scores obtained by the number connection test A and the number symbol test were consistent according to the normal value standards of the two studies in China and South Korea (Z = -0.982, -1.702; P = 0.326, 0.089), while the other three sub-tests had significant differences (P < 0.001). Conclusion: The prevalence rate of MHE in the cirrhotic population is high, but the prevalence of MHE obtained by using different diagnostic criteria or methods varies greatly. Therefore, in line with the current changes in demographics and disease spectrum, it is necessary to enroll a larger sample size of a healthy population as a control. Moreover, the establishment of more reliable diagnostic scoring criteria will serve as a basis for obtaining accurate MHE incidence and formulating diagnosis and treatment strategies in cirrhotic populations.

Accurate Registration of Cross-Modality Geometry via Consistent Clustering

The registration of unitary-modality geometric data has been successfully explored over past decades. However, existing approaches typically struggle to handle cross-modality data due to the intrinsic difference between different models. To address this problem, in this paper, we formulate the cross-modality registration problem as a consistent clustering process. First, we study the structure similarity between different modalities based on an adaptive fuzzy shape clustering, from which a coarse alignment is successfully operated. Then, we optimize the result using fuzzy clustering consistently, in which the source and target models are formulated as clustering memberships and centroids, respectively. This optimization casts new insight into point set registration, and substantially improves the robustness against outliers. Additionally, we investigate the effect of fuzzier in fuzzy clustering on the cross-modality registration problem, from which we theoretically prove that the classical Iterative Closest Point (ICP) algorithm is a special case of our newly defined objective function. Comprehensive experiments and analysis are conducted on both synthetic and real-world cross-modality datasets. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches with higher accuracy and robustness. Our code is publicly available at https://github.com/zikai1/CrossModReg.

GraphReg: Dynamical Point Cloud Registration With Geometry-Aware Graph Signal Processing

This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Our proposed method consists of four major modules. First, we leverage the graph signal processing (GSP) framework to define a new signature, i.e., point response intensity for each point, by which we succeed in describing the local surface variation, resampling keypoints, and distinguishing different particles. Then, to address the shortcomings of current physics-based approaches that are sensitive to outliers, we accommodate the defined point response intensity to median absolute deviation (MAD) in robust statistics and adopt the X84 principle for adaptive outlier depression, ensuring a robust and stable registration. Subsequently, we propose a novel geometric invariant under rigid transformations to incorporate higher-order features of point clouds, which is further embedded for force modeling to guide the correspondence between pairwise scans credibly. Finally, we introduce an adaptive simulated annealing (ASA) method to search for the global optimum and substantially accelerate the registration process. We perform comprehensive experiments to evaluate the proposed method on various datasets captured from range scanners to LiDAR. Results demonstrate that our proposed method outperforms representative state-of-the-art approaches in terms of accuracy and is more suitable for registering large-scale point clouds. Furthermore, it is considerably faster and more robust than most competitors. Our implementation is publicly available at https://github.com/zikai1/GraphReg.

Diffractive lensless imaging with optimized Voronoi-Fresnel phase

Lensless cameras are a class of imaging devices that shrink the physical dimensions to the very close vicinity of the image sensor by replacing conventional compound lenses with integrated flat optics and computational algorithms. Here we report a diffractive lensless camera with spatially-coded Voronoi-Fresnel phase to achieve superior image quality. We propose a design principle of maximizing the acquired information in optics to facilitate the computational reconstruction. By introducing an easy-to-optimize Fourier domain metric, Modulation Transfer Function volume (MTFv), which is related to the Strehl ratio, we devise an optimization framework to guide the optimization of the diffractive optical element. The resulting Voronoi-Fresnel phase features an irregular array of quasi-Centroidal Voronoi cells containing a base first-order Fresnel phase function. We demonstrate and verify the imaging performance for photography applications with a prototype Voronoi-Fresnel lensless camera on a 1.6-megapixel image sensor in various illumination conditions. Results show that the proposed design outperforms existing lensless cameras, and could benefit the development of compact imaging systems that work in extreme physical conditions.

Blending Surface Segmentation and Editing for 3D Models

Recognizing and fitting shape primitives from underlying 3D models are key components of many computer graphics and computer vision applications. Although a vast number of structural recovery methods are available, they usually fail to identify blending surfaces, which corresponds to small transitional regions among relatively large primary patches. To address this issue, we present a novel approach for automatic segmentation and surface fitting with accurate geometric parameters from 3D models, especially mechanical parts. Overall, we formulate the structural segmentation as a Markov random field (MRF) labeling problem. In contrast to existing techniques, we first propose a new clustering algorithm to build superfacets by incorporating 3D local geometric information. This algorithm extracts the general quadric and rolling-ball blending regions, and improves the robustness of further segmentation. Next, we apply a specially designed MRF framework to efficiently partition the original model into different meaningful patches of known surface types by defining the multilabel energy function on the superfacets. Furthermore, we present an iterative optimization algorithm based on skeleton extraction to fit rolling-ball blending patches by recovering the parameters of the rolling center trajectories and ball radius. Experiments on different complex models demonstrate the effectiveness and robustness of the proposed method, and the superiority of our method is also verified through comparisons with state-of-the-art approaches. We further apply our algorithm in applications such as mesh editing by changing the radius of the rolling balls.

