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NOISYmputer: genotype imputation in bi-parental populations for noisy low-coverage next-generation sequencing data
[article]
2019
bioRxiv
pre-print
This approach allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints, and minimize mapping intervals for quantitative-trait locus analysis ...
We compare its performance with Tassel-FSFHap, LB-Impute, and Genotype-Corrector using simulated data and three real datasets: a rice single seed descent (SSD) population genotyped by genotyping by sequencing ...
We found Genotype-Corrector to be the least accurate of the four methods, even for high-quality GBS data. ...
doi:10.1101/658237
fatcat:zi2t6p7j3nddzpwpvpzefpbk34
Score-Based Generative Modeling through Stochastic Differential Equations
[article]
2021
arXiv
pre-print
In particular, we introduce a predictor-corrector framework to correct errors in the evolution of the discretized reverse-time SDE. ...
transforms the prior distribution back into the data distribution by slowly removing the noise. ...
Yang Song was partially supported by the Apple PhD Fellowship in AI/ML. ...
arXiv:2011.13456v2
fatcat:jcft2ehkbfcklb2it5bppulkva
Linkage mapping and QTL analysis of flowering time using ddRAD sequencing with genotype error correction in Brassica napus
2020
BMC Plant Biology
A quantitative trait locus (QTL) on chromosome C2 was detected, covering eight flowering time genes including FLC. ...
Conclusions These findings demonstrate the effectiveness of the ddRAD approach to sample the B. napus genome. ...
Imputation and correction approaches used in crops include the hidden Markov model based LB-Impute [8] and FSFHap [9] , the sliding window based Genotype-Corrector [10] and simple heuristic approaches ...
doi:10.1186/s12870-020-02756-y
pmid:33287721
fatcat:geputy2z7rbk5b4r6xpdtpqwim
STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product
2020
Remote Sensing
The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. ...
In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. ...
Complete results of quantitative assessments of STAIR 2.0 fusion.
Acknowledgments: We thank the U.S. ...
doi:10.3390/rs12193209
fatcat:yixt3f7dv5hkrgmdkidax7wkny
Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing
2017
Remote Sensing
of observed hyperspectral data and the dominant noise sources, such as dark current, sensor temperature, atmosphere, and weather [20] . ...
In low cost UAV multiple sensor systems, it is not easy, or possible, to optically align sensors in a mount that will maintain the precise alignment in the field over multiple flight hours. ...
We are grateful to the anonymous reviewers who contributed to the improvement of this manuscript. ...
doi:10.3390/rs9070696
fatcat:qnowoggfuzcqflmhy4gwh5qerm
Linkage mapping and QTL analysis of flowering time using ddRAD sequencing with genotype error correction in Brassica napus
[article]
2020
bioRxiv
pre-print
A quantitative trail locus (QTL) on chromosome C2 was detected in the vicinity of flowering time genes including FT and FLC. ...
These findings demonstrate the effectiveness of the ddRAD approach to sample the B. napus genome. ...
). 422Genotypes were imputed and corrected using Genotype-Corrector 1.0 (79). ...
doi:10.1101/2020.06.26.162966
fatcat:la5lcmmtwfd5zhvui4imvn3uy4
Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series
2021
Remote Sensing
Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. ...
The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth's ...
Acknowledgments: We acknowledge the funding from the Academy of Finland for the SMART-LAND project (Environmental sensing of ecosystem services for developing climate smart landscape framework to improve ...
doi:10.3390/rs14010172
fatcat:i6xr2cvpqfbjvcvlvfbpl6wckm
Perverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Field Experiment
2021
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
We fnd causal evidence that being corrected decreases the quality, and increases the partisan slant and language toxicity, of the users' subsequent retweets (but has no signifcant efect on primary tweets ...
ACKNOWLEDGMENTS The authors thank Antonio Arechar for assistance with running the tweet-classifcation MTurk study and gratefully acknowledge funding from the William and Flora Hewlett Foundation, the John ...
