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Pedestrian pose prediction is an important topic, related closely to robotics and automation. Accurate predictions of human poses and motion can facilitate a more thorough understanding and analysis of human behavior, which benefits real-world applications such as human-robot interaction, humanoid and bipedal robot design, and safe navigation of mobile robots and autonomous vehicles. This article describes a deep predictive coding network (PredNet)-based approach for unsupervised pedestrian pose prediction from 2D camera imagery and provides experimental results of two real-world autonomous vehicle data sets. The article also discusses topics for future work in unsupervised and semisupervised pedestrian pose prediction and its potential applications in robotics and automation systems.

ObjectiveHepatocellular carcinoma (HCC) is one of the most common and malignant tumors with an insidious onset, difficult early diagnosis, and limited therapy options, resulting in a poor prognosis. Cell division cycle associated 2 (CDCA2), also known as Repo-Man, plays an important role in regulating mitosis and DNA repair, but the involvement of CDCA2 in HCC remains unclear.MethodsThe differentially expressed genes that were significantly upregulated in multiple RNA sequencing datasets of HCC were screened. Receiver operating characteristic (ROC) curve analysis was performed to identify diagnostic markers for HCC. Least absolute shrinkage and selection operator Cox regression analysis was performed to screen the prognosis-related genes. The screening and analyses identified CDCA2 as the target gene in this study. The expression of CDCA2 was analyzed in public databases and clinical specimens, and CDCA2 involvement in HCC was explored by both bioinformatic analysis and in vitro experiments.ResultsThe level of CDCA2 was enhanced in HCC compared with healthy livers. Overexpression of CDCA2 positively correlated with the pathological grade and TNM stage of the diseases. Furthermore, CDCA2 was found to be an independent prognostic predictor. An excellent prognostic model of HCC was successfully constructed with CDCA2 in combination with TNM stage. Bioinformatic analysis revealed that CDCA2 was closely associated with the cell cycle, apoptosis, and p53 signaling pathway. Silencing CDCA2 in Huh7 cells resulted in significant upregulation of p53 and the downstream PUMA and NOXA and a subsequently increased apoptosis. Inhibition of p53 signaling and apoptosis was found after overexpression of CDCA2 in L02 cells. Strikingly, the proliferation of cells was not affected by CDCA2.ConclusionsCDCA2 was a novel diagnostic marker for HCC, and overexpression of this gene reflected poor pathological grade, stage, and clinical prognosis. CDCA2 promoted the pathogenesis of HCC by suppressing the p53-PUMA/NOXA signaling and the subsequent apoptosis.

BackgroundOridonin (Ori), the main bioactive ingredient of the natural anti-inflammatory herb Rabdosia rubescens, could be a covalent inhibitor of the NLRP3 inflammasome. Solid organ transplantation provides a life-saving optional therapy for patients with end-stage organ dysfunction. The long-term survival of solid organ transplantation remains restricted because of the possibility of rejection and the toxicity, infection, cardiovascular disease, and malignancy related to immunosuppressive (IS) drugs. However, the pathogenic mechanisms involved remain unclear. The ideal IS drugs to prevent allograft rejection have not been identified. Here, we investigated whether Ori could prolong the in vivo survival of completely mismatched cardiac allografts.MethodsThe cardiac transplantation models were conducted among three groups of mice from C57BL/6NCrSlc (B6/N) or C3H/HeNSlc (C3H) to C3H: the syngeneic and the allogeneic group, whose recipients were treated with vehicle of Ori, and the Ori treatment group, in which the recipients were transplanted hearts from MHC-I mismatched donors and treated with different dosages of Ori from post-operative day (POD) 0 to 7. Then, we investigated the effect of Ori on bone marrow-derived dendritic cell (BMDC) and allogeneic mixed lymphocyte reaction in vitro.ResultsOri with 3, 10, and 15 mg/kg Ori could prolong the survival (MST = 22.8, 49.2, and 65.3 days, respectively). We found that infiltrating CD8+ T cells and macrophages were decreased, and regulatory T cells (Tregs) were expanded in allografts on POD7. The mRNA level of IL-1β and IFN-γ of allografts was downregulated. Mechanistically, Ori-treated BMDCs suppressed T-cell proliferation and IFN-γ+CD4+ T-cell differentiation, along with the expansion of Tregs and IL-10+CD4+ T cells. Ori inhibited NOD, LRR-, and pyrin domain-containing protein 3 (NLRP3) expression; attenuated NF-κB and IκBα phosphorylation in LPS-activated BMDCs; downregulated NLRP3, Caspase-1, IL-1β, IL-18, and IFN-γ; and upregulated IL-10 expression.ConclusionsOur findings highlight the potential of Ori as a novel and natural IS agent to improve transplant tolerance. Ori could exert IS activity through decreasing IL-1β and IL-18 production and Th1 differentiation and proliferation and expanding Tregs via inhibiting the NF-κB/NLRP3 signaling pathway.

