Search

View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenges are how to aggregate multi-view features and deal with 3D shapes in arbitrary poses. We propose two versions of a novel view-based Graph Convolutional Network, dubbed view-GCN and view-GCN++, to recognize 3D shape based on graph representation of multiple views. We first construct view-graph with multiple views as graph nodes, then design two graph convolutional networks over the view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. Specifically, view-GCN is a hierarchical network based on two pivotal operations, i.e., feature transform based on local positional and non-local graph convolution, and graph coarsening based on a selective view-sampling operation. To deal with rotation sensitivity, we further propose view-GCN++ with local attentional graph convolution operation and rotation robust view-sampling operation for graph coarsening. By these designs, view-GCN++ achieves invariance to transformations under the finite subgroup of rotation group SO(3). Extensive experiments on benchmark datasets (i.e., ModelNet40, ScanObjectNN, RGBD and ShapeNet Core55) show that view-GCN and view-GCN++ achieve state-of-the-art results for 3D shape classification and retrieval tasks under aligned and rotated settings.

Long-term use of glucocorticoids (GCs) is known to be a predominant cause of osteonecrosis of the femoral head (ONFH). Moreover, GCs can mediate apoptosis of various cell types by exaggerating oxidative stress. Previous studies have reported that the AKT pathway might be closely associated with apoptosis and metabolism of osteoblasts in ONFH mediated by GCs. We have previously found that cortistatin (CST) antagonizes oxidative stress and improves cell metabolism as well as apoptosis in several conditions. In this study, we detected that local and systemic CST expression levels were diminished in patients with ONFH compared with femoral neck fracture (FNF). In addition, a GC-induced rat ONFH model was established, which impaired bone quality and angiogenesis and improved osteoblastogenesis in the femoral head. Then, administration of CST attenuated these ONFH phenotypes in this model. Furthermore, osteoblast and endothelial cell lines were cultured and stimulated with dexamethasone (Dex) in the presence or absence of recombinant CST. As a result, Dex induced apoptosis and impaired anabolic metabolism of osteoblasts and suppressed tube formation as well as migration in endothelial cells, while additional treatment with CST reversed this damage to the cells. Moreover, blocking GHSR1a, a well-accepted receptor of CST, or blocking the AKT signaling pathway largely abolished the protective function of CST in Dex-induced disorder of the cells. Taken together, our results indicate that CST has the capability to prevent GC-induced apoptosis and metabolic disorder of osteoblasts in the pathogenesis of ONFH via the GHSR1a/AKT signaling pathway.

3D object recognition is one of the most important tasks in 3D data processing, and has been extensively studied recently. Researchers have proposed various 3D recognition methods based on deep learning, among which a class of view-based approaches is a typical one. However, in the view-based methods, the commonly used view pooling layer to fuse multi-view features causes a loss of visual information. To alleviate this problem, in this paper, we construct a novel layer called Dynamic Routing Layer (DRL) by modifying the dynamic routing algorithm of capsule network, to more effectively fuse the features of each view. Concretely, in DRL, we use rearrangement and affine transformation to convert features, then leverage the modified dynamic routing algorithm to adaptively choose the converted features, instead of ignoring all but the most active feature in view pooling layer. We also illustrate that the view pooling layer is a special case of our DRL. In addition, based on DRL, we further present a Dynamic Routing Convolutional Neural Network (DRCNN) for multi-view 3D object recognition. Our experiments on three 3D benchmark datasets show that our proposed DRCNN outperforms many state-of-the-arts, which demonstrates the efficacy of our method.

Abstract Intervertebral disc degeneration (IVDD) demonstrates a gradually increased incidence and has developed into a major health problem worldwide. The nucleus pulposus is characterized by the hypoxic and avascular environment, in which hypoxia-inducible factor-1α (HIF-1α) has an important role through its participation in extracellular matrix synthesis, energy metabolism, cellular adaptation to stresses and genesis. In this study, the effects of HIF-1α on mouse primary nucleus pulposus cells (MNPCs) exposed to TNF-α were observed, the potential mechanism was explored and a rabbit IVDD model was established to verify the protective role of HIF-1α on IVDD. In vitro results demonstrated that HIF-1α could attenuate the inflammation, apoptosis and mitochondrial dysfunction induced by TNF-α in MNPCs; promote cellular anabolism; and inhibit cellular catabolism. In vivo results demonstrated that after establishment of IVDD model in rabbit, disc height and IVD extracellular matrix were decreased in a time-dependent manner, MRI analysis showed a tendency for decreased T2 values in a time-dependent manner and supplementation of HIF-1α improved histological and imaginative IVDD while downregulation of HIF-1α exacerbated this degeneration. In summary, HIF-1α protected against IVDD, possibly through reducing ROS production in the mitochondria and consequent inhibition of inflammation, metabolism disorders and apoptosis of MNPCs, which provided a potential therapeutic instrument for the treatment of IVDD diseases.

