Novel PAX9 compound heterozygous variants in a Chinese family with non-syndromic oligodontia and genotype-phenotype analysis of PAX9 variants

Studies have reported that >91.9% of non-syndromic tooth agenesis cases are caused by seven pathogenic genes. Objective: To report novel heterozygous PAX9 variants in a Chinese family with non-syndromic oligodontia and summarize the reported genotype-phenotype relationship of PAX9 variants. Methodology: We recruited 28 patients with non-syndromic oligodontia who were admitted to the Hospital of Stomatology Hebei Medical University (China) from 2018 to 2021. Peripheral blood was collected from the probands and their core family members for whole-exome sequencing (WES) and variants were verified by Sanger sequencing. Bioinformatics tools were used to predict the pathogenicity of the variants. SWISS-MODEL homology modeling was used to analyze the three-dimensional structural changes of variant proteins. We also analyzed the genotype-phenotype relationships of PAX9 variants. Results: We identified novel compound heterozygous PAX9 variants (reference sequence NM_001372076.1) in a Chinese family with non-syndromic oligodontia: a new missense variant c.1010C>A (p.T337K) in exon 4 and a new frameshift variant c.330_331insGT (p.D113Afs*9) in exon 2, which was identified as the pathogenic variant in this family. This discovery expands the known variant spectrum of PAX9; then, we summarized the phenotypes of non-syndromic oligodontia with PAX9 variants. Conclusion: We found that PAX9 variants commonly lead to loss of the second molars

A Review on Cloud Computing: Design Challenges in Architecture and Security

Cloud computing is becoming a powerful network architecture to perform large-scale and complex computing.In this paper, we will comprehensively survey the concepts and architecture of cloud computing, as well as its security and privacy issues. We will compare different cloud models, trust/reputation models and privacy-preservation schemes. Their pros and cons are discussed for each cloud computing security and architecture strategy

Novel online data allocation for hybrid memories on tele-health systems

[EN] The developments of wearable devices such as Body Sensor Networks (BSNs) have greatly improved the capability of tele-health industry. Large amount of data will be collected from every local BSN in real-time. These data is processed by embedded systems including smart phones and tablets. After that, the data will be transferred to distributed storage systems for further processing. Traditional on-chip SRAMs cause critical power leakage issues and occupy relatively large chip areas. Therefore, hybrid memories, which combine volatile memories with non-volatile memories, are widely adopted in reducing the latency and energy cost on multi-core systems. However, most of the current works are about static data allocation for hybrid memories. Those mechanisms cannot achieve better data placement in real-time. Hence, we propose online data allocation for hybrid memories on embedded tele-health systems. In this paper, we present dynamic programming and heuristic approaches. Considering the difference between profiled data access and actual data access, the proposed algorithms use a feedback mechanism to improve the accuracy of data allocation during runtime. Experimental results demonstrate that, compared to greedy approaches, the proposed algorithms achieve 20%-40% performance improvement based on different benchmarks. (C) 2016 Elsevier B.V. All rights reserved.This work is supported by NSF CNS-1457506 and NSF CNS-1359557.Chen, L.; Qiu, M.; Dai, W.; Hassan Mohamed, H. (2017). Novel online data allocation for hybrid memories on tele-health systems. Microprocessors and Microsystems. 52:391-400. https://doi.org/10.1016/j.micpro.2016.08.003S3914005

Investigating the Impact of Perceived Micro-Level Neighborhood Characteristics on Housing Prices in Shanghai

It is widely accepted that houses in better-designed neighborhoods are found to enjoy a price premium. Prior studies have mainly examined the impact of macro-level neighborhood attributes (e.g., park accessibility using land use data) on housing prices. More recently, research has investigated the micro-level features using street view imagery (SVI) data, though scholars limited the scope to objective indicators such as the green view index and sky view index. The role of subjectively measured street qualities is less discussed due to the lack of large-scale perception data. To provide better explanations of whether and how the micro-level neighborhood environment affects housing prices, this article introduces a framework to collect designers’ perceptions on five subjective urban design perceptions from pairwise SVI rankings in Shanghai with an online visual survey and further predicted through machine learning (ML) algorithms. We also extracted ten important objective features from the scenes. The predictive power of micro-level neighborhood street perceptions (subjective perceptions and objective features) on housing prices was investigated using the hedonic price model (HPM) through ordinary least squares (OLS) and spatial regression, which considers spatial dependence. The findings prove the significance of the value of perceived qualities of the neighborhoods. It reveals that both objective perceived features and subjective perceptions significantly contribute to housing prices; while the objective features show more collective strengths, individual subjective perceptions have more explanatory power, and we argue that these two measures can complement each other. This study provides an important reference for decision makers when selecting street quality indicators to inform city planning, urban design, and community and housing development plans

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