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Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

Plant Phenomics 2023年第3期5卷 581-597页

作者： John Lagergren Mirko Pavicic Hari B.Chhetri Larry M.York Doug Hyatt David Kainer Erica M.Rutter Kevin Flores jack bailey-bale Marie Klein Gail Taylor Daniel Jacobson Jared Streich Biosciences Division Oak Ridge National LaboratoryOak RidgeTNUSA Department of Applied Mathematics University of CaliforniaMercedCAUSA Department of Mathematics North Carolina State UniversityRaleighNCUSA Department of Plant Sciences University of CaliforniaDavisCAUSA

Plant phenotyping is typically a time-consuming and expensive endeavor,requiring large groups of researchers to meticulously measure biologically relevant plant traits,and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population *** this work,we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the *** contrast to previous methods,our approach(a)does not require experimental or image preprocessing,(b)uses the raw RGB images at full resolution,and(c)requires very few samples for training(e.g.,just 8 images for vein segmentation).Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools,validated using real-world physical measurements,and used to conduct a genome-wide association study to identify genes controlling the *** this way,the current work is designed to provide the plant phenotyping community with(a)methods for fast and accurate image-based feature extraction that require minimal training data and(b)a new population-scale dataset,including 68 different leaf phenotypes,for domain scientists and machine learning *** of the few-shot learning code,data,and results are made publicly available.

关键词： networks neural image

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Understanding academic clinicians' intent to treat pediatric obesity

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World Journal of Clinical Pediatrics 2017年第1期6卷 60-68页

作者： Claudia Frankfurter Charles Cunningham Katherine M Morrison Heather Rimas Karen bailey Faculty of Medicine University of Toronto Department of Psychiatry and Behavioral Neurosciences McM aster University Department of Pediatrics Mc Master University Division of Pediatric Surgery Department of SurgeryMc Master University

AIM To examine the extent to which the theory of planned behavior(TPB) predicts academic clinicians' intent to treat pediatric *** A multi-disciplinary panel iteratively devised a Likert scale survey based on the constructs of the TPB applied to a set of pediatric obesity themes.A cross-sectional electronic survey was then administered to academic clinicians at tertiary care centers across Canada from January to April *** statistics were used to summarize demographic and item agreement data.A hierarchical linear regression analysis controlling for demographic variables was conducted to examine the extent to which the TPB subscales predicted intent to treat pediatric *** A total of 198 physicians,surgeons,and allied healthprofessionals across Canada(British Columbia,Alberta,Manitoba,Saskatchewan,Nova Scotia,Ontario and Quebec) completed the *** step 1,demographic factors accounted for 7.4% of the variance in intent *** in step 2,demographic variables and TPB subscales predicted 56.9% of the variance in a measure of the intent to treat pediatric *** behavioral control,that is,confidence in one's ability to manage pediatric obesity,and subjective norms,congruent with one's context of practice,were the most significant predictors of the intent to treat pediatric *** and barriers did not predict the intent to treat pediatric obesity in this *** Enhancing self-confidence in the ability to treat pediatric obesity and the existence of supportive treatment environments are important to increase clinician's intent to treat pediatric obesity.

关键词： Pediatric obesity Therapeutics Intention Decision Making Behavior

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