In this paper, we propose a novel network block, dubbed as second-order spectral transform block, for 3D shape retrieval and classification. This network block generalizes the second-order pooling to 3D surface by designing a learnable non-linear transform on the spectrum of the pooled descriptor. The proposed block consists of following two components. First, the second-order average (SO-Avr) and max-pooling (SO-Max) operations are designed on 3D surface to aggregate local descriptors, which are shown to be more discriminative than the popular average-pooling or max-pooling. Second, a learnable spectral transform parameterized by mixture of power function is proposed to perform non-linear feature mapping in the space of pooled descriptors, i.e., manifold of symmetric positive definite matrix for SO-Avr, and space of symmetric matrix for SO-Max. The proposed block can be plugged into existing network architectures to aggregate local shape descriptors for boosting their performance. We apply it to a shallow network for non-rigid 3D shape analysis and to existing networks for rigid shape analysis, where it improves the first-tier retrieval accuracy by 7.2% on SHREC’14 Real dataset and achieves state-of-the-art classification accuracy on ModelNet40. As an extension, we apply our block to 2D image classification, showing its superiority compared with traditional second-order pooling methods. We also provide theoretical and experimental analysis on stability of the proposed second-order spectral transform block.

Herein, N,P-rich carbon/carbon/Co

Shape classification and segmentation of point cloud data are two of the most demanding tasks in photogrammetry and remote sensing applications, which aim to recognize object categories or point labels. Point convolution is an essential operation when designing a network on point clouds for these tasks, which helps to explore 3D local points for feature learning. In this paper, we propose a novel point convolution (PSConv) using separable weights learned with polynomials for 3D point cloud analysis. Specifically, we generalize the traditional convolution defined on the regular data to a 3D point cloud by learning the point convolution kernels based on the polynomials of transformed local point coordinates. We further propose a separable assumption on the convolution kernels to reduce the parameter size and computational cost for our point convolution. Using this novel point convolution, a hierarchical network (PSNet) defined on the point cloud is proposed for 3D shape analysis tasks such as 3D shape classification and segmentation. Experiments are conducted on standard datasets, including synthetic and real scanned ones, and our PSNet achieves state-of-the-art accuracies for shape classification, as well as competitive results for shape segmentation compared with previous methods.

Summary: Background/objective: Neoadjuvant radiotherapy plays a vital role in rectal cancer treatment, but impairs postoperative bowel function, leading to low anterior resection syndrome (LARS). Neoadjuvant chemotherapy alone might avoid the negative effect of radiotherapy on bowel function. This study aims to assess the impact of neoadjuvant chemotherapy on LARS and the development of LARS over the first 6 months after surgery. Methods: Rectal cancer patients were prospectively recruited during June 30, 2018 and December 24, 2019. Bowel function was assessed by the LARS score, which was taken at 1 month, 3 months, and 6 months after surgery via phone call interview. Patients were divided into two groups based on whether they received neoadjuvant chemotherapy (group A) or not (group B). Results: A total of 97 patients were included in the analysis. There was no significant difference between the LARS scores at 1 month, 3 months, and 6 months of both groups. The LARS score at 6 months showed a significant decrease from that of 1 month and 3 months in group B (P

View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenge for view-based approach is how to aggregate multi-view features to be a global shape descriptor. In this work, we propose a novel view-based Graph Convolutional Neural Network, dubbed as view-GCN, to recognize 3D shape based on graph representation of multiple views in flexible view configurations. We first construct view-graph with multiple views as graph nodes, then design a graph convolutional neural network over view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. The view-GCN is a hierarchical network based on local and non-local graph convolution for feature transform, and selective view-sampling for graph coarsening. Extensive experiments on benchmark datasets show that view-GCN achieves state-of-the-art results for 3D shape classification and retrieval.

