Stuff and the Universe

A less than elegant foray into the world of data analysis, science, and a few other bits and pieces. Sometimes I use R, Excel, JavaScript, and Python to explore interesting questions. Sometimes I don't. Serious topics optional. @CamKieff LinkedIn

[stuffandtheuniverse]

84 Lumber Won the Advertisement Game in Super Bowl LI

Remember Super Bowl LI you guys? It happened, at minimum, five days ago and of course Tom Brady won what was actually one of the best Super Bowls in recent memory. Football, however, is only one half of the Super Bowl Sunday coin. The other half are the 60 second celebrations of capitalism: the Super Bowl Commercials.  Everyone has a list of favorites. Forbes has a list. Cracked has a video. But it is no longer politically correct in this Great country to hand out participation trophies, someone needs to decide who actually won the Advertisement Game.  

To tackle (AHAHA) this question I turned to the infinite online data repository, Google Trends, which tracks online search traffic. Using a list of commercials compiled during the game (AKA I got zero bathroom breaks) I downloaded the relative search volume in the United States for each company/product relative to the first commercial I saw for Google Home. [Author’s note: Only commercials shown in Nebraska, before the 4th quarter when my stream was cut, are included]. Here’s an example of what that looked like:

!he search traffic for a product instantly increased when a commercial was shown!  You can see exactly in which hour a commercial was shown based on the traffic spike.  Using the traffic spike as ground zero, I added up search traffic 24 hours prior to and after the commercial to see if the ad significantly increased the public’s interest in the product.

Below is a plot of each commercial, with the percent of search traffic after the commercial on the vertical axis and the highest peak search volume on the horizontal. If you look closely you will see that some of them are labeled. If a point is below the dotted line the product had less search traffic after the commercial than before (not good).

On average 86% of products had more traffic after their Super Bowl ad than before it.  But there are no participation trophies in the world of marketing and the clear winner is 84 Lumber. Damn. They are really in a league of their own (another sports reference!).  Almost no one was searching for them before the Super Bowl but oh boy was everyone searching for them afterwards.  They used the ole only-show-half-of-a-commercial trick where you need to see what happens next but can only do that by going to their website. Turns out its a construction supplies company

Pizza Hut had a pretty large spike during their commercial, but it actually was not their largest search volume of the night.  Turns out most people are searching for pizza BEFORE the Super Bowl.  Stranger Things 2 also drew a lot of searches for obvious reason.  We all love making small children face existential Lovecraftian horrors.

Other people loved the tightly-clad white knight Mr. Clean and his sensual mopping moves. The Fate of the Furious commercial drew lots of searches, most likely of people trying to decipher WTF the plot is about.  Finally there was the lovable Avocados from Mexico commercial.  No one was searching for Avocados from Mexico before the Super Bowl, but now, like, a couple of people are searching for them. Win.

So congratulations 84 Lumber on your victory in the Advertisement Game. I’m sure this will set a dangerous precedent for the half-ads in Super Bowl LII.

[stuffandtheuniverse]

Football Game in One Graphic

It’s possible to find play-by-play win probability graphs for every NFL game, but that does not tell me much about how the game itself was played. Additionally, I only sporadically have time to actually WATCH a game so using play-by-play data, R, and Inkscape I threw together this visualization of every play in this past Sunday’s game between the Kansas City Chiefs and New Orleans Saints. Why isn’t this done more often?  

[stuffandtheuniverse]

I Watched the NE02 Congressional Debate So You Don’t Have To Too

Absentee voting has already begun in Nebraska. And it turns out there are more names on the ballot than just the presidential candidates.  If you live in Nebraska’s 2nd congressional district then you also get to vote for your representative to the US House! Nate Silver’s FiveThirtyEight polls-only forecast has the district as a dead heat between presidential candidates Hillary Clinton and Donald Trump which could translate to a contested race for the House. Below I have some SparkNotes™ from the congressional debate so all of us in NE02 can get informed together. But first some formalities:

Brad Ashford was elected to represent the NE02 in 2014, the first Democrat to hold the seat since 1995.  Before that he served in Nebraska Unicameral (District 20) from 1987-1994 and from 2006-2015. You can read more about Brad Ashford at Ballotpedia or on his campaign website.

Don Bacon is a retired US Air Force brigadier general from Papillion. He is currently an assistant professor in leadership at Bellevue University. You can read more about Don Bacon at Ballotpedia or on his campaign website.

You can watch the debate and read the transcript on CSPAN here. Some questions below only have answers from one candidate; those are questions that they asked each other (how cute). The notes get longer as the debate progresses as the candidates begin having more of an open dialogue.

Some final thoughts: Mike’l Severe and Craig Nigrelli did an excellent job of moderating this (amazingly) civilized debate. Don clearly had some talking points that he wanted to squeeze in that took him off topic on occasion. Brad was very polished at the beginning of the debate, but while he maintained his substance, he lost some of that polish later on.  Brad also thanked Don for his service on multiple occasions while Don attacked Brad as a career politician while he himself was an outsider. Obviously this is not comprehensive and I would encourage you to look further into these candidates, but if you do not have time I hope this helped to inform your decision. Or it did not and you are still going to vote along party lines. That’s cool too just don’t forget to register to vote!

Online voter registration in NE ends at 5:00pm on October 21st. You can register here.

[stuffandtheuniverse]

How to Create Your Own Podcast for Zero Dollars

Podcasts are a big thing right now. They are perfect for commutes, washing dishes, long walks on the beach, whatever. Podcasts are a huge part of my day now. Serial (season 1 at least) changed the podcasting landscape and now they are everywhere. There are so many great choices, maybe too many, are we in a podcast bubble? Who cares. I get as many podcasts as I want, all for free.

On a recent episode of Question of the Day (a podcast of course) the hosts were discussing the future of podcasting. One of the co-hosts, James Altucher, posited that “it is worthwhile to do a podcast or to do an oral history...and the equipment is there.” He goes on to talk about how he records podcasts on his iPhone just for fun and uploads them “wherever.” This topic reemerged in a later episode on how to be an interesting person. The key, they agreed, was to ask interesting questions. Well I have an iPhone and I want to be an interesting person. So i looked into what it takes to make a podcast and what I found was alarming. It is so simple. Contributing to the podcast glut I made a podcast and in an effort to make more interesting people I wrote this guide.

