scholarly journals Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration—A machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254690
Author(s):  
Saurav Z. K. Sajib ◽  
Munish Chauhan ◽  
Oh In Kwon ◽  
Rosalind J. Sadleir
Keyword(s):  
Machine Learning ◽  
Electrical Conductivity ◽  
Magnetic Resonance ◽  
Magnetic Flux ◽  
Flux Density ◽  
Diffusion Tensor ◽  
Magnetic Flux Density ◽  
Learning Approach ◽  
Machine Learning Approach

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations.

scholarly journals Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

2019 ◽  
Author(s):  
Anton Levitan ◽  
Andrew N. Gale ◽  
Emma K. Dallon ◽  
Darby W. Kozan ◽  
Kyle W. Cunningham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Yeast Species ◽  
Transposon Mutagenesis ◽  
Learning Approach ◽  
Long Distance ◽  
Gene Essentiality ◽  
Genome Wide ◽  
Machine Learning Approach

ABSTRACTIn vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

scholarly journals Application of multi-omics data integration and machine learning approaches to identify epigenetic and transcriptomic differences between in vitro and in vivo produced bovine embryos

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252096
Author(s):  
Maria B. Rabaglino ◽  
Alan O’Doherty ◽  
Jan Bojsen-Møller Secher ◽  
Patrick Lonergan ◽  
Poul Hyttel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Integration ◽  
Focal Adhesion ◽  
Learning Approach ◽  
Omics Data ◽  
Machine Learning Approach ◽  
Cluster 2 ◽  
Omics Data Integration

Pregnancy rates for in vitro produced (IVP) embryos are usually lower than for embryos produced in vivo after ovarian superovulation (MOET). This is potentially due to alterations in their trophectoderm (TE), the outermost layer in physical contact with the maternal endometrium. The main objective was to apply a multi-omics data integration approach to identify both temporally differentially expressed and differentially methylated genes (DEG and DMG), between IVP and MOET embryos, that could impact TE function. To start, four and five published transcriptomic and epigenomic datasets, respectively, were processed for data integration. Second, DEG from day 7 to days 13 and 16 and DMG from day 7 to day 17 were determined in the TE from IVP vs. MOET embryos. Third, genes that were both DE and DM were subjected to hierarchical clustering and functional enrichment analysis. Finally, findings were validated through a machine learning approach with two additional datasets from day 15 embryos. There were 1535 DEG and 6360 DMG, with 490 overlapped genes, whose expression profiles at days 13 and 16 resulted in three main clusters. Cluster 1 (188) and Cluster 2 (191) genes were down-regulated at day 13 or day 16, respectively, while Cluster 3 genes (111) were up-regulated at both days, in IVP embryos compared to MOET embryos. The top enriched terms were the KEGG pathway "focal adhesion" in Cluster 1 (FDR = 0.003), and the cellular component: "extracellular exosome" in Cluster 2 (FDR<0.0001), also enriched in Cluster 1 (FDR = 0.04). According to the machine learning approach, genes in Cluster 1 showed a similar expression pattern between IVP and less developed (short) MOET conceptuses; and between MOET and DKK1-treated (advanced) IVP conceptuses. In conclusion, these results suggest that early conceptuses derived from IVP embryos exhibit epigenomic and transcriptomic changes that later affect its elongation and focal adhesion, impairing post-transfer survival.

scholarly journals Low Amplitude Burst Detection of Catecholamines

2021 ◽  
Author(s):  
Amnah Eltahir ◽  
Jason White ◽  
Terry Lohrenz ◽  
P. Read Montague
Keyword(s):  
Machine Learning ◽  
Electrochemical Detection ◽  
Perception And Action ◽  
Learning Approach ◽  
Voltage Waveform ◽  
Machine Learning Approach ◽  
Dependent Signal ◽  
Low Amplitude ◽  
New Machine

Abstract Machine learning advances in electrochemical detection have recently produced subsecond and concurrent detection of dopamine and serotonin during perception and action tasks in conscious humans. Here, we present a new machine learning approach to subsecond, concurrent separation of dopamine, norepinephrine, and serotonin. The method exploits a low amplitude burst protocol for the controlled voltage waveform and we demonstrate its efficacy by showing how it separates dopamine-induced signals from norepinephrine induced signals. Previous efforts to deploy electrochemical detection of dopamine in vivo have not separated the dopamine-dependent signal from a norepinephrine-dependent signal. Consequently, this new method can provide new insights into concurrent signaling by these two important neuromodulators.

scholarly journals Current Density Imaging Using Directly Measured HarmonicBzData in MREIT

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chunjae Park ◽  
Oh In Kwon
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Flux ◽  
Flux Density ◽  
Current Density ◽  
Background Field ◽  
Magnetic Flux Density ◽  
Reconstruction Procedure ◽  
Phantom Experiment ◽  
Impedance Tomography ◽  
Space Data

Magnetic resonance electrical impedance tomography (MREIT) measures magnetic flux density signals through the use of a magnetic resonance imaging (MRI) in order to visualize the internal conductivity and/or current density. Understanding the reconstruction procedure for the internal current density, we directly measure the second derivative ofBzdata from the measuredk-space data, from which we can avoid a tedious phase unwrapping to obtain the phase signal ofBz. We determine optimal weighting factors to combine the derivatives of magnetic flux density data,∇2Bz, measured using the multi-echo train. The proposed method reconstructs the internal current density using the relationships between the induced internal current and the measured∇2Bzdata. Results from a phantom experiment demonstrate that the proposed method reduces the scanning time and provides the internal current density, while suppressing the background field inhomogeneity. To implement the real experiment, we use a phantom with a saline solution including a balloon, which excludes other artifacts by any concentration gradient in the phantom.

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