Tag Archives: semantic

Spam, Spam, Spam: All Natural

Google-search-natural-junk-food

Parents through the ages have often decried the mangling of their mother tongue by subsequent generations. Language is fluid after all, particularly English, and our youth constantly add their own revisions to carve a divergent path from their elders. But, the focus of our disdain for the ongoing destruction of our linguistic heritage should really be corporations and their hordes of marketeers and lawyers. Take the once simple and meaningful word “natural”. You’ll see its oxymoronic application each time you stroll along the aisle at your grocery store: one hundred percent natural fruit roll-ups; all natural chicken rings; completely natural corn-dogs; totally naturally flavored cheese puffs. The word — natural — has become meaningless.

From NYT:

It isn’t every day that the definition of a common English word that is ubiquitous in common parlance is challenged in federal court, but that is precisely what has happened with the word “natural.” During the past few years, some 200 class-action suits have been filed against food manufacturers, charging them with misuse of the adjective in marketing such edible oxymorons as “natural” Cheetos Puffs, “all-natural” Sun Chips, “all-natural” Naked Juice, “100 percent all-natural” Tyson chicken nuggets and so forth. The plaintiffs argue that many of these products contain ingredients — high-fructose corn syrup, artificial flavors and colorings, chemical preservatives and genetically modified organisms — that the typical consumer wouldn’t think of as “natural.”

Judges hearing these cases — many of them in the Northern District of California — have sought a standard definition of the adjective that they could cite to adjudicate these claims, only to discover that no such thing exists.

Something in the human mind, or heart, seems to need a word of praise for all that humanity hasn’t contaminated, and for us that word now is “natural.” Such an ideal can be put to all sorts of rhetorical uses. Among the antivaccination crowd, for example, it’s not uncommon to read about the superiority of something called “natural immunity,” brought about by exposure to the pathogen in question rather than to the deactivated (and therefore harmless) version of it made by humans in laboratories. “When you inject a vaccine into the body,” reads a post on an antivaxxer website, Campaign for Truth in Medicine, “you’re actually performing an unnatural act.” This, of course, is the very same term once used to decry homosexuality and, more recently, same-sex marriage, which the Family Research Council has taken to comparing unfavorably to what it calls “natural marriage.”

So what are we really talking about when we talk about natural? It depends; the adjective is impressively slippery, its use steeped in dubious assumptions that are easy to overlook. Perhaps the most incoherent of these is the notion that nature consists of everything in the world except us and all that we have done or made. In our heart of hearts, it seems, we are all creationists.

In the case of “natural immunity,” the modifier implies the absence of human intervention, allowing for a process to unfold as it would if we did nothing, as in “letting nature take its course.” In fact, most of medicine sets itself against nature’s course, which is precisely what we like about it — at least when it’s saving us from dying, an eventuality that is perhaps more natural than it is desirable.

Yet sometimes medicine’s interventions are unwelcome or go overboard, and nature’s way of doing things can serve as a useful corrective. This seems to be especially true at the beginning and end of life, where we’ve seen a backlash against humanity’s technological ingenuity that has given us both “natural childbirth” and, more recently, “natural death.”

This last phrase, which I expect will soon be on many doctors’ lips, indicates the enduring power of the adjective to improve just about anything you attach it to, from cereal bars all the way on up to dying. It seems that getting end-of-life patients and their families to endorse “do not resuscitate” orders has been challenging. To many ears, “D.N.R.” sounds a little too much like throwing Grandpa under the bus. But according to a paper in The Journal of Medical Ethics, when the orders are reworded to say “allow natural death,” patients and family members and even medical professionals are much more likely to give their consent to what amounts to exactly the same protocols.

The word means something a little different when applied to human behavior rather than biology (let alone snack foods). When marriage or certain sexual practices are described as “natural,” the word is being strategically deployed as a synonym for “normal” or “traditional,” neither of which carries nearly as much rhetorical weight. “Normal” is by now too obviously soaked in moral bigotry; by comparison, “natural” seems to float high above human squabbling, offering a kind of secular version of what used to be called divine law. Of course, that’s exactly the role that “natural law” played for America’s founding fathers, who invoked nature rather than God as the granter of rights and the arbiter of right and wrong.

Read the entire article here.

Image courtesy of Google Search.