First a disclaimer: this method actually does cost money.  You need to have a cell phone and a computer, but since this is 2016 those hopefully are not insurmountable obstacles. I will be using my iPhone as an example but the process should be generalizable to Android phones etc.

First you need to record the audio for your podcast.  iPhones come pre-programmed with a voice memo app. Boom. Recording software. You’re basically halfway there. So find someone you want to interview or just write up a script and record it right there on your phone. Next you need to transfer your voice memos to your computer which can be done through iTunes or sent from the app.

If you recorded it perfectly the first time and don’t want to add music or effects you can skip this paragraph.  For most of you though you will want to filter out some of the noise or splice together various snippets of audio.  Audacity is a free, open-source audio editing program that does all of those things.  Mac folks could also use GarageBand. Unlike some open-source software (*cough* Gephi *cough*) Audacity is very stable and user-friendly.  You may need to download some plugins to import/export certain file extensions, but the program will forward you to the appropriate websites. Audacity has a great tutorial on mixing narration with background music so start there. If you want to try other effects their help wiki is...uh...helpful. I used the noise reduction and compressor effects first to level everything out and then the envelope tool to alter narration and music volume levels. Obviously you should listen to your podcast all the way through before exporting. I chopped out extraneous “uhs” and “ums” as well as any loud breaths. Be careful though because editing out too much can make the interview sound unnatural.

After you export your file as an MP3 you need to get it from your computer to the World Wide Web. iTunes does not host the podcast, but rather provides a distribution platform for audio files hosted elsewhere. Audacity has some recommendations for where and how to upload your file on their podcasting tutorial, but I chose a different route and used SoundCloud. You can also choose to host your file for free on Google Drive or WordPress. Soundcloud’s useful creator guide walks through how to use their service and how to get your podcast to iTunes. (NB Make sure the profile picture on your SoundCloud account/podcast files is at least 1400x1400 or iTunes will reject your podcast.) Once your podcast is uploaded to SoundCloud, go to the content tab on the settings page and copy the link for your RSS feed.

The last step is submitting your podcast to iTunes, the preeminent podcast repository. iTunes has a great walkthrough regarding the process. You basically just click “Submit a Podcast” from the podcast page of the iTunes store in iTunes, login with your Apple ID, and paste the link to your RSS feed from SoundCloud. Click “Verify” and your podcast is submitted.  It may take up to 1 or 2 days for your podcast to appear on the store.

Once your podcast is up, you and your friends can download it to your phones, subscribe, anything that you can do with a “real” podcast, because your podcast IS a real podcast. How easy was that? I made my first episode in a day and people were downloading it within 24 hours. For my first podcast I chose to interview my dad about his life and cut the interview into four different “episodes.” I am still not sure if he counts as an interesting person, but it was fun for both of us and I got to practice asking questions.  Maybe the best way to become an interesting person is just to tell others that you have a podcast.

You can find the podcast I made, “Papa Cam”, here or search for it in the iTunes store.

[stuffandtheuniverse]

Social Dynamics in Pharmacology

The Department of Pharmacology at Creighton University School of Medicine is small, but mighty. There are only 10 professors or principal investigators (PIs) in the department, but this small size has its advantages.  Or at least that is what we tell ourselves.  A recent paper in Nature argued  that bigger is not always better when it comes to labs and we are putting that to the test.  Ideally with a smaller faculty, there would be more collaboration. Everyone knows what everyone else is doing, more or less, so they can more efficiently leverage the various expertise found throughout the department.

To measure how interconnected the pharmacology department was I created a network analysis visualization based on who published with whom.  Using NCBI’s FLink tool I downloaded a list of the publications in the PubMed database for each PI in the pharmacology department at CU. A quick script in R formatted the authors and created a two-column “edge list” for each author, basically a list of every connection.  This was imported into the free, open-sourced network analysis program Gephi which crunched the numbers and produced a stunning map of the connections in the pharmacology dept:

Gephi automatically determines similar clusters (seen as different colors) which are unsurprisingly centered on the various PIs in the department since those are the publications I was looking at. Dr. Murray, the department chair, has the most connections, also known as the highest degree, at 292, followed by Dr. Abel. Drs. Dravid and Scofield are ranked 2nd and 3rd respectively for betweenness centrality, after Dr. Murray. They are the gatekeepers that connect Drs. Abel, Bockman, and Tu to Dr. Murray. Each point’s size is proportional to its eigenvalue centrality, similar to Google’s Pagerank metric of importance.

I was a bit surprised at how disperse the department was. 60% of the PIs could be connected, and many have strong relationships. However the rest are floating on their own islands. Dr. Oldenburg is relatively new so this is not surprising. The Simeones (who are married) are closely connected. Also unsurprising.

This was a quick and dirty analysis and a few of the finer points slipped through the cracks. Some of the names are common in PubMed (especially Tu). so I did my best to filter what was there and only look at publications affiliated with Creighton.  Unfortunately this filters out publications from other institutions by the same author. Also not everyone is attributed the same way on every manuscript. This is especially true for Drs. KA Simeone and Gelineau-Van Waes who have published under different last names, but also because sometimes a middle name is given and sometimes it is omitted.  I tried my best to standardize the spellings for each PI, but with over 700 nodes I could not double check every author to ensure there were not duplicates elsewhere. If more than one PI shows up on a paper, that paper may show up under both searches.  This should not increase the number of edges, but would affect the “strength” of those connections.

The connections are about what I had imagined. The brain people are on one side, everyone else is on the other. Expanding the search to include the papers from coauthors outside of the pharmacology department might discover more interesting connections. Just for fun I went ahead and pulled the data for every paper on PubMed with a Creighton affiliation. I could not even find my department on the visualization without searching for it. It is massive. The breadth of Creighton’s interconnected-ness forces me to marvel at how vast the community of scientists must truly be. So many people working to improve the body of knowledge of the human race. We are really just small bacteria in a very large petri dish.  

