The voltage and current is determined at the input of the boost-converter (corresponding to the output of the BFC) with a National Instrument NI USB-6211 16 bits 16-channels tension measurement module, with an additional resistance in parallel for measuring the current. The current and tension between the boost-converter and the flyback are respectively measured with a Keithley 2000 multimeter and the NI USB-6211. The independent voltages of the microelectrode and the platinum counter electrode in the electrolyser are also measured and referred to the common GND of the anode of the BFC. The hydrogen evolution is monitored with a full-HD camera (Logitech C930E).
Ni usb 6211
Manufactured by National Instruments
Sourced in United States
The NI USB-6211 is a multifunction data acquisition (DAQ) device that connects to a computer via a USB interface. It provides analog input, analog output, and digital I/O capabilities for measurement and automation applications.
Automatically generated - may contain errors
Lab products found in correlation
S3071, by Hamamatsu Photonics (4 mentions)
Tds 2012c, by National Instruments (2 mentions)
1999 amplifier, by National Instruments (2 mentions)
Det210, by Thorlabs (2 mentions)
Adxl330, by Analog Devices (1 mentions)
Angiocath, by BD (1 mentions)
Matlab r2017b, by MathWorks (1 mentions)
Matlab software, by MathWorks (1 mentions)
Labview version 8, by National Instruments (1 mentions)
Rhd2000 evaluation board, by Intan Technologies (1 mentions)
Mating sleeve, by Thorlabs (1 mentions)
Labview, by National Instruments (1 mentions)
Labview 2014, by National Instruments (1 mentions)
Rhd2000, by Intan Technologies (1 mentions)
Rhd2132, by Intan Technologies (1 mentions)
Matlab, by MathWorks (1 mentions)
Matlab r2012a, by MathWorks (1 mentions)
Labview program, by National Instruments (1 mentions)
Model 55 2222, by Harvard Apparatus (1 mentions)
Eclipse ti, by Nikon (1 mentions)
31 protocols using ni usb 6211
1
Boost-Converter Characterization for BFC
2
Simulating Realistic CPR Conditions
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We used a CPR manikin torso (Resusci Anne CPR, Laerdal Medical AS, Stavanger, Norway) and placed a resistive sensor (SP1-4, Celesco Transducer Products Inc., Chatsworth, CA, USA) inside its chest to measure the reference chest displacement signal. We placed distributed weight plates inside the manikin increasing its weight up to 20 kg to provide a more realistic simulation of a human torso. For CPR experiments, we used two types of mattresses: foam (800 × 2000 × 90 mm, Pardo, Zaragoza, Spain) and sprung (900 × 1800 × 100 mm, Pardo, Zaragoza, Spain). Some experiments were conducted with a backboard (CPR Board, Ferno, Wilmington, OH, USA) placed between the mattress and the manikin (Figure 2 ).
We used two triaxial accelerometers (ADXL330, Analog Devices, Norwood, MA, USA) each one encased in a metal box. One accelerometer was placed on the center of the manikin's chest and the other one beneath its back (Figure 1 ). During the experiments, we recorded the chest displacement and the two acceleration signals using an acquisition card (NI USB-6211, National Instruments, Austin, TX, USA) connected to a laptop computer, with a sampling rate of 250 Hz and 16-bit resolution.
For this study, we collected a database consisting of forty-eight 3-minute episodes, twelve per couple according to the protocol described inSection 2.1 .
We used two triaxial accelerometers (ADXL330, Analog Devices, Norwood, MA, USA) each one encased in a metal box. One accelerometer was placed on the center of the manikin's chest and the other one beneath its back (
For this study, we collected a database consisting of forty-eight 3-minute episodes, twelve per couple according to the protocol described in
3
Measuring Macrocirculation Parameters via Arterial Cannulation
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All macrocirculation parameters were blood-pressured derived. The radial artery was cannulated using a 20-gauge arterial catheter (Angiocath, Becton Dickinson Pty Ltd, Franklin Lake, NJ) which was connected to an arterial pressure monitoring set (Edwards. Lifesciences LLC, Irvine, Calif). The arterial blood pressure (ABP) signal was recorded on a laptop computer and stored on a hard disk with a sample rate of 200 Hz by an A/D converter (NI USB-6211, National Instrument, Austin, Tex) for off-line analysis. The ABP signal was analyzed using custom-made MATLAB scripts (Matlab R2017b, The MathWorks Inc, Mass). Mean arterial blood pressure (MAP) was acquired by taking a fourth order Butterworth low-pass filter with a cut-off frequency of 0.02 Hz from the raw ABP signal. Heart rate (HR) was acquired by automatic detection of R-peaks from the ECG-signal. The used pulse contour analysis (PCA) accounts for the dependence of arterial compliance on arterial pressure by scaling its cardiac output (CO) estimate to pulse pressure, with stroke volume (SV) equalling pulse pressure divided by the sum of systolic (SBP) and diastolic blood pressure (DBP) as proposed by Liljestrand and Zander (8, 9) . SV was subsequently multiplied by HR to calculate cardiac output (CO). Systemic vascular resistance (SVR) was approximated by dividing MAP by CO.
