Visualization of Material Functions through Collaboration between X-Ray Spectro-Ptychography and Data Science

2021 ◽  
Vol 87 (7) ◽  
pp. 7_597-7_600
Author(s):  
Nozomu ISHIGURO ◽  
Yukio TAKAHASHI
Keyword(s):  
Data Science ◽  
X Ray ◽  
Material Functions

Paleoenvironmental reconstruction of the central Levantine Corridor during the Pliocene: insights from lacustrine settings 

2021 ◽  
Author(s):  
John Greenlee ◽  
Silas Dean ◽  
Nicolas Waldmann
Keyword(s):  
High Resolution ◽  
Data Science ◽  
Environmental Changes ◽  
Laser Diffraction ◽  
Climatic Conditions ◽  
Paleoenvironmental Reconstruction ◽  
Cosmogenic Isotopes ◽  
X Ray ◽  
Geochemical Proxies ◽  
Favorable Conditions

<p>This study aims to reconstruct the paleoenvironmental and climatic conditions affecting the Levantine corridor during the early Pliocene. For the purpose of this study, a ~20 m continuous core sequence was retrieved out of the ~200 m long, tilted Erk el Ahmar sequence previously dated by cosmogenic isotopes to ~3.5 Ma. The record include intercalating units consisting of sands, silts, and clays that were sampled in high resolution in order to analyze a variety of sedimentological and geochemical proxies of past climate and environmental changes. We present new preliminary, high-resolution sedimentological (laser diffraction granulometry), petrophysical (magnetic susceptibility) and compositional (X-ray fluorescence) data along with accompanying statistical analysis performed with an advanced suite of data-science tools. These results reveal new cycles of environmental change in the area, which appears to be orbitally controlled, and include dramatic changes also indicated by discrete strata of fossil fragments. Moreover, cycles of deposition can also provide hints on the major hydrological controlling mechanisms. This project provides new light into favorable conditions for the subsistence of perennial lake environments in the Levantine Corridor, which in turn may have facilitated faunal migration between Africa and Eurasia.</p>

Present and Future Accelerator-Based X-ray Sources: A Perspective

2019 ◽  
Vol 10 (01) ◽  
pp. 33-48
Author(s):  
J. B. Hastings ◽  
L. Rivkin ◽  
G. Aeppli
Keyword(s):  
Data Science ◽  
High Temperature Superconductors ◽  
Three Dimensional ◽  
Structural Studies ◽  
X Ray ◽  
Protein Molecules ◽  
Complex Protein ◽  
Image Production ◽  
Photon Science ◽  
Computation Data

Accelerator-based X-ray sources have contributed uniquely to the physical, engineering and life sciences. There has been a constant development of the sources themselves as well as of the necessary X-ray optics and detectors. These advances have combined to push X-ray science to the forefront in structural studies, achieving atomic resolution for complex protein molecules, to meV scale dynamics addressing problems ranging from geoscience to high-temperature superconductors, and to spatial resolutions approaching 10[Formula: see text]nm for elemental mapping as well as three-dimensional structures. Here we discuss accelerator-based photon science in the frame of imaging and highlight the importance of optics, detectors and computation/data science as well as the source technology. We look to a bright future for X-ray systems, integrating all components from accelerator sources to digital image production algorithms, and highlight aspects that make them unique scientific tools.

scholarly journals COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare

2021 ◽  
Vol 18 (21) ◽  
pp. 11086
Author(s):  
Debaditya Shome ◽  
T. Kar ◽  
Sachi Nandan Mohanty ◽  
Prayag Tiwari ◽  
Khan Muhammad ◽  
...  
Keyword(s):  
Data Science ◽  
Disease Spread ◽  
Classification Task ◽  
Data Sets ◽  
Sudden Increase ◽  
X Rays ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Transformer Model

