A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Podsumowanie

We have developed a single platform to track animal behavior during two climbing fiber-dependent associative learning tasks. The low-cost design allows integration with optogenetic or imaging experiments directed towards climbing fiber-associated cerebellar activity.

Streszczenie

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

Wprowadzenie

Pavlovian conditioning of sub-second association between stimuli to elicit a conditioned response has long been used to probe cerebellar-dependent learning. For example, in classical delay eyeblink conditioning (DEC), animals learn to make a well-timed protective blink in response to a neutral conditional stimulus (CS; e.g., a flash of light or auditory tone) when it is paired repeatedly with an unconditional stimulus (US; e.g., a puff of air applied to the cornea) which always elicits a reflex blink, and which comes at or near the end of the CS. The learned response is referred to as a conditioned response (CR), while the reflex response is referred to as the unconditioned response (UR). In rabbits, cerebellum-specific lesions disrupt this form of learning^1,2,3,4. Further, Purkinje cell complex spikes, driven by their climbing fiber inputs⁵, provide a necessary^6,7 and sufficient^8,9 signal for the acquisition of properly-timed CRs.

More recently, climbing fiber-dependent associative learning paradigms have been developed for head-fixed mice. DEC was the first associative learning paradigm to be adapted to this configuration^10,11. DEC in head-fixed mice has been used to identify cerebellar regions^{11,12,13,14,15,16,17} and circuit elements^{11,12,13,14,15,18,19} that are required for task acquisition and extinction. This approach has also been used to demonstrate how the cellular-level physiological representation of task parameters evolves with learning^13,15,16.

In addition to eyeblink, the delayed startle tactile conditioning (DTSC) paradigm was recently developed as a novel associative learning task for head-fixed mice²⁰. Conceptually similar to DEC, DTSC involves the presentation of a neutral CS with a US, a tap to the face sufficient in intensity to engage a startle reflex^21,22 as the UR. In the DTSC paradigm, both the UR and CR are read out as backward locomotion on a wheel. DTSC has now been used to uncover how associative learning alters cerebellar activity and patterns of gene expression²⁰.

In this work, a method was developed for flexibly applying DEC or DTSC in a single platform. The stimulus and platform attributes are schematized in Figure 1. The design incorporates the capacity to track animal behavior with a camera as well as a rotary encoder to track mouse locomotion on a wheel. All aspects of data logging and trial structure are controlled by paired microcontrollers (Arduino) and a single-board computer (SBC; Raspberry Pi). These devices can be accessed through a provided graphical user interface. Here, we present a workflow for setup, experiment preparation and execution, and a customized analysis pipeline for data visualization.

Protokół

The animal protocols described here have been approved by the Animal Care and Use Committees of Princeton University.