Hepatoprotective Effects of Glycyrrhetinic Acid on Lithocholic Acid-Induced Cholestatic Liver Injury Through Choleretic and Anti-Inflammatory Mechanisms

Cholestasis is a clinical syndrome triggered by the accumulation and aggregation of bile acids by subsequent inflammatory responses. The present study investigated the protective effect of glycyrrhetinic acid (GA) on the cholestatic liver injury induced by lithocholic acid (LCA) from both anti-inflammatory and choleretic mechanistic standpoints. Male C57BL/6 mice were treated with LCA twice daily for 4 days to induce intrahepatic cholestasis. GA (50 mg/kg) and pregnenolone 16α-carbonitrile (PCN, 45 mg/kg) were intraperitoneally injected 3 days before and throughout the administration of LCA, respectively. Plasma biochemical indexes were determined by assay kits, and hepatic bile acids were quantified by LC-MS/MS. Hematoxylin and eosin staining of liver sections was performed for pathological examination. Protein expression of the TLRs/NF-κB pathway and the mRNA levels of inflammatory cytokines and chemokines were examined by Western blotting and PCR, respectively. Finally, the hepatic expression of pregnane X receptor (PXR) and farnesoid X receptor (FXR) and their target genes encoding metabolic enzymes and transporters was evaluated. GA significantly reversed liver necrosis and decreased plasma ALT and ALP activity. Plasma total bile acids, total bilirubin, and hepatic bile acids were also remarkably preserved. More importantly, the recruitment of inflammatory cells to hepatic sinusoids was alleviated. Additionally, the protein expression of TLR2, TLR4, and p-NF-κBp65 and the mRNA expression of CCL2, CXCL2, IL-1β, IL-6, and TNF-α were significantly decreased. Moreover, GA significantly increased the expression of hepatic FXR and its target genes, including BSEP, MRP3, and MRP4. In conclusion, GA protects against LCA-induced cholestatic liver injury by inhibiting the TLR2/NF-κB pathway and upregulating hepatic FXR expression.

Image Inpainting With Local and Global Refinement

Image inpainting has made remarkable progress with recent advances in deep learning. Popular networks mainly follow an encoder-decoder architecture (sometimes with skip connections) and possess sufficiently large receptive field, i.e., larger than the image resolution. The receptive field refers to the set of input pixels that are path-connected to a neuron. For image inpainting task, however, the size of surrounding areas needed to repair different kinds of missing regions are different, and the very large receptive field is not always optimal, especially for the local structures and textures. In addition, a large receptive field tends to involve more undesired completion results, which will disturb the inpainting process. Based on these insights, we rethink the process of image inpainting from a different perspective of receptive field, and propose a novel three-stage inpainting framework with local and global refinement. Specifically, we first utilize an encoder-decoder network with skip connection to achieve coarse initial results. Then, we introduce a shallow deep model with small receptive field to conduct the local refinement, which can also weaken the influence of distant undesired completion results. Finally, we propose an attention-based encoder-decoder network with large receptive field to conduct the global refinement. Experimental results demonstrate that our method outperforms the state of the arts on three popular publicly available datasets for image inpainting. Our local and global refinement network can be directly inserted into the end of any existing networks to further improve their inpainting performance. Code is available at https://github.com/weizequan/LGNet.git.

Parallel Computation of 3D Clipped Voronoi Diagrams

Computing the Voronoi diagram of a given set of points in a restricted domain (e.g., inside a 2D polygon, on a 3D surface, or within a volume) has many applications. Although existing algorithms can compute 2D and surface Voronoi diagrams in parallel on graphics hardware, computing clipped Voronoi diagrams within volumes remains a challenge. This article proposes an efficient GPU algorithm to tackle this problem. A preprocessing step discretizes the input volume into a tetrahedral mesh. Then, unlike existing approaches which use the bisecting planes of the Voronoi cells to clip the tetrahedra, we use the four planes of each tetrahedron to clip the Voronoi cells. This strategy drastically simplifies the computation, and as a result, it outperforms state-of-the-art CPU methods up to an order of magnitude.

