Matlab r2007b

Manufactured by MathWorks

Sourced in United States

MATLAB R2007b is a high-performance numerical computing environment and programming language developed by MathWorks. It provides an integrated platform for algorithm development, data analysis, visualization, and numerical computation.

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Lab products found in correlation

Tonocap, by GE Healthcare (1 mentions) Nexfin, by Edwards Lifesciences (1 mentions) Nexfin co trek, by Edwards Lifesciences (1 mentions) Jsm 6390, by JEOL (1 mentions) Achieva whole body scanner, by Philips (1 mentions) Labview, by National Instruments (1 mentions) Usb 6009, by National Instruments (1 mentions) Drosophila activity monitor system, by Trikinetics (1 mentions) X series, by Philips (1 mentions) Matlab r2013a, by MathWorks (1 mentions)

26 protocols using matlab r2007b

Comparison of Bovine and Ruminant Probes

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Statistical analyses were performed using Matlab R2007b (The MathWorks Inc, Natick, USA). Before each analysis, data were cross-checked by the scientific staff.
In order to compare the percentage of positive staining obtained in BBM and in PBM with the bovine probe, a statistical analysis was conducted using a two-sample Wilcoxon rank-sum (Mann-Whitney) test. The Fisher's exact test was used to compare the results obtained with the ruminant probe for BBM and PBM without or with contamination. The level of statistical significance for both tests was set at P < 0.05.

Lecrenier M.C., Ledoux Q., Berben G., Fumière O., Saegerman C., Baeten V, & Veys P. (2014). Determination of the ruminant origin of bone particles using fluorescence in situ hybridization (FISH). Scientific Reports, 4, 5730.

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Luminance-Matched Colored Circle Stimuli

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Stimuli were coloured circles (red or green), with a diameter subtending ~23° of visual angle. Green and red circles were matched in luminance (green circles CIE: 0.2858, 0.5939, 8.9311; red circles CIE: 0.6337, 0.3117, 8.9311). Stimuli were presented on a calibrated 20-in. CRT SyncMaster 1100p-Plus monitor, driven by a Cambridge Research Systems ViSaGe stimulus generator and custom Matlab R2007b (The MathWorks, Inc., 2007 ) software. The monitor had a resolution of 1,024 × 768 pixels and a refresh rate of 100 Hz. Participants viewed stimuli from 57 cm, from directly in front of the monitor with their chin placed on a chin rest.

Saurels B.W., Arnold D.H., Anderson N.L., Lipp O.V, & Yarrow K. (2022). The perceived duration of expected events depends on how the expectation is formed. Attention, Perception & Psychophysics, 84(5), 1718-1725.

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Visual Stimuli Generation for Cognitive Experiments

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We created the visual stimuli on a Windows^TM based PC using MATLAB R2007b (The MathWorks, Inc.) and Cogent Graphics developed by John Romaya at the LON at the Wellcome Department of Imaging Neuroscience. They were projected onto a translucent screen (size of the projected image: 28 deg x 37 deg visual angle; viewing distance: 92 cm) by means of a video projector (frame rate: 60 Hz; resolution: 1024 x 768 pixels). Our participants watched the projected stimuli on the translucent screen being placed behind them with the aid of a mirror that was mounted on the head coil.
We displayed the fixation cross of the baseline and delay phases in Arial font and a 2.44 degrees visual angle font size. The squared color cue was 2.44 deg x 2.44 deg. The modality cue images were 1.95deg x 1.76deg. The spatial cues (dots) were placed at 4.9 deg radius from the central fixation point and were approximately 1 degree in diameter, each.

Pilacinski A., Höller-Wallscheid M.S, & Lindner A. (2020). Remember how to use it: Effector-dependent modulation of spatial working memory activity in posterior parietal cortex. PLoS ONE, 15(8), e0238022.

