Wide Swath Stereo Mapping from Gaofen-1 Wide-Field-View (WFV) Images Using Calibration

The development of Earth observation systems has changed the nature of survey and mapping products, as well as the methods for updating maps. Among optical satellite mapping methods, the multiline array stereo and agile stereo modes are the most common methods for acquiring stereo images. However, differences in temporal resolution and spatial coverage limit their application. In terms of this issue, our study takes advantage of the wide spatial coverage and high revisit frequencies of wide swath images and aims at verifying the feasibility of stereo mapping with the wide swath stereo mode and reaching a reliable stereo accuracy level using calibration. In contrast with classic stereo modes, the wide swath stereo mode is characterized by both a wide spatial coverage and high-temporal resolution and is capable of obtaining a wide range of stereo images over a short period. In this study, Gaofen-1 (GF-1) wide-field-view (WFV) images, with total imaging widths of 800 km, multispectral resolutions of 16 m and revisit periods of four days, are used for wide swath stereo mapping. To acquire a high-accuracy digital surface model (DSM), the nonlinear system distortion in the GF-1 WFV images is detected and compensated for in advance. The elevation accuracy of the wide swath stereo mode of the GF-1 WFV images can be improved from 103 m to 30 m for a DSM with proper calibration, meeting the demands for 1:250,000 scale mapping and rapid topographic map updates and showing improved efficacy for satellite imaging

A ternary PEDOT-TiO2-reduced graphene oxide nanocomposite for supercapacitor applications

A ternary composite of PEDOT was prepared with TiO2 via emulsion polymerization method adjusting various weight ratios of TiO2 to PEDOT and synthesized rGO was then blended with this composite. The FTIR, UV–Vis and XRD analysis displayed characteristic features of PEDOT and TiO2. The morphology of the nano-hybrid structure was additionally investigated by SEM analysis. Pore size and surface area analysis of particles were characterized by BET method. The electrochemical analysis showed that the specific capacitance (Csp) for PEDOT-TiO2-15-rGO was 18.9 F.cm-2 at 0.1 mA g-1 current density

Melamine Impairs Renal and Vascular Function in Rats.

Melamine incident, linked to nephrotoxicity and kidney stone in infants previously exposed to melamine-contaminated milk products, was unprecedentedly grave in China in 2008 as little was known about the mechanistic process leading to renal dysfunction in affected children. This study investigates whether neonatal ingestion of melamine leads to renal and vascular dysfunction in adulthood; and whether ingestion of melamine in pregnant rats leads to renal dysfunction in their offspring. A combination of approaches employed includes functional studies in rat renal arteries, renal blood flow measurement by functional magnetic resonance imaging, assay for pro-inflammatory and fibrotic biomarkers, immunohistochemistry, and detection of plasma and renal melamine. We provide mechanistic evidence showing for the first time that melamine reduces renal blood flow and impairs renal and vascular function associated with overexpression of inflammatory markers, transforming growth factor-β1, bone morphogenic protein 4 and cyclooxygenase-2 in kidney and renal vasculature. Melamine also induces renal inflammation and fibrosis. More importantly, melamine causes nephropathies in offsprings from pregnant rat exposed to melamine during pregnancy, as well as in neonatal rat exposed to melamine afterbirth, thus supporting the clinical observations of kidney stone and acute renal failure in infants consuming melamine-contaminated milk products.This study was supported by the Food and Health Bureau Grant, Hong Kong SAR (MI-BS-12), CUHK Vice Chancellor’s Discretionary Fund and CUHK Investment Scheme B

Regression models for near-infrared measurement of subcutaneous adipose tissue thickness

Obesity is often associated with the risks of diabetes and cardiovascular disease, and there is a need to measure subcutaneous adipose tissue (SAT) thickness for acquiring the distribution of body fat. The present study aimed to develop and evaluate different model-based methods for SAT thickness measurement using an SATmeter developed in our laboratory. Near-infrared signals backscattered from the body surfaces from 40 subjects at 20 body sites each were recorded. Linear regression (LR) and support vector regression (SVR) models were established to predict SAT thickness on different body sites. The measurement accuracy was evaluated by ultrasound, and compared with results from a mechanical skinfold caliper (MSC) and a body composition balance monitor (BCBM). The results showed that both LR- and SVR-based measurement produced better accuracy than MSC and BCBM. It was also concluded that by using regression models specifically designed for certain parts of human body, higher measurement accuracy could be achieved than using a general model for the whole body. Our results demonstrated that the SATmeter is a feasible method, which can be applied at home and in the community due to its portability and convenience

