Igor pro 8

Manufactured by Wavemetrics

Sourced in United States

Igor Pro 8 is a powerful data analysis and visualization software. It provides a comprehensive suite of tools for scientific data processing, including functions for curve fitting, signal processing, and statistical analysis. Igor Pro 8 is designed to handle a wide range of data types, making it a versatile solution for researchers and scientists across various disciplines.

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Lab products found in correlation

Prism 9, by GraphPad (6 mentions) Prism 8, by GraphPad (5 mentions) Fitmaster, by HEKA Elektronik (4 mentions) Nicolet is50, by Thermo Fisher Scientific (3 mentions) D luciferin, by PerkinElmer (3 mentions) Triton x 100, by Merck Group (3 mentions) Matlab, by MathWorks (3 mentions) Patchmaster, by HEKA Elektronik (2 mentions) Victor multilabel plate reader, by PerkinElmer (2 mentions) Bf150 86 10, by Sutter Instruments (1 mentions) Spike2, by Cambridge Electronic Design (1 mentions) Coolsnap es camera, by Nikon (1 mentions) 1260 autosampler, by Agilent Technologies (1 mentions) Kinetex 2.6 mm xb c18100, by Phenomenex (1 mentions) P 1000 micropipette puller, by Sutter Instruments (1 mentions) Agilent 1260 hplc, by Agilent Technologies (1 mentions) Illustrator 2022, by Adobe (1 mentions) Digidata 1550b, by Molecular Devices (1 mentions) Axopatch 200b amplifier, by Molecular Devices (1 mentions) F 2710, by Hitachi (1 mentions)

110 protocols using igor pro 8

Analysis of Protein-Ligand Binding Kinetics

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The dwell times of the bound (t_on), and unbound (t_off) states were extracted from the TCC traces using the HMM fittings, as shown in Supplementary Fig. 8a. Considering a FRET threshold of 0.1, FRET > 0.1 was defined as bound, and 0 FRET was defined as unbound (Supplementary Fig. 8a). The dwell times were binned according to the web-based bin optimization algorithm (https://www.neuralengine.org//res/histogram.html), and histograms were generated in Igor Pro 8 (WaveMetrics). Single exponential functions were used to calculate the average τ_on and τ_off, where A₁ and A₂ are constants.

C o u n t s_{b o u n d} = A_{1} e^{- t_{o n} / τ_{o n}}

C o u n t s_{u n b o u n d} = A_{2} e^{- t_{o f f} / τ_{o f f}}

The transition counts for TCC traces were calculated using a FRET threshold of 0.1. Increasing FRET values that crossed the threshold was defined as transitions. Transition counts from individual traces were binned with 1-transition bins to build histograms in Igor Pro 8 (WaveMetrics).

Senavirathne G., London J., Gardner A., Fishel R, & Yoder K.E. (2023). DNA strand breaks and gaps target retroviral intasome binding and integration. Nature Communications, 14, 7072.

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Statistical Analysis of Neurological Outcomes

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Unless otherwise stated, statistical analyses and graph plotting were conducted in Igor Pro 8.02 (WaveMetrics, Portland, OR). Statistical significance was assessed by a Student’s t-test for most of the comparisons and by a Welch’s t-test for the seizure count comparison (the latter test was chosen because of unequal variance between the groups) ^{29 (link)}. Differences between groups were judged to be significant when p-values were smaller than 0.05. For multiple comparisons, a two-tailed t-test with a random field theory approach was used to achieve an adjusted significance threshold of p < 0.05 ^{29 (link)}. Error bars indicate the mean ± standard error of the mean (SEM) except when stated. No samples or animals were excluded from the analyses.

Farina M.G., Sandhu M.R., Parent M., Sanganahalli B.G., Derbin M., Dhaher R., Wang H., Zaveri H.P., Zhou Y., Danbolt N.C., Hyder F, & Eid T. (2021). Small loci of astroglial glutamine synthetase deficiency in the postnatal brain cause epileptic seizures and impaired functional connectivity. Epilepsia, 62(11), 2858-2870.