To reconstruct surface from unorganized points in three-dimensional Euclidean space, we propose a novel efficient and fast method by using l0 gradient minimization, which can directly measure the sparsity of a solution and produce sharper surfaces. Therefore, the proposed method is particularly effective for sharpening major edges and removing noise. Unlike the Poisson surface reconstruction approach and its extensions, our method does not depend on the accurate directions of normal vectors of the unorganized points. The resulting algorithm is developed using a half-quadratic splitting method and is based on decoupled iterations that are alternating over a smoothing step realized by a Poisson approach and an edge-preserving step through an optimization formulation. This iterative algorithm is easy to implement. Various tests are presented to demonstrate that our method is robust to point noise, normal noise and data holes, and thus produces good surface reconstruction results.

Noble metal nanoparticles strongly adhered to dielectric matrices have been extensively studied because of their potential applications in plasmonic devices based on tunable localized surface plasmon (LSP) excitation. Compared with conventional synthesis methods, the noble metal nanoparticles formed by ion-beam irradiation draw significant interest in recent years because a single layer dispersion of nanoparticles strongly bonded on the dielectric substrate can be obtained. In this paper, important phenomena related to ion-beam surface nanostructuring including ion-induced reshaping of metal nanoparticles, ion-induced core-satellite structure formation, and ion-induced burrowing of these nanoparticles are discussed, with their individual effects on LSP excitation. Consequently, ion-induced surface nanostructuring of Ag-Au bimetallic films on amorphous silica glass and sapphire with tunable LSP excitation are presented. In addition, theoretical studies of far-field and near-field optical properties of these nanoparticles under ion irradiation are introduced, and the enhanced localized electric field (hot spot) is interpreted. Finally, the futures and challenges of the emerging plasmonic applications based on tunable LSP excitations in bio-sensing and surface enhanced Raman spectroscopy (SERS) are presented.

Designing a network on 3D surface for non-rigid shape analysis is a challenging task. In this work, we propose a novel spectral transform network on 3D surface to learn shape descriptors. The proposed network architecture consists of four stages: raw descriptor extraction, surface second-order pooling, mixture of power function-based spectral transform, and metric learning. The proposed network is simple and shallow. Quantitative experiments on challenging benchmarks show its effectiveness for non-rigid shape retrieval and classification, e.g., it achieved the highest accuracies on SHREC’14, 15 datasets as well as the “range” subset of SHREC’17 dataset.

The oxygen reduction reaction (ORR) is a crucial cathodic process and a technology providing renewable energy. It is essential to create ORR catalysts not containing noble metals yet still affordable and possessing high-efficiency and long-term durability. Therefore, this work developed a highly porous N-doped carbon catalyst embedded with CeO2 nanoparticles (NPs). This composite catalyst was synthesized by pyrolysis of PANI-encapsulated CeO2 NPs (CeO2/PANI) combined with in situ polymerization. The resulting catalyst exhibited an outstanding ORR performance with 0.94 and 0.81 mV onset and positive half-wave potentials, respectively, and 5.52 mA cm−2 diffusion-limited current density. The catalyst also demonstrated excellent stability. These exceptional characteristics indicate that a synergy of the N-doped carbon and CeO2 NPs provide a novel strategy of fabrication of novel ORR catalysts without using noble-metals for applications related to fuel cells and metal-air batteries.

Herein is reported the dots alignment of Au nano-submicron particles (Au-NPs) on a chemical vapor-deposited β-SiC substrate through nanosecond-pulsed laser irradiation-induced dewetting of an Au thin film, which was successfully observed by in-situ high-voltage electron microscopy. The initial stages of the Au-NPs array arrangement were also successfully observed, and long-range quasi-two-dimensional Au-NPs arrangements were found after laser irradiation. Moreover, the effect of Rayleigh instability to form multiple Au-NPs in each line of the network structure after the hole growth in the Au thin film was confirmed.