1. Setting up the SBC

1. Connect the camera serial interface (CSI) cable to the Raspberry NoIR V2 camera and the camera port on the SBC.
2. Download the operating system for the SBC onto the host computer. Write the operating system image to a micro secure digital (microSD) card.
   NOTE: Detailed instructions for these procedures for a Raspberry Pi SBC can be found elsewhere²³. The system has been tested using the following operating systems: Stretch, Buster, Bullseye.
3. To enable secure shell communication, create an extensionless file called "ssh" in the boot partition of the microSD card. Once this is done, eject the microSD card from the host machine and insert it into the SBC microSD card slot. Power the SBC by plugging in its power supply.
4. Prepare the SBC to accept a wired connection to the host.
   1. Attach a monitor with an appropriate cable to the SBC. Open a terminal, type the command ifconfig and record the ethernet IP address of the SBC.
      NOTE: Raspberry Pi model 3B+ has an HDMI display port, while model 4B has a micro-HDMI port.
   2. Go to the Interface tab of the Raspberry Pi configuration setting and enable the options for Camera, secure shell network protocol (SSH), and Virtual Network Computing (VNC).
5. Establish a wired connection between the host computer and the SBC.
   1. Connect an ethernet cable to the ethernet port on the SBC and a host computer. Attach the other end of these cables to an ethernet switch.
   2. Use a virtual network computing client such as VNC viewer²⁴ and access the desktop using the SBC IP address and the default authentication (user = "pi", password = "raspberry").
6. Download required software included in the protocol steps.
   CAUTION: Change the default username and password to prevent unauthorized access to the SBC.
   1. Enter the following command in the SBC terminal to download the rig software:
      git clone --depth=1 https://github.com/gerardjb/assocLearnRig
   2. Enter the following commands to download the necessary python libraries.
      cd assocLearnRig
      python3 setup.py
   3. To allow direct control over the microcontroller, connect to the SBC and download the microcontroller integrated development environment (IDE) following steps 1.6.4-1.6.7.
   4. Open the web browser on the SBC desktop and navigate to https://arduino.cc/en/software. Download the latest Linux ARM 32 bit version of the IDE.
   5. Open a terminal window on the SBC desktop and navigate to the downloads directory by typing cd Downloads/
   6. To install the IDE, type the following commands in the terminal:
      tar -xf arduino-<version>-linuxarm.tar.xz
      sudo mv arduino-<version> /opt
      sudo /opt/arduino-<version>/install.sh
      (here <version> is the version of the downloaded IDE)
   7. Open an instance of the microcontroller IDE on the SBC desktop. Select menu option Tools > Manage Libraries. Install the "Encoder" library from Paul Stoffregen.
7. Expand SBC onboard memory with a USB thumb drive.
   1. Insert a thumb drive to a USB port on the SBC. Use a USB 3.0 port if available.
   2. Type in the terminal ls -l /dev/disk/by-uuid/ to find the thumb drive and its unique reference (UUID). Record the UUID.
   3. To allow the pi user to write to the USB device, type the following commands one by one into the terminal:
      sudo mkdir /media/usb
      sudo chown -R pi:pi /media/usb
      sudo mount /dev/sda1 /media/usb -o uid=pi,gid=pi
      NOTE: The thumb drive can be added as a device that will auto-mount when the SBC restarts by adding the following line to the end of the fstab file at /etc/fstab:
      ​UUID=<UUID from step 1.7.2> /media/usb vfat auto,nofail,noatime,users,rw,uid=pi,gid=pi 0 0