Shale gas reservoirs are tight reservoirs with ultralow porosity and ultralow permeability, and their matrix pores are mostly nanoscale. In addition, matrix particles and organic pore surfaces adsorb shale gas. These problems cause the production per well of shale gas to be lower than that of conventional natural gas. The use of hydraulic fracturing technology to exploit shale gas can achieve a good production increase effect. However, using this technology has some limitations caused by technical characteristics and geological conditions. Therefore, new technologies for shale gas exploitation need to be explored. In this study, we propose a method to improve the flow characteristics of shale gas by using ultrasonic waves to increase shale gas production and perform experimental tests to research the actual effect of this method. The lithology, mineral composition, pore structure, specific surface area, and pore size distribution of shale samples are tested. Then, the attenuation characteristics of ultrasonic waves propagating in shale are analyzed. Finally, the effect of ultrasonic waves on the adsorption, desorption, and seepage of shale gas is explored. Results show that the Langmuir adsorption isotherm can describe the adsorption characteristics of shale gas under the action of ultrasonic waves. The gas adsorption constant decreases with increasing ultrasonic wave power. The ultrasonic waves accelerate the gas desorption rate, significantly increase the desorption volume, and prolong the time taken to reach desorption equilibrium. They also increase the permeability of shale gas, and the growth is proportional to the power of the ultrasonic waves. These results indicate that the permeability of shale gas has a power function relationship with the effective stress under ultrasonic waves.

Liver transplantation is currently the most effective method for treating end-stage liver disease. However, recipients still need long-term immunosuppressive drug treatment to control allogeneic immune rejection, which may cause various complications and affect the long-term survival of the recipient. Many liver transplant researchers constantly pursue the induction of immune tolerance in liver transplant recipients, immunosuppression withdrawal, and the maintenance of good and stable graft function. Although allogeneic liver transplantation is more tolerated than transplantation of other solid organs, and it shows a certain incidence of spontaneous tolerance, there is still great risk for general recipients. With the gradual progress in our understanding of immune regulatory mechanisms, a variety of immune regulatory cells have been discovered, and good results have been obtained in rodent and non-human primate transplant models. As immune cell therapies can induce long-term stable tolerance, they provide a good prospect for the induction of tolerance in clinical liver transplantation. At present, many transplant centers have carried out tolerance-inducing clinical trials in liver transplant recipients, and some have achieved gratifying results. This article will review the current status of liver transplant tolerance and the research progress of different cellular immunotherapies to induce this tolerance, which can provide more support for future clinical applications.

In many remote sensing and hyperspectral image analysis applications, precise ground truth information is unavailable or impossible to obtain. Imprecision in ground truth often results from highly mixed or sub-pixel spectral responses over classes of interest, a mismatch between the precision of global positioning system (GPS) units and the spatial resolution of collected imagery, and misalignment between multiple sources of data. Given these sorts of imprecision, training of traditional supervised machine learning models which rely on the assumption of accurate and precise ground truth becomes intractable. Multiple instance learning (MIL) is a methodology that can be used to address these challenging problems. This chapter investigates the topic of hyperspectral image analysis given imprecisely labeled data and reviews MIL methods for hyperspectral target detection, classification, data fusion, and regression.