In this study, we investigated nanostructure formations on the surface of Au thin films deposited on SiO 2 glass substrates after nanosecond-pulsed laser irradiation, also the correlation between the nanostructures parameters and the photoabsorption peak. Spherical Au nanoparticle/SiO 2 glass nanocomposites were formed on the surface of the Au thin films deposited on the SiO 2 glass substrates after nanosecond-pulsed laser irradiation in air with a wavelength of 532 nm at a repetition rate of 2 Hz and a laser energy density of 0.7 kJ/m 2 . Au nanoparticles were periodically arranged on the substrates under laser irradiation perpendicular to the direction of the electrical field vector of the laser light, the average diameter of Au nanoparticles was increased from 59.3 to 67.4 nm and the average distance of the laser induced periodical structure was decreased from 1.3 to 1.0 μm as the number of laser pulses increased from 1000 to 1500. After 2000 pulses irradiation, an additional laser irradiation induced periodical structure was formed in the direction parallel to the electrical field vector of the laser. The average periodicity of this nanostructure perpendicular to the initial nanostructure was 560 nm, which is close to the wavelength of the nanosecond-pulsed laser used in this study. The average diameter of these Au nanoparticles is 41.9 nm which is smaller than that of the Au nanoparticles formed after 1000 pulses irradiation. Au nanoparticles were generally dispersed on the surface while some were embedded in the substrate. After 1500 pulses irradiation, the diameter of the Au nanoparticles on the Au(30 nm)/SiO 2 (0.8 mm) is relatively larger than that of the Au nanoparticles on the Au(20 nm)/SiO 2 (0.1 mm). Each of laser irradiated sample showed an own photoabsorption peak clearly in this study. Furthermore, effects of the average diameter of the Au nanoparticles on the photoabsorption peak are discussed.

Effects of Ar-ion induced surface nanostructuring were studied using 100 keV Ar-ion irradiation of 30 nm Ag–Au bimetallic films deposited on Al2O3 single crystals, under irradiation fluences ranging from 5.0 × 1015 cm−2 to 6.3 × 1016 cm−2. Scanning electron microscope was used to study the ion-beam-induced surface nanostructuring. As the irradiation fluence increased, dewetting of the bimetallic films on the Al2O3 substrate was observed, and formation of isolated Ag–Au nanostructures sustained on the substrate were obtained. Next, thermal annealing was performed under high vacuum at 1073 K for 2 h; a layer of photosensitive Ag–Au alloy nanoballs partially embedded in the Al2O3 substrate was obtained when higher fluence irradiation (>3.8 × 1016 cm−2) was used. The microstructures of the nanoballs were investigated using a transmission electron microscope, and the nanoballs were found to be single crystals with a FCC structure. In addition, photoabsorption spectra were measured, and localized surface plasmon resonance peaks were observed. With increase in the irradiation fluence, the size of the Ag–Au nanoballs on the substrate decreased, and a blue-shift of the LSPR peaks was observed. Further control of the LSPR frequency over a wide range was achieved by modifying the chemical components, and a red-shift of the LSPR peaks was observed as the Au concentration increased. In summary, ion irradiation is an effective approach toward surface nanostructuring, and the nanocomposites obtained have potential applications in optical devices.

Effects of the irradiation dose on surface nanostructuring accompanied with the dewetting process of Au films deposited on SiO2 glass were examined using an atomic force microscope and a scanning electron microscope. In addition, the microstructural evolution and the chemical concentration of Au films were investigated using a transmission electron microscope equipped with an energy-dispersive spectrometer. As increasing the Ar ion irradiation dose, the lattice expansion of Au nanoscale islands sustained on the SiO2 glass was observed and irradiation-induced lattice defects together with irradiation-induced interface ion mixing were accounted for this lattice expansion. Finally a layer of photosensitive Au nanoballs with highly spherical shape embedded in a SiO2 substrate was obtained after Ar ion irradiation to 10.0 × 1016/cm2 and some of Au nanoballs were found to be single crystals. As the irradiation energy of the Ar ions increased from 100 to 150 keV, the average diameter of the Au nanoballs in the substrate increased and the red shift of the SPR peak was observed. This tendency of the experimental SPR peaks corresponded with that of the theoretically calculated SPR peaks using Mie solution.

