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22 protocols using labview 2014

1

Broadband Noise Stimulus Calibration

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The sounds were broadcasted by a free-field ES1 speaker with an ED1 electrostatic speaker driver (Tucker Davis Technologies, USA).53 (link) The broadband noise (BBN, bandwidth 0-50 kHz, duration 50 ms) was generated by a custom-written software based on LabVIEW 2014 (National Instruments, USA) and transduced to an analog voltage by a PCI 6731 card (National Instruments, USA). During experiments, the speaker was put at a distance of ∼6 cm to the left ear of the animal. All sound levels were calibrated with a ¼-inch pressure pre-polarized condenser microphone system (377A01 microphone, 426B03 pre-amplifier, 480E09 signal conditioner, PCB Piezotronics Inc, USA). Data were sampled at 1 MHz via a high-speed data acquisition board USB-6361 from National Instruments and analyzed using custom-written software in LabVIEW 2014. The BBN was applied at ∼a 65 dB sound pressure level (SPL). The background noise (∼55 dB SPL) was below the BBN stimulation level.
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2

Two-Photon Imaging Data Analysis

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We analyzed our data with software including MATLAB 2014a (MathWorks), Igor Pro 5.0 (Wavemetrics) and LabVIEW 2014 (National Instruments). We identified individual neurons in two-photon imaging data and performed the regions of interest (ROIs) drawing for each of them. In each image frame, fluorescence changes (f) were calculated by averaging the image intensities within each ROI. The 25th percentile of the entire fluorescence changes in single-trial was calculated as the baseline f0, and then neuronal Ca2+ signals were expressed as the relative changes of fluorescence Δf/f = (f − f0)/f0.
As described in our previous studies5 (link),32 (link), Ca2+ transient detection was performed based on thresholding criteria about peak amplitude and rising rate, which is similar to previously described peeling algorithm49 (link). For the analysis of frequency tuning, we constructed the frequency tuning curves of individual neurons, and performed curve normalization and averaging as previously described37 (link). To improve visibility here, the frequency tuning curves were fitted by Gaussian function.
Summarized data are presented as mean ± standard error of the mean (SEM) in figures. We used Wilcoxon signed rank test and Wilcoxon rank sum test to determine statistical significances for paired and unpaired cases, respectively. P < 0.05 was considered statistically significant.
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3

Imaging and Quantifying Skate Movement

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Skates were placed in a 25 cm square glass tank and imaged from the ventral surface using a CMOS camera (Basler Ace a1920–155) with an 8 mm lens (Computar M0814-MP2). The bottom of the tank was covered with a thin (2 mm) Sylgard layer that was prepared by curing on a sheet of Glad Press’n Seal to create a textured surface. The tank was partially surrounded by dark infrared transmitting plastic (CYRO ACRYLITE® IR acrylic 11460) and illuminated obliquely and from below with multiple infrared (850 nm) illuminators (CMVIsion-IR200). Images were 8-bit, 960x600 pixels at 60 frames/sec. Data was analyzed in real-time with a custom program written in LabView 2014 (National Instruments). To estimate movement, we computed the number of pixels that changed above baseline noise (8/255) from frame to frame. Empirically, frames with changes greater than 0.7% of their pixels corresponded to movement of the skate’s body. These frames were timestamped and saved, uncompressed, for as long as the skate moved. Cessation of movement and video recording occurred once 150 consecutive frames had elapsed with < 0.7% pixel change.
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4

In Vivo Calcium Imaging of Prefrontal Cortex

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All procedures were performed as described in a previous study (50 (link)). Briefly, animals were anesthetized by inhalation of 1.5%. After removing the skin, a customized recording chamber with a hole at the front was cemented to the skull with cyanoacrylic glue (UHU). Next, a small craniotomy (1.5 × 1.5 mm) was created at the projecting point of the frontal cortex (1.5 mm lateral to the middle and 2.9 mm anterior to the bregma), bleeding was stopped, and 1.5% agarose was layered on the exposed cortex to suppress pulsation. Respiration rate was maintained between 90 and 110/min. Neurons in layer 2/3 of the prefrontal cortex were bulk loaded with 0.5 mM Ca2+ indicator Cal-520 AM (AAT-Bioquest) under a 2-photon microscope. Imaging was performed on a 2-photon microscope with a 12-kHz resonant scanner (model LotosScan 1.0, Suzhou Institute of Biomedical Engineering and Technology). A laser source provided excitation light (λ = 920 nm; Coherent) through a water-immersion objective (Nikon, 40X, NA 0.8) and a consecutive recording (4–6 mins) was acquired at a 40-Hz frame rate using custom-written software based on LabVIEW (National Instruments). Data analysis was performed using LabVIEW 2014 (National Instruments) and Igor Pro 5.0 (Wavemetrics). Glial cells were excluded based on morphology and time course of Ca2+ transients.
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5

Cardiorespiratory Simulator Model in LabVIEW

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The cardiorespiratory simulator is a lumped parameter model developed in LabVIEW 2014 (National Instrument, Austin, TX, USA). The overview of all its components is provided in Figure 1, the interface is shown in Figure 2. Table 1 reports a list of the main abbreviations used in the text.
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6

Adaptive Motion Control System for Robotics

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We used LabVIEW 2014 software (National Instruments, Austin, TX) to develop the control algorithm. The aforementioned sensing subsystems were connected to myRIO and transmitted data to LabVIEW on the mini PC for further analysis.
This control system could detect time markers from each sensing subsystem and subsequently resample the signals recorded by the sensors (IMU, 125 Hz; PIXY, 40 Hz; ultrasonic sensor, 20 Hz) to equal levels via the program in LabVIEW. When the distance between the subject and the platform was <1.5 m, the control system would increase or decrease the current motor signal by 10% to change the velocity of the motor system for acceleration or deceleration.
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7

