Fast characterization of multiplexed single-electron pumps with machine learning

We present an efficient machine learning based automated framework for the fast tuning of single-electron pump devices into current quantization regimes. It uses a sparse measurement approach based on an iterative active learning algorithm to take targeted measurements in the gate voltage parameter space. When compared to conventional parameter scans, our automated framework allows us to decrease the number of measurement points by about an order of magnitude. This corresponds to an eight-fold decrease in the time required to determine quantization errors, which are estimated via an exponential extrapolation of the first current plateau embedded into the algorithm. We show the robustness of the framework by characterizing 28 individual devices arranged in a GaAs/AlGaAs multiplexer array, which we use to identify a subset of devices suitable for parallel operation at communal gate voltages. The method opens up the possibility to efficiently scale the characterization of such multiplexed devices to a large number of pumps

Fast characterization of multiplexed single-electron pumps with machine learning

We present an efficient machine learning based automated framework for the fast tuning of single-electron pump devices into current quantization regimes. It uses a sparse measurement approach based on an iterative active learning algorithm to take targeted measurements in the gate voltage parameter space. When compared to conventional parameter scans, our automated framework allows us to decrease the number of measurement points by about an order of magnitude. This corresponds to an eightfold decrease in the time required to determine quantization errors, which are estimated via an exponential extrapolation of the first current plateau embedded into the algorithm. We show the robustness of the framework by characterizing 28 individual devices arranged in a GaAs/AlGaAs multiplexer array, which we use to identify a subset of devices suitable for parallel operation at communal gate voltages. The method opens up the possibility to efficiently scale the characterization of such multiplexed devices to a large number of pumps

The association of elevated HDL levels with carotid atherosclerosis in middle-aged women with untreated essential hypertension

High-density lipoprotein cholesterol (HDL-C), a negative risk factor, is positively associated with a decreased risk of coronary heart disease. We investigated the association between high HDL-C levels and target organ damage (TOD) in never treated women with hypertension. We measured HDL-C levels in 117 women followed by estimation of TODs, that is, pulse wave velocity, microalbuminuria, left ventricular mass index, coronary flow reserve, and carotid intima-media thickness (cIMT). Women were divided into 2 groups (HDLH and HDLL), regarding HDL-C quartiles (upper quartile vs the first 3 lower quartiles). In HDLH group (HDL ≥70 mg/dL), cIMT was nonindependently, negatively related to HDL-C (ρ = -.42, P <.05). Using receiver -operating characteristic curve (ROC) analysis in the HDLH group, we concluded that the cutoff value of HDL ≥76.5 mg/dL moderately predicted the absence of carotid atherosclerosis (area under the curve: 0.77, P =.02; confidence interval: 0.57-0.97; sensitivity 73% and specificity 67%). Increased HDL-C may predict the absence of carotid atherosclerosis in middle-age women with untreated essential hypertension and consequently contribute to total cardiovascular risk estimation and treatment planning. © 2015 SAGE Publications

Severity of Alopecia Predicts Coronary Changes and Arterial Stiffness in Untreated Hypertensive Men

An association between androgenic alopecia (AGA), coronary artery disease, and hypertension has been reported in previous epidemiological studies. The authors evaluated the relationship of target organ damage caused by hypertension with AGA in 101 newly diagnosed and untreated hypertension men with mild to moderate AGA (AGAm), severe AGA (AGAs), and non-AGA. Pulse wave velocity (PWV), office and 24-hour pulse pressure (PP), carotid intima-media thickness (IMT), left ventricular hypertrophy (LVH), coronary flow reserve (CFRd), and AGA severity by Hamilton-Norwood scale were estimated. CFRd was significantly impaired in AGAs patients compared with AGAm (P=.007) and non-AGA patients (P=.02). No differences were found within groups regarding PWV, PP, IMT, and LVH. AGA severity was related to CFRd (independently) and PP while AGA duration and age of onset were related to CFRd and PP, respectively. The authors conclude that impaired coronary microcirculation and aortic stiffness might precede the appearance of significant stenotic coronary lesions in hypertensive patients with severe AGA. In addition, hypertensive patients with severe and early AGA onset seem to be exposed to an augmented cardiovascular risk. ©2016 Wiley Periodicals, Inc