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custom-developed matlab software  (MathWorks Inc)


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    MathWorks Inc custom-developed matlab software
    Custom Developed Matlab Software, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/custom-developed+software/pm40389518-51-7-8
    Average 90 stars, based on 1 article reviews
    custom-developed matlab software - by Bioz Stars, 2026-10
    90/100 stars

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    Article Title: Foot kinematics and kinetics data for different static foot posture collected using a multi-segment foot model
    Article Snippet: All kinematic and kinetic computations were performed using custom-developed software in Matlab version R2022b (The MathWorks Inc., Natick, MA, USA).

    Article Title: The BE-UJI hand function activity set: a reduced set of activities for the evaluation of the healthy and pathological hand
    Article Snippet: The data analysis was completely performed with the custom-developed software in Matlab.

    Article Title: From Amorphous to Polycrystalline Rubrene: Charge Transport in Organic Semiconductors Paralleled with Silicon
    Article Snippet: The analysis of the Hall signal over time was performed using a custom-developed software developed in Matlab.

    Article Title: Membrane cholesterol and substrate stiffness co-ordinate to induce the remodelling of the cytoskeleton and the alteration in the biomechanics of vascular smooth muscle cells
    Article Snippet: 6 , 36 All force curve data were analysed using a custom-developed software written in MATLAB (R2016a, MathWorks Inc., Natick, MA, USA).

    Article Title: Multi-view object topography measurement with optical sectioning structured illumination microscopy
    Article Snippet: Hardware synchronization of the DMD, the CMOS camera, and the hexapod stage, are carried out by custom-developed software written in MATLAB.

    Article Title: Effects of geometric individualisation of a human spine model on load sharing: neuro-musculoskeletal simulation reveals significant differences in ligament and muscle contribution.
    Article Snippet: Selected muscle attachments matched those already implemented in the generic baseline model. Subject-specific CT-derived segmental torso weights for each vertebral level were determined using a custom-developed software (MATLAB R2017b, The Mathworks, Natick, MA, USA) to automatically calculate the volume and centroid of the transverse CT slice and rigidly connected to the respective vertebra.


    Article Title: The BE-UJI hand function activity set: a reduced set of activities for the evaluation of the healthy and pathological hand.
    Article Snippet: Detailed analysis Step 1—extracting kinematic PCs The data analysis was completely performed with the custom-developed software in Matlab.



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    Hippocampal neuronal activity represents object <t>exploration</t> during OPR task (A) Normalized firing rate of hippocampal single-units in relation to object exploration in the sample phase of the OPR task. Units increasing their firing rate over 2 SD for at least 200 ms from object exploration onset (time 0) were classified as activated neurons (object cells, n = 114). The remaining non-encoding neuronal population did not significantly change its firing rate in relation to object exploration ( n = 136). (B) Average pairwise crosscorrelograms between object cells (red, n = 353 pairs) and non-encoding neurons (black, n = 812 pairs). Synchrony between object cells was significantly larger ( p < 8.5 × 10e-10, Wilcoxon rank-sum test). Average peri-event crosscorrelograms between hippocampal units and sleep oscillations showing consistent differences between object cells and non-encoding cells: slow wave activity (C, p < 6.1 × 10e-4), spindles (D, p < 6.4 × 10e-3) and ripples (E, p < 1.6 × 10e-3). Wilcoxon rank-sum test.
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    custom developed matlab software tool  (MathWorks Inc)
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    MathWorks Inc custom developed matlab software tool
    Hippocampal neuronal activity represents object <t>exploration</t> during OPR task (A) Normalized firing rate of hippocampal single-units in relation to object exploration in the sample phase of the OPR task. Units increasing their firing rate over 2 SD for at least 200 ms from object exploration onset (time 0) were classified as activated neurons (object cells, n = 114). The remaining non-encoding neuronal population did not significantly change its firing rate in relation to object exploration ( n = 136). (B) Average pairwise crosscorrelograms between object cells (red, n = 353 pairs) and non-encoding neurons (black, n = 812 pairs). Synchrony between object cells was significantly larger ( p < 8.5 × 10e-10, Wilcoxon rank-sum test). Average peri-event crosscorrelograms between hippocampal units and sleep oscillations showing consistent differences between object cells and non-encoding cells: slow wave activity (C, p < 6.1 × 10e-4), spindles (D, p < 6.4 × 10e-3) and ripples (E, p < 1.6 × 10e-3). Wilcoxon rank-sum test.
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    Hippocampal neuronal activity represents object exploration during OPR task (A) Normalized firing rate of hippocampal single-units in relation to object exploration in the sample phase of the OPR task. Units increasing their firing rate over 2 SD for at least 200 ms from object exploration onset (time 0) were classified as activated neurons (object cells, n = 114). The remaining non-encoding neuronal population did not significantly change its firing rate in relation to object exploration ( n = 136). (B) Average pairwise crosscorrelograms between object cells (red, n = 353 pairs) and non-encoding neurons (black, n = 812 pairs). Synchrony between object cells was significantly larger ( p < 8.5 × 10e-10, Wilcoxon rank-sum test). Average peri-event crosscorrelograms between hippocampal units and sleep oscillations showing consistent differences between object cells and non-encoding cells: slow wave activity (C, p < 6.1 × 10e-4), spindles (D, p < 6.4 × 10e-3) and ripples (E, p < 1.6 × 10e-3). Wilcoxon rank-sum test.

