Team:Lambert GA/EPEN MODEL RESULTS
E L E C T R O P E N M O D E L
Theoretical Model
Voltage outputs from any piezoelectric-based system are dependent on the force exerted on the crystal, allowing for the reorganization of the ions, creating the corresponding output (Butt2016EffectHarvester). In order to characterize the capability of a small lighter to generate high voltages (on the order of kilovolts), the underlying mechanism within the lighter must first be described. The mechanism (which will henceforth be referred to as the “hammer action”) comprises of two springs, a hammer (metal piece striking the crystal), and the PZT crystal itself connected to a metal conductor. The hammer action functions in three phases: a loading phase, a release phase, and a relaxation phase, each directed by various components. During the loading phase, the hammer is held in a locked position as the lower spring and upper springs are being compressed using the user exerted force. The entire upper portion of the casing moves upwards, while the hammer is still locked in a loading position. The time interval of the loading phase is dependent on the force exerted by the user, and has no effect on the output voltage as the hammer's movement is dependent on the spring release. At the release phase, once the action begins to approach is critical point, the lower casing with a connected wedge pushes the hammer out of the latch while the spring is compressed at maximum, beginning to release the hammer. Then, the hammer switches from the lock to unlock state, allowing the lower spring to extend and project the hammer towards and onto the piezoelectric crystal, with the upper spring remaining compressed. The relaxation phase then constitutes the user pulling back, extending the upper spring, forcing the hammer downwards into its original state with no effect on the lower spring. Analysis of high-speed videos of the hammer releasing indicate that the hammer is able to reach a maximum velocity of 8 m/s at a peak acceleration of 30,000 m/s2. Further analysis indicates this rapid acceleration produces jerk of up to 300,000,000 m/s3 from this small hammer action, indicating the extreme nature of the design, allowing for the production of a powerful resultant force striking the crystal. In an effort to characterize the correlation between the experimentally obtained voltage outputs using the ElectroPen and the theoretical outputs, a piezoelectric static voltage theoretical model was used. Through this model and the data values established as constants , the theoretical voltage output of the crystal found within the lighter is a maximum of 2699 Volts, with a lower output under normal conditions due to the strain on the crystal from expanding towards its maximum length, as well as the resistance caused by the copper wires and metal conductors in the lighter. As the values from the described lighter are of the same magnitude, it can be declared that the theoretical basis confirms the obtained values from the experimental trials.
After conducting electroporation trials with the described protocol (Refer to Protocols below), growth of colonies as well as GFP expression were subsequently analyzed. Plates with growth were isolated and inoculated into liquid cultures, and quantified using the plate reader . GFP expression levels were thereby analyzed with a comparison of the ElectroPen to the outputs from an industrial electroporator (BioRad MicroPulser). With the negative control serving as the baseline comparison for validating positive expression of GFP, it can clearly be seen that there is a significant difference in fluorescence intensity (represented as Fluorescence/OD600) between the negative and positive control, confirming that GFP was successfully electroporated. The experimental samples were conducted using the ElectroPen and the positive control with the BioRad electroporator. It can be seen that the fluorescence intensity values for the ElectroPen trials are similar to the outputs produced by the standard electroporator, indicating successful electroporation, uptake of DNA, and expression of GFP by the \textit{Escherichia coli} bacteria. The obtained transformation efficiency (similar to the BioRad Micropulser) from the experimental trial additionally indicates the functionality of the ElectroPen, presenting it as a powerful device for infield and low-resource settings.
After conducting electroporation trials with the described protocol (Refer to Protocols below), growth of colonies as well as GFP expression were subsequently analyzed. Plates with growth were isolated and inoculated into liquid cultures, and quantified using the plate reader . GFP expression levels were thereby analyzed with a comparison of the ElectroPen to the outputs from an industrial electroporator (BioRad MicroPulser). With the negative control serving as the baseline comparison for validating positive expression of GFP, it can clearly be seen that there is a significant difference in fluorescence intensity (represented as Fluorescence/OD600) between the negative and positive control, confirming that GFP was successfully electroporated. The experimental samples were conducted using the ElectroPen and the positive control with the BioRad electroporator. It can be seen that the fluorescence intensity values for the ElectroPen trials are similar to the outputs produced by the standard electroporator, indicating successful electroporation, uptake of DNA, and expression of GFP by the \textit{Escherichia coli} bacteria. The obtained transformation efficiency (similar to the BioRad Micropulser) from the experimental trial additionally indicates the functionality of the ElectroPen, presenting it as a powerful device for infield and low-resource settings.
Color Q App
Color Q is a free mobile application developed in Java for the Google Play Store. The app was developed in the Android Studio v3.2.1 integrated development environment. It works alongside the Chrome-Q hardware also developed by Lambert iGEM in order to effectively quantify the result of a biological reporter, similar to the function of a plate reader. The app is able to use circle detection in order to find the samples on the base of the Chrome-Q hardware and then detect the red, green, and blue values (RGB) of the center of each circle. The way the circles are arranged allow for a range of values to be generated. The first row contains 4 circles. The average RGB values of the first two circles are calculated as the negative control and the average RGB values of the second pair of circles are calculated as the positive control. The distance between the positive and negative control is calculated in the 3D-coordinate plane using the following formula:
The Hough Circle Transform is used as the method of circle detection found in the app. Open Computer Vision (OpenCV) is a library that can be imported into Android Studio in order to perform image analysis-based methods. The image taken by the smartphone camera is converted into a grayscale photo. This essentially makes the image more readable in terms of edge detection and "round" estimation. There are several parameters that can be modified and calibrated in order to detect an accurate amount of circles:
The 6 by 6 grid located underneath the first row can be loaded with experimental samples and a percentage value can be determined on a scale from the negative control to the positive control. The circle detection process loops through until the maximum radius reaches 120 pixels. If anywhere from 35 to 40 circles are detected in total, then the loop stops. However, if there are fewer circles detected, then the loops restarts to finish through the maximum radius until anywhere from 25 to 40 circles are detected properly. If less than 25 circles are detected, then an error is caught and another picture is requested to be used. Zooming in or zooming out could possibly make the circle detection process easier for the system and more efficient. The following formula is used to calculate the relative percentage values:
The results are then displayed based upon the row in which the circles fall in on the base of the Chrome-Q hardware. The app is able to determine the row in which the circles fall in by comparing y-coordinates. If the y-values are similar to each other, then the circles are classified as being on the same row. The relative values are then transferred to another page within the app where the user is able to enter information that could help contribute to our machine learning model, CALM. The application uses the latitude, longitude, and timestamp values obtained from the phone's GPS to effectively determine where and when the test was run. When the user submits the data, the results are sent to a MySQL database, which is a part of the Relational Database Service (RDS) as a part of the Amazon Web Services (AWS) platform.
