Arterial stiffness and obesity as predictors of diabetes: Longitudinal cohort study

BACKGROUND: Previous studies have confirmed the separate effect of arterial stiffness and obesity on type 2 diabetes; however, the joint effect of arterial stiffness and obesity on diabetes onset remains unclear. OBJECTIVE: This study aimed to propose the concept of arterial stiffness obesity phenotype and explore the risk stratification capacity for diabetes. METHODS: This longitudinal cohort study used baseline data of 12,298 participants from Beijing Xiaotangshan Examination Center between 2008 and 2013 and then annually followed them until incident diabetes or 2019. BMI (waist circumference) and brachial-ankle pulse wave velocity were measured to define arterial stiffness abdominal obesity phenotype. The Cox proportional hazard model was used to estimate the hazard ratio (HR) and 95% CI. RESULTS: Of the 12,298 participants, the mean baseline age was 51.2 (SD 13.6) years, and 8448 (68.7%) were male. After a median follow-up of 5.0 (IQR 2.0-8.0) years, 1240 (10.1%) participants developed diabetes. Compared with the ideal vascular function and nonobese group, the highest risk of diabetes was observed in the elevated arterial stiffness and obese group (HR 1.94, 95% CI 1.60-2.35). Those with exclusive arterial stiffness or obesity exhibited a similar risk of diabetes, and the adjusted HRs were 1.63 (95% CI 1.37-1.94) and 1.64 (95% CI 1.32-2.04), respectively. Consistent results were observed in multiple sensitivity analyses, among subgroups of age and fasting glucose level, and alternatively using arterial stiffness abdominal obesity phenotype. CONCLUSIONS: This study proposed the concept of arterial stiffness abdominal obesity phenotype, which could improve the risk stratification and management of diabetes. The clinical significance of arterial stiffness abdominal obesity phenotype needs further validation for other cardiometabolic disorders

Implementation and control of a single-switch high step-up boost DC-DC converter based on a voltage multiplier

This report presents the design, implementation and control of a single-switch high step-up boost converter based on a voltage multiplier. The controllers implemented are the adaptive current-mode controller, the fixed current-mode controller and the sliding-mode controller. Experimental results of each controller operating under different operating conditions are presented to show the features of the controller.Bachelor of Engineering (Electrical and Electronic Engineering

Compressive Subspace Learning Based Wideband Spectrum Sensing for Multiantenna Cognitive Radio

Recently, sub-Nyquist sampling (SNS) based wideband spectrum sensing has emerged as a promising approach for cognitive radios. However, most of existing SNS-based approaches cannot effectively deal with the wireless channel fading due to the lack of space diversity exploitation, which would lead to poor sensing performance. To address the problem, we propose a multi-antenna system, referred to as the multiantenna generalized modulated converter (MAGMC), to realize the SNS, where spatially correlated multiple-input multiple-output (MIMO) channel is considered. Based on the multiantenna system, two compressive subspace learning (CSL) approaches (mCSL and vCSL) are proposed for signal subspace learning, where wideband sectrum sensing is realized based on the signal subspace. Both proposedCSLapproaches exploit space diversity, where the mCSL utilizes an antenna averaging temporal decomposition, and the vCSL (which is formulated based on a vectorization of sample matrix in the mCSL) uses a spatial-temporal joint decomposition. We further establish analytical relationships between eigenvalues of statistical covariance matrices in statistical sense in both multiantenna and single antenna scenarios. Space diversity and superiority over the single antenna scenario for both proposed CSL approaches are analyzed based on the derived analytical relationships. Moreover, the mCSL and vCSL based wideband spectrum sensing algorithms are proposed based on the system model of MAGMC and their computational complexities are given. The proposed CSL based wideband spectrum sensing algorithms can effectively dealwith the wireless channel fading and simulations show the improvement on performance of wideband spectrum sensing over related works

Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances

Sand dunes are one of the most abundant aeolian landforms and play an important role in understanding how aeolian environments evolve. Since the 1970s, remote sensing has enabled large-scale investigations of dunes at comparatively low costs and with temporally continuous observations, which greatly advances our knowledge of aeolian systems. In this context, we provide a review of recent progress in three research topics that have been greatly facilitated by remote sensing techniques. These topics are 1) mapping sand extent and dune types, 2) dune pattern quantification, and 3) monitoring dune dynamics. Sand dune mapping was the early focus of aeolian geomorphologists, and continued progress has been made in refining classification schemes and developing advanced classification techniques. Dune pattern quantification can be resolved in two geomorphometric approaches, and a careful design that takes into consideration the image resolution, the data quality, and the uncertainty in dune discretization is necessary. Dune dynamics typically exhibit as dune migration, dune interactions, and dune fine-scale morphodynamics. The wide application of change detection algorithms, especially COSI-Corr, provides great insights into dune migration, while the exploration of dune interactions is still in its infancy. Future directions are highlighted in four key areas: unifying classification schemes regarding dune morphology, developing methods that are capable of recognizing diverse dune forms at large spatial extents, designing modularized workflows and more complex matching rules to quantify dune migration, and improving quantitative analysis of dune interactions

Large-scale urban functional zone mapping by integrating remote sensing images and open social data

Urban functional zones (UFZs) are important for urban sustainability and urban planning and management, but UFZ maps are rarely available and up-to-date in developing countries due to frequent economic and human activities and rapid changes in UFZs. Current methods have focused on mapping UFZs in a small area with either remote sensing images or open social data, but large-scale UFZ mapping integrating these two types of data is still not be applied. In this study, a novel approach to mapping large-scale UFZs by integrating remote sensing images (RSIs) and open social data is proposed. First, a context-enabled image segmentation method is improved to generate UFZ units by incorporating road vectors. Second, the segmented UFZs are classified by coupling Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). In the classification framework, physical features from RSIs and social attributes from POI (Point of Interest) data are integrated. A case study of Beijing was performed to evaluate the proposed method, and an overall accuracy of 85.9% was achieved. The experimental results demonstrate that the presented method can provide fine-grained UFZs, and the fusion strategy of RSIs and POI data can distinguish urban functions accurately. The proposed method appears to be promising and practical for large-scale UFZ mapping

Electrochemical Biosensors for Circulating Tumor DNA Detection

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer-screening. Consequently, the detection of ctDNA in liquid biopsy is gaining much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from industry. However, it is difficult to achieve low-cost, real-time, and portable measurement of ctDNA in traditional gene-detection technology. Electrochemical biosensors have become a highly promising solution to ctDNA detection due to their unique advantages such as high sensitivity, high specificity, low cost, and good portability. Therefore, this review aims to discuss the latest developments in biosensors for minimally invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, designs of electrodes, detection strategies, preparation of samples, and figures of merit, sorted by type of electrode surface recognition elements. The development of biosensors for the Internet of Things, point-of-care testing, big data, and big health is analyzed, with a focus on their portable, real-time, and non-destructive characteristics