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MathWorks Inc data pre processing
Data Pre Processing, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2341 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/data+pre+processing/Database+Toolbox/pmc12563921-79-0-7
Average 96 stars, based on 2341 article reviews

data pre processing - by Bioz Stars, 2026-10

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Article Title: Multi-modal investigation of the bone micro- and ultrastructure, and elemental distribution in the presence of Mg-xGd screws at mid-term healing stages.
Article Snippet: Data pre-processing and tomographic reconstruction were performed in a MATLAB-based framework (version 2020a, The MathWorks Inc., USA) [28,] with the ASTRA toolbox for tomographic backprojection of the data obtained at PETRA III and using the open-source Savu framework with the TomoPy reconstruction package [29,30] for DLS data.

Article Title: Right-hemisphere coherence to speech at pre-reading stages predicts reading performance one year later
Article Snippet: Data pre-processing.We used the FieldTrip toolbox (Oostenveld et al., 2011) in Matlab 2014B (Mathworks, Natick, MA) to pre-process and analyse the data.

Article Title: Specific dynamic facial expression evoked responses show distinct perceptual and attentional features in autism connected to social communication and GABA phenotypes
Article Snippet: Data pre-processing and analysis were performed using Matlab ® ( Mathworks , version R2019b ) and the EEGLAB toolbox v2019_0 .

Article Title: Operational Modal Analysis of Near-Infrared Spectroscopy Measure of 2-Month Exercise Intervention Effects in Sedentary Older Adults with Diabetes and Cognitive Impairment.
Article Snippet: Data pre-processing was performed using HOMER3 toolbox [102] in Matlab (Mathworks Inc., Natick, MA, USA).

Article Title: EEG-Based Continuous Hand Movement Decoding Using Improved Center-Out Paradigm
Article Snippet: All data pre-processing was implemented by MATLAB 2021b (MathWorks, USA) and open source toolbox EEGLAB version 14.1.1 [34].

Article Title: Multi-modal investigation of the bone micro- and ultrastructure, and elemental distribution in the presence of Mg-xGd screws at mid-term healing stages
Article Snippet: Data pre-processing and tomographic reconstruction were performed in a MATLAB-based framework (version 2020a, The MathWorks Inc., USA) [ ,] with the ASTRA toolbox for tomographic backprojection of the data obtained at PETRA III and using the open-source Savu framework with the TomoPy reconstruction package [ , ] for DLS data.

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Article Title: Detection of EEG Activity in Response to the Surrounding Environment: A Neuro-Architecture Study
Article Snippet: .. Data pre-processing and analysis were performed in MATLAB 2023a (MathWorks Inc., Natick, MA, USA) using the EEGLAB toolbox [ ]. b illustrates a 10-s example after artifact removal, and the subsequent feature extraction process. ..

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Article Title: Chemometrics-Assisted Calibration of a Handheld LIBS Device for the Quantitative Determination of Major and Minor Elements in Artifacts from the Archaeological Park of Tindari (Italy)
Article Snippet: .. All data pre-processing, variable selection, and regression analysis steps were performed in a Matlab environment version 2023b (The MathWorks Inc., Natick, MA, USA) using PLS toolbox version 8.7.1 (Eigenvector Research, Inc., Manson, WA, USA). ..

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(A) Principal component analysis (PCA) plot of the bulk RNA-seq data from isolated TCR-transduced CD8 T cells following 48 hours of co-culture with each UM-SCC47 cell line and treatment condition (n = 4 donors per condition). (B-C) Log 2 normalized counts for (B) TNFRSF9 and (C) KLRC1 . (D) Heatmap of activation-induced genes defined as genes upregulated in the wildtype E7 11-20 SCT or HLA-A2 OE + IL-12p70 condition relative to the empty vector control. Genes are sorted by the log 2 fold change of gene expression in the comparison of HLA-A2 OE + IL-12p70 over wildtype E7 11-20 SCT. (E) Log 2 fold change of the differentially expressed genes for HLA-A2 OE + IL-12p70 versus HLA-A2 OE ( y-axis ) and wildtype E7 11-20 SCT versus HLA-A2 OE ( x-axis ). Points are colored by their respective k-means cluster from the heatmap in (D) . The diagonal lines indicate the 0.75 delta log 2 fold change threshold between the two comparisons. Colored overlays and labels denote the genes selected for the IL-12-specific, shared, and avidity-specific gene sets.

