Abstract

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Background & aims: Mild cognitive impairment (MCI) patients are at risk of cognitive decline, while elevated serum homocysteine is also associated with cognitive impairment. Thus, older people with MCI and hyperhomocysteinemia may be under greater risk of cognitive decline. We therefore performed a randomized trial of homocysteine-lowering by B vitamins supplementation to prevent cognitive decline in older MCI patients with elevated serum homocysteine. Methods: 279 MCI outpatients aged !65 years with serum homocysteine !10.0 mmol/L were randomly assigned to take either methylcobalamin 500 mg and folic acid 400 mg once daily, or two placebo tablets for 24 months. All subjects were followed up at 12 monthly intervals. The primary outcome was cognitive decline as defined by an increase in clinical dementia rating scale (CDR) sum of boxes (CDR_SOB). The secondary outcomes were global CDR, memory Z score, executive function Z score and Hamilton depression rating scale (HDRS) score. Results: The clinical characteristics between two groups were well matched, except that the supplement group had better executive function. The supplement effectively lowered serum homocysteine (mean 13.9 ± sd 3.5 mmol at baseline to 9.3 ± 2.4 mmol/L at month 24). At month 24, there was no significant group difference in CDR_SOB or any secondary outcomes (mean changes in CDR_SOB 0.36 versus 0.22 in supplement and placebo groups respectively). At month 12, the supplement group significantly improved in executive function and had lower HDRS score (P ¼ 0.004 and 0.012 respectively). Group difference was significant for HDRS, but borderline significant for executive function. (P ¼ 0.01; 0.06 respectively) These effects were not significant at month 24. Subgroup analysis showed that aspirin use had significant interaction with B supplements in CDR_SOB at month 24 (Beta 0.189, P ¼ 0.005). Conclusions: Vitamin B 12 and folic acid supplementation did not reduce cognitive decline in older people with MCI and elevated serum homocysteine, though the cognitive decline over two years in placebo group was small. The supplement led to a significant reduction in depressive symptoms at month 12, though this effect was not sustained. Aspirin use had a negative interaction effect on cognitive functioning with B supplements. Clinical trial registration: Centre for Clinical Research and Biostatistics (CCRB) Clinical Trials Registry: CUHK_CCT00373.

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more recent cohorts had higher levels of frailty than did earlier cohorts. Frailty interventions, coupled with early detection, should be developed to combat the increasing rates of frailty in Hong Kong Chinese.

Smartphones are now frequently used by end-users as the portals to cloud-based services, and smartphones are easily stolen or co-opted by an attacker. Beyond the initial login mechanism, it is highly desirable to re-authenticate endusers who are continuing to access security-critical services and data, whether in the cloud or in the smartphone. But attackers who have gained access to a logged-in smartphone have no incentive to re-authenticate, so this must be done in an automatic, non-bypassable way. Hence, this paper proposes a novel authentication system, iAuth, for implicit, continuous authentication of the end-user based on his or her behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We design a system that gives accurate authentication using machine learning and sensor data from multiple mobile devices. Our system can achieve 92.1% authentication accuracy with negligible system overhead and less than 2% battery consumption.

The growing number of instances of breaches in information security in the last few years has created a compelling case for efforts towards secure electronic systems. Embedded systems, which will be ubiquitously used to capture, store, manipulate, and access data of a sensitive nature, pose several unique and interesting security challenges. Security has been the subject of intensive research in the areas of cryptography, computing, and networking. However, despite these efforts, security is often mis-construed by designers as the hardware or software implementation of specific cryptographic algorithms and security protocols. In reality, it is an entirely new metric that designers should consider throughout the design process, along with other metrics such as cost, performance, and power.This paper is intended to introduce embedded system designers and design tool developers to the challenges involved in designing secure embedded systems. We attempt to provide a unified and holistic view of embedded system security by first analyzing the typical functional security requirements for embedded systems from an end-user perspective. We then identify the implied challenges for embedded system architects, as well as hardware and software designers (e.g., tamper-resistant embedded system design, processing requirements for security, impact of security on battery life for battery-powered systems, etc.). We also survey solution techniques to address these challenges, drawing from both current practice and emerging research, and identify open research problems that will require innovations in embedded system architecture and design methodologies.

Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and activity recognition. In order to leverage these data, automatic knowledge extraction -whereby we map from observations to interpretable states and transitions -must be done at scale. As such, we have seen many recent IoT data sets include annotations with a human expert specifying states, recorded as a set of boundaries and associated labels in a data sequence.ese data can be used to build automatic labeling algorithms that produce labels as an expert would. Here, we refer to human-specified boundaries as breakpoints. Traditional changepoint detection methods only look for statistically-detectable boundaries that are defined as abrupt variations in the generative parameters of a data sequence. However, we observe that breakpoints occur on more subtle boundaries that are non-trivial to detect with these statistical methods. In this work, we propose a new unsupervised approach, based on deep learning, that outperforms existing techniques and learns the more subtle, breakpoint boundaries with a high accuracy. rough extensive experiments on various real-world data sets -including human-activity sensing data, speech signals, and electroencephalogram (EEG) activity traces -we demonstrate the effectiveness of our algorithm for practical applications. Furthermore, we show that our approach achieves significantly be er performance than previous methods.