This study demonstrates that hyperhomocysteinemia is an independent risk factor for SBI, and provides the possibility of reducing the risk for SBI in the MTHFR 677TT genotype by folate supplementation.
This paper proposes a practical calibration solution for estimating the boresight and lever-arm parameters of the sensors mounted on a Mobile Mapping System (MMS). On our MMS devised for conducting the calibration experiment, three network video cameras, one mobile laser scanner, and one Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) were mounted. The geometric relationships between three sensors were solved by the proposed calibration, considering the GNSS/INS as one unit sensor. Our solution basically uses the point cloud generated by a 3-dimensional (3D) terrestrial laser scanner rather than using conventionally obtained 3D ground control features. With the terrestrial laser scanner, accurate and precise reference data could be produced and the plane features corresponding with the sparse mobile laser scanning data could be determined with high precision. Furthermore, corresponding point features could be extracted from the dense terrestrial laser scanning data and the images captured by the video cameras. The parameters of the boresight and the lever-arm were calculated based on the least squares approach and the precision of the boresight and lever-arm could be achieved by 0.1 degrees and 10 mm, respectively.
BackgroundLevodopa treatment in Parkinson's disease (PD) increases in serum homocysteine levels due to its metabolism via catechol O-methyltransferase. Endothelial progenitor cells (EPCs) have the capacity to differentiate into mature endothelial cells and are markers for endothelial functions and cardiovascular risks. Along with traditional vascular risk factors, hyperhomocysteinemia is known to decrease the level of EPCs. In the present study, we hypothesized that that levodopa-induced hyperhomocysteinemia leads to a change in EPC levels.Methodology/Principal FindingsWe prospectively enrolled PD patients who had been prescribed either levodopa/carbidopa (PD-L group, n = 28) or levodopa/carbidopa/COMT inhibitor (PD-LC group, n = 25) for more than 1 year. The number of circulating EPCs was measured by flow cytometry using dual staining of anti-CD34 and anti-KDR antibodies. The EPCs were divided into tertiles based on their distributions and a logistic regression analysis was used to estimate independent predictors of the highest tertile of EPCs. The number of endothelial progenitor cells was significantly decreased in PD-L patients (118±99/mL) compared with either PD-LC patients (269±258/mL, p = 0.007) or controls (206±204/mL, p = 0.012). The level of homocysteine was significantly increased in PD-L patients (14.9±5.3 µmol/L) compared with either PD-LC patients (11.9±3.0 µmol/L, p = 0.028) or controls (11.1±2.5 µmol/L, p = 0.012). The level of homocysteine was negatively correlated with endothelial progenitor cell levels (r = −0.252, p = 0.028) and was an independent predictor of the highest tertile of endothelial progenitor cell levels (OR; 0.749 [95% CI: 0.584–0.961]).Conclusions/SignificanceThese data indicate that a higher consumption of EPC for restoration of endothelial damage may be associated with chronic levodopa treatment in PD patients.
In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24–25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images.
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