Application of Earth Sciences Products for Use in Next Generation Numerical Aerosol Prediction Models

2009 ◽  
Author(s):  
Jeffrey S. Reid ◽  
Cynthia A. Curtis
Keyword(s):  
Earth Sciences ◽  
Prediction Models ◽  
Next Generation

Application of Earth Sciences Products for use in Next Generation Numerical Aerosol Prediction Models

2010 ◽  
Author(s):  
Jeffrey S. Reid ◽  
James R. Campbell ◽  
Cynthia A. Curtis ◽  
Edward J. Hyer
Keyword(s):  
Earth Sciences ◽  
Prediction Models ◽  
Next Generation

Application of Earth Sciences Products for use in Next Generation Numerical Aerosol Prediction Models

2008 ◽  
Author(s):  
Jeffrey S. Reid ◽  
Edward J. Hyer ◽  
James R. Campbell ◽  
Cindy A. Curtis
Keyword(s):  
Earth Sciences ◽  
Prediction Models ◽  
Next Generation

SCRUM-Japan genesis virtual sequencing (VSQ) project: A novel algorithm combining deep learning (DL) with pathological diagnostics to enable the prediction of BRAF mutations and microsatellite instability (MSI) in advanced colorectal cancer (CRC).

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 112-112
Author(s):  
Satoshi Fujii ◽  
Daisuke Kotani ◽  
Masahiro Hattori ◽  
Nishihara Masato ◽  
Toshihide Shikanai ◽  
...  
Keyword(s):  
Prediction Models ◽  
High Accuracy ◽  
Cancer Genome ◽  
Morphological Features ◽  
Braf V600e ◽  
Next Generation ◽  
Braf Mutations ◽  
Genetic Abnormalities ◽  
Image Prediction ◽  
Novel Algorithm

112 Background: Numerous genetic and epigenetic abnormalities may lead to various morphologies of cancer. However, exactly which gene abnormality causes which morphology is unknown. The VSQ Project aims at investigating a novel algorithm by synergistically fusing DL technology and pathological diagnostics for the prediction of cancer genome abnormalities. This was achieved by elucidating the association between the morphological findings and genetic abnormalities, including BRAF V600E mutations and MSI status directly linked to the therapeutic strategies for advanced CRC patients (pts). Methods: Clinicopathological-genomic integrated DB derived from SCRUM-Japan GI-SCREEN, a nation-wide cancer genome screening project including CRC, were used. A total of 1,657 images of thin sections (one representative image per pt) cut from formalin-fixed and paraffin-embedded (FFPE) tissue specimens from primary or metastatic tumors with genetic abnormalities confirmed by next-generation sequencing (NGS) were investigated; 1,234 and 423 images (one per pt) were used for training and validation cohorts, respectively. First, we developed image-prediction models based on the morphological features precisely annotated by the single central pathologist, and then constructed the DL algorithms (gene-prediction models) that enabled the prediction of gene abnormalities by using images filtered by the image-prediction models. Results: We achieved high accuracy of AUC > 0.90 for 12 features among the 33 morphological features analyzed. Next, we created several DL algorithms that enabled the prediction of BRAF mutations and MSI. The prediction level reached a high accuracy of AUC = 0.955 for the BRAF mutations and AUC = 0.857 for MSI in the training cohort. We verified the AUCs in the validation cohort and achieved AUC = 0.831 and 0.883 for BRAF mutations and MSI, respectively. Conclusions: Our findings suggest that VSQ can appropriately predict BRAF mutation and MSI status in advanced CRC, potentially without performing NGS tests. VSQ may also enable prompt initiation of systemic treatments in CRC patients as well as establish an unprecedented next-generation pathology in the near future.

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.

scholarly journals Predicting species distributions and community composition using satellite remote sensing predictors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jesús N. Pinto-Ledezma ◽  
Jeannine Cavender-Bares
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Direct Detection ◽  
Spatial Scales ◽  
Management Strategies ◽  
Model Performance ◽  
Next Generation ◽  
Distribution Models

AbstractBiodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m2). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km2). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.

scholarly journals Improving Harvey Forecasts with Next-Generation Weather Satellites: Advanced Hurricane Analysis and Prediction with Assimilation of GOES-R All-Sky Radiances

2019 ◽  
Vol 100 (7) ◽  
pp. 1217-1222 ◽  
Author(s):  
Fuqing Zhang ◽  
Masashi Minamide ◽  
Robert G. Nystrom ◽  
Xingchao Chen ◽  
Shian-Jian Lin ◽  
...  
Keyword(s):  
High Performance ◽  
Prediction Models ◽  
Gulf Coast ◽  
Weather Prediction ◽  
Next Generation ◽  
Emergency Managers ◽  
Advanced Analysis ◽  
Potential Improvement ◽  
Infrared Radiances ◽  
Gulf Coast Region

AbstractHurricane Harvey brought catastrophic destruction and historical flooding to the Gulf Coast region in late August 2017. Guided by numerical weather prediction models, operational forecasters at NOAA provided outstanding forecasts of Harvey’s future path and potential for record flooding days in advance. These forecasts were valuable to the public and emergency managers in protecting lives and property. The current study shows the potential for further improving Harvey’s analysis and prediction through advanced ensemble assimilation of high-spatiotemporal all-sky infrared radiances from the newly launched, next-generation geostationary weather satellite, GOES-16. Although findings from this single-event study should be further evaluated, the results highlight the potential improvement in hurricane prediction that is possible via sustained investment in advanced observing systems, such as those from weather satellites, comprehensive data assimilation methodologies that can more effectively ingest existing and future observations, higher-resolution weather prediction models with more accurate numerics and physics, and high-performance computing facilities that can perform advanced analysis and forecasting in a timely manner.

Sign in / Sign up

Export Citation Format

Share Document