Genomic Validation of 3-Tiered Clinical Subclassification of High-Risk Prostate Cancer

Recent data and National Comprehensive Cancer Network (NCCN) guidelines suggest that high-risk prostate cancer (cT3-4, Gleason score ≥8, or prostate-specific antigen [PSA] >20 ng/mL) is a heterogenous group in terms of long-term patient outcomes. We sought to determine whether subclassification of high-risk prostate cancer based on clinical factors correlates with genomic markers of risk. We identified 3220 patients with NCCN unfavorable intermediate-risk (n = 2000) or high-risk (n = 1220) prostate cancer from a prospective multi-institutional registry cohort. We defined the following subclassification of high-risk prostate cancer based on previously published data: favorable high risk (cT1c, Gleason 6, and PSA >20 ng/mL or cT1c, Gleason 4 + 4 = 8, PSA 20 ng/mL). We used a set of 33 previously developed genomic classifiers, including Decipher, to determine whether high-risk genomic features correlate with clinical subclasses of high-risk prostate cancer. Among those with favorable high-risk, standard high-risk, and very high-risk prostate cancer, 50.4%, 64.2%, and 81.6% had a high-risk Decipher score, respectively (P < .001). Among 32 other genomic signatures, 29 had a similar trend of increasing risk scores across the 3 subclasses of high-risk disease (P < .05 after correction for multiple hypothesis testing). Patients in the 3 subclasses of high-risk disease had a median of 4, 6, and 13 high-risk signatures, respectively. In comparison, among those with unfavorable intermediate-risk prostate cancer, 38.2% had a high-risk Decipher score, and the median number of high-risk signatures was 3. Although NCCN guidelines currently use a 2-tiered system for high-risk prostate cancer, genomic markers of risk correlate with the clinically validated subclassification of high-risk prostate cancer into favorable high-risk, standard high-risk, and very high-risk disease, further confirming the prognostic utility of this 3-tiered stratification

Development and validation of a novel clinical-genomic risk group classification for prostate cancer incorporating genomic and clinicopathologic risk

5000 Background: It is clinically challenging to integrate genomic classifier results that report a continuous numerical risk of recurrence into treatment decisions for prostate cancer (PCa). We aimed to develop a novel clinical-genomic risk system that can readily be incorporated into treatment guidelines for localized PCa. Methods: Four multi-center cohorts (n = 6928 men; 5937 prospective samples and 991 retrospective samples with long-term follow-up) were utilized to identify and validate our clinical-genomic risk system in radical prostatectomy (RP) samples and subsequently in pre-treatment biopsy samples. All patients’ FFPE tissue underwent microarray analysis, and the expression values for 22 prespecified biomarkers that constitute Decipher were extracted. Cumulative incidence curves were constructed to estimate metastasis risk. C-indices were calculated to compare NCCN and CAPRA score to our clinical-genomic system. Results: With a median follow-up of 8 years for men in our RP cohort, the 10-year distant metastasis rates for NCCN low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.8%, 9.4%, 40.1%, and 41.4%, respectively. Our 3-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.7%, 30.7%, and 57.7%, for low, intermediate, and high-risk, which were validated in our pre-treatment biopsy cohort with 10-year rate of distant metastasis of 0%, 30.3%, and 63.2%, respectively. C-indices for the clinical-genomic system (0.84, 95%CI 0.62-0.92) were significantly improved over NCCN (0.71, 95%CI 0.59-0.84) and CAPRA (0.71, 95%CI 0.60-0.81) score. A total of 33.4% of men would be reclassified by the clinical-genomic system, and specifically 17.1%, 41.3%, and 19.4% of men in NCCN low, intermediate and high risk groups would be reclassified by our new system. Conclusions: The use of a readily available genomic classifier in combination with clinicopathologic variables can generate a simple to use 3-tier clinical-genomic risk system that is highly prognostic for distant metastasis, is more accurate than clinical risk, and can be easily incorporated into NCCN guidelines to inform treatment decisions