The largest database of trusted experimental protocols

Matlab code

Manufactured by MathWorks
Sourced in United States

MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.

Automatically generated - may contain errors

15 protocols using matlab code

1

Quantifying QD-Apatite Fluorescence in Roots

Check if the same lab product or an alternative is used in the 5 most similar protocols
We next quantified QD‐apatite fluorescence in the roots (Whiteside et al., 2019). We prepared the ground roots by adding 150 µl 10 mM borate buffer per mg of root. From each root sample, we took five replicates of 150 µl and pipetted them in a 96‐well plate with a glass bottom (Eppendorf AG, Hamburg, Germany). To circumvent edge effects, we left the outermost wells empty. We measured the emission spectra using a standard 96‐well epifluorescence microplate reader (Syngery• Mx monochromator‐based multimode microplate reader; BioTek, Winooski, VT, USA). We measured the emission at 325 nm excitation, ranging from 450 to 800 nm, with interval steps of 2 nm.
We translated the emission spectra to QD‐apatite concentrations using emission finger printing. This technique allowed us to separate the emission curves from the three differently coloured QDs even if these curves were overlapping. We used a custom script in Matlab Code (MathWorks, Natick, MA, USA) to detect low levels of QDs (> 0.000 001 nmol quantum dot per mg of plant tissue). For specific details see Whiteside et al. (2019). We converted fluorescence intensities to specific QD‐apatite transfer rates using a calibration gradient of QD‐apatite for each colour, composing of seven concentrations: 13.1, 9.83, 7.37, 5.53, 4.15, 3.11 and 2.33 mM.
+ Open protocol
+ Expand
2

Normalizing EMG and MVC Data Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols
EMG and MVC data were exported via spreadsheets for further filtering with a customizable Matlab code (MathWorks, 2017)31 (link). Data were filtered twice (1) with the Telemyo and (2) with the Matlab code. Data were filtered with a fourth order bandpass Butterworth filter, with cutoffs between 10 to 400 Hz, before it was demeaned and rectified. A low pass filter (8 Hz) was used for smoothing.
The dynamic EMG data were normalized using the MVC collected prior to begin of the study protocol22 ,23 . Normalized data were multiplied by 100 to transform them to a percentage of the MVC EMG data22 ,23 . Peak Percentage EMGs (pEMG), peak activation of the muscle, and integrated EMG (iEMG), area under the curve, were exported to a Microsoft Excel Spreadsheet (Microsoft, 2016).
+ Open protocol
+ Expand
3

Dose Recalculation for Patient Plan Overshoot

Check if the same lab product or an alternative is used in the 5 most similar protocols
In order to find the clinically relevant patient dose error caused by the overshoot phenomenon, we recalculated the patient dose based on the centi‐MU counter records. For each patient plan, the TPS plan file which contains the detailed MLC positions and segment MUs was extracted from Pinnacle (Philips Healthcare, Andover, MA). An in‐house developed MATLAB code (MathWorks, Natick, MA) was used to overwrite the planned segment MUs in this file with the pulse counter‐recorded delivered segment MUs, creating a “delivered” plan file. The delivered plan file was then reimported to Pinnacle and patient dose was recalculated within TPS. The recalculated “delivered” patient dose was compared to the original TPS plan by evaluating the percent dose difference of every voxel. Furthermore, clinical DVH criteria were also evaluated, including the dose PTV D95, rectum D1cc and V50, bladder D1cc and Dmean, and bowel D1cc and V15.
+ Open protocol
+ Expand
4

3D Mapping of Focal Adhesions

Check if the same lab product or an alternative is used in the 5 most similar protocols
Cells with immunofluorescence-stained vinculin were imaged using confocal microscope (Leica SP8) with a X63 oil immersion objective (Zeiss, 1.6 NA), and the Z- direction re-construction was performed with customized MATLAB code (The MathWorks, Natick, MA). The code is accessible in supplementary information. We found the centroid coordinates of the re-constructed vinculin-stained focal adhesions and measured their depths relative to the zero plain level (at the top of the posts) based on the Z-coordinates. Then, we divided the vinculin-marked focal adhesions in individual cells into two groups, depending on their locations corresponding to the local post density, i.e., those in the sparser vs. denser post regions, and averaged their depths within each group. Relative depths were calculated by subtracting the mean depth of the focal adhesions on denser relative to the sparser region in an individual cell; positive relative depth indicates deeper average focal adhesions penetration on the sparser post region than on denser post region.
+ Open protocol
+ Expand
5

MRI Distortion Mapping and Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols
The MRI data was analyzed with the MriPlanner-software (v1.0.46, Spectronic Medical AB, Helsingborg, Sweden). The software used a geometrically accurate reference (i.e. our CT acquisition) and deformable registration to determine the distortions in the MR-images and produced text files that contained coordinates of real marker positions and deformed marker positions. Using the marker position information and a home-written MATLAB-code (R2019a, The MathWorks Inc., Natick, MA, US), the distortion magnitudes were determined in spherical volumes around the scanner isocenter with different diameters of the spherical volume (DSV), as well as in cylindrical volumes of interest (VOI) of different lengths along the z-axis (i.e. along the direction of the scanner bore; see Supplementary Fig. S1). Additionally, the one standard deviation (1SD) of the distortion magnitudes were determined marker by marker between the single-setups and the repeated-setups.
+ Open protocol
+ Expand
6

