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Report Finds Microsoft Excel Causes Errors in 20 Percent of Genomics Studies

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Microsoft Excel, that ubiquitous tool for data crunching, has been playing an unexpected role in the scientific world. The program has been screwing with data in genomics studies. A new report in the journal Genome Biology estimates that around 20 percent of scientific papers published in leading genome-focused journals that include gene lists from Excel contain errors due to the program’s default autocorrect settings, Slate reports.

The problem is, several genes have symbols that look a lot like dates. The program has a tendency to convert gene symbols like SEPT2 (Septin 2) and MARCH1 (Membrane Associated Ring-CH-Type Finger) into what Excel thinks is proper date form—turning them into 2-Sept and 1-Mar instead. In some, SEPT2 became “2006/09/02.”

"Inadvertent gene symbol conversion is problematic because these supplementary files are an important resource in the genomics community that are frequently reused," the paper’s authors write. They reviewed the supplementary gene list Excel files from 18 journals, examining studies published between 2005 and 2015—Excel’s gene-typo issue was first reported in 2004—for date formatting within lists of genes. The analysis was performed by a program that flagged supplementary materials that seemed to be lists of genes, then searched them for date formatting. Out of more than 35,000 supplementary files, they confirmed 987 files with gene errors that were published as part of 704 studies.

Overall, 19.6 percent of papers in the 18 journals contained gene name errors caused by Excel’s autocorrect function, but some journals were worse than others. High-impact journals, typically the most respected outlets to publish research in, actually had more affected gene lists, which the researchers speculate may be because studies published in these journals are more likely to have larger and more numerous data sets.

The highest proportion of gene lists with errors (more than 20 percent) came from the journals Nucleic Acids Research, Genome Biology, Nature Genetics, Genome Research, Genes and Development, and Nature; conversely, the journals Molecular Biology and Evolution, Bioinformatics, DNA Research, and Genome Biology and Evolution showed errors in less than 10 percent of genomics papers.

While this isn’t the worst scientific error to end up in a journal, since it’s pretty clear that 2006/09/02 isn’t a gene symbol, it’s also fairly disturbing that this many papers could make it through the editing process without anyone noticing that they contained lists of nonexistent genes.

The researchers highlight Google Sheets as a potential alternative for Excel, because it doesn’t suffer from the same symbol-date mixup, and it seems that when you open Sheets documents in other programs like Excel, the data is protected from Excel’s default autocorrection. They suggest that journal editors and reviewers should look out for these errors, pasting gene name lists into blank files and sorting them so that any dates that have been mistakenly inserted will become apparent.

[h/t Slate]

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]