[Studies of the norm of Karitane Parenting Confidence Scale(KPCS)among parents of infants in urban areas of China]

Objective: To establish the norm of the Chinese version of Karitane Parenting Confidence Scale (KPCS) in urban areas of China. Methods: From August to December 2017, the parents of 2 216 children (<36 months old) were selected from 15 cities (Beijing, Lianyungang, Hangzhou, Chengdu, Xi'an, Guangzhou, Changsha, Jinan, Guiyang, Ningbo, Dalian, Qinhuangdao, Maanshan, Chongqing and Wuhan) in 14 provinces by stratified random sampling. The general demographic characteristics and parents' parenting confidence were collected by a self-made questionnaire and KPCS Chinese version. The percentile norm was established. P₃, P₁₀ and P₂₅ were used as the criteria to define the degree of lack of parenting confidence. Results: The age of mothers was (30.67±4.29). The age of the father was (32.50±4.99) years old. There were 726 (32.76%), 759 (34.25%) and 731 (32.99%) infants in 6-12, 12-23 and 24-35 months old groups. The total scores of P₅₀, P₂₅, P₁₀ and P₃ of KPCS (Chinese version) of infant parents in urban areas in China were 41, 38, 33, and 29 respectively. When the scores of parents were 34-37, 30-33, and ≤ 29, they were judged as mild, moderate, and severe lack of parenting confidence. There was no significant difference in the Chinese version of KPCS between parents of different age groups and parents of different gender (χ²=3.53, P=0.171; χ²=1.41, P=0.236). Each factor score≤P₃ is defined as the boundary score, and the corresponding boundary scores of "parenting" "support" and "competence" were 13, 9, and 5 respectively. Conclusion: The Chinese version of KPCS can be used to assess the parenting confidence of infants in urban areas of China. It can used as one of the bases for scientific and objective evaluation of the parenting status of families.

Long non-coding RNA MEG3 regulates autophagy after cerebral ischemia/reperfusion injury

Severe cerebral ischemia/reperfusion injury has been shown to induce high-level autophagy and neuronal death. Therefore, it is extremely important to search for a target that inhibits autophagy activation. Long non-coding RNA MEG3 participates in autophagy. However, it remains unclear whether it can be targeted to regulate cerebral ischemia/reperfusion injury. Our results revealed that in oxygen and glucose deprivation/reoxygenation-treated HT22 cells, MEG3 expression was obviously upregulated, and autophagy was increased, while knockdown of MEG3 expression greatly reduced autophagy. Furthermore, MEG3 bound miR-181c-5p and inhibited its expression, while miR-181c-5p bound to autophagy-related gene ATG7 and inhibited its expression. Further experiments revealed that mir-181c-5p overexpression reversed the effect of MEG3 on autophagy and ATG7 expression in HT22 cells subjected to oxygen and glucose deprivation/reoxygenation. In vivo experiments revealed that MEG3 knockdown suppressed autophagy, infarct volume and behavioral deficits in cerebral ischemia/reperfusion mice. These findings suggest that MEG3 knockdown inhibited autophagy and alleviated cerebral ischemia/reperfusion injury through the miR-181c-5p/ATG7 signaling pathway. Therefore, MEG3 can be considered as an intervention target for the treatment of cerebral ischemia/reperfusion injury. This study was approved by the Animal Ethics Committee of the First Affiliated Hospital of Zhengzhou University, China (approval No. XF20190538) on January 4, 2019.

[Genetic characterization analysis of the whole genome sequence of Coxsackievirus A8 associated with hand, foot and mouth disease in China]

Objective: To study the genomic sequence of Coxsackievirus A8 (CV-A8) associated with hand, foot and mouth disease (HFMD) from 2013 to 2018 in China and to analyze the genetic evolution of each coding region of the full-length genome. Methods: The genome sequences of 11 CV-A8 strains isolated from patients with HFMD in different regions of China from 2013 to 2018 were determined. Sequence alignment and genetic evolution analysis were performed by Sequencher 5.0 and MEGA 7.0 software, etc. Results: Sequence alignment showed that the genome length of 11 CV-A8 strains ranged from 7 393 bp to 7 400 bp. There was no base insertion or deletion in the coding region compared with the prototype strain, but there were individual base insertion or deletion in the non-coding region. The nucleotide and amino acid similarities in the VP1 region of 11 CV-A8 strains were 78.3%-98.6% and 92.6%-99.7%, respectively, and the nucleotide and amino acid sequences identities with the CV-A8 prototype strain were 78.3%-98.2% and 92.6%-99.7%, respectively. Based on the phylogenetic analysis of VP1 region sequences, the CV-A8 can be divided into five genotypes: A, B, C, D and E. The 11 CV-A8 strains in this study belonged to genotypes C (1 strain), D (2 strains) and E (8 strains). The nucleotide and amino acid similarities of 11 CV-A8 full-length genomes were 81.3%-98.8% and 95.9%-99.5%, respectively. The phylogenetic tree of the P2 region showed that the eight E genotypes CV-A8 had the closest evolutionary distance with CV-A4, CV-A14, and CV-A16. The phylogenetic tree of the P3 region showed that the eight E genotypes CV-A8 had a close evolutionary distance with CV-A5, CV-A16, CV-A14 and CV-A4. Conclusions: The 11 CV-A8 stains in this study showed significant intra-genotype diversity in capsid region and recombinant diversity in non-capsid region which indicated that CV-A8 quasispecies were still undergoing dynamics variation. CV-A8 may become an important pathogen of HFMD and the monitoring of CV-A8 needs to be further strengthened.

Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud

We present a novel and efficient approach to estimate 6D object poses of known objects in complex scenes represented by point clouds. Our approach is based on the well-known point pair feature (PPF) matching, which utilizes self-similar point pairs to compute potential matches and thereby cast votes for the object pose by a voting scheme. The main contribution of this paper is to present an improved PPF-based recognition framework, especially a new center voting strategy based on the relative geometric relationship between the object center and point pair features. Using this geometric relationship, we first generate votes to object centers resulting in vote clusters near real object centers. Then we group and aggregate these votes to generate a set of pose hypotheses. Finally, a pose verification operator is performed to filter out false positives and predict appropriate 6D poses of the target object. Our approach is also suitable to solve the multi-instance and multi-object detection tasks. Extensive experiments on a variety of challenging benchmark datasets demonstrate that the proposed algorithm is discriminative and robust towards similar-looking distractors, sensor noise, and geometrically simple shapes. The advantage of our work is further verified by comparing to the state-of-the-art approaches.

Robust Ellipse Fitting Using Hierarchical Gaussian Mixture Models

Fitting ellipses from unrecognized data is a fundamental problem in computer vision and pattern recognition. Classic least-squares based methods are sensitive to outliers. To address this problem, in this paper, we present a novel and effective method called hierarchical Gaussian mixture models (HGMM) for ellipse fitting in noisy, outliers-contained, and occluded settings on the basis of Gaussian mixture models (GMM). This method is crafted into two layers to significantly improve its fitting accuracy and robustness for data containing outliers/noise and has been proven to effectively narrow down the iterative interval of the kernel bandwidth, thereby speeding up ellipse fitting. Extensive experiments are conducted on synthetic data including substantial outliers (up to 60%) and strong noise (up to 200%) as well as on real images including complex benchmark images with heavy occlusion and images from versatile applications. We compare our results with those of representative state-of-the-art methods and demonstrate that our proposed method has several salient advantages, such as its high robustness against outliers and noise, high fitting accuracy, and improved performance.

Porf-2 Inhibits Tumor Cell Migration Through the MMP-2/9 Signaling Pathway in Neuroblastoma and Glioma

Tumor migration and invasion are key pathological processes that contribute to cell metastasis as well as treatment failure in patients with malignant tumors. However, the mechanisms governing tumor cell migration remain poorly understood. By analyzing the tumor-related database and tumor cell lines, we found that preoptic regulatory factor-2 (Porf-2) is downexpressed in both neuroblastoma and glioma. Using in vitro assays, our data demonstrated that the expression of Porf-2 inhibits tumor cell migration both in neuroblastoma and glioma cell lines. Domain-mutated Porf-2 plasmids were then constructed, and it was found that the GAP domain, which plays a role in the inactivation of Rac1, is the functional domain for inhibiting tumor cell migration. Furthermore, by screening potential downstream effectors, we found that Porf-2 can reduce MMP-2 and MMP-9 expression. Overexpression of MMP-2 blocked the inhibitory effect of Porf-2 in tumor cell migration both in vitro and in vivo. Taken together, we show for the first time that Porf-2 is capable of suppressing tumor cell migration via its GAP domain and the downregulation of MMP-2/9, suggesting that targeting Porf-2 could be a promising therapeutic strategy for nervous system tumors.

Selection Expressions for Procedural Modeling

We introduce a new approach for procedural modeling. Our main idea is to select shapes using selection-expressions instead of simple string matching used in current state-of-the-art grammars like CGA shape and CGA++. A selection-expression specifies how to select a potentially complex subset of shapes from a shape hierarchy, e.g., "select all tall windows in the second floor of the main building facade". This new way of modeling enables us to express modeling ideas in their global context rather than traditional rules that operate only locally. To facilitate selection-based procedural modeling we introduce the procedural modeling language SelEx. An important implication of our work is that enforcing important constraints, such as alignment and same size constraints can be done by construction. Therefore, our procedural descriptions can generate facade and building variations without violating alignment and sizing constraints that plague the current state of the art. While the procedural modeling of architecture is our main application domain, we also demonstrate that our approach nicely extends to other man-made objects.