[stuffandtheuniverse]

Food Stamp Primer for the Upper Middle Class

Image source: NY Times 

Food stamps are mysterious. They are kind of like cobras. I have never seen one up close nor do I want to. Are they actually stamps? Like mail stamps? No idea.  But just because cobras are not a part of my daily life does not mean that they should be ignored. Food Stamps were utilized by over 45 million Americans in 2016 totaling $75 billion (less than 2% of the federal budget). That is not insignificant. So let’s check our privilege and become informed voters by (briefly) diving into the world of Food Stamps.

First, the term “Food Stamps” is passé.  The government renamed it the Supplemental Nutrition Assistance Program or SNAP in 2008, though states are free to call it whatever they want (a small victory for states’ rights). EBT is another term that occasionally appears in grocery store windows and that I surmised was loosely associated with food stamps. EBT, or Electronic Benefits Transfer, is essentially synonymous with SNAP. An EBT card has funds transferred to it at the beginning of every month which can then be used for SNAP purchases. So the “stamps” are not literal stamps. Nor were they ever really what I would consider stamps, but rather funny-colored tiny bills (see above).

A person with an EBT card loaded with SNAP $$$ can purchase just about anything at a grocery store with a nutrition label. It is probably easier to list what you cannot purchase with food stamps than what you can:

Fun stuff (wine, beer, liquor, cigarettes, misc. tobacco)

Nonfood items (soap, paper products, hammers, pet food, Arby’s)

Hot foods or foods consumable in the store (i.e. Taco Bell)

Medicine (this is what Medicaid is for)

Grocery stores have a lot of choices. Choosing which foods will keep you alive while also being as cheap as possible is a challenge. Luckily the US Department of Agriculture is here to help.  With the help of some sophisticated economists and the revised Food Pyramid (which I admittedly still do not understand) they have devised a definitive thrifty food plan (TFP). The government literally has a detailed plan for how SNAP dollars should be spent. Please check it out. Discover what Uncle Sam says you should be eating instead of mac-n-cheese and pizza rolls (hint: you have to eat a lot of beans). Two complaints with the TFP: 1) This report is pretty complex. Is the information getting out to people on SNAP? 2) Though the TFP is updated fairly regularly (especially the pricing data), it is based on some ACNielsen Homescan Panel data from 2001. As this was before the bacon craze it is doubtful that this data accurately reflects actual consumption patterns.

Applicants have to meet certain income tests to be eligible for SNAP.  They must have a net monthly income below the federal poverty level. Additionally some states have asset requirements that limit the amount of savings or property a recipient can own. Citizens can be considered categorically eligible if they meet the requirements for other federal programs. Several deductions factor into the calculations for benefits, including excessive housing costs.  If an applicant spends more than 50% of their income on rent, anything above 50% can be deducted from their income for SNAP calculations. Certain aged and disabled populations also have lower restrictions on benefits from SNAP.

Applying for SNAP is not easy and the application varies between states. Iowa for example has a 19 page form that looks way more complicated than a 1040 tax form. I did not even want to read it much less attempt to fill it out. The rigor in the application process is meant to curtail fraud but it also places a burden on the family receiving the benefits and increases the administrative costs for the case workers who have to review the forms.  

Benefits are calculated assuming a household spends 30% of its budget on food. So the difference between 30% of the net income and the maximum allowed federal benefits based on family size is the amount received.  For a family of 4 the maximum benefits are $649 which is about $6 less than the projected cost of the TFP for a family with two kids aged 6-8 and 9-11. The deficit is more pronounced for a family of two adults. This emphasizes the “supplemental” part in SNAP’s name. Even purchasing scant rations based on the TFP does not guarantee an adequate diet. Exacerbating this problem is state-to-state variability in food prices.  While the federal maximum benefits are fixed in the contiguous 48 states, food prices in Connecticut can be over 30% higher than the national average or 11% lower in Texas. 

SNAP does have some economic upside. SNAP spending by the government has a multiplier effect. For every $1 spent on SNAP the US GDP increases by $1.79. SNAP also decreases hospitalization costs and improves school attendance for children. 

SNAP has its benefits and drawbacks but for over 10% of Americans it is a necessity. If you want to learn more about SNAP or to try and live the SNAP life at home check out the “Food Stamped” documentary website for details. To find more specific statistics for SNAP in your state check out the interactive map at the Center on Budget and Policy Priorities. To find out more information about cobras click here.

[stuffandtheuniverse]

More Taylor Swift (GRAMMY edition)

I don’t have cable. So I did not get the chance to watch the Grammys this year.  I was, however, happy to hear that Taylor Swift won the Grammy for Album of the Year for 1989 (since I recently wrote a post about how great she is).  When I was writing the aforementioned post I did notice that she was nominated, but I felt pretty confident the National Academy of Recording Arts and Sciences would give it to Kendrick Lamar’s To Pimp a Butterfly. This is Swift’s second Grammy for Album of the year (she also won for Fearless as we all know).

Since the data have already been scraped from the Billboard Hot 100, I might as well get some mileage out of them. For each week since November of 2014 (around when 1989 and To Pimp a Butterfly were released) I assigned any song by any of the five artists nominated for Album of the Year a point value from 1 to 100 based on its position in the Hot 100. Songs ranked number 1 were given 100 points, and songs ranked 100 were given 1 point et cetera. Then for each artist I added up the point values for each week the results of which you can see below:

Notice someone missing from this visual? None of the songs from the Alabama Shakes’ album Sound & Color made it to the Hot 100. This is despite the fact that their album was on the Billboard 200 for album sales for 26 weeks, peaking at number 1. Chris Stapleton has a little purple blip around December, seven months after Traveller was released. Kendrick makes it on here and there, but the graph is clearly dominated by Taylor Swift and The Weeknd. The Weeknd has by far the highest peaks, but Taylor proves her popularity with the largest total area under the curve, 13,676 “points” vs. The Weeknd’s 11,156 “points”. TSwift also has the highest average per week, though not by much.

