Journal: Scientific Reports

Article Title: Early prediction of developing spontaneous activity in cultured neuronal networks

doi: 10.1038/s41598-021-99538-9

Figure Lengend Snippet: Development of spontaneous activity of cortical neurons in culture during the first three weeks in vitro. ( a ) Analysis of 18 electrophysiological features of neuronal network activity at DIV intervals: 6–8 (n = 50), 9–12 (n = 30), and 13–18 (n = 61). The box plots show the median and the interquartile range with Tukey whiskers. Description of features is provided in Table and p -values for ( a ) are reported in Table . (b ) Raster plots show the spontaneous activity recorded by 6 electrodes from a representative neuronal network at DIV 8, 12, and 15 with an archetypical increase of spontaneous activity and synchrony with days in culture. Vertical lines correspond to spikes. Horizontal scale bar, 20 s. ( c ) Maps of functional connectivity of the same neuronal network and period as in ( b ). The color of the connections (edges) between electrodes (nodes) represents Pearson’s correlation and shows a general increase in connectivity during early development. The figure was created using Graphpad 8.0 ( https://www.graphpad.com ) ( a ), Neuroexplorer 5 ( https://plexon.com/products/neuroexplorer/ ) ( b ), and Matlab 9.8 ( https://www.mathworks.com/products/matlab.html ) ( c ).

Article Snippet: The figure was created using Graphpad 8.0 ( https://www.graphpad.com ) ( a ), Neuroexplorer 5 ( https://plexon.com/products/neuroexplorer/ ) ( b ), and Matlab 9.8 ( https://www.mathworks.com/products/matlab.html ) ( c ).

Techniques: Activity Assay, In Vitro, Functional Assay

Journal: Scientific Reports

Article Title: Early prediction of developing spontaneous activity in cultured neuronal networks

doi: 10.1038/s41598-021-99538-9

Figure Lengend Snippet: Patterns of developing spontaneous activity in neuronal networks using clustering analysis. ( a , d ) Graphs display three developmental patterns for the features of Ch. bursts ( a ) and STTC identified by k-means clustering of the SOM. The lines represent the means of the observations, labelled by k-means, and the shaded areas represent the 95% confidence intervals. ( b , e ) Examples of raster plots (left panels) and connectivity maps (right panels) of neuronal networks included in either cluster 3 of Ch. bursts ( b ) or cluster 2 of STTC ( e ). Horizontal scale bar in raster plots, 20 s. Color of the edges between nodes in connectivity maps represents Pearson’s correlation. ( c , f ) Changes in Ch. bursts, STTC, Network spikes, Network bursts, and Efficiency between DIV 6–8 and 13–18 in each cluster of Ch. bursts ( c ) and STTC ( f ). The box plots show the median and the interquartile range with Tukey whiskers. Clusters are indicated on top of the graphs. The figure was created using Graphpad 8.0 ( https://www.graphpad.com ) ( c , f ), Neuroexplorer 5 ( https://plexon.com/products/neuroexplorer/ ), Matlab 9.8 ( https://www.mathworks.com/products/matlab.html ) ( b , e ), and R 4.03 ( https://www.r-project.org/ ) ( a , d ).

Article Snippet: The figure was created using Graphpad 8.0 ( https://www.graphpad.com ) ( a ), Neuroexplorer 5 ( https://plexon.com/products/neuroexplorer/ ) ( b ), and Matlab 9.8 ( https://www.mathworks.com/products/matlab.html ) ( c ).

Techniques: Activity Assay

Journal: Forensic Science, Medicine, and Pathology

Article Title: Discrimination of human and animal bloodstains using hyperspectral imaging

doi: 10.1007/s12024-023-00689-0

Figure Lengend Snippet: Results of first Bayesian optimisation for k-NN, bagged tree and 3rd-order polynomial SVM including optimised parameter values, the observed objective function value of 10-fold cross-validation, and corresponding F1 score

Article Snippet: Data balancing and data pre-processing were performed using custom scripts written in MATLAB (version 9.8; R2020a, The MathWorks Inc.), with machine learning algorithm selection and optimisation being performed using the Statistics and Machine Learning ToolboxTM and Imaging Processing ToolboxTM, both being provided by MATLAB.

Techniques:

Journal: Heliyon

Article Title: Estimating the sensory-associated metabolites profiling of matcha based on PDO attributes as elucidated by NIRS and MS approaches

doi: 10.1016/j.heliyon.2023.e21920

Figure Lengend Snippet: Partial least squares regression (PLSR) analysis for the potential of sensory prediction for matcha. (a) Correlations between the visual appearance scores and their NIRS profiles, (b) correlations between the interior quality scores and their NIRS profiles. (t 1 , t 2 ) refer to the first two PLSR components, 1 (green) refer to the special grade, 2 (blue) refer to the first grade, 3 (red) refer to the second grade.

Article Snippet: MATLAB (R2020a) (version 9.8, MathWorks Inc., USA) equipped with PLS Toolbox version 8.8.1 (Eigenvector Research Inc., Mason, USA) was utilized for PLSR and PCA analysis.

Techniques:

Journal: Heliyon

Article Title: Estimating the sensory-associated metabolites profiling of matcha based on PDO attributes as elucidated by NIRS and MS approaches

doi: 10.1016/j.heliyon.2023.e21920

Figure Lengend Snippet: Partial least squares discrimination analysis (PLS-DA) for the potential of grade prediction for matcha. (a) Correlations between the match grade and their NIRS profiles by the score plot based on the first two LVs; The projected test samples (external validation results) are symbolized by asterisks (b) score plot of LV1 and variation in contribution of wavelengths to LV1 for PLS-DA models.

Article Snippet: MATLAB (R2020a) (version 9.8, MathWorks Inc., USA) equipped with PLS Toolbox version 8.8.1 (Eigenvector Research Inc., Mason, USA) was utilized for PLSR and PCA analysis.

Techniques: Biomarker Discovery