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) Principal component analysis (PCA) plot of the bulk RNA-seq data from isolated TCR-transduced CD8 T cells following 48 hours of co-culture with each UM-SCC47 cell line and treatment condition (n = 4 donors per condition). (B-C) Log 2 normalized counts for (B) TNFRSF9 and (C) KLRC1 . (D) Heatmap of activation-induced genes defined as genes upregulated in the wildtype E7 11-20 SCT or HLA-A2 OE + IL-12p70 condition relative to the empty vector control. Genes are sorted by the log 2 fold change of gene expression in the comparison of HLA-A2 OE + IL-12p70 over wildtype E7 11-20 SCT. (E) Log 2 fold change of the differentially expressed genes for HLA-A2 OE + IL-12p70 versus HLA-A2 OE ( y-axis ) and wildtype E7 11-20 SCT versus HLA-A2 OE ( x-axis ). Points are colored by their respective k-means cluster from the heatmap in (D) . The diagonal lines indicate the 0.75 delta log 2 fold change threshold between the two comparisons. Colored overlays and labels denote the genes selected for the IL-12-specific, shared, and avidity-specific gene sets.

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: RNA Sequencing, Isolation, Co-Culture Assay, Activation Assay, Plasmid Preparation, Control, Gene Expression, Comparison

(A) Module scores of the bulk RNA-seq gene sets. Datapoints are the median module score per patient among the cells in the cluster (n = 30-32 patients per cluster; Dunnett’s multiple comparisons test). (B) Mean Z-scored RNA expression per cluster of the genes in the bulk RNA-seq gene sets. Genes are sorted by the delta Z-score of the Tex-KLR cluster minus the mean of the Tex-Conv and Tex-CCR6 clusters. Only genes detected in > 5% of cells in at least one cluster are shown. (C) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile (Q). The score distribution of all cells of the expanded Tex clones is overlaid. (D) Mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score and each gene are indicated in the quartile 1 (Q1) column. (E) RNA expression for the pseudo-bulked expanded Tex clonotypes following variance stabilized transformation (VST). The large points represent the mean expression for the quartile, and smaller points represent individual clonotypes (n = 82 clonotypes for Q1, 81 each for the other quartiles; DESeq2 Wald test). (F) Expression of the top avidity-specific gene set ( Methods ) and the Tex-KLR signature in FACS-sorted CD8+ TIL subsets from human melanoma samples as quantified by UCell (n = 364 clonotypes from 5 patients for singlet vs. cancer cell doublet and n = 137 clonotypes from 3 patients for singlet vs. APC doublet). Statistical significance was tested using linear regression with patient, clonotype, and log10(clonotype cell count) as fixed-effect covariates.

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) Module scores of the bulk RNA-seq gene sets. Datapoints are the median module score per patient among the cells in the cluster (n = 30-32 patients per cluster; Dunnett’s multiple comparisons test). (B) Mean Z-scored RNA expression per cluster of the genes in the bulk RNA-seq gene sets. Genes are sorted by the delta Z-score of the Tex-KLR cluster minus the mean of the Tex-Conv and Tex-CCR6 clusters. Only genes detected in > 5% of cells in at least one cluster are shown. (C) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile (Q). The score distribution of all cells of the expanded Tex clones is overlaid. (D) Mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score and each gene are indicated in the quartile 1 (Q1) column. (E) RNA expression for the pseudo-bulked expanded Tex clonotypes following variance stabilized transformation (VST). The large points represent the mean expression for the quartile, and smaller points represent individual clonotypes (n = 82 clonotypes for Q1, 81 each for the other quartiles; DESeq2 Wald test). (F) Expression of the top avidity-specific gene set ( Methods ) and the Tex-KLR signature in FACS-sorted CD8+ TIL subsets from human melanoma samples as quantified by UCell (n = 364 clonotypes from 5 patients for singlet vs. cancer cell doublet and n = 137 clonotypes from 3 patients for singlet vs. APC doublet). Statistical significance was tested using linear regression with patient, clonotype, and log10(clonotype cell count) as fixed-effect covariates.

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: RNA Sequencing, RNA Expression, Clone Assay, Marker, Transformation Assay, Expressing, Cell Characterization

(A) UMAP plot of the CD8 T cell atlas (Xue et al., 2024). (B) Feature plots showing module scores for each bulk RNA-seq gene set. Only the genes that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C) Violin plots of the module score for each bulk RNA-seq gene set. Datapoints indicate the median module score for each sample in the cluster. (D) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile. The distribution of all cells of expanded Tex clones is shown as an overlaid black line. (E) Smoothed histogram of the normalized avidity-specific gene set module score distributions for the cells in the expanded Tex clonotypes ( solid line ) with the inter-quartile range ( shaded ribbon ). (F) Heatmap of the mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score with each gene are indicated in the quartile 1 (Q1) column. (G) Pseudo-bulked RNA expression of expanded Tex clonotypes following variance stabilized transformation (VST). The large points connected by the line represent the mean expression, and small points represent individual clonotypes (n = 80 clonotypes for Q1-Q3, 79 for Q4; DESeq2 Wald test).