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Leaf and Petal Modeling Using AAM

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After images from all of the lines of the fourth and seventh leaves and the petals were prepared, the digital images were properly oriented (with the tip always pointing to the right and with good horizontality) using Photoshop CS5 software (Adobe Inc.). After that, we used MATLAB R2007b software (MathWorks Inc.) and the AAM Toolbox (version 6.5) [10 (link)] to construct the model of each individual leaf and petal separately [1 (link)]. The outline of each leaf and petal was represented by the Cartesian coordinates of its 25 points, which were placed around the leaf and petal outline using the leaf (Le) and petal (Pe) templates. These points were plotted to show the pattern of allometry in the data set, and PCA was used on the whole data set to identify trends in variation.

Li X., Zhang Y., Yang S., Wu C., Shao Q, & Feng X. (2020). The genetic control of leaf and petal allometric variations in Arabidopsis thaliana. BMC Plant Biology, 20, 547.

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Hemodynamic Monitoring in Critical Care

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Blood pressure (BP) was continuously measured using a non-invasive volume clamp method (Nexfin, Edwards Lifesciences BMEYE, Amsterdam, the Netherlands). Left ventricular SV was estimated by a pulse contour method (Nexfin CO-trek, Edwards Lifesciences BMEYE, Amsterdam, the Netherlands) [24 (link), 25 (link)] and CO was SV times heart rate (HR). SV index (SVI) was the ratio of SV and body surface area [26 ]. SPV and PPV were calculated from the BP signal:

100 \times \frac{A_{max} - A_{min}}{(A_{max} + A_{min}) / 2}

with A_max/min equal to, respectively, systolic arterial pressure (SAP) and pulse pressure (PP; SAP minus diastolic arterial pressure (DAP)). PPV and SPV were calculated for each breath and averaged over 5 consecutive breaths.
Airway flow and pressure were measured using an Alveotest flowmeter (Jaeger, Würzburg, Germany), tidal volume (TV) was the integral of airway flow (expressed in mL per kg predicted body weight) and end-tidal CO₂ (PetCO₂) was measured by capnography (Tonocap, Datex-Ohmeda, Madison, USA). Signals were visually inspected for artefacts and 60-second intervals were used for offline analysis (Matlab R2007b, Mathworks Inc. MA, USA).

Bronzwaer A.S., Ouweneel D.M., Stok W.J., Westerhof B.E, & van Lieshout J.J. (2015). Arterial Pressure Variation as a Biomarker of Preload Dependency in Spontaneously Breathing Subjects – A Proof of Principle. PLoS ONE, 10(9), e0137364.

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Measuring Contrast Thresholds via Adaptive Staircases

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To determine contrast thresholds of the subjects, an adaptive 2:1 staircase procedure (Levitt, 1971 ) was used for both medium-sf conditions to adjust the contrast of the Gabor patches online. The QUEST procedure (Watson & Pelli, 1983 (link)) was used to adjust the contrast of the Gabors in the low-sf and in the high-sf condition. The collected data was used to fit psychometric functions of detectability for every observer and all investigated target locations in all four conditions. This was done offline using the psignifit toolbox (Wichman & Hill, 2001 ) on Matlab R2007b (The MathWorks). Sensitivity data of the medium-sf conditions was compared with a 2 (viewing conditions) × 2 (eccentricities) repeated measures ANOVA using the statistical package R (R Development Core Team 2011).

Paulun V.C., Schütz A.C., Michel M.M., Geisler W.S, & Gegenfurtner K.R. (2015). Visual search under scotopic lighting conditions. Vision research, 113(Pt B), 155-168.

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Saccadic Eye Movements in Visual Search

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Saccades were detected with the EyeLink saccade detector, which uses a combination of a velocity and acceleration criterion. Data were then analyzed using customized software in Matlab R2007b (The MathWorks). We determined the search time for correct trials, i.e. between the onset of the trial and the first button press, which indicated that the subject found the target, the percentage of correct responses and the ratio between these two as measures of the search performance. Fixations were analyzed with regard to their number, duration and location on the search display. Additionally, we analyzed the direction and amplitude of saccades. Saccades and fixations were only considered for analysis if they were recorded during the search time and only analyzed for hits. Where applicable, we computed individual means for each observer, viewing condition and eccentricity (averaged across angles) to compare the scotopic and photopic medium-sf condition with repeated measures 2 (viewing condition) ×4 (eccentricity) – ANOVAs using the statistical package R (R Development Core Team 2011). This analysis could not be conducted for the low-sf and high-sf condition as only two observers completed these conditions.