2. Wiring stimulus hardware and assembling stage

1. Connect and prepare microcontrollers.
   1. Connect the SBC to the programming port of the microcontroller (Arduino Due) with a USB2 type A to USB2 micro cable.
      ​NOTE: Use a high-quality cable such as the product in the Table of Materials to ensure proper operation.
   2. Locate "dueAssocLearn.ino" in the downloaded project repository. Open the sketch with the microcontroller IDE and upload it to the microcontroller connected to the SBC.
   3. Download and install the appropriate version of the Arduino IDE on the host computer.
   4. Connect the host computer to the microcontroller (Arduino Uno) with a USB2 type B to USB2 type A cable.
   5. Go to the GitHub repository (https://github.com/gerardjb/assocLearnRig) and download the "DTSC_US.ino" sketch to the host computer.
   6. On the host computer, run the microcontroller IDE and open the "DTSC_US.ino" sketch, then upload it to the microcontroller.
2. Attach wires to the microcontrollers, breadboard, LEDs, rotary encoder, stepper motor with driver, and solenoid valve with driver as indicated in the Fritzing diagram in Figure 2.
3. Power the stepper motor and solenoid valve.
   1. Properly wire one channel of a power supply to the +V and GND pins of the stepper motor driver.
   2. Turn on the power supply and set the attached channel voltage to 25 V.
      ​NOTE: If the connections between the stepper motor, driver, and power supply are correctly configured, a green indicator LED on the stepper motor driver will turn on.
   3. Properly wire the positive lead of a power supply to the solenoid valve driver hold voltage pin and the other positive lead to the spike voltage pin.
   4. Attach the negative leads to a ground shared with the control signal.
   5. Turn on the power supply and set the channel connected to the hold voltage to about 2.5 V and the channel connected to spike voltage to about 12 V.
4. Connect an air source regulated to a pressure of ~20 PSI to the solenoid valve using the luer adapter.
5. Test that all stimulus components and camera are functioning properly.
   1. Open a terminal on the SBC and type cd ~/assocLearnRig to navigate to the cloned GitHub repository.
   2. In the terminal, type python3 assocLearnRig_app.py to start the control graphical user interface.
   3. Start the camera stream by hitting the Stream button.
   4. Select the DEC Radio button, upload to the microcontroller, and start a session with default parameters by hitting the Start Session button.
      ​NOTE: After this step, a printout of the data log should appear in the terminal, the message on the camera stream should disappear, and the LED CS and solenoid valve US should turn on and off at appropriate times during each trial.
   5. After the session ends, repeat the previous steps with the DTSC Radio button selected.
      NOTE: Sketches in the GitHub repository ("testStepper.ino", "testRotary.ino", and "testSolenoid.ino") can be used to test individual components if the above steps do not provide satisfactory results.
6. Make the running wheel.
   1. Cut a 3" wheel from a foam roller. Drill a 1/4" hole in the exact wheel center so that the wheel will not wobble when turned by the mouse's locomotion.
   2. Insert a 1/4" shaft into the wheel and fix it in place using clamping hubs placed on each side of the wheel.
7. Affix the rotary encoder to a 4.5" aluminum channel using an M3 bolt. Stabilize the aluminum channel on the aluminum breadboard using a right angle bracket with a 1/4" bolt, nut, and washer as shown.
8. Attach the wheel and rotary encoder using a shaft-coupling sleeve.
9. Stabilize the free side of the wheel shaft with a bearing inserted in a right-angle end clamp installed on a breadboard-mounted optical post.
   ​NOTE: Ensure the wheel spins freely without wobbling when rotated by hand.
10. Position the stimulus hardware, head restraint, infrared light array, and picamera around the assembled wheel.
    1. Position the head restraints using optical posts and right angle post clamps so that the head posts are 1.5 cm in front of the wheel axle and 2 cm above the wheel surface. (Values are for a 20 g mouse).
    2. Position the CS LED and solenoid valve outlet used for the DEC US less than 1 cm from the eye used for DEC.
    3. Mount the stepper motor used for the DTSC US
    4. Mount the picamera on an optical post ~10 cm from where the animal will be.
       ​NOTE: The design for the picamera mount can be made on a 3D printer from the file in "RaspPiCamMount1_1.stl" in the GitHub repository.
    5. Place the infrared light array slightly above and directly facing the position of the face on the same side as the picamera.
    6. Make a tactile stimulus for DTSC by taping foam to the edge of a piece of acrylic mounted to a 1/4" shaft using a clamping hub. Attach the tactile stimulus to the stepper motor shaft.
       ​NOTE: The design for the acrylic piece can be laser cut following the pattern in "TactileStimDesign.pdf" in the GitHub repository.

3. Preparing and running behavior experiments

1. Implanting mouse headplate.
   1. Anesthetize a mouse using 2% isoflurane and head fix in a stereotactic frame.
   2. Apply an ophthalmic ointment to the eyes.
   3. Shave the scalp using soapy water and a sterile scalpel. Inject lidocaine directly underneath the skin of the incision site and clean the surgical site with povidone.
   4. Make an incision with a scalpel along the midline of the scalp from the back edge of the eyes to the back edge of the skull, being careful not to press too hard on the skull.
   5. Spread the incision open and clamp both sides with sterile hemostats to hold it open. Gently remove the periosteum using a cotton swab dipped with ethanol and allow the surface of the exposed skull to dry.
   6. Position the headplate level on the skull, making sure to position the front of the headplate posterior to the eyes. Use cyanoacrylate glue to attach the headplate to the skull and allow the glue to dry fully.
   7. Mix the dental cement powder (1 scoop), solvent (2 drops), and catalyst (1 drop) in a mixing dish and apply to all areas of exposed bone. Add layers until the surface is flush with the top edge of the headplate, making sure the headplate is securely attached to the skull.
   8. Suture the skin closed behind and in front of the headplate if necessary.
   9. Inject post-operative analgesia such as carprofen per institutional guidelines while allowing the animal to recover for at least 5 days.
2. Preparing for behavior sessions.
   1. Allow the test animals to habituate to the platform by mounting them in the head restraint for 30-min sessions for 5 days preceding experiments.
      NOTE: By the end of the habituation sessions, animals should run comfortably on the wheel.
   2. (DEC only) Prior to sessions, ensure that the solenoid valve outlet is centered on the target eye positioned >1 cm away.
   3. (DEC only) Manually actuate an air puff using the push button. Ensure that the mouse promptly produces a blink without showing overt signs of stress such as adopting a hunched posture or grabbing the affected periocular region with the ipsilateral forepaw.
   4. (DTSC only) Prior to sessions, ensure that the tactile stimulus is centered on the animal's nose positioned ~1.5 cm away.
      NOTE: When a DTSC behavioral session is not running, the stepper motor is automatically inactivated to allow manual repositioning.
   5. (DTSC only) In the SBC terminal, type python3 assocLearnRig_app.py to start the GUI.
   6. (DTSC only) Run a test session of three trials with the default parameters by hitting the Start Session button in the GUI.
   7. (DTSC only) Ensure that the logged data that prints to the terminal show a deflection of greater than 20 but less than 100 steps logged on the rotary encoder following the US on each trial.
      ​CAUTION: To avoid harm and reduce stress to the animal, start the stimulus farther from the animal and move it closer until the required conditions are met.
3. Running behavioral sessions with data logging.
   1. Mount a mouse to the head restraint.
   2. In the terminal of the SBC, type python3 assocLearnRig_app.py to start the GUI.
   3. To allow camera recordings during the behavioral trials, hit the Stream button.
      NOTE: Sessions can be run without a camera. In this case, only data from the rotary encoder and stimulus presentation timestamps are logged.
   4. Input identifying information for the animal into the Animal ID field and hit the Set button.
   5. Select either the DEC or DTSC from the radio button under the Session Type heading depending on which behavioral paradigm is desired.
   6. Input the desired experiment parameters to the fields below the Animal ID field and hit the Upload to Arduino button.
      ​NOTE: Details of the experiment parameters can be found in the GitHub repository README section.
   7. Hit the Start Session button to begin the session.
   8. When a session is initialized, data will begin logging in a new directory created in "/media/usb" in the SBC thumb drive mount point.

