CryoDiscoveryTM: A machine learning platform for automated Cryogenic electron microscopy particle classification

2021 ◽  
Author(s):  
John Harkness ◽  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Learning Platform ◽  
Particle Classification

scholarly journals CryoDiscoveryTM: A Machine Learning Platform for Automated Cryo-electron Microscopy Particle Classification

2020 ◽  
Vol 26 (S2) ◽  
pp. 2308-2310
Author(s):  
Narasimha Kumar ◽  
John Harkness ◽  
Craig Yoshioka ◽  
Shiva Aditham ◽  
Tuan Phamdo ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Learning Platform ◽  
Cryo Electron Microscopy ◽  
Particle Classification

scholarly journals Multi-Agent System Observer: Intelligent Support for Engaged E-Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1370
Author(s):  
Igor Vuković ◽  
Kristijan Kuk ◽  
Petar Čisar ◽  
Miloš Banđur ◽  
Đoko Banđur ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Intelligent Support ◽  
Course Success ◽  
States Of Emergency ◽  
Learning Platform ◽  
Rapid Spread ◽  
E Learning ◽  
Multi Agent ◽  
Observer System

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

scholarly journals A machine learning platform for the discovery of materials

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carl E. Belle ◽  
Vural Aksakalli ◽  
Salvy P. Russo
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Gw Approximation ◽  
Dft Methods ◽  
Functional Theory ◽  
Unit Cell Size ◽  
Learning Platform ◽  
Photovoltaic Materials ◽  
Wide Range ◽  
Quantum Espresso

AbstractFor photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

scholarly journals Work Flows for Cellular Epidemiology, From Conception to Translation

2019 ◽  
Author(s):  
Anton D. Nathanson ◽  
Lucy Ngo ◽  
Tomasz Garbowski ◽  
Abhilash Srikantha ◽  
Christian Wojek ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Image Resolution ◽  
Machine Learning Algorithms ◽  
Human Anatomy ◽  
Test Case ◽  
Health State ◽  
Learning Approaches ◽  
Preparation Methods ◽  
Trade Offs

AbstractChanges in cell connectivity and morphology, observed and measured using microscopy, implicate a cellular basis of degenerative disease in tissues as diverse as bone, kidney and brain. To date, limitations inherent to sampling (biopsy sites) and/or microscopy (trade-offs between regions of interest and image resolution) have prevented early identification of cellular changes in specimen sizes of diagnostic relevance for human anatomy and physiology. This manuscript describes work flows for human tissue-based cell epidemiology studies. Using recently published sample preparation methods, developed and validated to maximize imaging quality, the largest-to-date scanning electron microscopy map was created showing cellular connections in the femoral neck of a human hip. The map, from a patient undergoing hip replacement, comprises an 11 TB dataset including over 7 million electron microscopy images. This map served as a test case to implement machine learning algorithms for automated detection of cells and identification of their health state. The test case showed a significant link between cell connectivity and health state in osteocytes of the human femur. Combining new, rapid throughput electron microscopy methods with machine learning approaches provides a basis for assessment of cell population health at nanoscopic resolution and in mesoscopic tissue and organ samples. This sets a path for next generation cellular epidemiology, tracking outbreaks of disease in populations of cells that inhabit tissues and organs within individuals.

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