Templeton Foundation, the Reset project of Omidyar Group's Luminate Project Limited, Jigsaw, and the National Science Foundation Graduate Research Fellowship (Grant No. 174530). ...
doi:10.1145/3411764.3445642
fatcat:cmkkd4j3end7ppqu47mkf54cqu
Similar Neutrophil-Driven Inflammatory and Antibacterial Responses in Elderly Patients with Symptomatic and Asymptomatic Bacteriuria
2015
Infection and Immunity
These data support the notion that the pathways mediating inflammation and pain in most elderly patients with ASB are not quantitatively different from those seen in most elderly patients with UTI and ...
The same enzymes contributing to the synthesis of leukotrienes LTB4and LTC4, mediators of inflammation and pain, were found in the UTI and ASB cohorts. ...
The contents of the paper are solely our responsibility and do not necessarily represent the official view of NIH. ...
doi:10.1128/iai.00745-15
pmid:26238715
pmcid:PMC4567619
fatcat:fqnclj4dtjbtbi6z2tublm57eu
BULLSEYE: A Compact, Camera-Based, Human-Machine Interface
1999
Presence - Teleoperators and Virtual Environments
This paper provides an overview of the concept and hardware, then examines the problems and design strategies associated with the interrelated areas of the environment, prop design, and color detection ...
The compact device, containing both the camera and processing engine, has been built and fielded. ...
The measured locations are projected back onto the prop plane to find imputed 3-D locations. We then interpolate along the 3-D line between the imputed locations, according to the prop geometry. ...
doi:10.1162/105474699566053
fatcat:j3xt3uejmzfffbo264zrm34xuy
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
This copy of my thesis is typeset for online dissemination to improve readability, whereas the thesis submitted to the University of Warwick in support of my application for the degree of Doctor of Philosophy ...
sensing with spiral, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise ...
Finally, the author acknowledges funding from EPSRC grant EP/N035437/1 and EPSRC Studentship 1917382.
Competing Interests The author declares no competing interests. ...
doi:10.5281/zenodo.4399748
fatcat:63ggmnviczg6vlnqugbnrexsgy
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
This version of my thesis is typeset for online dissemination to improve readability, whereas the thesis submitted to the University of Warwick in support of my application for the degree of Doctor of ...
sensing with spiral, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise ...
Finally, the author acknowledges funding from EPSRC grant EP/N035437/1 and EPSRC Studentship 1917382.
Competing Interests The author declares no competing interests. ...
doi:10.5281/zenodo.4598227
fatcat:hm2ksetmsvf37adjjefmmbakvq
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
This version of my thesis is typeset for online dissemination to improve readability, whereas the thesis submitted to the University of Warwick in support of my application for the degree of Doctor of ...
sensing with spiral, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise ...
Finally, the author acknowledges funding from EPSRC grant EP/N035437/1 and EPSRC Studentship 1917382.
Competing Interests The author declares no competing interests. ...
doi:10.5281/zenodo.4591029
fatcat:zn2hvfyupvdwlnvsscdgswayci
Review: Deep Learning in Electron Microscopy
[article]
2020
arXiv
pre-print
Finally, the author acknowledges funding from EPSRC grant EP/N035437/1 and EPSRC Studentship 1917382. ...
In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a creative commons 4.0 73 license. ...
Parameter noise is usually additive as it does not change an objective function being learned, whereas multiplicative noise can change the objective 1487 . ...
arXiv:2009.08328v4
fatcat:umocfp5dgvfqzck4ontlflh5ca
Advances in Electron Microscopy with Deep Learning
2020
Zenodo
This copy of my thesis is typeset for online dissemination to improve readability, whereas the thesis submitted to the University of Warwick in support of my application for the degree of Doctor of Philosophy ...
sensing with spiral, uniformly spaced and other fixed sparse scan paths; recurrent neural networks trained to piecewise adapt sparse scan paths to specimens by reinforcement learning; improving signal-to-noise ...
Finally, the author acknowledges funding from EPSRC grant EP/N035437/1 and EPSRC Studentship 1917382.
Competing Interests The author declares no competing interests. ...
doi:10.5281/zenodo.4413249
fatcat:35qbhenysfhvza2roihx52afuy
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