Next generation high resolution adaptive optics fundus imager

2005 ◽  
Author(s):  
P. Fournier ◽  
G. R. G. Erry ◽  
L. J. Otten ◽  
A. Larichev ◽  
N. Irochnikov
Keyword(s):  
High Resolution ◽  
Adaptive Optics ◽  
Next Generation

scholarly journals W. M. Keck Observatory's next-generation adaptive optics facility

2010 ◽  
Author(s):  
P. Wizinowich ◽  
S. Adkins ◽  
R. Dekany ◽  
D. Gavel ◽  
C. Max ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Next Generation

scholarly journals Microsatellite marker development for the tetraploid Veronica aragonensis (Plantaginaceae) using next-generation sequencing and high-resolution melting analyses

2018 ◽  
Vol 6 (5) ◽  
pp. e01154
Author(s):  
Nélida Padilla-García ◽  
Teresa Malvar-Ferreras ◽  
Josie Lambourdière ◽  
M. Montserrat Martínez-Ortega ◽  
Nathalie Machon
Keyword(s):  
Next Generation Sequencing ◽  
High Resolution ◽  
Microsatellite Marker ◽  
High Resolution Melting ◽  
Marker Development ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals High-Resolution Analysis of the 5′-End Transcriptome Using a Next Generation DNA Sequencer

PLoS ONE ◽  
2009 ◽  
Vol 4 (1) ◽  
pp. e4108 ◽  
Author(s):  
Shin-ichi Hashimoto ◽  
Wei Qu ◽  
Budrul Ahsan ◽  
Katsumi Ogoshi ◽  
Atsushi Sasaki ◽  
...  
Keyword(s):  
High Resolution ◽  
Next Generation ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Dna Sequencer

scholarly journals High Resolution Ancestry Deconvolution for Next Generation Genomic Data

2021 ◽  
Author(s):  
Helgi Hilmarsson ◽  
Arvind S. Kumar ◽  
Richa Rastogi ◽  
Carlos D. Bustamante ◽  
Daniel Mas Montserrat ◽  
...  
Keyword(s):  
High Resolution ◽  
Prediction Models ◽  
Association Studies ◽  
Training Data ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Next Generation ◽  
Genome Data ◽  
Computational Performance ◽  
Genetic Risk Prediction

ABSTRACTAs genome-wide association studies and genetic risk prediction models are extended to globally diverse and admixed cohorts, ancestry deconvolution has become an increasingly important tool. Also known as local ancestry inference (LAI), this technique identifies the ancestry of each region of an individual’s genome, thus permitting downstream analyses to account for genetic effects that vary between ancestries. Since existing LAI methods were developed before the rise of massive, whole genome biobanks, they are computationally burdened by these large next generation datasets. Current LAI algorithms also fail to harness the potential of whole genome sequences, falling well short of the accuracy that such high variant densities can enable. Here we introduce Gnomix, a set of algorithms that address each of these points, achieving higher accuracy and swifter computational performance than any existing LAI method, while also enabling portable models that are particularly useful when training data are not shareable due to privacy or other restrictions. We demonstrate Gnomix (and its swift phase correction counterpart Gnofix) on worldwide whole-genome data from both humans and canids and utilize its high resolution accuracy to identify the location of ancient New World haplotypes in the Xoloitzcuintle, dating back over 100 generations. Code is available at https://github.com/AI-sandbox/gnomix.

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