Realistic Procedural Plant Modeling from Multiple View Images

In this paper, we describe a novel procedural modeling technique for generating realistic plant models from multi-view photographs. The realism is enhanced via visual and spatial information acquired from images. In contrast to previous approaches that heavily rely on user interaction to segment plants or recover branches in images, our method automatically estimates an accurate depth map of each image and extracts a 3D dense point cloud by exploiting an efficient stereophotogrammetry approach. Taking this point cloud as a soft constraint, we fit a parametric plant representation to simulate the plant growth progress. In this way, we are able to synthesize parametric plant models from real data provided by photos and 3D point clouds. We demonstrate the robustness of the proposed approach by modeling various plants with complex branching structures and significant self-occlusions. We also demonstrate that the proposed framework can be used to reconstruct ground-covering plants, such as bushes and shrubs which have been given little attention in the literature. The effectiveness of our approach is validated by visually and quantitatively comparing with the state-of-the-art approaches.

A Semi-Explicit Surface Tracking Mechanism for Multi-Phase Immiscible Liquids

We introduce a new method to efficiently track complex interfaces among multi-phase immiscible fluids. Unlike existing techniques, we use a mesh-based representation for global liquid surfaces while selectively modeling some local surficial regions with regional level sets (RLS) to handle complex geometries that are difficult to resolve with explicit topology operations. Such a semi-explicit surface mechanism can preserve volume, fine features and foam-like thin films under a relatively low computational expenditure. Our method processes the surface evolution by sampling the fluid domain onto a spectrally refined grid (SRG) and performs efficient grid scanning, generalized interpolations and topology operations on the basis of this grid structure. For the RLS surface part, we propose an accurate advection scheme targeted at SRG. For the explicit mesh part, we develop a fast grid-scanning technique to voxelize the meshes and introduce novel strategies to detect grid cells that contain inconsistent mesh components. A robust algorithm is proposed to construct consistent local meshes to resolve mesh penetrations, and handle the coupling between explicit mesh and RLS surficial regions. We also provide further improvement on handling complicated topological variations, and strategies for remeshing mesh/RLS interconversions.

Prognostic value of preoperative hematological markers combined with molecular pathology in patients with diffuse gliomas

The prediction of clinical outcome for patients with infiltrative gliomas is challenging. Although preoperative hematological markers have been proposed as predictors of survival in glioma and other cancers, systematic investigations that combine these data with other relevant clinical variables are needed to improve prognostic accuracy and patient outcomes. We investigated the prognostic value of preoperative hematological markers, alone and in combination with molecular pathology, for the survival of 592 patients with Grade II-IV diffuse gliomas. On univariate analysis, increased neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR), and decreased albumin-to-globulin ratio (AGR), all predicted poor prognosis in Grade II/III gliomas. Multivariate analysis incorporating tumor status based on the presence of IDH mutations, TERT promoter mutations, and 1p/19q codeletion showed that in lower-grade gliomas, high NLR predicted poorer survival for the triple-negative, IDH mutation only, TERT mutation only, and IDH and TERT mutation groups. NLR was an independent prognostic factor in Grade IV glioma. We therefore propose a prognostic model for diffuse gliomas based on the presence of IDH and TERT promoter mutations, 1p/19q codeletion, and NLR. This model classifies lower-grade gliomas into nine subgroups that can be combined into four main risk groups based on survival projections.

[Practice of parenting and related factors on children aged 0-5 in the urban areas of China]

Objective: To characterize the relations between the practice of parenting and associated factors on children (0-5 years old) in urban areas of China, in order to provide evidence for promoting the early development of children and to provide positive guidance and service programs on parenting. Methods: A total of 4 515 parents from 15 cities (14 provinces) were surveyed with a self-administered questionnaire. Parenting and Family Adjustment Scales (PAFAS) was used, including parameters as: consistency and coercive parenting, positive encouragement, parent-child relationship and parental emotion adjustment, family relationship and parental teamwork aspects, etc. Both single factor analysis and multiple linear regression were used to examine the associations between parenting practice, individual, parental and family factors. Results: The mean score of PAFAS was 21.00 (15.00-28.00), associated with factors as children's age, only-child family, premature delivery, father's education level, confidence on parenting, problems regarding the parental mood, annual family income, family structure and behavior on seeking professional help, etc. Results showed that there were big differences on the practice of parenting in China and influenced by variety of factors. Conclusions: The general situation of parenting was well, in the urban areas of China. The practice of parenting was associated with a series of individual, parental and family factors. Programs on improving the parenting skills and promoting the early development of children, should be highlighted.

