scholarly journals Database support for efficiently maintaining derived data

1996 ◽  
pp. 223-240 ◽  
Author(s):  
Brad Adelberg ◽  
Ben Kao ◽  
Hector Garcia-Molina
Keyword(s):  
Database Support ◽  
Derived Data

Advanced type parameter features

2018 ◽  
Author(s):  
Michael Metcalf ◽  
John Reid ◽  
Malcolm Cohen
Keyword(s):  
Data Types ◽  
Derived Data ◽  
Type Parameter

The advanced type parameter features consist of type parameter enquiry and the ability to parameterize derived-data types.

scholarly journals Deriving Tree Size Distributions of Tropical Forests from Lidar

2021 ◽  
Vol 13 (1) ◽  
pp. 131
Author(s):  
Franziska Taubert ◽  
Rico Fischer ◽  
Nikolai Knapp ◽  
Andreas Huth
Keyword(s):  
Size Distribution ◽  
Spatial Scales ◽  
Basal Area ◽  
Tree Height ◽  
Tree Size ◽  
Diameter Distribution ◽  
Forest Inventories ◽  
Allometric Relations ◽  
Lidar Measurements ◽  
Derived Data

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

scholarly journals Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 453
Author(s):  
Vincenza Granata ◽  
Roberta Fusco ◽  
Antonio Avallone ◽  
Alfonso De Stefano ◽  
Alessandro Ottaiano ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Magnetic Resonance ◽  
Liver Metastases ◽  
Univariate Analysis ◽  
Features Selection ◽  
Ras Mutation ◽  
Mutation Status ◽  
Contrast Enhanced ◽  
Texture Parameters ◽  
Derived Data

Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36–80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.

Predicting alpha diversity of African rain forests: models based on climate and satellite-derived data do not perform better than a purely spatial model

2011 ◽  
Vol 38 (6) ◽  
pp. 1164-1176 ◽  
Author(s):  
Ingrid Parmentier ◽  
Ryan J. Harrigan ◽  
Wolfgang Buermann ◽  
Edward T. A. Mitchard ◽  
Sassan Saatchi ◽  
...  
Keyword(s):  
Spatial Model ◽  
Alpha Diversity ◽  
Rain Forests ◽  
Derived Data ◽  
Better Than

scholarly journals Exon Splice Enhancer Mutation (GH-E32A) Causes Autosomal Dominant Growth Hormone Deficiency

2007 ◽  
Vol 92 (11) ◽  
pp. 4427-4435 ◽  
Author(s):  
Vibor Petkovic ◽  
Didier Lochmatter ◽  
James Turton ◽  
Peter E. Clayton ◽  
Peter J. Trainer ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Autosomal Dominant ◽  
Secretory Granules ◽  
Cellular Level ◽  
Hormone Deficiency ◽  
Mutant Form ◽  
Splice Enhancer ◽  
Exon Splice ◽  
Microscopy Analysis ◽  
Derived Data

Abstract Context and Objective: Alteration of exon splice enhancers (ESE) may cause autosomal dominant GH deficiency (IGHD II). Disruption analysis of a (GAA) (n) ESE motif within exon 3 by introducing single-base mutations has shown that single nucleotide mutations within ESE1 affect pre-mRNA splicing. Design, Setting, and Patients: Confirming the laboratory-derived data, a heterozygous splice enhancer mutation in exon 3 (exon 3 + 2 A→C) coding for GH-E32A mutation of the GH-1 gene was found in two independent pedigrees, causing familial IGHD II. Because different ESE mutations have a variable impact on splicing of exon 3 of GH and therefore on the expression of the 17.5-kDa GH mutant form, the GH-E32A was studied at the cellular level. Interventions and Results: The splicing of GH-E32A, assessed at the protein level, produced significantly increased amounts of 17.5-kDa GH isoform (55% of total GH protein) when compared with the wt-GH. AtT-20 cells coexpressing both wt-GH and GH-E32A presented a significant reduction in cell proliferation as well as GH production after forskolin stimulation when compared with the cells expressing wt-GH. These results were complemented with confocal microscopy analysis, which revealed a significant reduction of the GH-E32A-derived isoform colocalized with secretory granules, compared with wt-GH. Conclusion: GH-E32A mutation found within ESE1 weakens recognition of exon 3 directly, and therefore, an increased production of the exon 3-skipped 17.5-kDa GH isoform in relation to the 22-kDa, wt-GH isoform was found. The GH-E32A mutant altered stimulated GH production as well as cell proliferation, causing IGHD II.

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