Optimizing Giant Magnetoimpedance Effect

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Two amorphous ribbons, with nominal composition P1 (Co0.93 Fe0.07)75 Si12.5 B12.5 (66 µm thickness, 0.40 mm width) and P2 (Co0.95 Fe0.05)75 Si12.5 B12.5 (80 µm thickness, 0.35 mm width) were obtained through the melt-spinning technique. Firstly, the conditions for optimum GMI effect (maximum impedance variations) were studied. Both ribbons were excited within a voltage divider configuration with an AC sinusoidal signal of frequency, f, and peak-to-peak current amplitude, Ipp, generated by a standard function generator (DS 345, Standford Research Systems, Sunnyvale, CA, USA). The output voltage changes, V=ZIpp (Z: electric impedance), were measured (oscilloscope MDO 3024, Tektronix, Beaverton, OR, USA) as a function of f under the effect of a null and a maximum DC axial magnetic field, H, (HMAX = 4.5 kA/m), generated by a homemade solenoid. The acquisition process was controlled with LabVIEW 2014 (National Instruments, Austin, TX, USA). Optimal conditions, namely, maximum GMI ratios defined as ΔZZ(%)=|Z(H=0)||Z(HMAX)||Z(HMAX)|×100 , were found at f = 600 kHz and Ipp = 69 mA and f = 500 kHz and Ipp = 101 mA for P1 and P2 samples, respectively. These conditions were employed in the impedance characterizations unless otherwise stated.
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8

Microfluidic Dielectrophoresis: Bead and Mitochondria Studies

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The experiments were performed using the microfluidic device as schematically shown in Figure 1. A 0.5 cm thick PDMS holder was employed to increase reservoir volume and provide stability for the electrodes. Solution A (0.5 mM F108 for bead studies and 1 mM F108 for mitochondrial studies, 10 mM HEPES, pH adjusted to 7.4 by KOH, sterile-filtered to 0.2 µm) was used to coat the channel surface with F108. Briefly, each channel was filled with solution A by capillarity, and the chip was placed in a humid environment overnight (16–24 h). Solution B (250 mM sucrose in solution A, pH 7.4, 0.03 S/m, sterile-filtered to 0.2 µm) was used to rinse the channel and to prepare the polystyrene bead or the mitochondrial suspension. The prepared bead or mitochondrial suspension was added to an inlet reservoir and solution B to another reservoir. Mineral oil was added on top of the liquid layer in both reservoirs to prevent evaporation. Platinum electrodes attached to the reservoirs were connected via microclamps (LabSmith, Livermore, CA, U.S.A.) to an AC power supply from a high voltage amplifier (AMT-3B20, Matsusada Precision Inc.) driven through a Multifunction DAQ card (USB X Series, National Instruments, TX, U.S.A.) programmed by LabVIEW 2014 (version 14.0, National Instruments).
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9

Microfluidic Device for Mitochondrial Manipulation

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All the experiments were performed using the microfluidic device schematically shown in Figure 1(a). After assembly, each channel was filled with solution A (1 mM F108, 10 mM HEPES, pH adjusted to 7.4 by KOH, sterile-filtered to 0.2 μm) by capillarity immediately and the chip was placed in a humid environment overnight (16 – 24 hours) to coat all surfaces with F108. Solution B (250 mM sucrose in solution A, pH 7.4, 0.03 S/m, sterile-filtered to 0.2 μm) was used to prepare the mitochondria or the polystyrene bead suspension (see also below) and to rinse the channel for three times right before use. A low conductivity buffer (0.03 S/m) was employed to minimize Joule heating effects.31 (link) A 0.5-cm-thick PDMS holder was employed to increase reservoir volume and provide stability for the electrodes on top of the assembled device. The prepared bead or mitochondrial suspension was added to an inlet reservoir and solution B to another reservoir. Mineral oil was added on top of the liquid layer in both reservoirs to prevent evaporation. Pt electrodes attached to the reservoirs were connected via micro-clamps (LabSmith, Livermore, CA, USA) to an AC power supply from a high voltage amplifier (AMT-3B20, Matsusada Precision Inc.) driven through a Multifunction DAQ card (USB X Series, National Instruments, TX, USA) programmed by LabVIEW 2014 (version 14.0, National Instruments).
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10

Trapezoidal Force Matching for Static Chuck Grip

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During the experimental chuck gripping trials, participants were asked to complete a force matching task with real-time visual force feedback provided in a custom program (LabVIEW 2014, National Instruments, Austin, TX). Specifically, participants were asked to match a trapezoidal force matching template, which consisted of ramping force up for 2 s, holding the force level (plateau) for 3 s, and ramping force down for 2 s. The force matching template was completed 3 times in succession with 3 s of rest between each trapezoidal profile (Fig. 3). The force ramps were completed at 10% and 40% of MVE within each of the 3 static wrist deviation positions, for a total of 18 static chuck gripping trials (2 chuck grip forces × 3 wrist deviation positions × 3 trials). Prior to the experimental trials, participants were provided with at least 2 practice trials at both force levels with the opportunity for additional practice if needed. The 3 wrist deviation positions were block randomized, and the 2 force levels were counter-balanced within each wrist deviation position to mitigate any potential fatigue or learning effects during the experimental trials.
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