    Journal: iScience

    Article Title: Sleep-dependent decorrelation of hippocampal spatial representations

    doi: 10.1016/j.isci.2024.110076

    Figure Lengend Snippet: Hippocampal neuronal activity represents object exploration during OPR task (A) Normalized firing rate of hippocampal single-units in relation to object exploration in the sample phase of the OPR task. Units increasing their firing rate over 2 SD for at least 200 ms from object exploration onset (time 0) were classified as activated neurons (object cells, n = 114). The remaining non-encoding neuronal population did not significantly change its firing rate in relation to object exploration ( n = 136). (B) Average pairwise crosscorrelograms between object cells (red, n = 353 pairs) and non-encoding neurons (black, n = 812 pairs). Synchrony between object cells was significantly larger ( p < 8.5 × 10e-10, Wilcoxon rank-sum test). Average peri-event crosscorrelograms between hippocampal units and sleep oscillations showing consistent differences between object cells and non-encoding cells: slow wave activity (C, p < 6.1 × 10e-4), spindles (D, p < 6.4 × 10e-3) and ripples (E, p < 1.6 × 10e-3). Wilcoxon rank-sum test.

    Article Snippet: Scoring was performed by quantifying the exploration time of each object using custom-developed software in MATLAB.

    Techniques: Activity Assay

    Place cells are more excitable and synchronized than non-spatial neurons during sleep (A and B) Example place cell (A) and non-spatial cell (B) simultaneously recorded in the same experimental session, during the test phase (rat GV11, session 03). Left, spatial trajectory (black) and single spikes (red) discharged during arena exploration. Large white circles depict object location. Right, heat maps. (C) Average firing rates of place cells (PC, n = 192) and non-spatial cells (NS, n = 324) according to task phase. Note place cells are consistently more active than non-spatial cells (two-way ANOVA, p = 3.7 × 10e-21). Task stages were also different within groups (Kruskal-Wallis test, p = 4.9 × 10e-9 for place cells and p = 1.4 × 10e-6 for non-spatial cells). (D) Average firing rates of place cells and non-spatial cells according to sleep phase. Note place cells are consistently more active than non-spatial cells (two-way ANOVA, p = 3.1 × 10e-10). Sleep phases were also different within groups (Wilcoxon signed rank test, p = 1.37 × 10e-10 for place cells and p = 6.6 × 10e-8 for non-spatial cells). (E) Peak pairwise crosscorrelogram amplitude for place cells and non-spatial cells according to task phase. Note place cells are consistently more synchronized than non-spatial cells, particularly during sleep (Kruskal-Wallis test, p = 4.8 × 10e-26 for place cells and p = 0.20 for non-spatial cells). (F) Peak pairwise crosscorrelogram amplitude for place cells and non-spatial cells according to sleep phase. Note place cells are consistently more synchronized than non-spatial cells (two-way ANOVA, p = 1.8 × 10e-167), particularly during NREM. Sleep phases were also different within groups (Wilcoxon rank-sum test, p = 7.9 × 10e-26 for place cells and p = 4.5 × 10e-33 for non-spatial cells). (G) Average pairwise crosscorrelograms between place cells (red, n = 1,503 pairs) and non-spatial neurons (black, n = 4,221 pairs). Synchrony between place cells was significantly larger (Wilcoxon rank-sum test, p = 3.8 × 10e-89). Average peri-event crosscorrelogram between hippocampal units and sleep oscillations showing consistent differences between place cells and non-spatial cells: slow wave activity (H, p = 2.3 × 10e-7), spindles (I, p = 1.6 × 10e-7), and ripples (J, p = 4.1 × 10e-8). Wilcoxon rank-sum test. Asterisks indicate significant differences, p < 0.05, pairwise Tukey’s test. See also <xref ref-type=Figures S9 and . " width="100%" height="100%">

