Journal: Diagnostics

Article Title: Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery

doi: 10.3390/diagnostics12041017

Figure Lengend Snippet: GUI for the resection segmentation. ( A ): Interface to select postoperative MRI and preoperative brain-mask files, or the patient’s Brainstorm anatomy folder that contains them. ( B ): Slider to select tolerance threshold (default: optimal value). ( C ): Interface to place the initial seed using an MRI viewer (see ) or entering its coordinates. The user can set an additional ROI for region growing by entering its ranges (this is optional, see ). ( D ): Options to exclude brain ventricles and/or generate the anatomical report through a brain parcellation file, which must be selected. E and F: the user can change the view ( E ) and slice ( F ) to visualize the postoperative MRI and resection model shown in G. ( G ): Preview of results (left: postoperative MRI; middle: resection model, right: automated report and resection volume in cm 3 ). ( H ): Buttons to run the segmentation (“Calculate”), or to export results to a file (NIfTI), MATLAB workspace, or directly to the initial Brainstorm anatomy folder. Status LED turns red in case of error (green otherwise). “Show 3D” button shows the resection model (see ), while “Reset” allows to reset all settings and run again.

Article Snippet: The GUI source code is freely available as a MATLAB-based application ( https://github.com/rbillardello/BrainResectionApp (accessed on 14 April 2022)).

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Journal: PLOS ONE

Article Title: EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI)

doi: 10.1371/journal.pone.0308873

Figure Lengend Snippet: Comparison of currently available software for flow cytometry analysis.

Article Snippet: Here we present the Matlab-based EasyFlow ( github.com/AntebiLab/easyflow ) and its derivative standalone Python EasyFlowQ ( ym3141.github.io/EasyFlowQ/ ), which are open source user-friendly GUI, can be run on multiple platforms (Windows, MacOS and Linux), and require no coding knowledge.

Techniques: Comparison, Software, Flow Cytometry

Journal: PLOS ONE

Article Title: EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI)

doi: 10.1371/journal.pone.0308873

Figure Lengend Snippet: Screenshots of the EasyFlow (Matlab) (A) and EasyFlowQ (Python) (B), with basic functions including managing FCS files, plotting and gating, annotated.

Article Snippet: Here we present the Matlab-based EasyFlow ( github.com/AntebiLab/easyflow ) and its derivative standalone Python EasyFlowQ ( ym3141.github.io/EasyFlowQ/ ), which are open source user-friendly GUI, can be run on multiple platforms (Windows, MacOS and Linux), and require no coding knowledge.

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Journal: PLOS ONE

Article Title: EasyFlow: An open-source, user-friendly cytometry analyzer with graphic user interface (GUI)

doi: 10.1371/journal.pone.0308873

Figure Lengend Snippet: T cells (Modified Jurkats, see ) were co-cultured with antigen-presenting cells (T2 line, B cells) and their cognate peptide to induce T cell activation. Cells were stained with antibodies to CD19 (B cell marker), CD3 (T cell receptor subunit), and CD69 (T cell activation marker). To gate live cells (A) and single cells (B), cells are plotted using the “Colored Dot Plot’’ option to visualize cell density and identify the sub-populations in the data. Cells are gated using a polygonal 2-dimensional gate, allowing to set the required gate to select for the desired population of cells. Next, a histogram display is used to identify the T cells and remove the B cells (C) and to identify T cells expressing CD3 that can respond to the added peptide (D). Using a 1-dimensional gate, we select the desired cells by choosing the range of values for the corresponding marker within a bi-modal population. In EasyFlow, gates are defined globally so that even if created for a single sample, gates can be applied to all samples in the analysis. In this way, the sequence of gates is applied to all samples in the analysis, enabling the comparison between different conditions. Finally, the percentage of activated cells as determined by the expression of CD69 is examined on the gated live single peptide-sensitive T cells. The percentage of CD69-expressing cells under three conditions: low, high, and no added peptide is examined (E). In all panels, the top row shows the EasyFlow (Matlab) UI, while the bottom row shows the EasyFlowQ (Python) UI.

Article Snippet: Here we present the Matlab-based EasyFlow ( github.com/AntebiLab/easyflow ) and its derivative standalone Python EasyFlowQ ( ym3141.github.io/EasyFlowQ/ ), which are open source user-friendly GUI, can be run on multiple platforms (Windows, MacOS and Linux), and require no coding knowledge.

Techniques: Modification, Cell Culture, Activation Assay, Staining, Marker, Expressing, Sequencing, Comparison

Journal: Frontiers in bioscience (Landmark edition)

Article Title: Enabling techniques for in vitro studies on mammalian spinal locomotor mechanisms

doi: 10.2741/4043

Figure Lengend Snippet: Commonly measured variables to describe locomotion analyzed with a custom Matlab-based GUI. SpinalMOD is designed to analyze ventral root, dorsal root, or muscle bursting for up to four channels. (a) Screenshot of the GUI with boxed regions enlarged in panel (b). Input variables are shown to the left and readily changed by the user by clicking in the box. The raw (green) and filtered (black) waveforms are graphed in the middle. Following burst detection, burst onset (red) and offset (blue) are denoted by vertical stems. "Run Updated Variables" allows the user to adjust the input variables and rerun the detection algorithm to correct misplaced burst markers. The stems can be also manually changed with “Change On/Off Stem Marks.” Once burst detection is acceptable, burst analysis is run by selecting “Run Burst Analysis.” The user then identifies the first burst to analyze and provides the number of bursts to be analyzed. Dashed black stems mark the beginning and end of the analyzed period. Once analyzed, the values calculated are shown in tabular and graphical form the Burst Analysis section to the right of the GUI. A phase plot figure as well as three of six other graphs, (average waveform, period, peak height, duty cycle, burst duration, and power spectrum), can be chosen for display. Finally, tabulated value and figures can be exported to Excel for subsequent use. (c). Blow-up of example raw and filtered waveform data shows commonly measured variables used to describe locomotion. For more information and to download SpinalMOD freely go to http://userwww.service.emory.edu/~shochm2/main_menu.html.

Article Snippet: Provision of a MATLAB-based code to analyze important measures of locomotion To aid in quantifying and comparing rhythmic locomotor patterns, we developed a custom MATLAB® Graphical User Interface (GUI) called SpinalMOD (Spinal Motor Output Detector) for the analysis of LLA ( ).

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