In the recent pandemic, accurate and rapid testing of patients remained a critical task in the diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of the sudden increase in cases, most countries have faced scarcity and a low rate of testing. Chest X-rays have been shown in the literature to be a potential source of testing for COVID-19 patients, but manually checking X-ray reports is time-consuming and error-prone. Considering these limitations and the advancements in data science, we proposed a Vision Transformer-based deep learning pipeline for COVID-19 detection from chest X-ray-based imaging. Due to the lack of large data sets, we collected data from three open-source data sets of chest X-ray images and aggregated them to form a 30 K image data set, which is the largest publicly available collection of chest X-ray images in this domain to our knowledge. Our proposed transformer model effectively differentiates COVID-19 from normal chest X-rays with an accuracy of 98% along with an AUC score of 99% in the binary classification task. It distinguishes COVID-19, normal, and pneumonia patient’s X-rays with an accuracy of 92% and AUC score of 98% in the Multi-class classification task. For evaluation on our data set, we fine-tuned some of the widely used models in literature, namely, EfficientNetB0, InceptionV3, Resnet50, MobileNetV3, Xception, and DenseNet-121, as baselines. Our proposed transformer model outperformed them in terms of all metrics. In addition, a Grad-CAM based visualization is created which makes our approach interpretable by radiologists and can be used to monitor the progression of the disease in the affected lungs, assisting healthcare.

scholarly journals JUNGFRAU detector for brighter x-ray sources: Solutions for IT and data science challenges in macromolecular crystallography

2020 ◽  
Vol 7 (1) ◽  
pp. 014305 ◽  
Author(s):  
Filip Leonarski ◽  
Aldo Mozzanica ◽  
Martin Brückner ◽  
Carlos Lopez-Cuenca ◽  
Sophie Redford ◽  
...  
Keyword(s):  
Data Science ◽  
Macromolecular Crystallography ◽  
X Ray

scholarly journals Open Universe for Blazars: a new generation of astronomical products based on 14 years of Swift-XRT data

2019 ◽  
Vol 631 ◽  
pp. A116 ◽  
Author(s):  
P. Giommi ◽  
C. H. Brandt ◽  
U. Barres de Almeida ◽  
A. M. T. Pollock ◽  
F. Arneodo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
High Energy ◽  
Space Mission ◽  
Science Data ◽  
Spectral Parameters ◽  
Data Set ◽  
X Ray ◽  
Research Education ◽  
Open Universe

Aims. Open Universe for Blazars is a set of high-transparency multi-frequency data products for blazar science, and the tools designed to generate them. Blazars are drawing growing interest following the consolidation of their position as the most abundant type of source in the extragalactic very high-energy γ-ray sky, and because of their status as prime candidate sources in the nascent field of multi-messenger astrophysics. As such, blazar astrophysics is becoming increasingly data driven, depending on the integration and combined analysis of large quantities of data from the entire span of observational astrophysics techniques. The project was therefore chosen as one of the pilot activities within the United Nations Open Universe Initiative, whose objective is to stimulate a large increase in the accessibility and ease of utilisation of space science data for the worldwide benefit of scientific research, education, capacity building, and citizen science. Methods. Our aim is to deliver innovative data science tools for multi-messenger astrophysics. In this work we report on a data analysis pipeline called Swift-DeepSky based on the Swift XRTDAS software and the XIMAGE package, encapsulated into a Docker container. Swift-DeepSky downloads and reads low-level data, generates higher level products, detects X-ray sources, and estimates several intensity and spectral parameters for each detection, thus facilitating the generation of complete and up-to-date science-ready catalogues from an entire space-mission data set. Results. As a first application of our innovative approach, we present the results of a detailed X-ray image analysis based on Swift-DeepSky that was run on all Swift-XRT observations including a known blazar, carried out during the first 14 years of operations of the Neil Gehrels Swift Observatory. Short exposures executed within one week of each other have been added to increase sensitivity, which ranges between ∼1 × 10−12 and ∼1 × 10−14 erg cm−2 s−1 (0.3–10.0 keV). After cleaning for problematic fields, the resulting database includes over 27 000 images integrated in different X-ray bands, and a catalogue, called 1OUSXB, that provides intensity and spectral information for 33 396 X-ray sources, 8896 of which are single or multiple detections of 2308 distinct blazars. All the results can be accessed online in a variety of ways, from the Open Universe portal through Virtual Observatory services, via the VOU-Blazar tool and the SSDC SED builder. One of the most innovative aspects of this work is that the results can be easily reproduced and extended by anyone using the Docker version of the Swift-DeepSky pipeline, which runs on Linux, Mac, and Windows machines, and does not require any specific experience in X-ray data analysis.