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) UMAP plot of the CD8 T cell atlas (Xue et al., 2024). (B) Feature plots showing module scores for each bulk RNA-seq gene set. Only the genes that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C) Violin plots of the module score for each bulk RNA-seq gene set. Datapoints indicate the median module score for each sample in the cluster. (D) Avidity-specific gene set module score of the cells within representative expanded Tex clonotypes from each quartile. The distribution of all cells of expanded Tex clones is shown as an overlaid black line. (E) Smoothed histogram of the normalized avidity-specific gene set module score distributions for the cells in the expanded Tex clonotypes ( solid line ) with the inter-quartile range ( shaded ribbon ). (F) Heatmap of the mean Z-scored RNA expression for the pseudo-bulked expanded Tex clonotypes of each quartile for selected general Tex genes and Tex-KLR marker genes. The sign and significance of the Spearman correlation between the avidity-specific gene set module score with each gene are indicated in the quartile 1 (Q1) column. (G) Pseudo-bulked RNA expression of expanded Tex clonotypes following variance stabilized transformation (VST). The large points connected by the line represent the mean expression, and small points represent individual clonotypes (n = 80 clonotypes for Q1-Q3, 79 for Q4; DESeq2 Wald test).

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: RNA Sequencing, Clone Assay, RNA Expression, Marker, Transformation Assay, Expressing

(A) UMAP plot of an scRNA-seq dataset of murine CD8 T cells collected during the acute and chronic lymphocytic choriomeningitis virus (LCMV) infection models from the spleen, lung, and liver (Daniel et al., 2022). (B) Feature plots of the module score for each bulk RNA-seq gene set. Genes were converted to the equivalent mouse gene, and only the genes within the gene set that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C-E) Module scores of the (C) avidity-specific, (D) IL-12-specific, and (E) shared gene sets in the CD8 T cell clusters from day 21 post-infection with the chronic LCMV strain. (F) Spearman correlation of Klr genes in the dataset with the avidity-specific gene set module score at day 21 post-infection with the chronic LCMV strain. Bars are colored by -log 10 (FDR). (G) Dot plot of selected activation-associated genes among the Tex clusters from day 21 post-infection with the chronic LCMV model.

Journal: bioRxiv

Article Title: High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response

doi: 10.64898/2026.05.29.728765

Figure Lengend Snippet: (A) UMAP plot of an scRNA-seq dataset of murine CD8 T cells collected during the acute and chronic lymphocytic choriomeningitis virus (LCMV) infection models from the spleen, lung, and liver (Daniel et al., 2022). (B) Feature plots of the module score for each bulk RNA-seq gene set. Genes were converted to the equivalent mouse gene, and only the genes within the gene set that were present in > 5% of the cells in at least one cluster were used to apply the module score. (C-E) Module scores of the (C) avidity-specific, (D) IL-12-specific, and (E) shared gene sets in the CD8 T cell clusters from day 21 post-infection with the chronic LCMV strain. (F) Spearman correlation of Klr genes in the dataset with the avidity-specific gene set module score at day 21 post-infection with the chronic LCMV strain. Bars are colored by -log 10 (FDR). (G) Dot plot of selected activation-associated genes among the Tex clusters from day 21 post-infection with the chronic LCMV model.

Article Snippet: Bulk RNA-seq data pre-processing was performed by Plasmidsaurus.

Techniques: Virus, Infection, RNA Sequencing, Activation Assay

Association of BEND2 fusions with poor clinical outcomes (A) Diagram of sample composition for the pooled BEND2 cohort of pNET. (B) Kaplan-Meier curve of disease-specific survival rate regarding BEND2 rearrangement status. (C) Forest plot showing the hazard ratio (95% CI) in the univariate Cox regression and multivariate regression after adjusting for major clinicopathological features and the corresponding p values. The total number for the cohort, the number of cases per variable category, and the number of events (disease-specific deaths) for each level were also indicated.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Association of BEND2 fusions with poor clinical outcomes (A) Diagram of sample composition for the pooled BEND2 cohort of pNET. (B) Kaplan-Meier curve of disease-specific survival rate regarding BEND2 rearrangement status. (C) Forest plot showing the hazard ratio (95% CI) in the univariate Cox regression and multivariate regression after adjusting for major clinicopathological features and the corresponding p values. The total number for the cohort, the number of cases per variable category, and the number of events (disease-specific deaths) for each level were also indicated.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques:

