FIRST PART: The design ideas

After we have designed the core component. When the receptor on core cells surfaces bind to the antigen of cancer cells surfaces. It can initiate a downstream signaling pathway, which could activate DNA, we transfected using plasmid or phage, transcription into mRNA. Then the mRNA through the nuclear pore into the cytoplasm translated into the signal molecules we needed (generally some interleukins, such as IL-15). Signal molecules finally secreting into the extracellular fluid into the blood. If we want to detect the released signal molecules, the only way is to puncture skin and take the peripheral blood for detection. Although this method can quickly determine the existence and concentration of the signal molecules, also has its shortage such as, must to collecting the peripheral blood, can not continuous monitoring the concentration and need to go to the professional medical institution to operate, etc.

In our extension part we have designed another engineering cell which could bind to the signal molecules mentioned above, and then express the fluorescent protein floating in cytoplasm. It could bind with the different type of signal molecules and the corresponding cell initiated in order to distinguish the dominant tumor cell population. So that we could detect the optical signal to determine the concentration of molecules.

Our method allows us to monitor the signal molecules expressed by our engineered cells in real time without collecting peripheral blood so that we can monitor cancer development simply and dynamically.

SECOND PART: Principle of the software and modeling

The principle flow chart is as follows:

[ About Our Process ]

The extensions in our overall project can be extended to a variety of fields.（After the core cells generated signal molecules, we could connect a variety of engineered cell for monitoring or treatment, such as importing the gene sequences into engineering cell to produce anticancer drug. Here we have access to a dynamic signal molecule monitoring device, which could initiate the downstream expression to generate fluorescent protein after the detection of signal molecules. At this time, the external device is used to take photos to analyze the corresponding RGB channel strength and forecast the corresponding relative concentration.）

[ About Our Software ]

We use Java as our programming language, which relies on two development packages. We used OpenCV engine for image recognition and jfreechart for final result output method.

Our camera model in laboratory is Canon EOS 6D, in order to guaranteed variable uniformity. Our parameter is: F number: f/14; Exposure time: 1/15; Focal length: 48mm; ISO: 1600.

Processing image by binarization: Using OpenCV engine for binarization, we used the parameter: Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU. The THRESH_OTSU parameter can be automatically binarized according to different picture scenes. How this algorithm works? First, we divide the image into two parts that are larger than the threshold and smaller than the threshold, which is the foreground and the background. Then first, we divide the image into two parts that are larger than the threshold and smaller than the threshold, that is, the foreground and the background. We can calculate the variance between the two parts of the class. The larger the variance between classes, the greater the difference in gray scale between the two parts. Try the 256 thresholds from 0 to 255 and find the value with the largest variance between classes, which is usually the best threshold we are looking for.

Using algorithm to find contours can be expanded in two parts. The first part is use FindContours parameter to find the edge of the image directly. However, this method is suitable for the single color of the image and it require the recognition area is conspicuous. Otherwise we use the Sobel Operator for edge detection. Technically, it is a discrete difference operator that is used to calculate the approximation of the gradient of the image brightness function. Using this operator at any point in the image will generate the corresponding gradient vector or its normal vector. The advantage is that the noise of the image can be filtered or minimized to enhance the accuracy of the search edge, and the Sobel Operator can be used to find the edge when the image color is more complicated.

After identifying the area, we can filter out the desired area according to the preset maximum and minimum values and the ratio of the area so as to improve the recognition rate of the area and reduce the errors. We also introduce HSV channels here to correct the average value of RGB we got to improve the recognition accuracy. The specific implementation of method is shown below.

Finally, we input the RGB average value of the recognition area into our model to get the relative concentration so as to make the chart to display the relative protein concentration we predicted visually.

[ About Our Model ]

We build a standard predictive curve suitable for our laboratory conditions in the laboratory. First, we took the values from the concentration 0% to 100%. Then we used the same parameters to take photograph, which we mentioned above, and get the relative fluorescence intensity and relative concentration using our algorithm. Then these values used to curve fitting and finally to determine the available model categories for predictions.

THIRD PART: Description and Demonstration

[ About Our Process ]

Image four is the main interface of our software. Select the photo that we have taken using “Choose File” Button on the right and click “Recognize” to get the standard model curve and the relative density of the identified position in the model.

[ Progress Indicator ]

Progress Indicator is a non-resizable and closed window that is only displayed when the image is processing. It is used to show the current recognition progress and observed which step the software stopped when some of the unrecognizable pictures are selected in order to adjust the corresponding parameter to solve the problems. It can also enhance the visualization of the software processing procedure.

[ Contours Screen ]

As the description in the second part. This part of the code can filter the image noise we do not need and identify the wrong contour, as the image below, the identified image noise will be removed.

THIRD PART: Description and Demonstration

[ About Our Process ]

Image four is the main interface of our software. Select the photo that we have taken using “Choose File” Button on the right and click “Recognize” to get the standard model curve and the relative density of the identified position in the model.

THIRD PART: Description and Demonstration

[ About Our Process ]

Image four is the main interface of our software. Select the photo that we have taken using “Choose File” Button on the right and click “Recognize” to get the standard model curve and the relative density of the identified position in the model.