Fun fact: Swift’s “Bad Blood” was minimally successful until she added some bars by Kendrick Lamar...which went on to win the Grammy for Best Music Video. Does that say more about Taylor or Kendrick?

We can debate whether song popularity should be the metric by which we measure the value of an album. Obviously a lot of people thought Sound & Color was a world-class album despite its absence from the Hot 100. In fact the National Academy of Recording Arts and Sciences insists that Album of the Year is to “honor artistic achievement, technical proficiency and overall excellence in the recording industry, without regard to album sales or chart position.” However, of those nominated this year, they did pick the one with the best album sales and chart position.

[stuffandtheuniverse]

Taylor Swift’s Data is Beautiful

“If you're horrible to me, I'm going to write a song about it, and you won't like it. That's how I operate.”  – Taylor Swift

Taylor Swift. Love her, hate her, love her, or love her. There really is not much of a choice as everyone seems to be constantly showering Ms. Swift with praise. While her fans are eager to show Tay some love she also has no problem sharing that love with others.  As Gawker points out, she has dated what appears to be every man in the universe.

Regardless of Taylor Allison Swift’s extracurricular activities you cannot deny that she is a powerhouse in the music industry. Of Taylor’s five studio albums, three have sold over a million copies in their first week making Taylor the first female artist to have done so.  That is remarkable considering that I cannot recall the last time I actually bought any music.  John Lennon famously claimed that The Beatles were “more popular than Jesus.”  TSwift is not quite that popular, but she is close according to Google Search traffic:

Whether Taylor or The Beatles before her, these musicians are having a large impact on in the lives of the kids. Perhaps an impact comparable to The JC. (Justin Bieber actually IS a more popular search term than Jesus over the same time span, which should be worrisome.)

Being popular is nothing new for TSwift and we can measure that popularity using the charts produced by Billboard. Taylor first appeared on the Billboard Hot 100 on September 23, 2006 with her song “Tim McGraw” off of her debut album Taylor Swift.  Billboard started tracking the hits of the day on its list Best Sellers in Stores way back in 1939. Shortly thereafter it started publishing two other lists, Most Played by Jockeys and Most Played in Jukeboxes. Three years later Billboard published its magnum opus, the Hot 100, which coalesced these various chart into a single definitive ranking across genres.

Today the Hot 100 is composed of three components: sales (35-45%), airplay (30-40%) and streaming (20-30%) which can vary weekly in order to hit the target of 100 songs. The Streaming Songs chart is the newest and began in 2007 with data from AOL and Yahoo.  It expanded to include services such as Spotify in January of 2013 and one month later added YouTube views.  The Digital Songs chart tracks sales data for digital downloads while the Radio Music chart tracks radio airplay audience impressions.  Both charts are generated from data furnished by Nielsen Music. Additionally Billboard publishes its YouTube data as a separate chart on its website.

The Billboard charts provide a rich, publicly available data set.  Using the tools over at Kimono I scraped the available data from the Hot 100, Radio Music, Digital Songs, Streaming Songs and YouTube charts since 2000. Now that I have data on hundreds of artists over the last one and a half decades I feel like anything is possible.  Taylor Swift is our test case because (1) She is very popular (2) many of her songs make it onto the Hot 100 (3) her most recent album, 1989, was released after Spotify and YouTube were included on the Streaming Songs chart. Here is what 1989′s travel through the Hot 100 looks like:

Yes it’s a bunch of lines and dots, but they tell some interesting stories. Compare the difference between “Shake It Off” and its debut at the #1 spot with the slow methodical rise of “Style.” Or Bad Blood’s resurgence after the addition of Kendrick Lamar and the release of its star-studded music video. Overall, nine of the 15 songs from Swift’s fifth album appeared on the Hot 100.  At first it seemed odd that Taylor’s songs would drop off the chart once they dropped to position #50.  Was this some brilliant marketing strategy? Is it not worth promoting a song anymore after it has fallen so far? Unfortunately the answer is much more mundane. Billboard labels songs as “recurrent” and removes them after 20 weeks on the chart and after falling below the 50th position. 

In November 2014 Swift pulled her music from the popular streaming site Spotify. This severely crippled her ability to gain on the charts through the streaming component of the Hot 100.  TSwift did agree to have her music featured on Apple Music’s new service which launched June 30, 2015 after some controversy regarding its 3-month free trial period.  Her ability to sway the actions of the second most valuable public company legitimizes her powerhouse status and further validates all of this work.

Swift can make up for her refusal to stream by dominating on another “streaming” service, YouTube. Below is a breakdown of her trends on each of the Billboard charts I analyzed.  While the Hot 100 ranks songs 1-100, the other charts only display the top 25 songs in each category.  

(NB physical sales of a CD are also a component of the Hot 100 ranking, but are not included above.)

Some take aways: Each song had a huge increase in digital downloads when 1989 was released at the end of October, but only “Blank Space” was able to maintain that (”Shake It Off” was released prior to 1989). However, the increase in digital downloads was enough for both “Bad Blood” and “Style” to make it onto the Hot 100. YouTube views are almost always above streaming views.  This makes sense since Taylor did not allow streaming on other services.  If “Shake It Off” and “Blank Space” had not been labeled as recurrent they would probably still be on the Hot 100 based on their YouTube views which are very persistent. YouTube views were a leading indicator for “Bad Blood.” It made it to the top 5 of the YouTube chart a week before charting on digital downloads, the Hot 100, or radio songs.  It would appear that TSwift’s mastery of music videos can single-handedly make a single.

Finally I calculated the area under the curve (AUC) for each of Taylor’s songs off of 1989 and compared it to the number of YouTube views for each song.  The AUC was calculated by assigning a 100 point value to position 1, 99 points to position 2 etc. and multiplied by a song’s weeks at that position.  Without access to other streaming services like Spotify or Tidal and given the visual correlation between the YouTube and Streaming Songs charts I expected a strong relationship between YouTube views and success on the charts.  The regression for TSwift was statistically significant. However Justin Bieber, who does not have the same qualms with distributing his music on Spotify had an even stronger correlation.  This might have something to do with the fact that J-Biebs has a music video for every song off of his new album (although many of them have relatively few views) while Taylor only has videos for six of her songs.  Would more music videos add buoyancy to some of her other songs? I suspect that it would.