Evaluating Upper Extremity Kinematics and Strength Ratios

Check if the same lab product or an alternative is used in the 5 most similar protocols
Upper extremity kinematic findings during the humeral elevation task were first
processed by use of Nexus software (v 1.8.5; Vicon) and using a standard plug-in
gait–full body model, a modified version of the Newington–Helen Hayes gait
model. Raw kinematics were filtered by use of a Woltring filter routine.30 The static capture was used for anatomic reference, and Euler rotation
angles were calculated from 3D coordinate data. Finally, a custom-written Matlab
code (v R2014a; MathWorks) was used to identify all kinematic variables,
including scapular protraction/retraction, downward/upward rotation, and
anterior/posterior tilt. All variables were identified at 90° and 120° of
humeral elevation and calculated as the average of 5 repetitions.
Bilateral strength asymmetries were calculated by the following formula, where
NDL indicates the average peak torque for the nondominant
limb and DL indicates the same for the dominant limb:
Ipsilateral protraction/retraction and external/internal ratios were simply
calculated by dividing the average peak torque of the protractor or external
rotators by that of the retractor or internal rotators, respectively. For
reference, values of zero would indicate no difference for bilateral strength
asymmetries, and values of 1.0 would indicate no difference for ipsilateral
strength ratios.
+ Open protocol
+ Expand
7

3D Mapping of Focal Adhesions

Check if the same lab product or an alternative is used in the 5 most similar protocols
Cells with immunofluorescence-stained vinculin were imaged using confocal microscope (Leica SP8) with a X63 oil immersion objective (Zeiss, 1.6 NA), and the Z- direction re-construction was performed with customized MATLAB code (The MathWorks, Natick, MA). The code is accessible in supplementary information. We found the centroid coordinates of the re-constructed vinculin-stained focal adhesions and measured their depths relative to the zero plain level (at the top of the posts) based on the Z-coordinates. Then, we divided the vinculin-marked focal adhesions in individual cells into two groups, depending on their locations corresponding to the local post density, i.e., those in the sparser vs. denser post regions, and averaged their depths within each group. Relative depths were calculated by subtracting the mean depth of the focal adhesions on denser relative to the sparser region in an individual cell; positive relative depth indicates deeper average focal adhesions penetration on the sparser post region than on denser post region.
+ Open protocol
+ Expand
8

Statistical Analyses for Research Data

Check if the same lab product or an alternative is used in the 5 most similar protocols
Analyses were performed using custom-written Matlab code (2015B; 2017A, Mathworks) and JMP software (Version 12, SAS Institute, NC). One-way, two-way and three-way (repeated measures) ANOVAs were used as appropriate. When data were not normally distributed or the group variances were unequal, one of the following non-parametric tests was used, as appropriate: two-way ANOVA on ranks, Kruskal-Wallis or Welch’s one-way ANOVA. Fisher’s LSD were performed for post-hoc pair-wise comparisons. Statistical analyses are reported in the figure legends or in the RESULTS section, when not corresponding to a figure. The significance level is set to 0.05 and all tests are two-tailed.
+ Open protocol
+ Expand
9

Multimodal Acoustic Feature Extraction

Check if the same lab product or an alternative is used in the 5 most similar protocols
The raw and aggregated data, stimulus files and Matlab code (The MathWorks, 2012 ) to reproduce the analyses and figures in this article are available at the Open Science Framework: https://osf.io/a8g32. The files are annotated and demonstrate how to extract the stimulus features from the audio files, how to create figures and perform the QDA analysis. In addition, the raw data is available in spreadsheet format.
+ Open protocol
+ Expand
10

Imaging-Guided Tumor Tissue Analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols
Co-registered images captured of targeted and untargeted fluorescence were processed and used to create DDSI images of each tumor-adipose tissue pair. Image processing was completed using custom-written MatLab Code (MathWorks, Natick, MA). Image processing began by subtracting the median background signal from the entire image in a user selected region of interest (ROI) in which no tissue was present. To account for any fluorescence variance between experiments, the staining solutions for each study were imaged and a user defined ROI was quantified for each probe pair and concentration used for staining. Images from each fluorescence channel were normalized by dividing each pixel by the average intensity value of the ROI representing the DDSI staining solution corresponding to the probe pair and concentration used for staining. A mask was then applied to each normalized image so that only pixels of measurable fluorescence (0.8-1.2x the average pixel value of the area containing tissue) were used in the DDSI image calculation. The DDSI image was then calculated as IDDSI = (ITargeted - IUntargeted) / IUntargeted. Tumor and normal tissue areas were determined via user selected ROIs encompassing the entire tissue area and intersected with the tissue mask for statistical analysis.
+ Open protocol
+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!