4. Exporting and analyzing data

1. To export all the recorded sessions to the host computer, open a command prompt and input the command pscp -r pi@Pi_IP_address:/media/usb* host_computer_destination, then authenticate with the SBC password.
   NOTE: The above command is for a Windows machine. On Mac and Linux machines, use terminal and replace "pscp" with "scp".
2. Install Anaconda²⁵ or another python package manager (PPM) on the host computer.
3. Go to the GitHub repository and download "analyzeSession.py", "summarizeSessions.py", "session2mp4s.py", and "requirementsHost.txt".
4. Open a PPM prompt and type conda install --file directory_containing_requirementsHostrequirements Host.txt to ensure that the Python package installation has the required python libraries.
5. In the prompt, type cd directory_containing_analyzeData to navigate to the directory containing "analyzeData.py" and "session2mp4s.py". Run the analysis program by typing python analyzeSession.py
   NOTE: An error message will be generated if using a Python 2 version as python. To check the version, type python -V in the prompt.
6. Select the directory containing the data when prompted. Directories with multiple subdirectories will be analyzed sequentially.
7. For DEC sessions, for each session directory analyzed, select a region of interest (ROI) containing the mouse's eye from a trial average image.
   NOTE: Final analysis data files and summary graphs will populate to a subdirectory of each analyzed session directory.
8. Type python summarizeSessions.py to generate summary data across multiple sessions.
9. Type in the prompt python session2mp4s.py to convert imaging data files into viewable .mp4 files.

Wyniki

Workflow for DEC experiments and analysis
Proper experimental parameter selection is important for successful delay eyeblink conditioning (DEC) training. For the data presented here, the GUI was used to choose a CS duration of 350 ms and a US duration of 50 ms. This pairing results in an inter-stimulus interval of 300 ms: long enough to prevent low-amplitude CR production¹⁰ and short enough to avoid getting into the regime of poor learning or trace conditioning, a process th...

Dyskusje

The platform with associated protocols outlined here can be used to reliably track animal behavior in two sensory associative learning tasks. Each task depends on intact communication through the climbing fiber pathway. In the design described here, we incorporate elements to facilitate learning and recording/perturbation of cerebellar response. These include a wheel to allow for free locomotion^11,18 as well as head fixation. The wheel allows mouse subjects to lo...