I should quickly note that analyzing the Billboard Hot 100 is not a novel idea.  On his blog Modern Insights (and minor observations) Michael Kling has plotted similar analyses looking at the rise and fall of artists and songs in the Billboard Hot 100.  Also very recently Cristian Cibils from Stanford University published a brief paper where he used machine learning to predict a song’s future Hot 100 trajectory based on past chart performance. I would recommend you check their posts out. Next Big Sounds is a company that tracks streaming and social media to provide unique insights into what makes songs popular and actually makes money doing it.

This is just a quick overview of some of the interesting features that can be investigated using data from Billboard.  Full disclosure I did most of this as a part of WNYC’s Note to Self’s Infomagical week.  Tweet me which other artists you think might have interesting stories to uncover.  Taylor Swift is here to stay, and while she is here she will continue to win over our hearts and most of the Grammys.

[stuffandtheuniverse]

Lungs, Electricity, and D3

In my research I create a lot of graphs. Most of them are boring. But the world does not have to be this way. So I decided to try and spruce them up by adding color and making them interactive. 

My research focuses on lung diseases.  Specifically I am looking at how your nervous system controls airways and blood vessels in your lungs.  We do this by slicing mouse lungs paper-thin and stimulating them with electricity.  This stimulation causes the nerves to release their signaling molecules (called neurotransmitters) which in turn cause the airways to constrict or relax.  We hope to use this technique to look into new drug treatments that target nerves in diseases such as asthma and pulmonary hypertension.

Below is a graph of some of my results. This is from a group of airways that have been electrically stimulated at different frequencies.  The higher the frequency, the greater the release of neurotransmitters.  The percent constriction of the airways is on the y-axis. The treatment groups include the control (with no drugs), stimulation in the presence of a drug called atropine (which blocks some of the neurotransmitters from constricting the airways) and magnesium sulfate (MgSO4, which blocks release of  neurotransmitters from lungs). 

0 20 40 60 1 3 7 10 15 30 Frequency(Hz) Response(%) Treatments Control Atropine MgSO4 AW Frequency Response Curves ourdata= [[{"Dose":" 1","variable":"AW","value":"14.3161099"}],[{"Dose":" 3","variable":"AW","value":"19.5406948"}],[{"Dose":" 7","variable":"AW","value":"24.7079560"}],[{"Dose":"10","variable":"AW","value":"35.3996151"}],[{"Dose":"15","variable":"AW","value":"39.6948869"}],[{"Dose":"30","variable":"AW","value":"52.8271691"}],[{"Dose":" 1","variable":"A","value":"-0.5890159"}],[{"Dose":" 3","variable":"A","value":" 5.7389871"}],[{"Dose":" 7","variable":"A","value":"18.3817052"}],[{"Dose":"10","variable":"A","value":"27.6805522"}],[{"Dose":"15","variable":"A","value":"30.7087604"}],[{"Dose":"30","variable":"A","value":"41.1434394"}],[{"Dose":" 1","variable":"M","value":" 3.2402510"}],[{"Dose":" 3","variable":"M","value":" 2.9960416"}],[{"Dose":" 7","variable":"M","value":" 2.4840646"}],[{"Dose":"10","variable":"M","value":" 2.2161551"}],[{"Dose":"15","variable":"M","value":" 1.5570279"}],[{"Dose":"30","variable":"M","value":" 2.1489746"}]] dataToBind = d3.entries(ourdata.map(function(d,i) {return d[0]})) scatterPoints = d3.select(".points").selectAll("use"); scatterPoints.data(dataToBind) scatterPoints .on("mouseover", function(d) { //Change color and outline d3.select(this) .style("fill-opacity","") .style("fill-opacity",1) //Create the tooltip label var tooltip = d3.select(this.parentNode).append("g"); tooltip .attr("id","tooltip") .attr("transform","translate("+(d3.select(this).attr("x")+10)+","+d3.select(this).attr("y")+")") .append("rect") .attr("stroke","white") .attr("stroke-opacity",1) .attr("fill",d3.select(this).attr("fill")) .attr("fill-opacity",0.60) .attr("height",30) .attr("width",40) .attr("rx",5) .attr("x",2) .attr("y",5); tooltip.append("text") .attr("transform","scale(1,-1)") .attr("x",5) .attr("y",-22) .attr("text-anchor","start") .attr("stroke","black") .attr("fill","black") .attr("fill-opacity",1) .attr("opacity",1) .style("font-size", "10px") .text(Math.round((d.value.Dose)*100)/100 + " Hz"); tooltip.append("text") .attr("transform","scale(1,-1)") .attr("x",5) .attr("y",-10) .attr("text-anchor","start") .attr("stroke","black") .attr("fill","black") .attr("fill-opacity",1) .attr("opacity",1) .style("font-size", "10px") .text(Math.round(d.value.value*100)/100 + "%"); }) .on("mouseout", function(d) { d3.select("#tooltip").remove(); //change opacity d3.select(this) .style("fill-opacity","") .style("fill-opacity",0.5) });

What I find the most interesting is that atropine is able to block almost all of the constriction at low frequencies, but has a diminished effect at higher frequencies.  This indicates that there are neurotransmitters being released that are not blocked by atropine which is very exciting.  Maybe these will be the new targets we can attack.

The chart itself was generated in R and then exported as an SVG file.  The interactive portions are created using a JavaScript library called D3.  If you are interested in D3 at all, Mike Bostock has a great tutorial called “Three Little Circles.” 

So there you have it.  Interactive electrical airway stimulation results.  Just as God intended.

[stuffandtheuniverse]

Hobbit, Spanish for...Hobbit

“En un agujero en el suelo, vivía un hobbit”

Did you ever get your report card and have to masterfully change some of those F’s to B’s on the bus ride home? I doubt that this has ever successfully fooled anyone, but this movie trope illustrates that report cards can be terrifying. They do, however, provide a useful piece of information: feedback.  Without feedback, it is difficult to tell if learning is happening. Am I closer to mastery now than I was when I started?

