scholarly journals In vivo quantification of plant starch reserves at micrometer resolution using X-ray microCT imaging and machine learning

2018 ◽  
Vol 218 (3) ◽  
pp. 1260-1269 ◽  
Author(s):  
J. Mason Earles ◽  
Thorsten Knipfer ◽  
Aude Tixier ◽  
Jessica Orozco ◽  
Clarissa Reyes ◽  
...  
Keyword(s):  
Machine Learning ◽  
X Ray ◽  
Microct Imaging

scholarly journals Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

MENDEL ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 9-17
Author(s):  
Hiam Alquran ◽  
Mohammad Alsleti ◽  
Roaa Alsharif ◽  
Isam Abu Qasmieh ◽  
Ali Mohammad Alqudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Texture Features ◽  
Automated System ◽  
Rt Pcr ◽  
False Negatives ◽  
X Ray ◽  
Organ Systems ◽  
Wide Range ◽  
Chest X Ray

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

scholarly journals Digitally Deconstructing Leaves in 3D Using X-ray Microcomputed Tomography and Machine Learning

2019 ◽  
Author(s):  
Guillaume Théroux-Rancourt ◽  
Matthew R. Jenkins ◽  
Craig R. Brodersen ◽  
Andrew McElrone ◽  
Elisabeth J. Forrestel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Microcomputed Tomography ◽  
Plant Phenotyping ◽  
3D Segmentation ◽  
Tissue Segmentation ◽  
Holistic View ◽  
X Ray ◽  
Single Leaf ◽  
Time Required ◽  
Microct Imaging

ABSTRACTPremise of the studyX-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organisation. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small datasets, restricting its utility for phenotyping experiments and limiting our confidence in the conclusion of these studies due to low replication numbers.Methods and ResultsWe present a Python codebase for random-forest machine learning segmentation and 3D leaf anatomical trait quantification which dramatically reduces the time required to process single leaf microCT scans into detailed segmentations. By training the model on each scan using 6 hand segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation.ConclusionOverall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.

Preparation of cultured astrocytes for x-ray microanalysis

1989 ◽  
Vol 47 ◽  
pp. 988-989
Author(s):  
N.K.R. Smith ◽  
K.E. Hunter ◽  
P. Mobley ◽  
L.P. Felpel
Keyword(s):  
Cultured Cells ◽  
Elemental Content ◽  
Elemental Distribution ◽  
Sprague Dawley ◽  
X Ray ◽  
Cultured Astrocytes ◽  
Freeze Dried ◽  
Ultimate Objective

Electron probe energy dispersive x-ray microanalysis (XRMA) offers a powerful tool for the determination of intracellular elemental content of biological tissue. However, preparation of the tissue specimen , particularly excitable central nervous system (CNS) tissue , for XRMA is rather difficult, as dissection of a sample from the intact organism frequently results in artefacts in elemental distribution. To circumvent the problems inherent in the in vivo preparation, we turned to an in vitro preparation of astrocytes grown in tissue culture. However, preparations of in vitro samples offer a new and unique set of problems. Generally, cultured cells, growing in monolayer, must be harvested by either mechanical or enzymatic procedures, resulting in variable degrees of damage to the cells and compromised intracel1ular elemental distribution. The ultimate objective is to process and analyze unperturbed cells. With the objective of sparing others from some of the same efforts, we are reporting the considerable difficulties we have encountered in attempting to prepare astrocytes for XRMA.Tissue cultures of astrocytes from newborn C57 mice or Sprague Dawley rats were prepared and cultured by standard techniques, usually in T25 flasks, except as noted differently on Cytodex beads or on gelatin. After different preparative procedures, all samples were frozen on brass pins in liquid propane, stored in liquid nitrogen, cryosectioned (0.1 μm), freeze dried, and microanalyzed as previously reported.

