scholarly journals Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches

2021 ◽  
Vol 9 ◽  
Author(s):  
Alexis Caro ◽  
Thomas Condom ◽  
Antoine Rabatel
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Mass Loss ◽  
Surface Area ◽  
Explanatory Power ◽  
Learning Approaches ◽  
Spatial Variance ◽  
Glacier Surface ◽  
The Andes ◽  
Glacier Changes

Over the last decades, glaciers across the Andes have been strongly affected by a loss of mass and surface areas. This increases risks of water scarcity for the Andean population and ecosystems. However, the factors controlling glacier changes in terms of surface area and mass loss remain poorly documented at watershed scale across the Andes. Using machine learning methods (Least Absolute Shrinkage and Selection Operator, known as LASSO), we explored climatic and morphometric variables that explain the spatial variance of glacier surface area variations in 35 watersheds (1980–2019), and of glacier mass balances in 110 watersheds (2000–2018), with data from 2,500 to 21,000 glaciers, respectively, distributed between 8 and 55°S in the Andes. Based on these results and by applying the Partitioning Around Medoids (PAM) algorithm we identified new glacier clusters. Overall, spatial variability of climatic variables presents a higher explanatory power than morphometric variables with regards to spatial variance of glacier changes. Specifically, the spatial variability of precipitation dominates spatial variance of glacier changes from the Outer Tropics to the Dry Andes (8–37°S) explaining between 49 and 93% of variances, whereas across the Wet Andes (40–55°S) the spatial variability of temperature is the most important climatic variable and explains between 29 and 73% of glacier changes spatial variance. However, morphometric variables such as glacier surface area show a high explanatory power for spatial variance of glacier mass loss in some watersheds (e.g., Achacachi with r2 = 0.6 in the Outer Tropics, Río del Carmen with r2 = 0.7 in the Dry Andes). Then, we identified a new spatial framework for hydro-glaciological analysis composed of 12 glaciological zones, derived from a clustering analysis, which includes 274 watersheds containing 32,000 glaciers. These new zones better take into account different seasonal climate and morphometric characteristics of glacier diversity. Our study shows that the exploration of variables that control glacier changes, as well as the new glaciological zones calculated based on these variables, would be very useful for analyzing hydro-glaciological modelling results across the Andes (8–55°S).

scholarly journals Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexandre Hild Aono ◽  
Estela Araujo Costa ◽  
Hugo Vianna Silva Rody ◽  
James Shiniti Nagai ◽  
Ricardo José Gonzaga Pimenta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rust Resistance ◽  
Predictive Accuracy ◽  
Explanatory Power ◽  
Genotyping By Sequencing ◽  
Brown Rust ◽  
Learning Approaches ◽  
Molecular Approaches ◽  
Genomic Regions ◽  
Sugarcane Brown Rust

AbstractSugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% when using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. As a result of this approach, we achieved an accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

scholarly journals Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

2020 ◽  
Author(s):  
Alexandre Hild Aono ◽  
Estela Araujo Costa ◽  
Hugo Vianna Silva Rody ◽  
James Shiniti Nagai ◽  
Ricardo José Gonzaga Pimenta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Rust Resistance ◽  
Predictive Accuracy ◽  
Explanatory Power ◽  
Genotyping By Sequencing ◽  
Brown Rust ◽  
Learning Approaches ◽  
Genomic Regions ◽  
Sugarcane Brown Rust

ABSTRACTSugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

scholarly journals Glacier changes from a new inventory, Nianchu river basin, Tibetan Plateau

2009 ◽  
Vol 50 (53) ◽  
pp. 87-92 ◽  
Author(s):  
Caiping Zhou ◽  
Wenbin Yang ◽  
Liang Wu ◽  
Shiyin Liu
Keyword(s):  
Tibetan Plateau ◽  
Surface Area ◽  
River Basin ◽  
Glacier Surface ◽  
Air Temperatures ◽  
Digital Elevation ◽  
Glacier Changes ◽  
Southern Tibetan Plateau ◽  
The Mean ◽  
Topographical Maps

AbstractThe ice cover of the Nianchu river basin, southern Tibetan Plateau, was mapped for 2005, using a SPOT5 scene, and for 1990 and 2000 from Landsat TM/ETM. Digital elevation models (DEMs) were generated from 1 : 50 000 scale topographical maps. The results show that in 2005 there were 136 glaciers in this basin, with a total area of 224 km2. Of these, 37 glaciers had an area >1 km2 and 10 were larger than 5 km2; the average snout altitude was 5608m a.s.l. A comparison of outlines from the last 15 years shows that most glaciers have decreased in size; none have advanced. From 1990 to 2005, Xiaquepu glacier No. 56 and Shimozongpu glacier No. 38 retreated 310 and 560 m, respectively. The mean reduction in glacier surface area was 5% (10 a)–1 while the area of glacial lakes expanded by 10%; nine new lakes formed in this basin over the 15 year period. Because air temperatures here have been increasing, while precipitation has remained steady, glacier retreat is considered to be related to rising temperature.

scholarly journals Identification of REM Sleep Behavior Disorder by Structural Magnetic Resonance Imaging and Machine Learning

2021 ◽  
Author(s):  
Jie Mei ◽  
Shady Rahayel ◽  
Christian Desrosiers ◽  
Ronald B Postuma ◽  
Jacques Montplaisir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Surface Area ◽  
Behavior Disorder ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Learning Approaches ◽  
Structural Magnetic Resonance Imaging ◽  
Sleep Behavior

Background Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a major risk factor for synucleinopathies, and patients often present with clinical signs and morphological brain changes. However, there is an important heterogeneity in the presentation and progression of these alterations, and the brain regions that are more vulnerable to neurodegeneration remain to be determined. Objectives To assess the feasibility of morphology-based machine learning approaches in the identification and subtyping of iRBD. Methods For the three classification tasks [iRBD (n=48) vs controls (n=41); iRBD vs Parkinson's disease (n=29); iRBD with mild cognitive impairment (n=16) vs without mild cognitive impairment (n=32)], machine learning models were trained with morphometric measurements (thickness, surface area, volume, and deformation) extracted from T1- weighted structural magnetic resonance imaging. Model performance and the most discriminative brain regions were analyzed and identified. Results A high accuracy was reported for iRBD vs controls (79.6%, deformation of the caudal middle frontal gyrus and putamen, thinning of the superior frontal gyrus, and reduced volume of the inferior parietal cortex and insula), iRBD vs Parkinson's disease (82%, larger volume and surface area of the insula, thinning of the entorhinal cortex and lingual gyrus, and reduced volume of the fusiform gyrus), and iRBD with vs without mild cognitive impairment (84.8%, thinning of the pars triangularis, superior temporal gyrus, transverse temporal cortex, larger surface area of the superior temporal gyrus, and deformation of isthmus of the cingulate gyrus). Conclusions Morphology-based machine learning approaches may allow for automated detection and subtyping of iRBD, potentially enabling efficient preclinical identification of synucleinopathies.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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