scholarly journals Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junya Ohyama ◽  
Airi Hirayama ◽  
Nahoko Kondou ◽  
Hiroshi Yoshida ◽  
Masato Machida ◽  
...  
Keyword(s):  
Surface Area ◽  
Inductively Coupled Plasma ◽  
Data Science ◽  
Supervised Machine Learning ◽  
Area Measurement ◽  
Emission Spectrometry ◽  
Direct Oxidation ◽  
X Ray ◽  
Pairwise Correlation ◽  
Structural Variables

AbstractDozens of Cu zeolites with MOR, FAU, BEA, FER, CHA and MFI frameworks are tested for direct oxidation of CH4 to CH3OH using H2O2 as oxidant. To investigate the active structures of the Cu zeolites, 15 structural variables, which describe the features of the zeolite framework and reflect the composition, the surface area and the local structure of the Cu zeolite active site, are collected from the Database of Zeolite Structures of the International Zeolite Association (IZA). Also analytical studies based on inductively coupled plasma-optical emission spectrometry (ICP-OES), X-ray fluorescence (XRF), N2 adsorption specific surface area measurement and X-ray absorption fine structure (XAFS) spectral measurement are performed. The relationships between catalytic activity and the structural variables are subsequently revealed by data science techniques, specifically, classification using unsupervised and supervised machine learning and data visualization using pairwise correlation. Based on the unveiled relationships and a detailed analysis of the XAFS spectra, the local structures of the Cu zeolites with high activity are proposed.

scholarly journals Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

2021 ◽  
Vol 13 ◽  
pp. 175628722110448
Author(s):  
B.M. Zeeshan Hameed ◽  
Gayathri Prerepa ◽  
Vathsala Patil ◽  
Pranav Shekhar ◽  
Syed Zahid Raza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Data Science ◽  
Image Interpretation ◽  
X Ray ◽  
X Ray Imaging ◽  
Potential Applications ◽  
Efficiency And Productivity

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

scholarly journals Machine Learning Approach using CNN for COVID-19 Pandemic Detection

2021 ◽  
Vol 11 (2) ◽  
pp. 63-67
Author(s):  
Dr. Vikas S ◽  
◽  
Dr. Thimmaraju S N ◽  
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Data Science ◽  
Corrective Action ◽  
Learning Approach ◽  
Data Generation ◽  
X Ray ◽  
Domain Names ◽  
Machine Learning Approach ◽  
Chest X Ray

Data science and machine learning are domain names in which data generation can assist with inside the fight towards the disease. Early caution systems which can are expecting how much a disease might effect society and permit the authorities to take suitable measures without disrupting the economy are extremely important. In the confrontation towards COVID-19 methods for forecasting the future cases primarily based totally on present data are extremely beneficial. The preceding are three strategies of machine learning which are discussed: Two for predicting the wide variety of positive cases in the coming ten days, and one for identifying COVID-19 infection via way of means of analyzing the patient's chest x-ray image. Various algorithms had been tested, and the only that produced the maximum accurate consequences become selected for use on this take a look at to forecast confirmed cases in India. Various government entities can leverage the findings to take corrective action. Now that methods for forecasting infectious disease are available, COVID-19 can be less complicated to combat.

Sign in / Sign up

Export Citation Format

Share Document