Materiały

Name	Company	Catalog Number	Comments
"B" Quick Base For C&B METABOND - 10 mL bottle	Parkell	S398	Dental cement solvent
"C" Universal TBB Catalyst - 0.7 mL	Parkell	S371	Catalyst
#8 Washers	Thorlabs	W8S038	Washers
0.250" (1/4") x 8.00" Stainless Steel Precision Shafting	Servocity	634172	1/4" shaft
0.250” (0.770") Clamping Hub	Servocity	545588	Clamping hub
1/4" to 6 mm Set Screw Shaft Coupler- 5 pack	Actobotics	625106	Shaft-coupling sleeve
1/4"-20 Cap Screws, 3/4" Long	Thorlabs	SH25S075	1/4" bolt
100 pcs 5 mm 395–400 nm UV Ultraviolet LED Light Emitting Diode Clear Round Lens 29 mm Long Lead (DC 3V) LEDs Lights +100 pcs Resistors	EDGELEC	‎ED_YT05_U_100Pcs	CS LEDs
2 m Micro HDMI to DVI-D Cable - M/M - 2 m Micro HDMI to DVI Cable - 19 pin HDMI (D) Male to DVI-D Male - 1920 x 1200 Video	Star-tech	‎HDDDVIMM2M	Raspberry Pi4B to monitor cable
256 GB Ultra Fit USB 3.1 Flash Drive	SanDisk	‎SDCZ430-256G-G46	USB thumb drive
3.3 V–5 V 4 Channels Logic Level Converter Bi-Directional Shifter Module	Amazon	B00ZC6B8VM	Logic level shifter
32 GB 95 MB/s (U1) microSDHC EVO Select Memory Card	Samsung	‎MB-ME32GA/AM	microSD card
4.50" Aluminum Channel	Servocity	585444	4.5" aluminum channel
48-LED CCTV Ir Infrared Night Vision Illuminator	Towallmark	SODIAL	Infrared light array
4PCS Breadboards Kit Include 2PCS 830 Point 2PCS 400 Point Solderless Breadboards for Proto Shield Distribution Connecting Blocks	REXQualis	B07DL13RZH	Breadboard
5 Port Gigabit Unmanaged Ethernet Network Switch	TP-Link	‎TL-SG105	Ethernet switch
5 V 2.5 A Raspberry Pi 3 B+ Power Supply/Adapter	Canakit	‎DCAR-RSP-2A5	Power supply for Raspberry Pi 3B+
5-0 ETHILON BLACK 1 x 18" C-3	Ethicon	668G	Sutures
6 mm Shaft Encoder 2000 PPR Pushpull Line Driver Universal Output Line Driver Output 5-26 V dc Supply	Calt	B01EWER68I	Rotary encoder
Ø1/2" Optical Post, SS, 8-32 Setscrew, 1/4"-20 Tap, L = 1", 5 Pack	Thorlabs	TR1-P5	Optical posts
Ø1/2" Optical Post, SS, 8-32 Setscrew, 1/4"-20 Tap, L = 2", 5 Pack	Thorlabs	TR2-P5	Optical posts
Ø1/2" Optical Post, SS, 8-32 Setscrew, 1/4"-20 Tap, L = 4", 5 Pack	Thorlabs	TR4-P5	Optical posts
Ø1/2" Optical Post, SS, 8-32 Setscrew, 1/4"-20 Tap, L = 6", 5 Pack	Thorlabs	TR6-P5	Optical posts
Ø1/2" Post Holder, Spring-Loaded Hex-Locking Thumbscrew, L = 2"	Thorlabs	PH2	Optical post holder
Adapter-062-M X LUER LOCK-F	The Lee Co.	TMRA3201950Z	Solenoid valve luer adapter
Aeromat Foam Roller Size: 36" Length	Aeromat	B002H3CMUE	Foam roller
Aluminum Breadboard 10" x 12" x 1/2", 1/4"-20 Taps	Thorlabs	MB1012	Aluminum breadboard
Amazon Basics HDMI to DVI Adapter Cable, Black, 6 Feet, 1-Pack	Amazon	HL-007347	Raspberry Pi3B+ to monitor cable
Arduino  Uno R3	Arduino	A000066	Arduino Uno (microcontroller board)
Arduino Due	Arduino	‎A000062	Arduino Due (microcontroller board)
Bench Power Supply, Single, Adjustable, 3 Output, 0 V, 24 V, 0 A, 2 A	Tenma	72-8335A	Power supply