Frontiers in biomolecular mesh generation and molecular visualization systems

With the development of biomolecular modeling and simulation, especially implicit solvent modeling, higher requirements are set for the stability, efficiency and mesh quality of molecular mesh generation software. In this review, we summarize the recent works in biomolecular mesh generation and molecular visualization. First, we introduce various definitions of molecular surface and corresponding meshing software. Second, as the mesh quality significantly influences biomolecular simulation, we investigate some remeshing methods in the fields of computer graphics and molecular modeling. Then, we show the application of biomolecular mesh in the boundary element method (BEM) and the finite element method (FEM). Finally, to conveniently visualize the numerical results based on the mesh, we present two types of molecular visualization systems.

Isotropic Surface Remeshing without Large and Small Angles

We introduce a novel algorithm for isotropic surface remeshing which progressively eliminates obtuse triangles and improves small angles. The main novelty of the proposed approach is a simple vertex insertion scheme that facilitates the removal of large angles, and a vertex removal operation that improves the distribution of small angles. In combination with other standard local mesh operators, e.g., connectivity optimization and local tangential smoothing, our algorithm is able to remesh efficiently a low-quality mesh surface. Our approach can be applied directly or used as a post-processing step following other remeshing approaches. Our method has a similar computational efficiency to the fastest approach available, i.e., real-time adaptive remeshing [1]. In comparison with state-of-the-art approaches, our method consistently generates better results based on evaluations using different metrics.

Molecular Surface Remeshing with Local Region Refinement

Molecular surface mesh generation is a prerequisite for using the boundary element method (BEM) and finite element method (FEM) in implicit-solvent modeling. Molecular surface meshes typically have small angles, redundant vertices, and low-quality elements. In the implicit-solvent modeling of biomolecular systems it is usually required to improve the mesh quality and eliminate low-quality elements. Existing methods often fail to efficiently remove low-quality elements, especially in complex molecular meshes. In this paper, we propose a mesh refinement method that smooths the meshes, eliminates invalid regions in a cut-and-fill strategy, and improves the minimal angle. We compared our method with four different state-of-the-art methods and found that our method showed a significant improvement over state-of-the-art methods in minimal angle, aspect ratio, and other meshing quality measurements. In addition, our method showed satisfactory results in terms of the ratio of regular vertices and the preservation of area and volume.

A Simple Push-Pull Algorithm for Blue-Noise Sampling

We describe a simple push-pull optimization (PPO) algorithm for blue-noise sampling by enforcing spatial constraints on given point sets. Constraints can be a minimum distance between samples, a maximum distance between an arbitrary point and the nearest sample, and a maximum deviation of a sample's capacity (area of Voronoi cell) from the mean capacity. All of these constraints are based on the topology emerging from Delaunay triangulation, and they can be combined for improved sampling quality and efficiency. In addition, our algorithm offers flexibility for trading-off between different targets, such as noise and aliasing. We present several applications of the proposed algorithm, including anti-aliasing, stippling, and non-obtuse remeshing. Our experimental results illustrate the efficiency and the robustness of the proposed approach. Moreover, we demonstrate that our remeshing quality is superior to the current state-of-the-art approaches.

Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement

Surface remeshing is a key component in many geometry processing applications. The typical goal consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application (e.g., the minimum interior angle is above an application-dependent threshold). Our algorithm is designed to address all three optimization goals simultaneously by targeting prescribed bounds on approximation error , minimal interior angle and maximum mesh complexity (number of vertices). The approximation error bound is a hard constraint, while the other two criteria are modeled as optimization goals to guarantee feasibility. Our optimization framework applies carefully prioritized local operators in order to greedily search for the coarsest mesh with minimal interior angle above and approximation error bounded by . Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that for reasonable angle bounds ( ) our approach delivers high-quality meshes with implicitly preserved features (no tagging required) and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.

Characterization of a highly virulent and antimicrobial-resistant Acinetobacter baumannii strain isolated from diseased chicks in China

Poultry husbandry is a very important aspect of the agricultural economy in China. However, chicks are often susceptible to infectious disease microorganisms, such as bacteria, viruses and parasites, causing large economic losses in recent years. In the present study, we isolated an Acinetobacter baumannii strain, CCGGD201101, from diseased chicks in the Jilin Province of China. Regression analyses of virulence and LD50 tests conducted using healthy chicks confirmed that A. baumannii CCGGD201101, with an LD50 of 1.81 (±0.11) × 10(4) CFU, was more virulent than A. baumannii ATCC17978, with an LD50 of 1.73 (±0.13) × 10(7) CFU. Moreover, TEM examination showed that the pili of A. baumannii CCGGD201101 were different from those of ATCC17978. Antibiotic sensitivity analyses showed that A. baumannii CCGGD201101 was sensitive to rifampicin but resistant to most other antibiotics. These results imply that A. baumannii strain CCGGD201101 had both virulence enhancement and antibiotic resistance characteristics, which are beneficial for A. baumannii survival under adverse conditions and enhance fitness and invasiveness in the host. A. baumannii CCGGD20101, with its high virulence and antimicrobial resistance, may be one of the pathogens causing death of diseased chicks.

