Navigation Instruction Validation Tool and Indoor Wayfinding Training System for People with Disabilities

According to World Health Survey, there are 785 million (15.6%) people in the world that live with a disability. It is a well-known fact that lack of access to public transportation is a barrier for people with disabilities in seeking work or accessing health care. In this research, we seek to increase access to public transportation by introducing a virtual pre-travel training system that enables people with disabilities to get familiar with a public transportation venue prior to arriving at the venue. Using this system, users establish a mental map of the target environment prior to their arrival to the physical space, increasing their confidence and therefore increasing their chances of using public transportation. First, we have to guarantee that all navigation instructions sent to our training system are correct. Since the number of navigation instruction increases dramatically, instruction validation becomes a challenge. We propose a video game based validation tool which includes a game scene that represents in 2D the physical environment and uses a game avatar to verify the navigation instructions automatically in the game scene. The avatar traverses the virtual space following the corresponding navigation instructions. Only in case that it successfully reaches the planned destination, the current navigation instruction can be considered as correct. Then, we introduce a virtual reality based pre-travel wayfinding training system to assist people with disabilities to get familiar with a venue prior to their arrival at the physical space, which provides two modes: 1) Self-Guided mode in which the path between a source and a destination is shown to the user from third person perspective, and 2) Exploration mode in which the user explores and interacts with the environment. In the end, we have implemented visual analytics tools that track and evaluate trainees’ performance and help us optimize the game. These tools identify the difficulties faced by the trainees as well as obtain overall statistics on the trainees’ behavior in the indoor environment, helping us understand how to modify the system and adjust it to different classes of disabilities

A study on fault diagnosis in nonlinear dynamic systems with uncertainties

In this draft, fault diagnosis in nonlinear dynamic systems is addressed. The objective of this work is to establish a framework, in which not only model-based but also data-driven and machine learning based fault diagnosis strategies can be uniformly handled. Instead of the well-established input-output and the associated state space models, stable image and kernel representations are adopted in our work as the basic process model forms. Based on it, the nominal system dynamics can then be modelled as a lower-dimensional manifold embedded in the process data space. To achieve a reliable fault detection as a classification problem, projection technique is a capable tool. For nonlinear dynamic systems, we propose to construct projection systems in the well-established framework of Hamiltonian systems and by means of the normalised image and kernel representations. For nonlinear dynamic systems, process data form a non-Euclidean space. Consequently, the norm-based distance defined in Hilbert space is not suitable to measure the distance from a data vector to the manifold of the nominal dynamics. To deal with this issue, we propose to use a Bregman divergence, a measure of difference between two points in a space, as a solution. Moreover, for our purpose of achieving a performance-oriented fault detection, the Bregman divergences adopted in our work are defined by Hamiltonian functions. This scheme not only enables to realise the performance-oriented fault detection, but also uncovers the information geometric aspect of our work. The last part of our work is devoted to the kernel representation based fault detection and uncertainty estimation that can be equivalently used for fault estimation. It is demonstrated that the projection onto the manifold of uncertainty data, together with the correspondingly defined Bregman divergence, is also capable for fault detection

Changes in zebrafish (Danio rerio) lens crystallin content during development.

PurposeThe roles that crystallin proteins play during lens development are not well understood. Similarities in the adult crystallin composition of mammalian and zebrafish lenses have made the latter a valuable model for examining lens function. In this study, we describe the changing zebrafish lens proteome during development to identify ontogenetic shifts in crystallin expression that may provide insights into age-specific functions.MethodsTwo-dimensional gel electrophoresis and size exclusion chromatography were used to characterize the lens crystallin content of 4.5-day to 27-month-old zebrafish. Protein spots were identified with mass spectrometry and comparisons with previously published proteomic maps, and quantified with densitometry. Constituents of size exclusion chromatography elution peaks were identified with sodium dodecyl sulfate-polyacrylamide gel electrophoresis.ResultsZebrafish lens crystallins were expressed in three ontogenetic patterns, with some crystallins produced at relatively constant levels throughout development, others expressed primarily before 10 weeks of age (βB1-, βA1-, and γN2-crystallins), and a third group primarily after 10 weeks (α-, βB3-, and γS-crystallins). Alpha-crystallins comprised less than 1% of total lens protein in 4.5-day lenses and increased to less than 7% in adult lenses. The developmental period between 6 weeks and 4 months contained the most dramatic shifts in lens crystallin expression.ConclusionsThese data provide the first two-dimensional gel electrophoresis maps of the developing zebrafish lens, with quantification of changing crystallin abundance and visualization of post-translational modification. Results suggest that some crystallins may play stage specific roles during lens development. The low levels of zebrafish lens α-crystallin relative to mammals may be due to the high concentrations of γ-crystallins in this aquatic lens. Similarities with mammalian crystallin expression continue to support the use of the zebrafish as a model for lens crystallin function

Energy-Efficient β

As the first priority of query processing in wireless sensor networks is to save the limited energy of sensor nodes and in many sensing applications a part of skyline result is enough for the user’s requirement, calculating the exact skyline is not energy-efficient relatively. Therefore, a new approximate skyline query, β-approximate skyline query which is limited by a guaranteed error bound, is proposed in this paper. With an objective to reduce the communication cost in evaluating β-approximate skyline queries, we also propose an energy-efficient processing algorithm using mapping and filtering strategies, named Actual Approximate Skyline (AAS). And more than that, an extended algorithm named Hypothetical Approximate Skyline (HAS) which replaces the real tuples with the hypothetical ones is proposed to further reduce the communication cost. Extensive experiments on synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings

Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems

This paper is dedicated to control theoretically explainable application of autoencoders to optimal fault detection in nonlinear dynamic systems. Autoencoder-based learning is a standard method of machine learning technique and widely applied for fault (anomaly) detection and classification. In the context of representation learning, the so-called latent (hidden) variable plays an important role towards an optimal fault detection. In ideal case, the latent variable should be a minimal sufficient statistic. The existing autoencoder-based fault detection schemes are mainly application-oriented, and few efforts have been devoted to optimal autoencoder-based fault detection and explainable applications. The main objective of our work is to establish a framework for learning autoencoder-based optimal fault detection in nonlinear dynamic systems. To this aim, a process model form for dynamic systems is firstly introduced with the aid of control and system theory, which also leads to a clear system interpretation of the latent variable. The major efforts are devoted to the development of a control theoretical solution to the optimal fault detection problem, in which an analog concept to minimal sufficient statistic, the so-called lossless information compression, is introduced for dynamic systems and fault detection specifications. In particular, the existence conditions for such a latent variable are derived, based on which a loss function and further a learning algorithm are developed. This learning algorithm enables optimally training of autoencoders to achieve an optimal fault detection in nonlinear dynamic systems. A case study on three-tank system is given at the end of this paper to illustrate the capability of the proposed autoencoder-based fault detection and to explain the essential role of the latent variable in the proposed fault detection system

Proteomic Studies on the Mechanism of Myostatin Regulating Cattle Skeletal Muscle Development

Myostatin (MSTN) is an important negative regulator of muscle growth and development. In this study, we performed comparatively the proteomics analyses of gluteus tissues from MSTN+/− Mongolian cattle (MG.MSTN+/−) and wild type Mongolian cattle (MG.WT) using a shotgun-based tandem mass tag (TMT) 6-plex labeling method to investigate the regulation mechanism of MSTN on the growth and development of bovine skeletal muscle. A total of 1,950 proteins were identified in MG.MSTN+/− and MG.WT. Compared with MG.WT cattle, a total of 320 differentially expressed proteins were identified in MG.MSTN cattle, including 245 up-regulated differentially expressed proteins and 75 down-regulated differentially expressed proteins. Bioinformatics analysis showed that knockdown of the MSTN gene increased the expression of extracellular matrix and ribosome-related proteins, induced activation of focal adhesion, PI3K-AKT, and Ribosomal pathways. The results of proteomic analysis were verified by muscle tissue Western blot test and in vitro MSTN gene knockdown test, and it was found that knockdown MSTN gene expression could promote the proliferation and myogenic differentiation of bovine skeletal muscle satellite cells (BSMSCs). At the same time, Co-Immunoprecipitation (CO-IP) assay showed that MSTN gene interacted with extracellular matrix related protein type I collagen α 1 (COL1A1), and knocking down the expression of COL1A1 could inhibit the activity of adhesion, PI3K-AKT and ribosome pathway, thus inhibit BSMSCs proliferation. These results suggest that the MSTN gene regulates focal adhesion, PI3K-AKT, and Ribosomal pathway through the COL1A1 gene. In general, this study provides new insights into the regulatory mechanism of MSTN involved in muscle growth and development

Associations between anthropometric indicators and refraction in school-age children during the post-COVID-19 era

PurposeTo explore the associations between anthropometric indicators and refraction in school-aged children in the post-COVID-19 era.MethodsData were collected from 25,644 children aged 7 to 12 years in 48 elementary schools in Tianjin. The comprehensive examination included height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), refraction, and calculation of BMI, with a follow-up visit after 6 months. Myopia was defined as spherical equivalent refraction (SER) ≤-0.50 diopter (D). Bivariate correlation coefficients and multiple linear regression models were used to explore the cross-sectional and longitudinal associations between anthropometric indicators (height, weight, BMI, SBP, and DBP) and refraction.ResultsThe mean changes in height, weight, BMI, SBP, DBP, and SER of the participants were 4.03 ± 2.18 cm, 3.10 ± 2.39 kg, 0.45 ± 1.16 kg/m2, 2.26 ± 14.74 mmHg, 2.18 ± 11.79 mmHg and −0.17 ± 0.51 D, respectively. Overall, height, weight, BMI, SBP, and DBP were all correlated with SER (r = −0.324, r = −0.234, r = −0.121, r = −0.112, r = −0.066, both p < 0.001), and changes in height and weight were correlated with changes in SER (r = −0.034, −0.031, both p < 0.001). Furthermore, multiple linear regression analysis revealed that the association of BMI, SBP, and DBP with SER was significant in myopic children but not in non-myopic children. The association between changes in weight and changes in SER was only present in non-myopic children but not in myopic children.ConclusionHeight and weight were negatively correlated with SER in both cross-sectional analysis and longitudinal changes, indicating that children's height, weight and growth rate may be used as a reference indicator for myopia risk prediction and myopia progression monitoring

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research