 

Meta-Research: Discoveries From Research on Discoveries

Discoveries through scientific research don’t just happen in the lab. Many of course do. Some discoveries now come through data analysis of research papers. Here, sophisticated data mining tools and semantic software sift through hundreds of thousands of research papers looking for patterns and links that would otherwise escape the eye of human researchers.

From Technology Review:

Software that read tens of thousands of research papers and then predicted new discoveries about the workings of a protein that’s key to cancer could herald a faster approach to developing new drugs.

The software, developed in a collaboration between IBM and Baylor College of Medicine, was set loose on more than 60,000 research papers that focused on p53, a protein involved in cell growth, which is implicated in most cancers. By parsing sentences in the documents, the software could build an understanding of what is known about enzymes called kinases that act on p53 and regulate its behavior; these enzymes are common targets for cancer treatments. It then generated a list of other proteins mentioned in the literature that were probably undiscovered kinases, based on what it knew about those already identified. Most of its predictions tested so far have turned out to be correct.

“We have tested 10,” Olivier Lichtarge of Baylor said Tuesday. “Seven seem to be true kinases.” He presented preliminary results of his collaboration with IBM at a meeting on the topic of Cognitive Computing held at IBM’s Almaden research lab.

Lichtarge also described an earlier test of the software in which it was given access to research literature published prior to 2003 to see if it could predict p53 kinases that have been discovered since. The software found seven of the nine kinases discovered after 2003.

“P53 biology is central to all kinds of disease,” says Lichtarge, and so it seemed to be the perfect way to show that software-generated discoveries might speed up research that leads to new treatments. He believes the results so far show that to be true, although the kinase-hunting experiments are yet to be reviewed and published in a scientific journal, and more lab tests are still planned to confirm the findings so far. “Kinases are typically discovered at a rate of one per year,” says Lichtarge. “The rate of discovery can be vastly accelerated.”

Lichtarge said that although the software was configured to look only for kinases, it also seems capable of identifying previously unidentified phosphatases, which are enzymes that reverse the action of kinases. It can also identify other types of protein that may interact with p53.

The Baylor collaboration is intended to test a way of extending a set of tools that IBM researchers already offer to pharmaceutical companies. Under the banner of accelerated discovery, text-analyzing tools are used to mine publications, patents, and molecular databases. For example, a company in search of a new malaria drug might use IBM’s tools to find molecules with characteristics that are similar to existing treatments. Because software can search more widely, it might turn up molecules in overlooked publications or patents that no human would otherwise find.

“We started working with Baylor to adapt those capabilities, and extend it to show this process can be leveraged to discover new things about p53 biology,” says Ying Chen, a researcher at IBM Research Almaden.

It typically takes between $500 million and $1 billion dollars to develop a new drug, and 90 percent of candidates that begin the journey don’t make it to market, says Chen. The cost of failed drugs is cited as one reason that some drugs command such high prices (see “A Tale of Two Drugs”).

Software that read tens of thousands of research papers and then predicted new discoveries about the workings of a protein that’s key to cancer could herald a faster approach to developing new drugs.

The software, developed in a collaboration between IBM and Baylor College of Medicine, was set loose on more than 60,000 research papers that focused on p53, a protein involved in cell growth, which is implicated in most cancers. By parsing sentences in the documents, the software could build an understanding of what is known about enzymes called kinases that act on p53 and regulate its behavior; these enzymes are common targets for cancer treatments. It then generated a list of other proteins mentioned in the literature that were probably undiscovered kinases, based on what it knew about those already identified. Most of its predictions tested so far have turned out to be correct.

“We have tested 10,” Olivier Lichtarge of Baylor said Tuesday. “Seven seem to be true kinases.” He presented preliminary results of his collaboration with IBM at a meeting on the topic of Cognitive Computing held at IBM’s Almaden research lab.

Lichtarge also described an earlier test of the software in which it was given access to research literature published prior to 2003 to see if it could predict p53 kinases that have been discovered since. The software found seven of the nine kinases discovered after 2003.

“P53 biology is central to all kinds of disease,” says Lichtarge, and so it seemed to be the perfect way to show that software-generated discoveries might speed up research that leads to new treatments. He believes the results so far show that to be true, although the kinase-hunting experiments are yet to be reviewed and published in a scientific journal, and more lab tests are still planned to confirm the findings so far. “Kinases are typically discovered at a rate of one per year,” says Lichtarge. “The rate of discovery can be vastly accelerated.”