To study ultrasound’s attenuation law propagating in shale, we first tested the attenuation rate of ultrasound in air and shale by assembling an experimental device by ourselves. The experimentally measured ultrasonic attenuation coefficient in the air is close to the value obtained from most kinds of literature, so our self-assembled practical device is very reliable. By fitting the experimental data, we found that the average attenuation coefficient of ultrasound in the air is 0.0258 dB/m. The average attenuation coefficient in shale is 0.0657 dB/m. As the ultrasonic power increases, the attenuation coefficient of ultrasound gradually increases. Through calculation and analysis using the ultrasonic intensity formula, we found that the effective propagation distance of ultrasound in shale is about 1.2 m.

The isogeometric method is used to study the free vibration of thick plates based on Mindlin theory. The Non-uniform Rational B-Spline (NURBS) basis functions are employed to build the thick plate’s geometry models and serve as the shape functions for solution field approximation in finite element analysis. The Reissner–Mindlin plates built with multiple NURBS patches are investigated, in which several patches of the model have multi-interface and different patches may share a common point. In order to solve the non-conforming interface problems, Nitsche method is employed to glue different NURBS patches and only refers to the coupling conditions in this work. Various plate shapes, different boundary conditions and several kinds of thickness-span ratios are considered to verify the validity of the presented method. The dimensionless frequencies for different cases are obtained by solving the eigenvalue equation problems and compared with the existing reference solutions or the results calculated by ABAQUS software. Several numerical examples exhibit the effectiveness of the isogeometric approach. It shows that the natural frequencies of the Reissner–Mindlin plate can be successfully predicted by the combination of isogeometric analysis and Nitsche method.

Globalization has changed the landscape for higher education, and many students decide to improve their educational qualifications outside their home countries. As a result, Canadian higher education has witnessed an increasing number of international students, and the People’s Republic of China has been the main source country (Canadian Bureau for International Education, Facts and Figures: Canada’s performance and potential in international education. Retrieved from http://cbie.ca/media/facts-and-figures/, 2018). Therefore, the current demographic context calls for a further investigation of Chinese international students’ experiences while studying abroad. Many research studies have investigated different aspects of international student experiences (Sovic, Art, Design & Communication in Higher Education, 6(3), 145–158, 2008; Zhai, Studying international students: Adjustment issues and social support. Retrieved from http://files.eric.ed.gov.proxy1.lib.uwo.ca/fulltext/ED474481.pdf, 2002; Zhang & Zhou, Canadian and International Education/Education canadienne et international, 39(3), 43–58, 2010), but little is known about student perspectives. This chapter focuses on Chinese international students’ lived experiences in one Canadian institution using a narrative inquiry into student experiences from a sociocultural perspective, listening to students’ stories, recognizing their voices, and understanding their choices and practices in order to reconsider what seems familiar and to reveal some ordinary yet “sophisticated” stories. Suggestions are made to help educators and administrators to further support international students in Canadian higher education.

Urban environments pose a significant challenge for autonomous vehicles (AVs) as they must safely navigate while in close proximity to many pedestrians. It is crucial for the AV to correctly understand and predict the future trajectories of pedestrians to avoid collision and plan a safe path. Deep neural networks (DNNs) have shown promising results in accurately predicting pedestrian trajectories, relying on large amounts of annotated real-world data to learn pedestrian behavior. However, collecting and annotating these large real-world pedestrian datasets is costly in both time and labor. This paper describes a novel method using a stochastic sampling-based simulation to train DNNs for pedestrian trajectory prediction with social interaction. Our novel simulation method can generate vast amounts of automatically-annotated, realistic, and naturalistic synthetic pedestrian trajectories based on small amounts of real annotation. We then use such synthetic trajectories to train an off-the-shelf state-of-the-art deep learning approach Social GAN (Generative Adversarial Network) to perform pedestrian trajectory prediction. Our proposed architecture, trained only using synthetic trajectories, achieves better prediction results compared to those trained on human-annotated real-world data using the same network. Our work demonstrates the effectiveness and potential of using simulation as a substitution for human annotation efforts to train high-performing prediction algorithms such as the DNNs., Comment: 8 pages, 6 figures and 2 tables