4
Neurophysiological Monitoring of Crayfish
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Once crayfish were implanted, they were returned to their individual aquaria and left undisturbed for at least 24 h. Then, we videotaped and recorded the brain and cardiorespiratory electrical activities from isolated crayfish during 8 continuous hours (start at 12:00 h, end at 20:00 h).
The brain electrical activity was bandpass filtered between 3 Hz and 3 kHz; a 60-Hz notch filter was sometimes used. Electrical signals were preamplified with AC amplifiers (CWE, BM400, United States) and in parallel sampled at 2 kHz by an A/D converter (National Instruments, NI-USB-6211, Austin, TX, United States).
The cardiorespiratory electrical activity was band-pass filtered between 1 Hz and 1 kHz and sampled at 100 Hz. We acquired all data using a MATLAB software (MathWorks)-based algorithm developed in our laboratory and stored on a personal computer for off-line analysis. The experiments were approved by the Ethical Committee of the Faculty of Medicine at UNAM 023/2018.
The brain electrical activity was bandpass filtered between 3 Hz and 3 kHz; a 60-Hz notch filter was sometimes used. Electrical signals were preamplified with AC amplifiers (CWE, BM400, United States) and in parallel sampled at 2 kHz by an A/D converter (National Instruments, NI-USB-6211, Austin, TX, United States).
The cardiorespiratory electrical activity was band-pass filtered between 1 Hz and 1 kHz and sampled at 100 Hz. We acquired all data using a MATLAB software (MathWorks)-based algorithm developed in our laboratory and stored on a personal computer for off-line analysis. The experiments were approved by the Ethical Committee of the Faculty of Medicine at UNAM 023/2018.
5
Muscle Fatigue Evaluation Protocol
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Muscle performance during the fatiguing protocol was evaluated by peak torque adjusted to body weight (peak torque(BW)), fatigue rate, and total work done. Fatigue rate, expressed as a percentage, was defined as the difference between the mean force production of the first 10 and last 10 contractions divided by the mean of the first 10 contractions. Total work, quantified as the area under the torque curve × angular displacement over the contraction period, was recorded and analyzed by the custom script software (LabView version 8.6, National Instruments Corporation, Austin) and data acquisition device (NI-USB6211, National Instruments Corporation, TX) at a sampling rate of 1000 Hz.
6
Multiunit Neural Recordings with Heating
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Recordings were made with Intan RHD2132 16-channel amplifiers, connected to an RHD-2000 Evaluation Board. Heating was triggered by a square pulse generated by an NI-USB 6211 (National Instruments, TX, USA) data acquisition system. All signals were sampled at 20 kHz and synchronized through the analog input of the Intan system, which was connected to an x86 based PC.
Raw LFP channels were band pass filtered between 0.4–7 kHz, and multi-units were detected with an absolute threshold. The unit activity was combined from multiple neighboring channels, downsampled to 1 kHz and smoothed with a 10 ms moving average filter. This data was used for calculation of peri-stimulus time histogram (PSTH) of heating events.
Raw LFP channels were band pass filtered between 0.4–7 kHz, and multi-units were detected with an absolute threshold. The unit activity was combined from multiple neighboring channels, downsampled to 1 kHz and smoothed with a 10 ms moving average filter. This data was used for calculation of peri-stimulus time histogram (PSTH) of heating events.
7
Transient Absorption Spectrometer Setup for μs-s Timescales
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The setup of transient absorption spectrometer (μs–s) has been described previously.20 Briefly, a Nd:YAG laser (Big Sky Laser Technologies, Ultra CFR Nd:YAG laser system) was used as the excitation source (355 nm, 3rd harmonic, pulsed laser, 6 ns band width). The laser flash rate was 0.33 Hz; the laser excitation intensity was set at 100 μJ cm–2, unless otherwise stated; the probe light source was a 100 W Bentham IL1 tungsten lamp equipped with a monochromator (OBB-2001, Photon Technology International); the transmitted light was filtered using long pass filters and a band pass filter (Comar Optics) to block the scattered laser light into the detector (Si photodiode, Hamamatsu S3071). The signal collected by the detector was sent to an amplifier (Costronics) and recorded by an oscilloscope (Tektronics TDS 2012c) on μs–ms timescales and a DAQ card (National Instruments, NI USB-6211) on ms–s timescales. Each decay was averaged by 300–500 times. All data were acquired by home-programmed software based on the LabVIEW software.
