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Edge computing provides a new platform to support real-time connected vehicle applications. One of the challenges is to ensure communication security among vehicles, roadside devices, and edge servers, while addressing group communication nature and fast changing group membership. This paper presents the design and evaluation of a secure communication scheme for vehicular edge computing applications based on decentralized attribute-based encryption (ABE), which provides flexible data encryption and access control according to attribute-based policies, without requirement for knowing the recipient identities and establishment of a secure communication channel between the sender and recipient. Key-policy ABE and ciphertext-policy ABE are employed to collect sensed data for process and disseminate processed results to the involved entities, respectively. The communication protocols are designed and an experimental prototype considering an edge cloud-assisted collision warning application is implemented for proof of the concept. The evaluation results show that the proposed ABE-based scheme is efficient and feasible.

Yu-Cheng Cheng,1 Yu-Hsuan Li,1,2 Chiann-Yi Hsu,3 I-Te Lee1,4– 6 1Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan; 2Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan; 3Biostatistics Task Force of Taichung Veterans General Hospital, Taichung, Taiwan; 4School of Medicine, National Yang-Ming University, Taipei, Taiwan; 5School of Medicine, Chung Shan Medical University, Taichung, Taiwan; 6College of Science, Tunghai University, Taichung, TaiwanCorrespondence: I-Te Lee Email itlee@vghtc.gov.twPurpose: Carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are tumor-associated antigens. An increased serum level of CEA and CA19-9 separately has been reported in diabetes. In this study, we examined the composite effect of elevated serum levels of both CEA and CA19-9 on subjects with type 2 diabetes and prediabetes.Patients and Methods: A total of 3568 adults who attended a health examination were enrolled into this cross-sectional study. Subjects were grouped into four groups according to the median serum CEA and CA19-9 levels.Results: Subjects with high CEA and high CA19-9 levels had the highest proportions of diabetes (43.9%) and prediabetes (33.04%). There was a statistically significant trend in the proportion of diabetes across the four groups (P < 0.001). Multivariable logistic regression analysis revealed higher risks of type 2 diabetes in subjects with high CEA and low CA19-9 levels (odds ratio [OR] = 2.10, 95% confidence interval [CI]: 1.39– 3.18, P < 0.001) and in those with high CA19-9 and low CEA levels (OR = 2.18, 95% CI: 1.42– 3.34, P < 0.001) than in those with low CEA and low CA19-9 levels; among these four groups, the highest risk of type 2 diabetes was observed in subjects with high CEA and high CA19-9 levels (OR = 2.65, 95% CI: 1.81– 3.88, P < 0.001). The risk of prediabetes was significantly higher only in subjects with high CEA and high CA19-9 levels compared to those with low CEA and low CA19-9 levels (OR = 1.32, 95% CI: 1.08– 1.61, P = 0.006).Conclusion: CEA and CA19-9 had a synergistic ability to increase the risk of type 2 diabetes and prediabetes.Keywords: CA19-9, CEA, diabetes, prediabetes, tumor marker

Man-in-the-Middle (MITM) attacks can significantly compromise the security of the Internet and cloud computing applications, where an attacker intercepts the packets transmitted between the clients and servers over the network to steal confidential information and/or change the packets. It is essential and challenging to detect MITM attacks. In this paper, we build a virtual network testbed to emulate a real-world cloud environment and study the detection of MITM attacks. We consider an MITM detection approach that utilizes network packet analysis and machine learning techniques to measure the changes in the packet Round-Trip Time (RTT) between a client and a server. Specifically, we use the machine learning algorithms in TensorFlow to analyze the RTT data collected on the testbed to determine the detectability of MITM attacks. If the attacker's link speed is much higher than the client's link speed in an access network, e.g. an attacker connecting the network through a wired Ethernet and a normal client connecting the network through a wireless link, it would be difficult to discern the RTT difference with and without the MITM attacks. We are able to deduce a threshold below which the MITM attacks can be detected based on the RTT difference with a certain accuracy. Our experiments show the detectability accuracy becomes lower.

Asian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7×, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with 0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.

Due to the rapid increase in wireless network traffic, especially video traffic, innovative network architectures and algorithms need be developed to reduce congestion and improve the quality of service (QoS). Multi-access edge computing or mobile edge computing (MEC) is a new paradigm that integrates computing and storage capabilities at the edge of the wireless network. In this paper, we design and implement a wireless access network-aware video streaming system based on the MEC concept, called Edge-Controlled Adaptive Streaming (ECAS). ECAS employs in-network video bitrate adaptation to improve data delivery efficiency. In our design, a MEC function intercepts HTTP requests from the client, and a rate adaptation mechanism is employed to decide the best video representation for the client. Updated HTTP requests, with modified video rates based on the adaptation decision, are forwarded to the video server. We also design a resource allocation and streaming bitrate adaptation algorithm to achieve the overall optimization of multiple video streams, with fairness, subject to wireless transmission capacity constraint. This network-assisted adaptive streaming approach allows more accurate estimation of wireless network states and can enhance the QoS of multiple video streams. A prototype is implemented to prove the concept, and the experimental results demonstrate that the proposed scheme significantly improves video streaming performance compared to the de facto standard video streaming technique, Dynamic Adaptive Streaming over HTTP (DASH).

Glaucoma is a multi-factorial blinding disease in which genetic factors play an important role. Elevated intraocular pressure is a highly heritable risk factor for primary open angle glaucoma and currently the only target for glaucoma therapy. Our study helps to better understand underlying genetic and molecular mechanisms that regulate intraocular pressure, and identifies a new candidate gene, Cacna2d1, that modulates intraocular pressure and a promising therapeutic, pregabalin, which binds to CACNA2D1 protein and lowers intraocular pressure significantly. Because our study utilizes a genetically diverse population of mice with known sequence variants, we are able to determine that the intraocular pressure-lowering effect of pregabalin is dependent on the Cacna2d1 haplotype. Using human genome-wide association study (GWAS) data, evidence for association of a CACNA2D1 single-nucleotide polymorphism and primary open angle glaucoma is found. Importantly, these results demonstrate that our systems genetics approach represents an efficient method to identify genetic variation that can guide the selection of therapeutic targets., Elevated intraocular pressure (IOP) is a heritable risk factor for primary open angle glaucoma. Using forward mouse genetics, cell biology, pharmacology and human genetic data, the authors identify CACNA2D1 as an IOP risk gene that can be therapeutically targeted by the drug pregabalin in animal models.

In this paper, well-developed eigenstructure assignment (ESA) and linear exponential quadratic Gaussian and loop transfer recovery (LEQG/LTR) techniques are originally unified and applied for the design of a spinning type satellite with double gimbals single rotor-control moment gyro (DGSR-CMG). By the combination of both ESA and LEQG/LTR control design methods, the control system can simultaneously provide prescribed stability and overcome some limitations in existence for two design techniques. This paper also derives the algorithms, which can take all the time domain, frequency domain, and robust decoupling design techniques into a unified method. Finally, numerical results show that the proposed method is more robust to disturbance, sensor noise, and parameter variations. In addition, the time-domain responses are also better.