The Hough Circle Transform is used as the method of circle detection found in the app. Open Computer Vision (OpenCV) is a library that can be imported into Android Studio in order to perform image analysis-based methods. The image taken by the smartphone camera is converted into a grayscale photo. This essentially makes the image more readable in terms of edge detection and "round" estimation. There are several parameters that can be modified and calibrated in order to detect an accurate amount of circles:
- Maximum Radius: The smallest value for the radius of a detected circle
- Minimum Radius: The largest value for the radius of a detected circle
- Minimum Distance: The smallest distance between the centers of any two detected circles
- Edge Gradient Value: The roundness of each detected circle
- Threshold Value: The amount of memory the system has to store the detected circles
The 6 by 6 grid located underneath the first row can be loaded with experimental samples and a percentage value can be determined on a scale from the negative control to the positive control. The circle detection process loops through until the maximum radius reaches 120 pixels. If anywhere from 35 to 40 circles are detected in total, then the loop stops. However, if there are fewer circles detected, then the loops restarts to finish through the maximum radius until anywhere from 25 to 40 circles are detected properly. If less than 25 circles are detected, then an error is caught and another picture is requested to be used. Zooming in or zooming out could possibly make the circle detection process easier for the system and more efficient. The following formula is used to calculate the relative percentage values:
The results are then displayed based upon the row in which the circles fall in on the base of the Chrome-Q hardware. The app is able to determine the row in which the circles fall in by comparing y-coordinates. If the y-values are similar to each other, then the circles are classified as being on the same row. The relative values are then transferred to another page within the app where the user is able to enter information that could help contribute to our machine learning model, CALM. The application uses the latitude, longitude, and timestamp values obtained from the phone's GPS to effectively determine where and when the test was run. When the user submits the data, the results are sent to a MySQL database, which is a part of the Relational Database Service (RDS) as a part of the Amazon Web Services (AWS) platform.
CALM
There are two main components of the CALM platform; the SMS component and the machine learning component. The entirety of the platform is written in Python 3.6+, and several libraries, including the pandas, numpy, scikit-learn, beautifulsoup, xgboost, and flask libraries are utilized. In order to make predictions, the machine learning aspect of CALM ___
To distribute SMS notifications, Michael Koohang graciously allowed Lambert iGEM to modify his RatWatch project (developed at Georgia Tech) to create CALM’s SMS component. The code for SMS distribution is located on a server using the Flask microframework for logic and computation. The Flask server interacts with Twilio’s (an SMS-survey provider) Python API in order to send out text messages to a specified population. The population’s survey results are aggregated and stored on the Flask server using pandas.
We hope to see CALM in use throughout the cholera field within the next few years as medical organizations begin using it to prevent outbreaks and better distribute medical supplies. As cholera already has a cure, a machine-learning based approach to predicting and preventing cholera, especially one that is open-source and free to use, will drastically reduce the time, energy, and money required to treat an infected population. Finally, we believe the CALM project will not only treat millions of people affected with cholera, but will also begin efforts to use CALM’s foundation to predict other diseases such as malaria and parasitic infections.
CALM began as a subcomponent of Lambert’s 2018 project and rapidly developed throughout the beginning of the 2018 season. In late May Lambert participated in the Day One Challenge, an Atlanta-based AI competition, and won. Through further collaboration and outreach with the Day One organization Lambert has been able to receive feedback and advice from professionals in a variety of fields, such as epidemiology, computer science, machine learning, and business. As CALM develops further, we hope to not only see other teams adopt the platform to address other issues, but also for healthcare organizations across the world to utilize CALM and adapt it to other diseases.
We hope to see CALM in use throughout the cholera field within the next few years as medical organizations begin using it to prevent outbreaks and better distribute medical supplies. As cholera already has a cure, a machine-learning based approach to predicting and preventing cholera, especially one that is open-source and free to use, will drastically reduce the time, energy, and money required to treat an infected population. Finally, we believe the CALM project will not only treat millions of people affected with cholera, but will also begin efforts to use CALM’s foundation to predict other diseases such as malaria and parasitic infections.
CALM began as a subcomponent of Lambert’s 2018 project and rapidly developed throughout the beginning of the 2018 season. In late May Lambert participated in the Day One Challenge, an Atlanta-based AI competition, and won. Through further collaboration and outreach with the Day One organization Lambert has been able to receive feedback and advice from professionals in a variety of fields, such as epidemiology, computer science, machine learning, and business. As CALM develops further, we hope to not only see other teams adopt the platform to address other issues, but also for healthcare organizations across the world to utilize CALM and adapt it to other diseases.