Fuzzy logic-based implicit authentication for mobile access control

In order to address the increasing compromise of user privacy on mobile devices, a Fuzzy Logic based implicit authentication scheme is proposed in this paper. The proposed scheme computes an aggregate score based on selected features and a threshold in real-time based on current and historic data depicting user routine. The tuned fuzzy system is then applied to the aggregated score and the threshold to determine the trust level of the current user. The proposed fuzzy-integrated implicit authentication scheme is designed to: operate adaptively and completely in the background, require minimal training period, enable high system accuracy while provide timely detection of abnormal activity. In this paper, we explore Fuzzy Logic based authentication in depth. Gaussian and triangle-based membership functions are investigated and compared using real data over several weeks from different Android phone users. The presented results show that our proposed Fuzzy Logic approach is a highly effective, and viable scheme for lightweight real-time implicit authentication on mobile devices.

Development and Evaluation of a Computer-Based, Self-Management Tool for People Recently Diagnosed with Type 2 Diabetes

Aim. The purpose of this study was to develop and evaluate a computer-based, dietary, and physical activity self-management program for people recently diagnosed with type 2 diabetes. 
Methods. The computer-based program was developed in conjunction with the target group and evaluated in a 12-week randomised controlled trial (RCT). Participants were randomised to the intervention (computer-program) or control group (usual care). Primary outcomes were diabetes knowledge and goal setting (ADKnowl questionnaire, Diabetes Obstacles Questionnaire (DOQ)) measured at baseline and week 12. User feedback on the program was obtained via a questionnaire and focus groups. Results. Seventy participants completed the 12-week RCT (32 intervention, 38 control, mean age 59 (SD) years). After completion there was a significant between-group difference in the “knowledge and beliefs scale” of the DOQ. Two-thirds of the intervention group rated the program as either good or very good, 92% would recommend the program to others, and 96% agreed that the information within the program was clear and easy to understand. 
Conclusions. The computer-program resulted in a small but statistically significant improvement in diet-related knowledge and user satisfaction was high. With some further development, this computer-based educational tool may be a useful adjunct to diabetes self-management.

HDL-C and HDL-C/ApoA-I predict long-term progression of glycemia in established type 2 diabetes

OBJECTIVE: Low HDL cholesterol (HDL-C) and small HDL particle size may directly promote hyperglycemia. We evaluated associations of HDL-C, apolipoprotein A-I (apoA-I), and HDL-C/apoA-I with insulin secretion, insulin resistance, HbA1c, and long-term glycemic deterioration, reflected by initiation of pharmacologic glucose control.

RESEARCH DESIGN AND METHODS: The 5-year Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study followed 9,795 type 2 diabetic subjects. We calculated baseline associations of fasting HDL-C, apoA-I, and HDL-C/apoA-I with HbA1c and, in those not taking exogenous insulin (n = 8,271), with estimated β-cell function (homeostasis model assessment of β-cell function [HOMA-B]) and insulin resistance (HOMA-IR). Among the 2,608 subjects prescribed lifestyle only, Cox proportional hazards analysis evaluated associations of HDL-C, apoA-I, and HDL-C/apoA-I with subsequent initiation of oral hypoglycemic agents (OHAs) or insulin.

RESULTS: Adjusted for age and sex, baseline HDL-C, apoA-I, and HDL-C/apoA-I were inversely associated with HOMA-IR (r = -0.233, -0.134, and -0.230; all P < 0.001; n = 8,271) but not related to HbA1c (all P > 0.05; n = 9,795). ApoA-I was also inversely associated with HOMA-B (r = -0.063; P = 0.002; n = 8,271) adjusted for age, sex, and HOMA-IR. Prospectively, lower baseline HDL-C and HDL-C/apoA-I levels predicted greater uptake (per 1-SD lower: hazard ratio [HR] 1.13 [CI 1.07-1.19], P < 0.001; and HR 1.16 [CI 1.10-1.23], P < 0.001, respectively) and earlier uptake (median 12.9 and 24.0 months, respectively, for quartile 1 vs. quartile 4; both P < 0.01) of OHAs and insulin, with no difference in HbA1c thresholds for initiation (P = 0.87 and P = 0.81). Controlling for HOMA-IR and triglycerides lessened both associations, but HDL-C/apoA-I remained significant.

CONCLUSIONS: HDL-C, apoA-I, and HDL-C/apoA-I were associated with concurrent insulin resistance but not HbA1c. However, lower HDL-C and HDL-C/apoA-I predicted greater and earlier need for pharmacologic glucose control.

Alleles that increase risk for type 2 diabetes mellitus are not associated with increased risk for Alzheimer's disease

Although epidemiological studies suggest that type 2 diabetes mellitus (T2DM) increases the risk of late-onset Alzheimer's disease (LOAD), the biological basis of this relationship is not well understood. The aim of this study was to examine the genetic comorbidity between the 2 disorders and to investigate whether genetic liability to T2DM, estimated by a genotype risk scores based on T2DM associated loci, is associated with increased risk of LOAD. This study was performed in 2 stages. In stage 1, we combined genotypes for the top 15 T2DM-associated polymorphisms drawn from approximately 3000 individuals (1349 cases and 1351 control subjects) with extracted and/or imputed data from 6 genome-wide studies (>10,000 individuals; 4507 cases, 2183 controls, 4989 population controls) to form a genotype risk score and examined if this was associated with increased LOAD risk in a combined meta-analysis. In stage 2, we investigated the association of LOAD with an expanded T2DM score made of 45 well-established variants drawn from the 6 genome-wide studies. Results were combined in a meta-analysis. Both stage 1 and stage 2 T2DM risk scores were not associated with LOAD risk (odds ratio = 0.988; 95% confidence interval, 0.972-1.004; p = 0.144 and odds ratio = 0.993; 95% confidence interval, 0.983-1.003; p = 0.149 per allele, respectively). Contrary to expectation, genotype risk scores based on established T2DM candidates were not associated with increased risk of LOAD. The observed epidemiological associations between T2DM and LOAD could therefore be a consequence of secondary disease processes, pleiotropic mechanisms, and/or common environmental risk factors. Future work should focus on well-characterized longitudinal cohorts with extensive phenotypic and genetic data relevant to both LOAD and T2DM.