Non-Obtuse Remeshing with Centroidal Voronoi Tessellation

We present a novel remeshing algorithm that avoids triangles with small (acute) angles and those with large (obtuse) angles. Our solution is based on an extension of Centroidal Voronoi Tesselation (CVT). We augment the original CVT formulation with a penalty term that penalizes short Voronoi edges, while the CVT term helps to avoid small angles. Our results show significant improvements in remeshing quality over the state of the art.

Automatic Constraint Detection for 2D Layout Regularization

In this paper, we address the problem of constraint detection for layout regularization. The layout we consider is a set of two-dimensional elements where each element is represented by its bounding box. Layout regularization is important in digitizing plans or images, such as floor plans and facade images, and in the improvement of user-created contents, such as architectural drawings and slide layouts. To regularize a layout, we aim to improve the input by detecting and subsequently enforcing alignment, size, and distance constraints between layout elements. Similar to previous work, we formulate layout regularization as a quadratic programming problem. In addition, we propose a novel optimization algorithm that automatically detects constraints. We evaluate the proposed framework using a variety of input layouts from different applications. Our results demonstrate that our method has superior performance to the state of the art.

Disk Density Tuning of a Maximal Random Packing

We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.

Expression, purification and characterization of recombinant toxins consisting of truncated gastrin 17 and pseudomonas exotoxin

Gastric cancer is a major cause of mortality and morbidity around world. However the effectiveness of the current approaches to the diagnosis and treatment of gastric cancer is limited. Recombinant targeted toxins may represent a novel direction of cancer therapy. In this study, we aimed to explore whether recombinant toxins fused with the truncated forms of G17 could target to kill cancer cells by recognizing CCK2R. Four recombinant Pseudomonas toxins PE38 fused with the forward or reverse truncated forms of G17 (G14 and G13) were successfully constructed, expressed, and purified. Their characteristics were further analyzed by SDS-PAGE, western blot and indirect immunofluorescence assay. The cytotoxicity assay demonstrated that only reversely fused recombinant toxins rG14PE38 and rG13PE38 exhibited certain toxicity on several cancer cell lines, and a competition assay indicated that the binding of the reverse gastrin-endotoxin to CCK2R (+) cells may be mediated by interaction between gastrin/gastrin-like and CCK2R.

Low-Resolution Remeshing Using the Localized Restricted Voronoi Diagram

A big problem in triangular remeshing is to generate meshes when the triangle size approaches the feature size in the mesh. The main obstacle for Centroidal Voronoi Tessellation (CVT)-based remeshing is to compute a suitable Voronoi diagram. In this paper, we introduce the localized restricted Voronoi diagram (LRVD) on mesh surfaces. The LRVD is an extension of the restricted Voronoi diagram (RVD), but it addresses the problem that the RVD can contain Voronoi regions that consist of multiple disjoint surface patches. Our definition ensures that each Voronoi cell in the LRVD is a single connected region. We show that the LRVD is a useful extension to improve several existing mesh-processing techniques, most importantly surface remeshing with a low number of vertices. While the LRVD and RVD are identical in most simple configurations, the LRVD is essential when sampling a mesh with a small number of points and for sampling surface areas that are in close proximity to other surface areas, e.g., nearby sheets. To compute the LRVD, we combine local discrete clustering with a global exact computation.

Production of monoclonal antibody and application in indirect competitive ELISA for detecting okadaic acid and dinophytoxin-1 in seafood

BACKGROUND, AIM, AND SCOPE

Okadaic acid (OA) and analogues of dinophysistoxin (DTX) are key diarrheic shellfish poisoning (DSP) toxins, which possibly arouse DSP symptoms by consuming the contaminated shellfish. Because of the stable toxicity in high temperature and the long-term carcinogenicity, the outbreaks of DSP related to consumption of bivalve mollusks contaminated by DSP toxins pose a hazard to public health. Therefore, it is worth developing a fast and reliable analytical method for the detection of OA and analogues in shellfish. In this paper, an indirect competitive enzyme-linked immunosorbent assay (ELISA) (icELISA) for detecting OA and DTX-1 in seafood was developed based on monoclonal antibody (McAb).