The in situ observation of Au dot formation and the self-assembly dynamics of Au nanoparticles (NPs) was successfully demonstrated via dewetting of Au thin films on SiO2 glass substrates under nano-second pulsed laser irradiation using a multi-quantum beam high-voltage electron microscope. Moreover, using electron energy-loss spectroscopy (EELS) performed in a scanning transmission electron microscope (STEM), the plasmonic properties of the formed Au/SiO2 nanostructure were analyzed to demonstrate its validity in advanced optical devices. The uniformly distributed Au NPs evolved into a dot alignment through movement and coalescence processes was demonstrated in this in situ observation. We carried out the plasmon-loss images of the plan view and the cross-section of the Au/SiO2 nanostructures were obtained at the plasmon-loss peak energy for investigate the three-dimensional distribution of surface plasmon. Furthermore, discrete-dipole approximation (DDA) calculations were used to simulate the plasmonic properties, such as the surface plasmon resonance and the surface plasmon field distribution, of isolated single Au/SiO2 nanostructures. This STEM-EELS-acquired surface plasmon map of the cross-sectional sample is in excellent agreement with the DDA calculations. This results demonstrated the influence of the contact condition between Au NP and SiO2 glass on the plasmonic properties, and may improve the technology for developing advanced optical devices.

The influence of a directly adjacent or an anisotropic surrounding medium alters the plasmonic properties of a nanoparticle because it provides a mechanism for symmetry breaking of the scattering. Given the success of ion irradiation induced embedment of rigid metallic nanospheroids into amorphous substrate, it is possible to examine the effect of the silica glass substrate on the plasmonic properties of these embedded nanospheroids. In this work presented here, discrete dipole approximation (DDA) calculations for the Au nanospheroids' optical properties were performed based on 3-dimensional (3D) configuration extracted from planar SEM micrographs and cross-sectional TEM micrographs of the Au nanospheroids partially embedded in the silica glass, and the well-matched simulations with respect to the experimental measurements could demonstrate the dielectric constant at the near surface of silica glass decreased after Ar-ion irradiation.

Ion irradiation and short-pulsed laser irradiation can be used to form nanostructures on the surfaces of substrates. This work investigates the synergistic effects of ion and nanosecond-pulsed laser co-irradiation on surface nanostructuring of Au thin films deposited under vacuum on SiO2 glass substrates. Gold nanoparticles are randomly formed on the surface of the substrate after nanosecond-pulsed laser irradiation under vacuum at a wavelength of 532 nm with a repetition rate of 10 Hz and laser energy density of 0.124 kJ/m(2). Gold nanoparticles are also randomly formed on the substrate after 100-keV Ar+ ion irradiation at doses of up to 3.8 x 10(15) ions/cm(2), and nearly all of these nanoparticles are fully embedded in the substrate. With increasing ion irradiation dose (number of incident laser pulses), the mean diameter of the Au nanoparticles decreases (increases). However, Au nanoparticles are only formed in a periodic surface arrangement after co-irradiation with 6000 laser pulses and 3.8 x 10(15) ions/cm(2). The periodic distance is similar to 540 nm, which is close to the wavelength of the nanosecond-pulsed laser, and the mean diameter of the Au nanoparticles remains at similar to 20 nm with a relatively narrow distribution. The photoabsorption peaks of the ion-or nanosecond-pulsed laser-irradiated samples clearly correspond to the mean diameter of Au nanoparticles. Conversely, the photoabsorption peaks for the co-irradiated samples do not depend on the mean nanoparticle diameter. This lack of dependence is likely caused by the periodic nanostructure formed on the surface by the synergistic effects of co-irradiation.

Ag-Au bimetallic nanospheroids with tunable localized surface plasmon resonance (LSPR) were synthesized by 100 keV Ar-ion irradiation of 30 nm Ag-Au bimetallic films deposited on SiO2 glass substrates. A shift of the LSPR peaks toward shorter wavelengths was observed up to an irradiation fluence of 1.0 x 10(17) cm(-2), and then shifted toward the longer wavelength because of the increase of fragment volume under ion irradiation. Further control of LSPR frequency over a wider range was realized by modifying the chemical components. The resulting LSPR frequencies lie between that of the pure components, and an approximate linear shift of the LSPR toward the longer wavelength with the Au concentration was achieved, which is in good agreement with the theoretical calculations based on Gans theory. In addition, the surface morphology and compositions were examined with a scanning electron microscope equipped with an energy dispersive spectrometer, and microstructural characterizations were performed using a transmission electron microscope. The formation of isolated photosensitive Ag-Au nanospheroids with a FCC structure partially embedded in the SiO2 substrate was confirmed, which has a potential application in solid-state devices. (C) 2013 AIP Publishing LLC.