Well, over a year ago I travelled to Mexico to visit my good friend Dave in Chiapas.  I had worked my way through Duolingo and even did Rosetta Stone for a bit, so while I would not have considered myself fluent, I figured I could get by.  This assumption was quickly smashed against the rocks of reality at Customs and I spent the remainder of the trip staring blankly at anyone who had the misfortune of attempting to speak to me.

The trip was still a blast, but I made a commitment to improve my Spanish over the next year. In the Spanish language section of Barnes & Noble I picked up a copy of El Hobbit by the legendary J.R.R. Tolkien. Maybe you’ve heard of it.  I had been wanting to reread it for a while so why not try in Spanish.  My goal was to read one page per day with a goal of finishing it within a year. At 283 pages, this seemed more than achievable.

Just last week I finally learned how the story ended (happily ever after), but I had no way of knowing if I actually improved my Spanish at all.  There was no feedback.

There was one piece of information that I could leverage to my advantage, the number of words I translated per page. In an effort to actually retain some information I would write the translation of words or phrases I did not know over the words in the book.  So I counted these up for each page.  You can see the breakdown of the page totals sorted by chapter in the graph below. The average for that chapter is shown in red.

0 5 10 15 20 25 30 35 40 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Chapter Number of Searched Words El Hobbit as a Learning Tool

Overall the chapters had a statistically significant downward trend! I may have learned something after all.  This works out to be about 2/5 of a word improvement per chapter on average, from an average of 20 words per page in chapter 1 to just under 12 words per page in chapter 19.

I am still far from being a fluent speaker.  In fact this did not teach me anything about speaking Spanish.  But at least I have a report card to show my parents.

P.S. When I told my dad about this he responded, “Son, you’re a huge nerd.”  Thanks pops.

[stuffandtheuniverse]

Macklemore: Moped Market Maker

“And I’m like ‘Honestly, I don’t know nothing about mopeds’” -Macklemore

Mopeds are a thing. A thing that exists. A thing that has had approximately zero influence on my life. People in Europe drive mopeds, people in America drive literally anything else. But then I watched the music video of Macklemore’s & Ryan Lewis’s new song “Downtown.” The video begins with Macklemore naively purchasing a moped which he proceeds to ride around the ‘Downtown’ for the remainder of the video.

“Oh yeah mopeds, those exist” I thought, and headed to Google to see if a moped is right for me [Spoiler: It is not]. But then I wondered how many other people were similarly inspired to dive into the fabulous(?) world of mopeds.

Google, of “Don’t be Evil” fame, obviously tracks every search performed on its site and the aggregated data is available through Google Trends. Below is the Google Trends plot for the search term “moped” from 2004 - present in the United States.  An interactive version of this plot can be found here.

The most prominent feature is the large spike in the summer of 2008.  When the financial crisis hit people were thinking of ditching their Hummers and opting for something that gets 70 mpg.  What is curious is that this was not a persistent trend, even though people were supposedly strapped for cash for the next few years of the recession. Everyone did their moped research in 2008 and decided that mopeds were not for them. Conversely, everyone bought a moped in 2008 and they all still have them and have no need to ever search for anything moped related. Anecdotally, the number of mopeds I see is still approximately none so I will choose to believe the former.

Because Google Trends only displays relative search numbers, and the 2008 spike was such a huge outlier, I chose to rerun the analysis from December 2008 until the present to achieve greater “resolution” to answer the question of interest.  Explicitly, how has “Downtown” affected searches for mopeds?

Because searches for mopeds are cyclical, we can average these to get an idea of how Macklemore’s song has deviated interest in mopeds.  Here is a plot of the average number of relative moped searches by month (bold). Each individual year is also shown, with 2015 highlighted in red. 

Downtown Drops 40 60 80 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Relative Search Traffic Moped Search Traffic

As you can see, 2015 has been a bit below average, but there was a small increase at the end of the month in August, which is when “Downtown” was released (indicated as a dotted line). Then in September, instead of dramatically plunging as Fall approaches, the number of searches for mopeds stayed relatively stable.  September’s search traffic scored a 74, almost 17 points higher than the average for this time of year, and statistically different than the mean. More people than usual are actually looking for mopeds because of this ridiculous song.

There is no way to know if this increase in traffic will actually lead to more mopeds on the streets, especially when Winter is Coming. Nevertheless there appears to be a causal relationship between this song and moped searches. As “Downtown” continues to rise in the BillBoard Top 100 (it has only been on there since the September third and is currently sitting at number 16 at the time of writing) the last question we have to ask ourselves is: How much is Big Moped paying Macklemore?

Click Below to Watch the “Downtown” Music Video.

[stuffandtheuniverse]

Watch Me Move

"Now Break your legs. Tell 'em break your legs" - Silento

Groundbreaking new study finds a perfect correlation between “Whips” and “Nae Naes”

[stuffandtheuniverse]

Home sweet home opener

Nebraska’s 29-season opener winning streak was busted on Saturday by BYU with a final score of 33-28.  That streak, which has lasted since 1985, was the longest standing streak in the FBS. Not all of Nebraska’s openers during that streak have been cake-walks either including four openers against teams ranked in the top 25. The new claimants to the title are the Florida Gators who won their game against New Mexico State 61-13 and have not been defeated in a season opener since 1989.  

To quote a friend: “Nebraska isn’t good and BYU isn’t bad so this could get interesting.”  It was interesting which appears to be unusual for season openers.  Most teams prefer to start the year with a beat-down, the average point differential being over 30 points.  The highest differential goes to Ole Miss who took the University of Tennessee at Martin behind a barn and put them out of their misery with a score of 76-3.   In contrast this year’s initial game for Nebraska was one of the closest and coincidentally most exciting.  Nebraska had the 5th lowest point differential of any game on Saturday.

