Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor

Systematic Comparison of Power Corridor Classification Methods from ALS Point Clouds

Study examines factors that affect power corridor classification using LiDAR (light detection and ranging) point clouds, including the class distribution, feature selection, classifier type and neighborhood radius for classification feature extraction

Power Pylon Reconstruction Based on Abstract Template Structures Using Airborne LiDAR Data

Paper describes the development and testing of a three-dimensional reconstruction method using airborne LiDAR (Light Detection And Ranging) point cloud

Metabolically Healthy Obesity and High Carotid Intima-Media Thickness in Children and Adolescents: International Childhood Vascular Structure Evaluation Consortium

OBJECTIVE It has been argued that metabolically healthy obesity (MHO) does not increase cardiovascular disease (CVD) risk. This study examines the association of MHO with carotid intima-media thickness (cIMT), a proxy of CVD risk, in children and adolescents. RESEARCH DESIGN AND METHODS Data were available for 3,497 children and adolescents aged 6–17 years from five population-based cross-sectional studies in Brazil, China, Greece, Italy, and Spain. Weight status categories (normal, overweight, and obese) were defined using BMI cutoffs from the International Obesity Task Force. Metabolic status (defined as "healthy" [no risk factors] or "unhealthy" [one or more risk factors]) was based on four CVD risk factors: elevated blood pressure, elevated triglyceride levels, reduced HDL cholesterol, and elevated fasting glucose. High cIMT was defined as cIMT ≥90th percentile for sex, age, and study population. Logistic regression model was used to examine the association of weight and metabolic status with high cIMT, with adjustment for sex, age, race/ethnicity, and study center. RESULTS In comparison with metabolically healthy normal weight, odds ratios (ORs) for high cIMT were 2.29 (95% CI 1.58–3.32) for metabolically healthy overweight and 3.91 (2.46–6.21) for MHO. ORs for high cIMT were 1.44 (1.03–2.02) for unhealthy normal weight, 3.49 (2.51–4.85) for unhealthy overweight, and 6.96 (5.05–9.61) for unhealthy obesity. CONCLUSIONS Among children and adolescents, cIMT was higher for both MHO and metabolically healthy overweight compared with metabolically healthy normal weight. Our findings reinforce the need for weight control in children and adolescents irrespective of their metabolic status

A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods

Estimation of the fraction of absorbed photosynthetically active radiation (fPAR) in maize canopies using LiDAR data and hyperspectral imagery.

Accurate estimation of the fraction of absorbed photosynthetically active radiation (fPAR) for maize canopies are important for maize growth monitoring and yield estimation. The goal of this study is to explore the potential of using airborne LiDAR and hyperspectral data to better estimate maize fPAR. This study focuses on estimating maize fPAR from (1) height and coverage metrics derived from airborne LiDAR point cloud data; (2) vegetation indices derived from hyperspectral imagery; and (3) a combination of these metrics. Pearson correlation analyses were conducted to evaluate the relationships among LiDAR metrics, hyperspectral metrics, and field-measured fPAR values. Then, multiple linear regression (MLR) models were developed using these metrics. Results showed that (1) LiDAR height and coverage metrics provided good explanatory power (i.e., R2 = 0.81); (2) hyperspectral vegetation indices provided moderate interpretability (i.e., R2 = 0.50); and (3) the combination of LiDAR metrics and hyperspectral metrics improved the LiDAR model (i.e., R2 = 0.88). These results indicate that LiDAR model seems to offer a reliable method for estimating maize fPAR at a high spatial resolution and it can be used for farmland management. Combining LiDAR and hyperspectral metrics led to better performance of maize fPAR estimation than LiDAR or hyperspectral metrics alone, which means that maize fPAR retrieval can benefit from the complementary nature of LiDAR-detected canopy structure characteristics and hyperspectral-captured vegetation spectral information

Estimating FPAR of Maize Canopy Using Airborne Discrete-Return LiDAR Data

This article tests a method to estimate the fraction of absorbed photosynthetically active radiation (FPAR) of maize from airborne discrete-return LiDAR data

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H C V of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H C V combined with H m e a n performed better than one LiDAR-derived metric alone in biomass estimation (R2 = 0.835, RMSE = 374.655 g/m2, RMSECV = 393.573 g/m2). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R2 = 0.883, RMSE = 321.092 g/m2, RMSECV = 337.653 g/m2) compared with LiDAR or hyperspectral data alone