METHODS

The OA was conjugated to human immunoglobulin G (IgG) and bovine serum albumin (BSA) by the active ester method as the immune antigen and the detective antigen. The spleen cells from BALB/c mice immunized with OA-IgG were fused with SP2/0 myeloma cells. A hybridoma cell line, which secreted McAb against OA, was selected by "limiting dilution" cloning. An icELISA was developed based on immobilized conjugate (OA-BSA) competing the McAb with the free OA in seafood sample.

RESULTS

A hybridoma cell line, which secreted IgG1 subclass monoclonal antibody (McAb) against OA, was selected. The IC(50) of the McAb for OA and dinophytoxin-1 (DTX-1) were 4.40 and 3.89 ng/mL, respectively. Based on the McAb, an indirect competitive ELISA for detection of OA and DTX-1 in seafood was developed. The regression equation was y = 54.713x - 25.879 with a coefficient correlation of R (2) = 0.9729. The linear range and the limit of detection were 0.4-12.5 and 0.45 ng/mL, respectively. The average recovery of OA and DTX-1 spiked shellfish was 82.29% with the coefficient of variation of 7.67%.

CONCLUSION

The developed icELISA is a fast, sensitive, and convenient assay for detecting of total amount of OA and DTX-1 in seafood.

Unfolded protein response suppresses cisplatin-induced apoptosis via autophagy regulation in human hepatocellular carcinoma cells

It has been shown that drug resistance is extremely common in hepatocellular carcinoma (HCC) and is one of the major problems in HCC chemotherapy. However, the detailed mechanisms remain largely unknown. We have previously shown that endoplasmic reticulum (ER) stress is involved in the tumorigenesis of HCC. Here, we demonstrated that the unfolded protein response (UPR) inhibits cisplatin-induced HCC cell apoptosis. In HCC cells, cisplatin treatment triggers the UPR, which subsequently inhibits cisplatin-induced apoptosis. Importantly, mild ER stress precondition suppresses the sensitivity of HCC cells to cisplatin-induced apoptosis through autophagy regulation. Furthermore, heat-shock protein 27 (Hsp27) is involved in the cytoprotective role of the UPR in cisplatin-induced apoptosis. We also demonstrated that Hsp27 inhibits cisplatin- induced HCC cell death through autophagy activation. Taken together, our results indicate that the UPR inhibits cisplatin-induced apoptosis in HCC cells, at least in part, by Hsp27-mediated autophagy activation.

PI3K/Akt promotes GRP78 accumulation and inhibits endoplasmic reticulum stress-induced apoptosis in HEK293 cells

The potential pro-survival role of phosphatidylinositol 3-kinase (PI3K)/Akt during endoplasmic reticulum stress has been well-characterized. However, the detailed mechanisms remain largely unknown. Here, we showed that PI3K/Akt inhibition promoted endoplasmic reticulum stress-induced apoptosis in a glucose-regulated protein 78 (GRP78)-dependent manner. During endoplasmic reticulum stress, high levels of Akt phosphorylation were sustained for at least 18 h in HEK293 cells. Importantly, PI3K/Akt enhanced GRP78 accumulation through increasing its stability following endoplasmic reticulum stress. Furthermore, Akt1, but not Akt2 or Akt3, was involved in GRP78 stability regulation. These results suggest that PI3K/Akt inhibits endoplasmic reticulum stress-induced apoptosis in HEK293 cells, at least in part, by promoting GRP78 protein stability.

Effective constituents in Xiexin Decoction for anti-inflammation

AIM OF THE STUDY

To ascertain the effective constituents in Xiexin Decoction for anti-inflammation and the interactions of these constituents at the pharmacodynamic level.

MATERIALS AND METHODS

Rats were administered oral Xiexin Decoction 1h before intraperitoneal lipopolysaccharide. Nitric oxide production and Xiexin Decoction constituents in venous serum samples were quantified and the correlation between nitric oxide production and each constituent in serum was calculated. Raw264.7 cells were stimulated with lipopolysaccharide and one or more Xiexin Decoction constituents; cell viability and nitric oxide production was quantified.

RESULTS

Xiexin Decoction significantly decreased nitric oxide production in vivo, which correlated well with rhein, baicalin, emodin and aloe-emodin. All the typical constituents of Xiexin Decoction, with the exception of physcione and chrysophanol, dose-dependently inhibited nitric oxide production in vitro. In an orthogonal designed in vitro study, rhein was the most powerful constituent, followed by baicalin then berberine and no synergy was found among these constituents.

CONCLUSIONS

Rhein was the most effective anti-inflammatory constituent in Xiexin Decoction followed by baicalin; no synergy was observed between rhein, baicalin and berberine at the pharmacodynamic level in vitro.