Paulun V.C., Schütz A.C., Michel M.M., Geisler W.S, & Gegenfurtner K.R. (2015). Visual search under scotopic lighting conditions. Vision research, 113(Pt B), 155-168.

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Beam-Blanking SEM Imaging of Biological Specimens

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A beam-blanking unit (Sanyu Electron Co., Japan) consisting of deflection plates was introduced into the thermionic emission SEM (JSM-6390, JEOL, Japan). The unit was controlled by a function generator (WF1974, NF Co., Japan) using a square wave between 0 and 10 V and 30−60 kHz frequency. The atmospheric sample holder was fixed onto an aluminium stage on the upper side of the W–Ni-coated SiN film. The sample holder with the biological specimens in water was mounted onto the sample stage, and its measurement terminal was connected to the pre-amplifier (Figure 1A). The electric frequency signal from the pre-amplifier was entered into a lock-in amplifier (LI5640, NF Co., Japan). The XY scanning signal and the output of lock-in amplifier were recorded using a data recorder (EZ7510, NF Co., Japan). The data files were transferred to a personal computer (Intel Core i7, 2.8 GHz, Windows 7), and the FTE images were calculated using Matlab R2007b (Math Works Inc., USA). The observation conditions of SEM were captured under the following parameters: 1500−4000× magnifications, 1280 × 960 pixels, 160-s scanning time, 7-mm working distance, 3−4 kV accelerating EB, and 300−700 pA current.

, & Ogura T. (2014). Direct Observation of Unstained Biological Specimens in Water by the Frequency Transmission Electric-Field Method Using SEM. PLoS ONE, 9(3), e92780.

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Spectator Breathing Synchrony in Duet Performance

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Individual level analysis: For each spectator, we fitted the data to four (one per duet) multi-linear regression models using MATLAB R2007b (The MathWorks, Inc., Natick, Massachusetts, USA). The regression equation was YBR(t) = aX1BR(t) + bX2BR(t). YBR is the breathing rate of the spectator during the performance of the duet. X1BR and X2BR are the breathing rates of the two dancers from the duet. To study the combined effect of the two dancers’ breathing rate we extracted the model’s R2 (amount of variance explained by the model) rather than the coefficients of the individual dancers. In order to estimate the overall effect of the 4 duets we then averaged the four R2 values. We shall name this measure the R2 score.
Group level analysis: Individual responses to the four target questions were modeled using linear mixed models (lm4 package for R; Bates et al., 2014 ) with “session” and “R2 score” as fixed effects and “subject” as random effect. A linear mixed model was used rather than a simple correlation test since 4 subjects participated twice (see above).

Bachrach A., Fontbonne Y., Joufflineau C, & Ulloa J.L. (2015). Audience entrainment during live contemporary dance performance: physiological and cognitive measures. Frontiers in Human Neuroscience, 9, 179.

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High-Resolution SE-ADM Image Processing

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SE-ADM signal data from the AD converter were transferred to a personal computer (Intel Core i7, 2.8 GHz, Windows 7), and high-resolution SE-ADM images were processed from the LPF signal and scanning signal using the image-processing toolbox of MATLAB R2007b (Math Works Inc., Natick, MA, USA). Original SE-ADM images were filtered using a two-dimensional (2D) Gaussian filter (GF) with a kernel size of 7 × 7 pixels and a radius of 1.2 σ. Background subtraction was achieved by subtracting SE-ADM images from the filtered images using a broad GF (400 × 400 pixels, 200 σ).

Okada T, & Ogura T. (2017). High-resolution imaging of living mammalian cells bound by nanobeads-connected antibodies in a medium using scanning electron-assisted dielectric microscopy. Scientific Reports, 7, 43025.

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