Below is a plot of the winning team’s score versus the score of the losing team.  The line indicates a score differential of zero, or a tie (which we are thankfully spared in American Football).  The closer to the line, the lower the point differential.  The Nebraska-BYU game is indicated in red. Every game with a lower point differential than Nebraska-BYU was a “shootout” with a higher total number of points scored.  

If every opener was easy, then Nebraska’s streak would not have been at all impressive and if every game Saturday was a blowout then why would anyone bother watching? Someone has to bite the bullet and make college football worth writing blog posts about.

Next year Nebraska will start off against Fresno State and (maybe?) begin the streak all over again.

Click below to see a breakdown of the score differential for each game from Saturday.

[stuffandtheuniverse]

Interactive Babes of the Day

In a quest to provide a more hands on data experience for you, I have created an updated version of the map from my TFM babes of the day post where I mapped out the density of featured TFM girls by state.  The map was creating using the data I collected on May 19th, 2015 and using the tools over at HighCharts.

Because Tumblr is terrible, you will likely have to allow the map to run on the page.  If you are using Google Chrome click the small shield icon in the right side of the URL bar and disable it. I can assure you with the utmost confidence that it is completely safe.

Enjoy your only hands on experience with these lovely TFM babes.

Click here to see the original post.

[stuffandtheuniverse]

Hallelujah it’s raining bots

[Dusk. Three 20-something white men sit around a rectangular kitchen table, several piles of white and black cards stacked in front of them. Oh and some Busch Light cans.]

This all started when no one would play Cards Against Humanity with us.  So we started pulling pairs of black and white cards off the stacks and sharing the ones that we thought were funny. “Someone should make a Twitter bot that automatically posts random combinations of these,” I thought aloud.  No one responded because that is a stupid thing to say at a party. When we finally finished all of the black cards I went home and did it (two days later) anyway.

To be fair this HAS been done before. @CAH_bot is a fine example of one. And its about 1600 tweets ahead of me.  But I did it anyway because the world deserves more Twitter bots (and because I didn’t find that account until after I did all this work).

How did I do it? Well I’m glad you asked.  After I tracked down some text lists of the cards, I imported them into Excel and used its RANDBETWEEN() and INDIRECT() functions to pull cards from each list and paste them together.  Once I copied them into a new Notepad document I used the code in the appropriately titled “How to Write a Twitter Bot with Python and tweepy” tutorial to automatically post to Twitter. All I had to do then was create a Twitter account and away I went.

So here it is: @bot_CAH

This little guy is more of a rough approximation of a Twitter bot.  First, I should probably write up some Python that automatically generates the posts.  Also it currently posts every 15 minutes, but only when I am using my Surface.  So it won’t completely spam your Twitter feed.  In a perfect world I would have it post every hour from a constantly running Raspberry Pi (basically just a tiny $35 computer that’s useful for things like this).

This bot works in a completely different way from my first foray into Twitter automation.  My other bot, @PH_papers, is based off this post and uses dlvr.it to automatically update the account based off a Google Alerts style search from PubMed.  I would recommend you follow it if you are interested in hearing about the most up-to-date Pulmonary Hypertension research.  So far most of my followers are doctors from Mexico. That’s how you know you’ve made it. 

In closing I would just like to say that this was a fun little experiment and that it has helped to reveal some deep truths about the universe.

White people like Kanye West

— CAH Bot (@bot_CAH) August 11, 2015

[stuffandtheuniverse]

Computer Skills for Scientists: Integral Tools of the Scientific Process

This post was originally hosted on the American Society for Pharmacology and Experimental Therapeutics Blog - PharmTalk.  The original post can be found here.

Computer Skills for Scientists

The dawn of the computer age is over. Computers are no longer just hobbyist toys or specialized equipment; they have become an integral part of the scientific process. In fact you are almost certainly reading this on a computer. As science’s reliance on computing grows, so too will its need for able-minded computer users. Graduate school or post-graduate training is not only a time to develop your bench top skills, but also a time to focus on honing the “soft skills” of independent critical thinking, teamwork, networking, and scientific writing. Allow me to propose that you should also spend some of your (limited) time expanding your computing prowess.

Streamline Workflow and Save Time

Many commonly used programs, like Excel or ImageJ, allow you to write macros that will repeat a task over and over. Macros are sets of instructions that tell a program to repeat a specific sequence of tasks. Need to perform the same analysis on hundreds of images? A macro might work. Do you need to import, manipulate, and format a series of data sets in Excel? Try a macro. Some require a bit of coding knowledge, while others are able to record a sequence of clicks. You could potentially save yourself hundreds of hours by automating some of your more monotonous work. Take a day to look through your favorite program’s documentation or help files to learn what options are available to you. When in doubt, Google it. Chances are someone has already done something similar.

Improve Clarity of Scientific Reporting

Mistakes are inevitable, but they are a huge problem for data reproducibility. Automated computing tasks help to remove unintended biases in data analysis by essentially “blinding” the researcher leading to increased reproducibility. Journals are pushing for researchers to publish their raw data and program code. Peer review only works though if you, as a trained scientist, can interpret that computational information. Systemic statistics illiteracy is also a common problem that is closely tied to computational proficiency. The more you practice and explore a particular program, the more positive you can be that you are using the appropriate statistical tests. If you are familiar with GraphPad, check out their thorough documentation which can answer most of your stats questions.