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Quantifying Protein-Ligand Interactions via Hill Equation

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All data was fit to the models using Igor Pro 8.02 (WaveMetrics, Inc., Portland, OR, USA), a data analysis and display software package with extensive capabilities, including curve fitting. Both Equations (1) and (7) may be fit to the Hill Equation [23 ,24 (link)], which is one of the built-in functions in Igor Pro, Equation (8):

y = b a s e + \frac{(\max - b a s e)}{1 + {(\frac{x_{h a l f}}{x})}^{r a t e}}

In all cases, we have fixed the base at zero, and y is taken to be the measured RFU signal.
To model Equation (1) using the Hill Equation, the rate is either fixed at 1, fixed at 2, or allowed to float. The following quantities were used for the terms in the Hill equation: x_half = K_Ds, x = [P_s] = P_Stot, and max = R A_tot, where R is the conversion between antibody concentration and RFU (generally unknown).
To model Equation (7) using the Hill equation, the rate is fixed at −1, and x = P_Ftot. The numerator and denominator of Equation (7) must be divided through by (K_DF + P_Stot) to achieve the form of the Hill Equation, so that, Equation (9):

\max = \frac{R A_{t o t} P_{S t o t}}{K_{D s} + P_{S t o t}}

and Equation (10)

x_{h a l f} = m_{C} = \frac{K_{D s} + P_{S t o t}}{K_{D s} / K_{D F}}

If K_Ds is found by fitting to Equation (1), m_c is found by fitting to Equation (7), and P_Stot is known, then K_DF (the dissociation constant in solution) is found to be:

K_{D F} = \frac{K_{D S} m_{c}}{K_{D S} + P_{S t o t}}

Mattison C.P., Vant-Hull B., de Castro A.C., Chial H.J., Bren-Mattison Y., Bechtel P.J, & de Brito E.S. (2021). Characterization of Anti-Ana o 3 Monoclonal Antibodies and Their Application in Comparing Brazilian Cashew Cultivars. Antibodies, 10(4), 46.

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Extracellular Vestibular Afferent Recording

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Sharp microelectrodes, with impedances of 40–120 MΩ, were pulled from borosilicate glass tubing (BF150-86-10, Sutter Instrument), filled with 3 M KCl, and inserted into an electrode sleeve connected to a single axis motorized micromanipulator (IVM, Scientifica). After connecting to a preamplifier headstage (Biomedical Engineering, Thornwood, NY, United States), microelectrodes were lowered into the superior division of nerve VIII in mouse or the posterior crista nerve of turtle to record extracellular spike activity from spontaneously-discharging vestibular afferents. Afferent signals were low-pass filtered (1 kHz, four-pole Bessel; Wavetek), sampled at 10 kHz, and recorded using in-house acquisition scripts in Spike2 (Cambridge Electronic Design) on a PC with a micro1401 interface. Spike2 data files, exported as general text files, were processed with custom macros in IgorPro 8.02 (WaveMetrics). Afferent discharge in mice and turtle was classified according to CV*, a normalized measurement of discharge regularity (Brichta and Goldberg, 2000a (link); Schneider et al., 2021 (link)). Mouse afferents were classified as regularly-discharging when CV* < 0.1, while afferents with CV* > 0.1 were classified as irregularly-discharging. A total of 58 mice and 16 turtles were used for afferent recordings in this study.

Lee C., Sinha A.K., Henry K., Walbaum A.W., Crooks P.A, & Holt J.C. (2021). Characterizing the Access of Cholinergic Antagonists to Efferent Synapses in the Inner Ear. Frontiers in Neuroscience, 15, 754585.

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Quantitative Analysis of Retinal Neuron Morphology

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For each experiment and time point a minimum of 3 retinas from three different mice were analyzed. For analysis of neuron morphology and tracer coupling, at least 5 neurons were analyzed from at least 3 animals. For all datasets, the variance was reported as mean ± SEM. Each dataset was first tested for normality. Analysis between two groups was completed by using unpaired Student’s t test (parametric) or Mann-Whitney U test (nonparametric). For analysis between more than two groups, we used either a one-way analysis of variance (ANOVA) with Tukey’s multiple comparison test (parametric) or Kruskal-Wallis with Dunn’s multiple comparison test (nonparametric). All statistical significance tests were completed using Prism 8 Software (Graphpad Software, Inc.) and Igor Pro 8.02 (WaveMetrics, Inc.) for electrophysiological analyses.