Inter-tumor heterogeneity at single-nuclei level (A) UMAP representation of 43,619 nuclei isolated from nine pNET samples with tumor purity annotated at the top left corner. (B) UMAP showing a separation between tumor cells and non-tumor cells. Tumor cells were grouped according to the five-subtype bulk classification, referred as pseudo-bulk sn-clusters. (C) Dot plot of canonical marker genes across all identified cell populations. Dot size indicates the proportion of cells expressing a gene, and color intensity reflects mean expression levels. (D) Heatmap of the Hallmark signaling pathways specific for each of the five pseudo-bulk sn-clusters based on GSVA enrichment scores. (E) Regulon specificity plot showing the top six regulons identified for each of the five pseudo-bulk sn-clusters. The x axis of the plot represents the genes within the regulon, while the y axis represents the specificity score. (F) Validation of bulk subtype-specific regulon on the bulk pNET cohort using GSVA based on a set of target genes within a regulon.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Inter-tumor heterogeneity at single-nuclei level (A) UMAP representation of 43,619 nuclei isolated from nine pNET samples with tumor purity annotated at the top left corner. (B) UMAP showing a separation between tumor cells and non-tumor cells. Tumor cells were grouped according to the five-subtype bulk classification, referred as pseudo-bulk sn-clusters. (C) Dot plot of canonical marker genes across all identified cell populations. Dot size indicates the proportion of cells expressing a gene, and color intensity reflects mean expression levels. (D) Heatmap of the Hallmark signaling pathways specific for each of the five pseudo-bulk sn-clusters based on GSVA enrichment scores. (E) Regulon specificity plot showing the top six regulons identified for each of the five pseudo-bulk sn-clusters. The x axis of the plot represents the genes within the regulon, while the y axis represents the specificity score. (F) Validation of bulk subtype-specific regulon on the bulk pNET cohort using GSVA based on a set of target genes within a regulon.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: Isolation, Marker, Expressing, Protein-Protein interactions, Biomarker Discovery

Cell-cell communication network and in silico drug sensitivity prediction in pNET subtypes (A) Cell-cell communication network visualized in Cytoscape, depicting the number and strength of interactions between tumor and microenvironmental cell populations. Nodes represent cell types, and edges represent intercellular interactions, with edge width and color reflecting interaction strength and frequency. (B) Bubble plot of selected ligand-receptor pairs between tumor subtypes and non-tumor microenvironmental cell populations. (C) Correlation plots showing associations between gene expression and deconvoluted cell-type proportions in bulk pNET samples. (D) Kaplan-Meier survival curves showing different DSS rates between pNET patients with high and low expression of genes of interest, including NOTCH3 (left), CD74 (middle), and the geometric mean value of these two genes (right). Patients were categorized into different groups according to the optimal thresholding. (E) Mechanisms of action of the 10 compounds predicted by Connectivity Map (CMap) analysis to preferentially target the BEND2 fusion. (F) Predicted sensitivities to 12 HDAC inhibitors across pNET subtypes based on GDSC database analyses. Statistical comparisons were performed using the Kruskal-Wallis test. p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Cell-cell communication network and in silico drug sensitivity prediction in pNET subtypes (A) Cell-cell communication network visualized in Cytoscape, depicting the number and strength of interactions between tumor and microenvironmental cell populations. Nodes represent cell types, and edges represent intercellular interactions, with edge width and color reflecting interaction strength and frequency. (B) Bubble plot of selected ligand-receptor pairs between tumor subtypes and non-tumor microenvironmental cell populations. (C) Correlation plots showing associations between gene expression and deconvoluted cell-type proportions in bulk pNET samples. (D) Kaplan-Meier survival curves showing different DSS rates between pNET patients with high and low expression of genes of interest, including NOTCH3 (left), CD74 (middle), and the geometric mean value of these two genes (right). Patients were categorized into different groups according to the optimal thresholding. (E) Mechanisms of action of the 10 compounds predicted by Connectivity Map (CMap) analysis to preferentially target the BEND2 fusion. (F) Predicted sensitivities to 12 HDAC inhibitors across pNET subtypes based on GDSC database analyses. Statistical comparisons were performed using the Kruskal-Wallis test. p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: In Silico, Gene Expression, Expressing