Clear Scratch- and UV-Resistant Cast Acrylic Sheet, 12" x 24" x 1/8"	McMaster Carr	8560K257	Acrylic sheet
CNC Stepper Motor Driver 1.0–4.2 A 20–50 V DC 1/128 Micro-Step Resolutions for Nema 17 and 23 Stepper Motor	Stepper Online	B06Y5VPSFN	Stepper motor driver
Compact Compressed Air Regulator, Inline Relieving, Brass Housing, 1/4 NPT	McMaster Carr	6763K13	Air source regulator
Cotton Swab	Puritan	806-WC	Cotton swab
Dell 1908FP 19" Flat Panel Monitor - 1908FPC	Dell	1908FPC	Computer monitor
Flex Cable for Raspberry Pi Camera	Adafruit	2144	camera serial interface cable
High Torque Nema 17 Bipolar Stepper Motor 92 oz·in/65 N·cm 2.1 A Extruder Motor	Stepper Online	17HS24-2104S	Stepper motor
Isoflurane	Henry Schein	66794001725	Isoflurane
Krazy Maximum Bond Permanent Glue, 0.18 oz.	Krazy Glue	KG483	Cyanoacrylate glue
Lidocaine HCl	VetOne	510212	Lidocaine
Low-Strength Steel Hex Nut, Grade 2, Zinc-Plated, 1/4"-20 Thread Size	McMaster Carr	90473A029	Nuts
M3 x 50 mm Partially Threaded Hex Key Socket Cap Head Screws 10 pcs	Uxcell	A16040100ux1380	M3 bolt
NEMA 17 Stepper Motor Mount	ACTOBOTICS	555152	Stepper motor mount
Official Raspberry Pi Power Supply 5.1 V 3 A with USB C - 1.5 m long	Adafruit	4298	Power supply for Raspberry Pi 4B
Optixcare Dog & Cat Eye Lube Lubricating Gel, 0.70-oz tube	Optixcare	142422	Opthalimic ointment
Precision Stainless Steel Ball Bearing, Shielded, Trade No. R188-2Z, 13000 rpm Maximum Speed	McMaster-Carr	3759T57	Bearing
Premium Female/Female Jumper Wires - 40 x 6"	Adafruit	266	Wires
Premium Female/Male 'Extension' Jumper Wires - 40 x 6" (150 mm)	Adafruit	826	Wires
Premium Male/Male Jumper Wires - 40 x 6"	Adafruit	758	Wires
Radiopaque L-Powder for C&B METABOND - 5 g	Parkell	S396	Dental cement powder
Raspberry Pi (3B+ or 4B)	Adafruit	3775 or 4295	Raspberry Pi
Raspberry Pi NoIR Camera Module V2 - 8MP 1080P30	Raspberry Pi Foundation	RPI3-NOIR-V2	Raspberry NoIR V2 camera
Right-Angle Bracket, 1/4" (M6) Counterbored Slot, 8-32 Taps	Thorlabs	AB90E	Right-angle bracket
Right-Angle Clamp for Ø1/2" Posts, 3/16" Hex	Thorlabs	RA90	Right-angle optical post clamp
Right-Angle End Clamp for Ø1/2" Posts, 1/4"-20 Stud and 3/16" Hex	Thorlabs	RA180	Right-angle end clamp
RJ45 Cat-6 Ethernet Patch Internet Cable	Amazon	‎CAT6-7FT-5P-BLUE	Ethernet cable
Rotating Clamp for Ø1/2" Posts, 360° Continuously Adjustable, 3/16" Hex	Thorlabs	SWC	Rotating optical post clamps
Spike & Hold Driver-0.1 TO 5 MS	The Lee Co.	IECX0501350A	Solenoid valve driver
Swivel Base Adapter	Thorlabs	UPHA	Post holder adapter
USB 2.0 A-Male to Micro B Cable, 6 feet	Amazon	‎7T9MV4	USB2 type A to USB2 micro cable
USB 2.0 Printer Cable - A-Male to B-Male, 6 Feet (1.8 m)	Amazon	B072L34SZS	USB2 type B to USB2 type A cable
VHS-M/SP-12 V	The Lee Co.	INKX0514900A	Solenoid valve
Zinc-Plated Steel 1/4" washer, OD 1.000"	McMaster Carr	91090A108	Washers