In classifier (or regression) fusion, the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accurate and precise training labels. However, accurate labels may be difficult to obtain in many remote sensing applications. This paper proposes novel classification and regression fusion models that can be trained given ambiguously and imprecisely labeled training data in which the training labels are associated with sets of data points (i.e., “bags”) instead of individual data points (i.e., “instances”) following a multiple-instance learning framework. Experiments were conducted based on the proposed algorithms on both synthetic data and applications such as target detection and crop yield prediction given remote sensing data. The proposed algorithms show effective classification and regression performance.

An approach to image labeling by seabed context based on multiple-instance learning via embedded instance selection (MILES) is presented. Sonar images are first segmented into superpixels with associated intensity and texture feature distributions. These superpixels are defined as the "instances" and the sonar images are defined as the "bags" within the MILES classification framework. The intensity feature distributions are discrete while the texture feature distributions are continuous, thus the Cauchy-Schwarz divergence metric is used to embed the instances in a higher-dimensional discriminatory space. Results are given for labeled synthetic aperture sonar (SAS) image database containing images with a variety of seabed textures.

Characteristics of underwater targets displayed in synthetic aperture sonar (SAS) imagery vary depending on their environmental context. Discriminative features in sea grass may differ from the features that are discriminative in sand ripple, for example. Environmentally-adaptive target detection and classification systems that take into account environmental context, therefore, have the potential for improved results. This paper presents an end-to-end environmentally-adaptive target detection system for SAS imagery that performs target recognition while accounting for environmental context. First, locations of interest are identified in the imagery using the Reed-Xiaoli (RX) detector and a Non-Gaussian detector based on the multivariate Laplace distribution. Then, the Multiple Instance Learning via Embedded Instance Selection (MILES) approach is used to identify the environmental context of the targets. Finally, target features are extracted and a set of environmentally-specific k-Nearest Neighbors (k-NN) classifiers are applied. Experiments were conducted on a collection of both high and low frequency SAS imagery with a variety of environmental contexts and results show improved classification accuracy between target classes when compared with classification results with no environmental consideration.

This paper introduces the beta compositional model (BCM) for hyperspectral unmixing and four algorithms for unmixing given the BCM. Hyperspectral unmixing estimates the proportion of each endmember at every pixel of a hyperspectral image. Under the BCM, each endmember is a random variable distributed according to a beta distribution. By using a beta distribution, spectral variability is accounted for during unmixing, the reflectance values of each endmember are constrained to a physically realistic range, and skew can be accounted for in the distribution. Spectral variability is incorporated to increase hyperspectral unmixing accuracy. Two BCM-based spectral unmixing approaches are presented: BCM-spectral and BCM-spatial. For each approach, two algorithms, one based on quadratic programming (QP) and one using a Metropolis-Hastings (MH) sampler, are developed. Results indicate that the proposed BCM unmixing algorithms are able to successfully perform unmixing on simulated data and real hyperspectral imagery while incorporating endmember spectral variability and spatial information.

This paper proposes a possibilistic context identification approach for synthetic aperture sonar (SAS) imagery. SAS seabed imagery can display a variety of textures that can be used to identify seabed types such as sea grass, sand ripple and hard-packed sand, etc. Target objects in SAS imagery often have varying characteristics and features due to changing environmental context. Therefore, methods that can identify the seabed environment can be used to assist in target classification and detection in an environmentally adaptive or context-dependent approach. In this paper, a possibilistic context identification approach is used to identify the seabed contexts. Alternative methods, such as crisp, fuzzy or probabilistic methods, would force one type of context on every sample in the imagery, ignoring the possibility that the test imagery may include an environmental context that has not yet appeared in the training process. The proposed possibilistic approach has an advantage in that it can both identify known contexts as well as identify when an unknown context has been encountered. Experiments are conducted on a collection of SAS imagery that display a variety of environmental features.