A Preliminary Study of the Relationship Between Built Environment of Open Space and Cognitive Health of Older People

Many older people are facing various risks of cognitive impairment, while outdoor activities in open spaces may be helpful for their cognitive health.  However, the effect of open spaces on cognitive health is unclear.  This study aims to investigate the relationships between the cognitive health of older people and the built environment of open spaces.  A questionnaire survey of 60 older people aged 60 and above was conducted.  Results identified three major components of the built environment of open spaces, namely, planning, supporting facilities, and building services.  According to the correlation and regression analysis, it is revealed that 8 BEOS items, including green ratio, a width of the pathway, maintenance of the whole garden, the color of green space, diversity of plants, location, and font of signage, artificial light of sitting area were positively related to memory, while only the size was negatively associated with memory.  Only the green ratio could positively predict the concentration.  The judgment was positively influenced by the green ratio, width of pathways, maintenance of the whole garden, color of green space and diversity of plants.  A BEOS – cognitive health model for older people was built in this study.  The results highlighted the importance of plants for cognitive health.  Several recommendations, such as not-so-large sizes and diverse plants with vivid colors and signages with big fonts, etc., were proposed to improve the built environment of the open spaces and support the declining cognitive health of older people

Two-person Graph Convolutional Network for Skeleton-based Human Interaction Recognition

Graph convolutional networks (GCNs) have been the predominant methods in skeleton-based human action recognition, including human-human interaction recognition. However, when dealing with interaction sequences, current GCN-based methods simply split the two-person skeleton into two discrete graphs and perform graph convolution separately as done for single-person action classification. Such operations ignore rich interactive information and hinder effective spatial inter-body relationship modeling. To overcome the above shortcoming, we introduce a novel unified two-person graph to represent inter-body and intra-body correlations between joints. Experiments show accuracy improvements in recognizing both interactions and individual actions when utilizing the proposed two-person graph topology. In addition, We design several graph labeling strategies to supervise the model to learn discriminant spatial-temporal interactive features. Finally, we propose a two-person graph convolutional network (2P-GCN). Our model achieves state-of-the-art results on four benchmarks of three interaction datasets: SBU, interaction subsets of NTU-RGB+D and NTU-RGB+D 120

Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas

BackgroundLow-grade gliomas (LGG) are one of the most prevalent types of brain cancers. The efficacy of immunotherapy in LGG is limited compared to other cancers. Immunosuppression in the tumor microenvironment (TME) of LGG is one of the main reasons for the low efficacy of immunotherapy. Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. However, their role in the TME of LGG has not been investigated. This study aimed to construct a risk model based on these TPRs and to detect the significance of immunotypes in predicting LGG prognosis and immunotherapy efficacy.MethodsA total of 688 LGGs and 202 normal brain tissues were extracted from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases. The NMF R package was used to identify TRP-related subtypes. The TPR prognostic model was established using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival of LGG samples.ResultsThe Subtype 2 patients had worse survival outcomes, suppressed immune function, and higher immune cell infiltration. A risk regression model consisting of 14 TPRs was established, and its performance was validated in CGGA325 cohorts. The low-risk group exhibited better overall survival, immune microenvironment, and immunotherapy response, as determined via the TIDE algorithm, indicating that increasing the level of immune infiltration can effectively improve the response to immunotherapy in the low-risk group. The risk score was determined to be an independent hazard factor (p<0.001) although other clinical features (age, sex, grade, IDH status, 1p19q codel status, MGMT status, and accepted radiotherapy) were considered. Lastly, high-risk groups in both cohorts revealed optimal drug responses to rapamycin, paclitaxel, JW-7-52-1, and bortezomib.ConclusionsOur study identified two distinct TPR subtypes and built a TPR signature to elucidate the characteristics of T cell proliferation in LGG and its association with immune status and prognosis. These findings shed light on possible immunotherapeutic strategies for LGGs

Family association study between INSR gene polymorphisms and PCOS in Han Chinese

<p>Abstract</p> <p>Background</p> <p>Polycystic ovary syndrome (PCOS) is a complex disease having both genetic and environmental components. Candidate genes with insulin metabolism have been hypothesized to be involved in the etiology of this syndrome. In the present study, we investigated the genetic association between polymorphisms in the insulin receptor (INSR) gene and PCOS.</p> <p>Methods</p> <p>A total of 260 family trios were recruited and performed a family-based analysis to assess linkage and association between four single nucleotide polymorphisms (SNPs) (rs1799817, rs2059807, rs8108622 and rs10500204) of INSR gene and PCOS.</p> <p>Results</p> <p>Using the transmission disequilibrium test (TDT), we failed to find that rs1799817 (p = 0.486), rs2059807 (p = 0.195), rs8108622 (p = 0.866) and rs10500204 (p = 1.0) were significantly overtransmitted to PCOS offspring from their parents.</p> <p>Conclusion</p> <p>No significant evidence of association or linkage was found in the four tested markers, indicating that our family samples did not support susceptibility of the INSR gene to PCOS.</p

A Pull Model IPv6 Duplicate Address Detection

Abstract-In IPv6 network, before configuring any address, a node must perform Duplicate Address Detection (DAD) to ensure the address is unique on link. However, original DAD is unreliable and vulnerable. In this article, a pull model DAD is designed, which achieves improvements both in reliability and security through changing the solicitation model. Comparing with SEcure Neighbor Discovery (SEND), this proposal has advantage in lightweight overhead and flexibility of address generation. Through evaluation, it is found to be feasible and cost effective

Cloud services for children early development via wearable devices

Voice4Baby is a multi-disciplinary project which aims at facilitating the language learning environment of babies. This is achieved by designing a comprehensive product to record and analyze human voices around babies. Interactions between the family and the product are best supported with a cloud platform connecting mobile devices, wearable devices and cloud server. At initial stage, the heavy development at cloud server was based on Django, a content-centric web framework. As project progresses, some research challenges emerged in voice separation and recognition. In previous research, voice separation and recognition were researched and implemented separately; however, in this seamless processing, separation and recognition have to be incorporated together to produce better performance. Last stage of the project included sentiment analysis at Django, recording function and Bluetooth communication at Raspberry-Pi. The intense development and research solution have resulted in seamless workflow and functional effectiveness. However, there is more room for improvement in the precision of separation and recognition algorithms. Overall, the project has met the design requirement and project objective.Bachelor of Engineering (Computer Engineering

Secure and Smartphone-Assisted Reprogramming for Wireless Sensor Networks Based on Visible Light Communication

During the period of over-the-air reprogramming, sensor nodes are easy to eavesdrop and even controlled by unauthorized person. That reminds us that security is key issue for over-the-air reprogramming. Most of previous studies discussed this problem from the aspect of data encryption, but give little consideration to the physical level. In this paper, we attempt to improve the security of reprogramming by changing the physical-level communication mode. We apply unidirectional Visible Light Communication (VLC) to the over-the-air reprogramming and use Commercial Off-The-Shelf device such as smartphone and sensor node to improve applicability. However, the unstable light source and low-cost light sensor make the procedure of reprogramming difficult. For this end, we put forward a novel reprogramming approach named ReVLC, which is twofold: firstly, we design a code block mechanism based on function similarity to reduce transmitting code. Secondly, we use compressing representation to optimize the Dual Header-Pulse Interval Modulation (DH-PIM) to save transmission time. The experiment results illustrate the effectiveness of ReVLC at the cost of extra 49.1% energy overhead compared with a traditional reprogramming approach