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.

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.

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.

A magnetic particles-based chemiluminescence enzyme immunoassay for rapid detection of ovalbumin.

Egg allergy is an important public health and safety concern, so quantification and administration of food or vaccines containing ovalbumin (OVA) are urgently needed. This study aimed to establish a rapid and sensitive magnetic particles-chemiluminescence enzyme immunoassay (MPs-CLEIA) for the determination of OVA. The proposed method was developed on the basis of a double antibodies sandwich immunoreaction and luminol-H2O2 chemiluminescence system. The MPs served as both the solid phase and separator, the anti-OVA MPs-coated polyclonal antibodies (pAbs) were used as capturing antibody, and the horseradish peroxidase (HRP)-labeled monoclonal antibody (mAb) was taken as detecting antibody. The parameters of the method were evaluated and optimized. The established MPs-CLEIA method had a linear range from 0.31 to 100ng/ml with a detection limit of 0.24ng/ml. The assays showed low reactivities and less than 5% of intraassay and interassay coefficients of variation (CVs), and the average recoveries were between 92 and 97%. Furthermore, the developed method was applied in real samples analysis successfully, and the correlation coefficient with the commercially available OVA kit was 0.9976. Moreover, it was more rapid and sensitive compared with the other methods for testing OVA.

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.

A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting.

This work presents a novel and effective method for fitting multidimensional ellipsoids (i.e., ellipsoids embedded in [Formula: see text]) to scattered data in the contamination of noise and outliers. Unlike conventional algebraic or geometric fitting paradigms that assume each measurement point is a noisy version of its nearest point on the ellipsoid, we approach the problem as a Bayesian parameter estimate process and maximize the posterior probability of a certain ellipsoidal solution given the data. We establish a more robust correlation between these points based on the predictive distribution within the Bayesian framework, i.e., considering each model point as a potential source for generating each measurement. Concretely, we incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain, ensuring ellipsoid-specific results regardless of inputs. We then establish the connection between measurement point and model data via Bayes' rule to enhance the method's robustness against noise. Due to independent of spatial dimensions, the proposed method not only delivers high-quality fittings to challenging elongated ellipsoids but also generalizes well to multidimensional spaces. To address outlier disturbances, often overlooked by previous approaches, we further introduce a uniform distribution on top of the predictive distribution to significantly enhance the algorithm's robustness against outliers. Thanks to the uniform prior, our maximum a posterior probability coincides with a more tractable maximum likelihood estimation problem, which is subsequently solved by a numerically stable Expectation Maximization (EM) framework. Moreover, we introduce an ε-accelerated technique to expedite the convergence of EM considerably. We also investigate the relationship between our algorithm and conventional least-squares-based ones, during which we theoretically prove our method's superior robustness. To the best of our knowledge, this is the first comprehensive method capable of performing multidimensional ellipsoid-specific fitting within the Bayesian optimization paradigm under diverse disturbances. We evaluate it across lower and higher dimensional spaces in the presence of heavy noise, outliers, and substantial variations in axis ratios. Also, we apply it to a wide range of practical applications such as microscopy cell counting, 3D reconstruction, geometric shape approximation, and magnetometer calibration tasks. In all these test contexts, our method consistently delivers flexible, robust, ellipsoid-specific performance, and achieves the state-of-the-art results.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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-1ß, 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.

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.

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.

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.