BEND2 fusions induce transcriptional reprogramming and morphological plasticity in pNET tumor cells (A) Phase-contrast microscopy images of BON1 cells at 48 h post-induction showing morphological changes following overexpression of BEND2 -only, CHD7 - BEND2 , EWSR1 - BEND2 , or mCherry control. (B) Bar plot showing the number of differentially expressed genes at 12 and 48 h across all BON1 cell line models, including EWSR1 (E), BEND2 (B), CHD7 - BEND2 (CB), and EWSR1 - BEND2 (EB), compared to the mCherry (M) control. (C) Heatmap illustrating transcriptomic clustering of BON1 cell lines at 48 h (D) Venn diagram showing overlapping significantly upregulated transcription factors (TFs) in CHD7 - BEND2 and EWSR1 - BEND2 models compared to controls at 12 h (E) Bar plot of ASCL1 expression across BON1 cell line models at 12 and 48 h. Data are represented as mean ± SEM. (F) Heatmap showing transcriptional activation of neurodevelopmental, mesenchymal, and immune-related TFs in fusion-expressing BON1 cells at 48 h, accompanied by ASCL1 downregulation, activation of GAST-high subtype-specific regulons, and upregulation of immune checkpoint genes PDCD1 and CD274 . (G) Heatmap illustrating temporal transcriptomic shifts in BON1 fusion models using a 50-gene classifier (25 neuroendocrine and 25 non-neuroendocrine genes) derived from human SCLC lines. (H) GSEA results showing transcriptional reprogramming at 48 h in BEND2 fusion lines compared to 12 h (I) Bar plot showing GATA6 expression uniquely and robustly upregulated in BEND2 fusion lines at both 12 and 48 h. Data are represented as mean ± SEM. (J) Bar plot showing specific upregulation of POMC in BON1 cells expressing EWSR1 - BEND2 at 48 h. Data are represented as mean ± SEM. (K) Bar plot showing markedly higher POMC expression in the EWSR1 - BEND2 -positive tumor compared to the CHD7 - BEND2 -positive tumor in the clinical cohort.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: BEND2 fusions induce transcriptional reprogramming and morphological plasticity in pNET tumor cells (A) Phase-contrast microscopy images of BON1 cells at 48 h post-induction showing morphological changes following overexpression of BEND2 -only, CHD7 - BEND2 , EWSR1 - BEND2 , or mCherry control. (B) Bar plot showing the number of differentially expressed genes at 12 and 48 h across all BON1 cell line models, including EWSR1 (E), BEND2 (B), CHD7 - BEND2 (CB), and EWSR1 - BEND2 (EB), compared to the mCherry (M) control. (C) Heatmap illustrating transcriptomic clustering of BON1 cell lines at 48 h (D) Venn diagram showing overlapping significantly upregulated transcription factors (TFs) in CHD7 - BEND2 and EWSR1 - BEND2 models compared to controls at 12 h (E) Bar plot of ASCL1 expression across BON1 cell line models at 12 and 48 h. Data are represented as mean ± SEM. (F) Heatmap showing transcriptional activation of neurodevelopmental, mesenchymal, and immune-related TFs in fusion-expressing BON1 cells at 48 h, accompanied by ASCL1 downregulation, activation of GAST-high subtype-specific regulons, and upregulation of immune checkpoint genes PDCD1 and CD274 . (G) Heatmap illustrating temporal transcriptomic shifts in BON1 fusion models using a 50-gene classifier (25 neuroendocrine and 25 non-neuroendocrine genes) derived from human SCLC lines. (H) GSEA results showing transcriptional reprogramming at 48 h in BEND2 fusion lines compared to 12 h (I) Bar plot showing GATA6 expression uniquely and robustly upregulated in BEND2 fusion lines at both 12 and 48 h. Data are represented as mean ± SEM. (J) Bar plot showing specific upregulation of POMC in BON1 cells expressing EWSR1 - BEND2 at 48 h. Data are represented as mean ± SEM. (K) Bar plot showing markedly higher POMC expression in the EWSR1 - BEND2 -positive tumor compared to the CHD7 - BEND2 -positive tumor in the clinical cohort.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: Microscopy, Over Expression, Control, Expressing, Activation Assay, Derivative Assay

In healthy younger (n = 20) and older (n = 20) individuals, continuous and discontinuous passive movements were performed at three different speeds while 64-channelelectroencephalography (EEG)and biceps and triceps electromyography (EMG) data were acquired. bpm, beats per minute.

Journal: bioRxiv

Article Title: Neurophysiological correlates of passive movements are speed- and type-dependent

doi: 10.1101/2025.07.09.663910

Figure Lengend Snippet: In healthy younger (n = 20) and older (n = 20) individuals, continuous and discontinuous passive movements were performed at three different speeds while 64-channelelectroencephalography (EEG)and biceps and triceps electromyography (EMG) data were acquired. bpm, beats per minute.

Article Snippet: The EMG data was pre-processed in MATLAB R2022b (MathWorks).

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