Tele-Substitution Reactions in the Synthesis of a Promising Class of Antimalarials

2020 ◽  
Author(s):  
Marat Korsik ◽  
Edwin Tse ◽  
David Smith ◽  
William Lewis ◽  
Peter J. Rutledge ◽  
...  
Keyword(s):  
Heterocyclic Ring ◽  
Ring System ◽  
Substitution Reactions ◽  
Laboratory Notebook ◽  
Polar Solvents ◽  
X Ray ◽  
X Ray Crystallography ◽  
The Core ◽  
Nmr Data

<p></p><p>We have discovered and studied a <i>tele</i>substitution reaction in a biologically important heterocyclic ring system. Conditions that favour the <i>tele</i>-substitution pathway were identified: the use of increased equivalents of the nucleophile or decreased equivalents of base, or the use of softer nucleophiles, less polar solvents and larger halogens on the electrophile. Using results from X-ray crystallography and isotope labelling experiments a mechanism for this unusual transformation is proposed. We focused on this triazolopyrazine as it is the core structure of the <i>in vivo </i>active anti-plasmodium compounds of Series 4 of the Open Source Malaria consortium.</p> <p> </p> <p>Archive of the electronic laboratory notebook with the description of all conducted experiments and raw NMR data could be accessed via following link <a href="https://ses.library.usyd.edu.au/handle/2123/21890">https://ses.library.usyd.edu.au/handle/2123/21890</a> . For navigation between entries of laboratory notebook please use file "Strings for compounds in the article.pdf" that works as a reference between article codes and notebook codes, also this file contain SMILES for these compounds. </p><br><p></p>

scholarly journals Integrating Machine/Deep Learning Methods and Filtering Techniques for Reliable Mineral Phase Segmentation of 3D X-ray Computed Tomography Images

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4595
Author(s):  
Parisa Asadi ◽  
Lauren E. Beckingham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ct Images ◽  
Ct Imaging ◽  
Learning Method ◽  
Learning Methods ◽  
X Ray ◽  
Machine Learning Methods ◽  
Filtering Techniques

X-ray CT imaging provides a 3D view of a sample and is a powerful tool for investigating the internal features of porous rock. Reliable phase segmentation in these images is highly necessary but, like any other digital rock imaging technique, is time-consuming, labor-intensive, and subjective. Combining 3D X-ray CT imaging with machine learning methods that can simultaneously consider several extracted features in addition to color attenuation, is a promising and powerful method for reliable phase segmentation. Machine learning-based phase segmentation of X-ray CT images enables faster data collection and interpretation than traditional methods. This study investigates the performance of several filtering techniques with three machine learning methods and a deep learning method to assess the potential for reliable feature extraction and pixel-level phase segmentation of X-ray CT images. Features were first extracted from images using well-known filters and from the second convolutional layer of the pre-trained VGG16 architecture. Then, K-means clustering, Random Forest, and Feed Forward Artificial Neural Network methods, as well as the modified U-Net model, were applied to the extracted input features. The models’ performances were then compared and contrasted to determine the influence of the machine learning method and input features on reliable phase segmentation. The results showed considering more dimensionality has promising results and all classification algorithms result in high accuracy ranging from 0.87 to 0.94. Feature-based Random Forest demonstrated the best performance among the machine learning models, with an accuracy of 0.88 for Mancos and 0.94 for Marcellus. The U-Net model with the linear combination of focal and dice loss also performed well with an accuracy of 0.91 and 0.93 for Mancos and Marcellus, respectively. In general, considering more features provided promising and reliable segmentation results that are valuable for analyzing the composition of dense samples, such as shales, which are significant unconventional reservoirs in oil recovery.

Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning

2021 ◽  
Vol 143 (10) ◽  
pp. 3779-3793
Author(s):  
Paula C. Ortet ◽  
Samantha N. Muellers ◽  
Lauren A. Viarengo-Baker ◽  
Kristina Streu ◽  
Blair R. Szymczyna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
X Ray ◽  
X Ray Crystallography ◽  
Cyclic Heptapeptide

scholarly journals An Integrated Framework with Machine Learning and Radiomics for Accurate and Rapid Early Diagnosis of COVID-19 from Chest X-ray

2021 ◽  
pp. 115152
Author(s):  
Mahbubunnabi Tamal ◽  
Maha Alshammari ◽  
Meernah Alabdullah ◽  
Rana Hourani ◽  
Hossain Abu Alola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Diagnosis ◽  
X Ray ◽  
Integrated Framework ◽  
Chest X Ray
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