A community-based topological distance for brain-connectome classification

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Juan Luis Villareal–Haro ◽  
Alonso Ramirez–Manzanares ◽  
Juan Antonio Pichardo-Corpus
Keyword(s):  
Euclidean Distance ◽  
Brain Connectivity ◽  
Ground Truth ◽  
Jaccard Index ◽  
Community Based ◽  
Large Dataset ◽  
Magnetic Resonance Data ◽  
Brain Connectome ◽  
Resonance Data ◽  
Diverse Groups

Abstract Measuring differences among complex networks is a well-studied research topic. Particularly, in the context of brain networks, there are several proposals. Nevertheless, most of them address the problem considering unweighted networks. Here, we propose a metric based on modularity and Jaccard index to measure differences among brain-connectivity weighted networks built from diffusion-weighted magnetic resonance data. We use a large dataset to test our metric: a synthetic Ground Truth network (GT) and a set of networks available from a tractography challenge, three sets computed from GT perturbations, and a set of classic random graphs. We compare the performance of our proposal with the most used methods as Euclidean distance between matrices and a kernel-based distance. Our results indicate that the proposed metric outperforms those previously published distances. More importantly, this work provides a methodology that allows differentiating diverse groups of graphs based on their differences in topological structure.

Methods to Determine Phytopharmaceuticals in Tobacco and Tobacco Products. 4th Report: A Procedure to Determine Vamidothion and its Metabolites/Methoden zur Bestimmung von Phytopharmaka in Tabak und Tabakerzeugnissen: IV. Mitteilung: Ein Bestimmungsverfahren fuer Vamidothion und seine Metaboliten

1977 ◽  
Vol 9 (2) ◽  
Author(s):  
A.N. Sagredos ◽  
R. Moser
Keyword(s):  
Mass Spectrometry ◽  
Nuclear Magnetic Resonance ◽  
Acetic Acid ◽  
Magnetic Resonance ◽  
Aluminium Oxide ◽  
Nuclear Magnetic Resonance Data ◽  
Gas Chromatograph ◽  
Tobacco Products ◽  
Magnetic Resonance Data ◽  
Resonance Data

AbstractBased on previous work (7) a method to simultaneously determine vamidothion [I], vamidothion-sulfoxide [II] and vamidothion sulfone [III] in tobacco has been developed. The compounds are extracted with water/acetone/acetic acid from the tobacco, cleansed over an aluminium oxide column and then determined on the gas chromatograph with the specific sulphur detector. Rates of recovery are 70 % - 92 %, the determination level is 0.1 ppm. Mass spectrometry and nuclear magnetic resonance data of vamidothion [I], vamidothion-sulfoxide [ II ] and vamidothion-sulfone [III] are given.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

2-Epi- and 15-dihydro-2-epilubimin: new stress compounds from the potato

1980 ◽  
Vol 58 (19) ◽  
pp. 2069-2072 ◽  
Author(s):  
A. Stoessl ◽  
J. B. Stothers
Keyword(s):  
Magnetic Resonance ◽  
Pathogenic Fungus ◽  
Reduction Product ◽  
Magnetic Resonance Data ◽  
Resonance Data

The complement of sesquiterpenoidal stress compounds produced in the interaction of potato with the pathogenic fungus, Alternariasolani, includes representatives of a new stereochemical series, 2-epilubimin (1) and its reduction product, 15-dihydro-2-epilubimin (3). Their structures were deduced from 13C and 1H magnetic resonance data.

Lignans of western red cedar (Thuja plicata Donn). VI. Dihydroxythujaplicatin methyl ether

1967 ◽  
Vol 45 (3) ◽  
pp. 305-309 ◽  
Author(s):  
Harold MacLean ◽  
Koji Murakami
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Methyl Ether ◽  
Degradation Products ◽  
Parent Compound ◽  
Thuja Plicata ◽  
Red Cedar ◽  
Magnetic Resonance Data ◽  
Resonance Data ◽  
Thuja Plicata Donn

Proof of structure is presented for another lignan of the thujaplicatin series, 2,3-dihydroxy-2-(4″-hydroxy-3″,5″-dimethoxybenzyl)-3-(4′-hydroxy-3′-methoxybenzyl)-butyrolactone (I) (dihydroxythujaplicatin methyl ether). Analytical and spectral (ultraviolet, infrared, and nuclear magnetic resonance) data on derivatives and degradation products, in addition to the parent compound, are presented.

scholarly journals Effective clustering for single cell sequencing cancer data

2019 ◽  
Author(s):  
Simone Ciccolella ◽  
Murray Patterson ◽  
Paola Bonizzoni ◽  
Gianluca Della Vedova
Keyword(s):  
Single Cell ◽  
Categorical Data ◽  
Euclidean Distance ◽  
Missing Values ◽  
False Negative ◽  
Ground Truth ◽  
Sequencing Data ◽  
Large Space ◽  
Cancer Data ◽  
Single Cell Sequencing

AbstractBackgroundSingle cell sequencing (SCS) technologies provide a level of resolution that makes it indispensable for inferring from a sequenced tumor, evolutionary trees or phylogenies representing an accumulation of cancerous mutations. A drawback of SCS is elevated false negative and missing value rates, resulting in a large space of possible solutions, which in turn makes infeasible using some approaches and tools. While this has not inhibited the development of methods for inferring phylogenies from SCS data, the continuing increase in size and resolution of these data begin to put a strain on such methods.One possible solution is to reduce the size of an SCS instance — usually represented as a matrix of presence, absence and missing values of the mutations found in the different sequenced cells — and infer the tree from this reduced-size instance. Previous approaches have used k-means to this end, clustering groups of mutations and/or cells, and using these means as the reduced instance. Such an approach typically uses the Euclidean distance for computing means. However, since the values in these matrices are of a categorical nature (having the three categories: present, absent and missing), we explore techniques for clustering categorical data — commonly used in data mining and machine learning — to SCS data, with this goal in mind.ResultsIn this work, we present a new clustering procedure aimed at clustering categorical vector, or matrix data — here representing SCS instances, called celluloid. We demonstrate that celluloid clusters mutations with high precision: never pairing too many mutations that are unrelated in the ground truth, but also obtains accurate results in terms of the phylogeny inferred downstream from the reduced instance produced by this method.Finally, we demonstrate the usefulness of a clustering step by applying the entire pipeline (clustering + inference method) to a real dataset, showing a significant reduction in the runtime, raising considerably the upper bound on the size of SCS instances which can be solved in practice.AvailabilityOur approach, celluloid: clustering single cell sequencing data around centroids is available at https://github.com/AlgoLab/celluloid/ under an MIT license.

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