Kerstein P.C., Leffler J., Sivyer B., Taylor W.R, & Wright K.M. (2020). Gbx2 Identifies Two Amacrine Cell Subtypes with Distinct Molecular, Morphological, and Physiological Properties. Cell reports, 33(7), 108382.

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Quantitative Analysis of Retinal Neuron Morphology

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Membrane Tension Measurement Protocol

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Imaging was performed as described (Lewis and Grandl, 2015 (link)). Briefly, images were captured at a rate of ~13 frames/s at a resolution of 61.5 pixels/µm using a Plan Apo (100×) DIC oil objective coupled with a Coolsnap ES camera and 4× relay lens (Nikon Instruments). Images were analyzed in Igor Pro 8.02 (WaveMetrics) using custom scripts available in our Github repository (github.com/GrandlLab). The membrane was identified by performing a line scan parallel to the pipette walls and localizing the minimum pixel intensity over a rolling average of 5–9 pixels and fitting the output with a circle to obtain radius (R). Tension (T) was then calculated for each pressure step (p) using Laplace’s law: T=R*p/2. The surface area of each patch (Figure 5—figure supplement 1) was calculated from the radius at 0 mmHg by fitting the patch dome as a spherical cap and manually estimating the contact points between the membrane and the pipette walls.

Lewis A.H, & Grandl J. (2021). Piezo1 ion channels inherently function as independent mechanotransducers. eLife, 10, e70988.

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Comprehensive Statistical Analysis of Neuron Morphology

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For each experiment and time point a minimum of 3 retinas from three different mice were analyzed. For analysis of neuron morphology and tracer coupling, at least 5 neurons were analyzed from at least 3 animals. For all data sets, the variance was reported as mean ± SEM. Each data set was first tested for normality. Analysis between two groups was completed by using unpaired Student's t-test (parametric) or Mann-Whitney U test (nonparametric). For analysis between more than two groups, we used either a one-way analysis of variance (ANOVA) with Tukey's multiple comparison test (parametric) or Kruskal-Wallis with Dunn's multiple comparison test (nonparametric). All statistical significance tests were completed using Prism 8 Software (Graphpad Software, Inc.) and Igor Pro 8.02 (WaveMetrics, Inc.) for electrophysiological analyses.

Kerstein P.C., Leffler J., Sivyer B., Taylor W.R., & Wright K.M. (2020). Gbx2 identifies two amacrine cell subtypes with distinct molecular, morphological, and physiological properties.

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Kinetic Analysis of GRB2 Membrane Dynamics

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Kinetics and statistical analyses of GRB2 on the plasma membrane were performed with Igor Pro 8.0 (WaveMetrics) as described previously [34 (link)].

Yoshizawa R., Umeki N., Yamamoto A., Murata M, & Sako Y. (2021). Biphasic spatiotemporal regulation of GRB2 dynamics by p52SHC for transient RAS activation. Biophysics and Physicobiology, 18, 1-12.

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HPLC Analysis of Chemical Compounds

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HPLC analyses were conducted using ChemStation on an Agilent 1260 quaternary pump and Agilent 1260 Autosampler with DAD UV-vis detector, with a path length of 1.0 cm. Samples were separated using a Phenomenex Kinetex 2.6 mm xB-C18 100 Å, LC column 150 × 2.1 mm. Column temp: 25 °C. 10 µL Injection. Solvents: Solvent gradient was: (A) 0.1% formic acid in LC-MS grade water, (B) LC-MS grade acetonitrile. Flow Rate: 0.3 mL/min. Gradient: 5 min 100% A, 0% B; 20 min ramp to 45% A, 55% B; 10 min 0% A, 100% B; 1 min ramp 100% A, 0% B; 14 min 100% A, 0% B. Wavelengths monitored: 210 and 220 nm, with entire spectrum 180–400 nm collected in 2 nm steps. Processing of HPLC data were conducted using either Excel or a suite of macros within Igor Pro 8.0 (Wavemetrics).

Frenkel-Pinter M., Bouza M., Fernández F.M., Leman L.J., Williams L.D., Hud N.V, & Guzman-Martinez A. (2022). Thioesters provide a plausible prebiotic path to proto-peptides. Nature Communications, 13, 2569.

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