Lichtarge said that although the software was configured to look only for kinases, it also seems capable of identifying previously unidentified phosphatases, which are enzymes that reverse the action of kinases. It can also identify other types of protein that may interact with p53.

The Baylor collaboration is intended to test a way of extending a set of tools that IBM researchers already offer to pharmaceutical companies. Under the banner of accelerated discovery, text-analyzing tools are used to mine publications, patents, and molecular databases. For example, a company in search of a new malaria drug might use IBM’s tools to find molecules with characteristics that are similar to existing treatments. Because software can search more widely, it might turn up molecules in overlooked publications or patents that no human would otherwise find.

“We started working with Baylor to adapt those capabilities, and extend it to show this process can be leveraged to discover new things about p53 biology,” says Ying Chen, a researcher at IBM Research Almaden.

It typically takes between $500 million and $1 billion dollars to develop a new drug, and 90 percent of candidates that begin the journey don’t make it to market, says Chen. The cost of failed drugs is cited as one reason that some drugs command such high prices (see “A Tale of Two Drugs”).

Lawrence Hunter, director of the Center for Computational Pharmacology at the University of Colorado Denver, says that careful empirical confirmation is needed for claims that the software has made new discoveries. But he says that progress in this area is important, and that such tools are desperately needed.

The volume of research literature both old and new is now so large that even specialists can’t hope to read everything that might help them, says Hunter. Last year over one million new articles were added to the U.S. National Library of Medicine’s Medline database of biomedical research papers, which now contains 23 million items. Software can crunch through massive amounts of information and find vital clues in unexpected places. “Crucial bits of information are sometimes isolated facts that are only a minor point in an article but would be really important if you can find it,” he says.

Read the entire article here.

Lemonade without the Lemons: New Search Engine Looks for Uplifting News

[div class=attrib]From Scientific American:[end-div]

Good news, if you haven’t noticed, has always been a rare commodity. We all have our ways of coping, but the media’s pessimistic proclivity presented a serious problem for Jurriaan Kamp, editor of the San Francisco-based Ode magazine—a must-read for “intelligent optimists”—who was in dire need of an editorial pick-me-up, last year in particular. His bright idea: an algorithm that can sense the tone of daily news and separate the uplifting stories from the Debbie Downers.

Talk about a ripe moment: A Pew survey last month found the number of Americans hearing “mostly bad” news about the economy and other issues is at its highest since the downturn in 2008. That is unlikely to change anytime soon: global obesity rates are climbing, the Middle East is unstable, and campaign 2012 vitriol is only just beginning to spew in the U.S. The problem is not trivial. A handful of studies, including one published in the Clinical Psychology Review in 2010, have linked positive thinking to better health. Another from the Journal of Economic Psychology the year prior found upbeat people can even make more money.

Kamp, realizing he could be a purveyor of optimism in an untapped market, partnered with Federated Media Publishing, a San Francisco–based company that leads the field in search semantics. The aim was to create an automated system for Ode to sort and aggregate news from the world’s 60 largest news sources based on solutions, not problems. The system, released last week in public beta testing online and to be formally introduced in the next few months, runs thousands of directives to find a story’s context. “It’s kind of like playing 20 questions, building an ontology to find either optimism or pessimism,” says Tim Musgrove, the chief scientist who designed the broader system, which has been dubbed a “slant engine”. Think of the word “hydrogen” paired with “energy” rather than “bomb.”

Web semantics developers in recent years have trained computers to classify news topics based on intuitive keywords and recognizable names. But the slant engine dives deeper into algorithmic programming. It starts by classifying a story’s topic as either a world problem (disease and poverty, for example) or a social good (health care and education). Then it looks for revealing phrases. “Efforts against” in a story, referring to a world problem, would signal something good. “Setbacks to” a social good, likely bad. Thousands of questions later every story is eventually assigned a score between 0 and 1—above 0.95 fast-tracks the story to Ode’s Web interface, called OdeWire. Below that, a score higher than 0.6 is reviewed by a human. The system is trained to only collect themes that are “meaningfully optimistic,” meaning it throws away flash-in-the-pan stories about things like sports or celebrities.

[div class=attrib]More from theSource here.[end-div]