    Journal: iScience

    Article Title: Sleep-dependent decorrelation of hippocampal spatial representations

    doi: 10.1016/j.isci.2024.110076

    Figure Lengend Snippet: Place cells are more excitable and synchronized than non-spatial neurons during sleep (A and B) Example place cell (A) and non-spatial cell (B) simultaneously recorded in the same experimental session, during the test phase (rat GV11, session 03). Left, spatial trajectory (black) and single spikes (red) discharged during arena exploration. Large white circles depict object location. Right, heat maps. (C) Average firing rates of place cells (PC, n = 192) and non-spatial cells (NS, n = 324) according to task phase. Note place cells are consistently more active than non-spatial cells (two-way ANOVA, p = 3.7 × 10e-21). Task stages were also different within groups (Kruskal-Wallis test, p = 4.9 × 10e-9 for place cells and p = 1.4 × 10e-6 for non-spatial cells). (D) Average firing rates of place cells and non-spatial cells according to sleep phase. Note place cells are consistently more active than non-spatial cells (two-way ANOVA, p = 3.1 × 10e-10). Sleep phases were also different within groups (Wilcoxon signed rank test, p = 1.37 × 10e-10 for place cells and p = 6.6 × 10e-8 for non-spatial cells). (E) Peak pairwise crosscorrelogram amplitude for place cells and non-spatial cells according to task phase. Note place cells are consistently more synchronized than non-spatial cells, particularly during sleep (Kruskal-Wallis test, p = 4.8 × 10e-26 for place cells and p = 0.20 for non-spatial cells). (F) Peak pairwise crosscorrelogram amplitude for place cells and non-spatial cells according to sleep phase. Note place cells are consistently more synchronized than non-spatial cells (two-way ANOVA, p = 1.8 × 10e-167), particularly during NREM. Sleep phases were also different within groups (Wilcoxon rank-sum test, p = 7.9 × 10e-26 for place cells and p = 4.5 × 10e-33 for non-spatial cells). (G) Average pairwise crosscorrelograms between place cells (red, n = 1,503 pairs) and non-spatial neurons (black, n = 4,221 pairs). Synchrony between place cells was significantly larger (Wilcoxon rank-sum test, p = 3.8 × 10e-89). Average peri-event crosscorrelogram between hippocampal units and sleep oscillations showing consistent differences between place cells and non-spatial cells: slow wave activity (H, p = 2.3 × 10e-7), spindles (I, p = 1.6 × 10e-7), and ripples (J, p = 4.1 × 10e-8). Wilcoxon rank-sum test. Asterisks indicate significant differences, p < 0.05, pairwise Tukey’s test. See also Figures S9 and .

    Article Snippet: Scoring was performed by quantifying the exploration time of each object using custom-developed software in MATLAB.

    Techniques: Activity Assay

    Stability of spatial representations decreases across sleep Examples of place cells recorded in the exploration phases of the OPR task (A) or the open field (C). Average spatial correlation of place cells in relation to NREM duration for the OPR task (B, computed between test and sample phases, r = −0.53, p = 0.03, Spearman’s correlation) or the open field (D, computed between after and before sleep, r = 0.07, p = 0.97, Spearman’s correlation). Examples of population vector correlation maps obtained from sessions with different NREM durations for the OPR task (E, n = 15 sessions) and the open field (G, n = 9 sessions). Population vector correlation (stability index) in relation to NREM duration for the OPR task (F, r = −0.55, p = 0.028, Spearman’s correlation) and open field (H, r = 0.35, p = 0.36, Spearman’s correlation). Only significant linear regressions are plotted.

    Journal: iScience

    Article Title: Sleep-dependent decorrelation of hippocampal spatial representations

    doi: 10.1016/j.isci.2024.110076

    Figure Lengend Snippet: Stability of spatial representations decreases across sleep Examples of place cells recorded in the exploration phases of the OPR task (A) or the open field (C). Average spatial correlation of place cells in relation to NREM duration for the OPR task (B, computed between test and sample phases, r = −0.53, p = 0.03, Spearman’s correlation) or the open field (D, computed between after and before sleep, r = 0.07, p = 0.97, Spearman’s correlation). Examples of population vector correlation maps obtained from sessions with different NREM durations for the OPR task (E, n = 15 sessions) and the open field (G, n = 9 sessions). Population vector correlation (stability index) in relation to NREM duration for the OPR task (F, r = −0.55, p = 0.028, Spearman’s correlation) and open field (H, r = 0.35, p = 0.36, Spearman’s correlation). Only significant linear regressions are plotted.

    Article Snippet: Scoring was performed by quantifying the exploration time of each object using custom-developed software in MATLAB.

    Techniques: Plasmid Preparation