Cannabinoid receptor 2 deficiency enhances isoflurane-induced spatial cognitive impairment in adult mice by affecting neuroinflammation, neurogenesis and neuroplasticity.

Isoflurane (Iso) is a commonly used inhalational anesthetic and is associated with the incidence of postoperative cognitive dysfunction (POCD). Cannabinoid receptor 2 (CB2R) was previously reported to have a promising neuroprotective function in cases of POCD, but the specific mechanisms have remained to be fully explored. The aim of the present study was to investigate the effect of CB2R deficiency on spatial cognitive performance in adult mice exposed to Iso. A total of 20 adult CB2R knockout (KO) and 20 wild-type (WT) mice were exposed to Iso (1.4% in oxygen for 4 h) or 100% oxygen. The Morris water maze (MWZ) test was performed 10 days after Iso exposure. Immunofluorescence staining and reverse transcription-quantitative PCR were performed to assess the expression of microglial marker ionized calcium-binding adaptor molecule-1 (Iba1) and the mRNA expression levels of microglial phenotype markers (M1: Interleukin-6, tumor necrosis factor-α, inducible nitric oxide synthase; M2: Chitinase-3 like protein) in the hippocampus. Changes in hippocampal neurogenesis and neuroplasticity were assessed by 5-bromodeoxyuridine (BrdU) immunostaining and Golgi staining. Compared with control mice, WT Iso-exposed mice had impaired spatial performance in the MWZ test. Furthermore, hippocampal Iba1 immunoreactivity and the number of microglial branches were notably increased in Iso-exposed WT mice. This was paralleled by significant upregulation of M1-associated markers and downregulation of M2-associated markers in the hippocampus. An obviously reduced number of BrdU+ neurons and decreased spine density were observed in WT Iso-exposed mice compared with control mice. Of note, CB2R deficiency exacerbated the spatial cognition impairment induced by Iso in the MWZ test. The alterations in the activation, morphology and M1 polarization of microglia, the number of BrdU+ neurons and spine density were more pronounced in CB2R-deficient Iso-exposed KO mice than in WT Iso-exposed mice. These results suggested that CB2R has a crucial role in Iso-induced cognitive impairment, which may be related to changes in hippocampal neuroinflammation, neurogenesis and neuroplasticity.

Hypergraph-Induced Convolutional Networks for Visual Classification.

At present, convolutional neural networks (CNNs) have become popular in visual classification tasks because of their superior performance. However, CNN-based methods do not consider the correlation of visual data to be classified. Recently, graph convolutional networks (GCNs) have mitigated this problem by modeling the pairwise relationship in visual data. Real-world tasks of visual classification typically must address numerous complex relationships in the data, which are not fit for the modeling of the graph structure using GCNs. Therefore, it is vital to explore the underlying correlation of visual data. Regarding this issue, we propose a framework called the hypergraph-induced convolutional network to explore the high-order correlation in visual data during deep neural networks. First, a hypergraph structure is constructed to formulate the relationship in visual data. Then, the high-order correlation is optimized by a learning process based on the constructed hypergraph. The classification tasks are performed by considering the high-order correlation in the data. Thus, the convolution of the hypergraph-induced convolutional network is based on the corresponding high-order relationship, and the optimization on the network uses each data and considers the high-order correlation of the data. To evaluate the proposed hypergraph-induced convolutional network framework, we have conducted experiments on three visual data sets: the National Taiwan University 3-D model data set, Princeton Shape Benchmark, and multiview RGB-depth object data set. The experimental results and comparison in all data sets demonstrate the effectiveness of our proposed hypergraph-induced convolutional network compared with the state-of-the-art methods.

Beyond Pairwise Matching: Person Reidentification via High-Order Relevance Learning.

Person reidentification has attracted extensive research efforts in recent years. It is challenging due to the varied visual appearance from illumination, view angle, background, and possible occlusions, leading to the difficulties when measuring the relevance, i.e., similarities, between probe and gallery images. Existing methods mainly focus on pairwise distance metric learning for person reidentification. In practice, pairwise image matching may limit the data for comparison (just the probe and one gallery subject) and yet lead to suboptimal results. The correlation among gallery data can be also helpful for the person reidentification task. In this paper, we propose to investigate the high-order correlation among the probe and gallery data, not the pairwise matching, to jointly learn the relevance of gallery data to the probe. Recalling recent progresses on feature representation in person reidentification, it is difficult to select the best feature and each type of feature can benefit person description from different aspects. Under such circumstances, we propose a multihypergraph joint learning algorithm to learn the relevance in corporation with multiple features of the imaging data. More specifically, one hypergraph is constructed using one type of feature and multiple hypergraphs can be generated accordingly. Then, the learning process is conducted on the multihypergraph structure, and the identity of a probe is determined by its relevance to each gallery data. The merit of the proposed scheme is twofold. First, different from pairwise image matching, the proposed method jointly explores the relationships among different images. Second, multimodal data, i.e., different features, can be formulated in the multihypergraph structure, which can convey more information in the learning process and can be easily extended. We note that the proposed method is a general framework to incorporate with any combination of features, and thus is flexible in practice. Experimental results and comparisons with the state-of-the-art methods on three public benchmarking data sets demonstrate the superiority of the proposed method.

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks.

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.

Enhancing pyridinic nitrogen level in graphene to promote electrocatalytic activity for oxygen reduction reaction.

We develop an efficient approach to fabricate nitrogen-doped graphene with tunable pyridinic nitrogen levels (from 1.1 to 1.8 at.%), abundant in-plane holes and high surface areas (623 m(2) g(-1)) via a hydrothermal treatment of graphene oxide with hydrogen peroxide and subsequent annealing under ammonia gas. It is found that the chemical etching is beneficial to the formation of pyridinic nitrogen in graphene during the nitrogen-doping process, which is crucial to enhancing the electrocatalytic properties of graphene for oxygen reduction reaction (ORR). Hence, the optimized NG exhibits good electrocatalytic activity, more positive onset potential than Pt-C (-0.08 V versus -0.09 V), good durability, and high selectivity when it is employed as a metal-free catalyst for ORR. This approach may uncover a mechanism in escalation of pyridinic N atoms doped on the graphene basal edge and provide an efficient platform for the synthesis of a series of heteroatom-doped graphene with tunable heteroatom content for broad applications.

Efficient example-based painting and synthesis of 2D directional texture.

We present a new method for converting a photo or image to a synthesized painting following the painting style of an example painting. Treating painting styles of brush strokes as sample textures, we reduce the problem of learning an example painting to a texture synthesis problem. The proposed method uses a hierarchical patch-based approach to the synthesis of directional textures. The key features of our method are: 1) Painting styles are represented as one or more blocks of sample textures selected by the user from the example painting; 2) image segmentation and brush stroke directions defined by the medial axis are used to better represent and communicate shapes and objects present in the synthesized painting; 3) image masks and a hierarchy of texture patches are used to efficiently synthesize high-quality directional textures. The synthesis process is further accelerated through texture direction quantization and the use of Gaussian pyramids. Our method has the following advantages: First, the synthesized stroke textures can follow a direction field determined by the shapes of regions to be painted. Second, the method is very efficient; the generation time of a synthesized painting ranges from a few seconds to about one minute, rather than hours, as required by other existing methods, on a commodity PC. Furthermore, the technique presented here provides a new and efficient solution to the problem of synthesizing a 2D directional texture. We use a number of test examples to demonstrate the efficiency of the proposed method and the high quality of results produced by the method.