Open New Doors to Different Career Options

Bench top skills make you a good technician. Pick your favorite bench top technique. Are you really good at it? Then chances are high that so is everyone else in your field. Your competition likely has a similar publication record. They probably even took the same basic classes as you. How are you going to stand out in the academic field? Computer skills are, as they say, another tool in your toolbox that may help you stand out from the crowd. Universities are looking for faculty and post-docs that will add something to their team. That something could be working knowledge of a unique (and hopefully useful) computer program. While almost any skill may give you a competitive edge when applying for jobs in general, certain careers are looking for scientists with specific skills. Clinical trials and high throughput screening create monstrous mounds of information. While the clinicians and technicians are running the experiments someone has to translate the results into information that the decision makers can use. Data management tools like SQL, R Tool, SPSS, or SAS are great ways to practice making sense out of huge amounts of information, are broadly applicable, and are sought after skills in industry. “Big data” is an emerging field that may provide a potentially satisfying career path for PhD level scientists. If you are interested in lab management as a career post graduate school then experiences with software like Labguru and Quartzy are good places to start. Because this software can link specific reagents to each protocol they can add reproducibility to your current lab’s data too. Software like these allow for better sharing of data between lab members and/or collaborators. Finally, “Open science” tools like the Open Science Data Cloud allow researchers to access public datasets as well as make their own data available to collaborators across the planet. Data visualization is a trendy new field that revolves around transforming raw numbers into thought provoking graphics. Journalist organizations, both traditional and web-based ones, need people who are familiar with turning complex scientific ideas or large data sets into attractive and informative images. Professional biostatisticians and bioinformaticists also should be able to communicate their complex results to diverse audiences. Plotly has a Python library all about visualizations that might be a good place to start. While not every job outside of academia will require a PhD and each discipline has its own set of heavily used programs, it would be unreasonable to try and learn them all. However, the simple act of independently learning a new skill demonstrates that you are driven to learn and can readily adapt to whatever technology is presented to you. Data visualization is a trendy new field that revolves around transforming raw numbers into thought provoking graphics. Journalist organizations, both traditional and web-based ones, need people who are familiar with turning complex scientific ideas or large data sets into attractive and informative images. Professional biostatisticians and bioinformaticists also should be able to communicate their complex results to diverse audiences. Plotly has a Python library all about visualizations that might be a good place to start. While not every job outside of academia will require a PhD and each discipline has its own set of heavily used programs, it would be unreasonable to try and learn them all. However, the simple act of independently learning a new skill demonstrates that you are driven to learn and can readily adapt to whatever technology is presented to you.

How to Get Started

One option is to ask your PI if they or your research group has a website. If they do not, help make one to showcase your lab’s current research and makes it accessible to potential new members browsing the web. You can also make a website for yourself. This can serve as both an online resume and tangible display of your HTML/CSS abilities. A completed website gives you a concrete “finish line” to your independent study. Plus with websites like Squarespace creating a new website is easier than ever. You could enroll in a computing class at your university or seek out online courses. Codecademy has free lessons on programming basics and website design. Another potential avenue for learning is Software Carpentry.Software Carpentry is a global network of courses and instructors that seek to teach basic computational workflow skills to scientists. Classes like these can help you to start thinking like a programmer and open your mind to potential uses for code in your own research. These are not your only options. You could make a video of one of your laboratory methodologies for YouTube. Learn a new program to calculate ANOVAs. Start a podcast with members of your lab. Ask someone in another lab if they can show you what software they use. It doesn’t have to be a huge time commitment. You can get a free week-long trial at Lynda.com where they have hundreds of videos on cutting edge software, but you have to start somewhere. Even practicing some of the advanced functions in Excel can go a long way towards being an effective, tech-savvy scientist. Is this list comprehensive? No. If you learn a new skill are you guaranteed a job? No. But computers will only become a more integral part of quality science. With a toolbox full of computer based skills you will be able to more nimbly assert yourself in the job market and the scientific community. The key is to try something new that you find interesting and maybe you can turn a hobby into a new career.

Additional Information

http://www.nature.com/nbt/journal/v31/n11/full/nbt.2740.html

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001745

[stuffandtheuniverse]

Pretty girls and graphs

I know what you are all thinking: which state has the most beautiful college-aged women? A question for the ages to be sure, one that if I ask again in a few years would probably end with me in jail.  Total Frat Move has been faithfully performing the Lord’s work and compiling a database answering that very question.

TFM’s ‘Instagram Babe of the Day’ showcases one Instagram account featuring a woman wearing clothes that her father must...just...not know about.  Using the tools at Kimono Labs I assembled a list of 299 IBoDs and sorted them by state.  Here are the results in a fraternity-approved-salmon colored map:

Florida is the winner and it is not even close at 67 ‘Babes’ of the 299, or 22%.  California is the next closest at 49 followed by Arizona at 38, Texas at 21 and then Virginia and New York with 8 and 7 respectively.   In fact together the top 3 states beat out the bottom 48 (including DC) combined.  The entire country of Australia had 1 which puts it ahead of the six states with zero.   America’s friendly neighbor to the North, Canada, came in at 4 beating out 34 states.

Clearly not all states are equally gifted.  Warmer states seem to be dominating and the upper portion of the Midwest is just a deserted wasteland. But Wyoming only has 6 people per square mile. I didn’t expect there to be bronze bombshells patrolling the streets of Cheyenne.  But even adjusting for the number of female college students per state does not even the playing field.  Here is a graph of the total number of IBoDs plotted against female college students by state (data are in thousands, from 2008; available at the US Census. Thanks Obama).

Florida and Arizona are really just doing a great job.  Keep it up gals. Texas and especially New York are under-performing their potential.  Both have fewer TFM ‘Babes’ than would be expected based on their student population.  Hawaii, with its low student population, actually has the highest rate of IBoDs. Around 1 in every 8500 college girls is on TFM.  Florida’s rate is the only close competition at 1:9000 followed by a distant Arizona.

Babes of the Day is only one metric though.  Is it really accurate in predicting the most beautiful states?  I looked up some other rankings and chose two (the first two that came up on Google). The first was from the internet’s bastion of journalism, Buzzfeed, and the other was from Comcast Xfinity (even they have lists?). Both used a combination of arbitrary numbers to reach their equally dubious conclusions about the most attractive states.  Here are the top 5 for each in descending order.  (I switched back to using total number of IBoDs for this one because it gives a better list. Rigor is not the goal of this analysis)

No surprise, California shows up on each list.  We also see Florida and Virginia show up on one of the other lists. The other two lists have Hawaii which would have been there if we had taken into account total number of college girls.  So overall 3 of our top 5 ended up on the other lists which gives us a 60%. Plus 10% extra credit for Hawaii almost being on there gives us 70%. And C’s get degrees.

There is no way to differentiate between correlation and causation. Either warm weather attracts beautiful women, makes women beautiful or coerces them to send their names to [email protected]. Who cares though? I’m not complaining.