REFERENCES/DATABASE USED:

1. Differentiation Between Viral vs. Bacterial Infections:

For machine-learning (SVM) to differentiate between viral and bacterial infections, we used MicroArray dataset GLP96 from:
Ramilo O, Allman W, Chung W, Mejias A et al. Gene expression patterns in blood leukocytes discriminate patients with
acute infections. Blood 2007 Mar 1;109(5):2066-77. PMID: 17105821

We used GEO2R to compare the bacterial infected datasets and viral infected datasets to get the top 100 differentially expressed genes. We then used the expression of these top differentially expressed genes for SVM.

We used sklearn to train our SVM. We do a logarithmic grid search from 10^-3 to 10^3 for tuning parameters C and gamma. We train using accuracy as our metric and by performing 5 fold cross validation in order to choose the best parameters.
Our final parameters were C=1 and gamma=0.001. Our five fold cross validation had an accuracy of 90.8% and our test set had a 100% accuracy.

For gene set enrichment analysis, we used GSEA to run enrichment tests against C7: immunological signatures (189 gene sets)
of GSEA's MSigDB database to compare patient sample against healthy samples.

1. Mapping bacterial genome to find antibiotic-resistance conferring genes

For our analysis, we used the Antibiotic Resistance Genes Database from:
Liu B, Pop M. ARDB-Antibiotic Resistance Genes Database. Nucleic Acids Res. 2009 Jan;37(Database issue):D443-7

1. For the web front end, we used a python django server. Our pages were built using the bootstrap styling framework, and analytics were provided using the highcharts.js plotting library.
## Inspiration

I was interested in exploring the health datasets given by John Snow Labs in order to give users the ability to explore meaningful datasets. The datasets selected were Vaccination Data Immunization Kindergarten Students 2011 to 2014, Mammography Data from Breast Cancer Surveillance Consortium, 2014 State Occupational Employment and Wage Estimate dataset from the Bureau of Labor Statistics, and Mental Health Data from the CDC and Behavioral Risk Factor Surveillance System.

Vaccinations are crucial to ending health diseases as well as deter mortality and morbidity rates and has the potential to save future generations from serious disease. By visualization the dataset, users are able to better understand the current state of vaccinations and help to create policies to improve struggling states. Mammography is equally important in preventing health risks. Mental health is an important fact in determining the well-being of a state. Similarly, the visualization allows users to better understand correlations between preventative steps and cancerous outcomes.

## What it does

The data visualization allows users to observe possible impacts of preventative steps on breast cancer formation and the current state of immunizations for kindergarten students and mental health in the US. Using this data, we can analyze specific state and national trends and look at interesting relationships they may have on one another.

## How I built it

The web application's backend used node and express. The data visualizations and data processing used d3. Specific d3 packages allowed to map and spatial visualizations using network/node analysis. D3 allowed for interactivity between the user and visualization, which allows for more sophisticated exploration of the datasets.

## Challenges I ran into

Searching through the John Snow Labs datasets required a lot of time. Further processing and finding the best way to visualize the data took much of my time as some datasets included over 40,000 entries! Working d3 also took awhile to understand.

## Accomplishments that I'm proud of

In the end, I created a working prototype that visualizes significant data that may help a user understand a complex dataset. I learned a lot more about d3 and building effective data visualizations in a very constrained amount of time.

## What I learned

I learned a lot more about d3 and building effective data visualizations in a very constrained amount of time.

## What's next for Finder

I hope to add more interaction for users, such as allowing them to upload their own dataset to explore their data.
## Inspiration

We are a team of goofy engineers and we love making people laugh. As Western students (and a stray Waterloo engineer), we believe it's important to have a good time. We wanted to make this game to give people a reason to make funny faces more often.

## What it does

We use OpenCV to analyze webcam input and initiate signals using winks and blinks. These signals control a game that we coded using PyGame.
See it in action here: <https://youtu.be/3ye2gEP1TIc>

## How to get set up

##### Prerequisites

* Python 2.7
* A webcam
* OpenCV

1. [Clone this repository on Github](https://github.com/sarwhelan/hack-the-6ix)
2. Open command line
3. Navigate to working directory
4. Run `python maybe-a-game.py`

## How to play

**SHOW ME WHAT YOU GOT**

You are playing as Mr. Poopybutthole who is trying to tame some wild GMO pineapples. Dodge the island fruit and get the heck out of there!

##### Controls

* Wink left to move left
* Wink right to move right
* Blink to jump

**It's time to get SssSSsssSSSssshwinky!!!**

## How we built it

Used haar cascades to detect faces and eyes. When users' eyes disappear, we can detect a wink or blink and use this to control Mr. Poopybutthole movements.

## Challenges we ran into

* This was the first game any of us have ever built, and it was our first time using Pygame! Inveitably, we ran into some pretty hilarious mistakes which you can see in the gallery.
* Merging the different pieces of code was by-far the biggest challenge. Perhaps merging shorter segments more frequently could have alleviated this.

## Accomplishments that we're proud of

* We had a "pineapple breakthrough" where we realized how much more fun we could make our game by including this fun fruit.

## What we learned

* It takes a lot of thought, time and patience to make a game look half decent. We have a lot more respect for game developers now.

## What's next for ShwinkySwhink

We want to get better at recognizing movements. It would be cool to expand our game to be a stand-up dance game! We are also looking forward to making more hacky hackeronis to hack some smiles in the future.