8
Optogenetic Stimulation of Neural Circuits
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Behavioral experiments started 3 to 5 weeks after the viral injection. Implanted animals were connected to a laser source (473 nm DPSS system, LaserGlow Technologies, Toronto, Canada) through a mating sleeve (Thorlabs). The laser was triggered by the output of a National Instruments interface (NI-USB 6211) and the timings of light activations were delivered using the NI MAX v19.6 software, as we did previously44 (link). For CnF, RTNPhox2b/Atoh1, PAG or IC long photostimulation, light was delivered in trains of pulses of 20 ms (5 to 20 Hz) and of 15 ms (30 and 40 Hz) for a duration of 1 s. Each stimulation frequency was repeated three times with several minutes of rest between trials. We used the minimal laser power sufficient to evoke a response, which was measured to be between 5-12 mW at the fiber tip using a power meter (PM100USB with S120C silicon power head, Thorlabs) to restrict photoactivations unilaterally47 (link), prevent heat, and exclude an unintentional silencing by over-activation. For randomized short light-pulses, 50 ms light stimulations (50-70 pulses/experiment) were applied randomly in the respiratory cycles.
9
Detailed Protocol for CS-FET Sensor Characterization
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CS-FET device chips were wire bonded to a 84-pin J-bend leaded chip carrier.
Pure dry air was used as diluent gas and was procured from Praxair Technology Inc. For H 2 (Fig. 234) and NO 2 (Fig. 5) sensing experiments, 1%
H 2 in N 2 (Gasco) and 1 ppm NO 2 (Gasco) in N 2 were used as source respectively. Selectivity measurements in Fig. 3e were performed with 2.5% CH 4 , 100 ppm CO 2 , 50 ppm NH 3 , 5 ppm NO 2 , 50 ppm SO 2 and 50 ppm H 2 S in N 2 (Mesa gas) as sources. Typical gas flow rates were from 1 to 100 sccm, and diluent (air) flow rate was approximately 1000 sccm. Gas delivery was controlled by mass flow controllers (Alicat Scientific Inc.). Measurements involving relative humidity and temperature changes were done in ESPEC Humidity and Temperature Cabinet LHU-113 with gas outlet 1-2 cm from the sensor chip, otherwise in a walk-in fumehood. CS-FET sensors were biased using a Keithley 428 current preamplifier, and the current signals were acquired using a LabVIEW-controlled data acquisition unit (National Instruments, NI USB-6211). The microheaters were powered by the Agilent E3631A DC Power Supply and all the measurements were performed with microheaters placed on the adjacent die to the one with the CS-FET. Infrared images in Supplementary figure S3 were taken using FLIR ETS320.
Pure dry air was used as diluent gas and was procured from Praxair Technology Inc. For H 2 (Fig. 234) and NO 2 (Fig. 5) sensing experiments, 1%
H 2 in N 2 (Gasco) and 1 ppm NO 2 (Gasco) in N 2 were used as source respectively. Selectivity measurements in Fig. 3e were performed with 2.5% CH 4 , 100 ppm CO 2 , 50 ppm NH 3 , 5 ppm NO 2 , 50 ppm SO 2 and 50 ppm H 2 S in N 2 (Mesa gas) as sources. Typical gas flow rates were from 1 to 100 sccm, and diluent (air) flow rate was approximately 1000 sccm. Gas delivery was controlled by mass flow controllers (Alicat Scientific Inc.). Measurements involving relative humidity and temperature changes were done in ESPEC Humidity and Temperature Cabinet LHU-113 with gas outlet 1-2 cm from the sensor chip, otherwise in a walk-in fumehood. CS-FET sensors were biased using a Keithley 428 current preamplifier, and the current signals were acquired using a LabVIEW-controlled data acquisition unit (National Instruments, NI USB-6211). The microheaters were powered by the Agilent E3631A DC Power Supply and all the measurements were performed with microheaters placed on the adjacent die to the one with the CS-FET. Infrared images in Supplementary figure S3 were taken using FLIR ETS320.
10
LED Light Stimulation of the Eye
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A thin layer of eye cream was put on the contralateral eye. A 5 mm green LED was inserted in custom-made black rubber cone. The cone was positioned on the contralateral eye, with the help of a FISSO arm (XS-130, Baitella, Switzerland), such that the LED illuminates on the eye 3–4 mm away from the cornea. This configuration allowed sufficient intensity light stimulation to the eye, as the cone covered the eye and blocked any light from outside. The LED was triggered with a 10 ms TTL pulse, which was generated in LabView (National Instruments) and buffered through a NI-USB-6211 (National Instruments) board. Stimulus presentation was synchronized with the electrophysiological recordings with the same TTL signal used for stimulation. The eye was continuously stimulated at a repetition rate of 0.3 Hz. The ipsilateral eye was covered with a black rubber cone, with the help of another FISSO arm (XS-130, Baitella, Switzerland), after putting sufficient amount of eye cream. The whole recording session was done while all the lights in the room were turned off.
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