Elmendorf and Spencer used data from the 2008 National Annenberg Election Survey, which asked non-blacks to rank their own racial group and blacks regarding intelligence, trustworthiness, and work ethic. Respondents ranked their racial group above blacks by an average of 15 points in each of these categories, perhaps proving the Avenue Q claim that “everyone’s a little bit racist.” Elmendorf and Spencer, however, only counted a person as “prejudiced” if he thought his racial group was more superior to blacks than the average person—and only if he thought so in two or more of the three categories. That is, a respondent could think his race was a lot better than blacks and still not count as racist under their methodology.
The results were striking: the researchers’ mathematical model suggests that of the seven states in the country with the highest percentage of people who are biased against black people, six are Southern states—Louisiana, Mississippi, Texas, Alabama, Georgia, and South Carolina—required to seek federal approval for election law changes under the VRA. Arizona and Alaska, the other two states required to get the feds’ permission before changing their election laws, ranked much lower in anti-black bias. But as Elmendorf and Spencer note, these states are presumably required to seek that permission because of other bias—anti-Latino in Arizona and anti-Native American in Alaska—which their study did not measure. (Besides the eight states mentioned above, the VRA requires some counties and municipalities in seven other states to seek federal permission to change election rules.)
The researchers crunched the data several different ways to make sure they were getting valid results. But “whichever approach you pick, the Deep South states are close to the top,” Elmendorf says.
Elmendorf and Spencer’s study may have come too late: The Supreme Court is widely expected to strike down the portion of the VRA that governs which states are and are not required to seek the feds’ permission to change their election rules. If that happens, Congress will have to come up with new rules to determine which states this section of the VRA should cover. If lawmakers decide to embrace Roberts’ implication that states with more racist attitudes should receive special scrutiny, Elmendorf and Spencer’s study suggests they could end up with a list of VRA-covered states that looks a lot like today’s.
WHERE do new words come from? On Twitter at least, they often begin life in cities with large African American populations before spreading more widely, according to a study of the language used on the social network.
Jacob Eisenstein at the Georgia Institute of Technology in Atlanta and colleagues examined 30 million tweets sent from US locations between December 2009 and May 2011. Several new terms spread during this period, including “bruh”, an alternative spelling of “bro” or “brother”, which first arose in a few south-east cities before eventually hopping to parts of California. Residents of Cleveland, Ohio, were the first to use “ctfu”, an abbreviation of “cracking the fuck up”, usage that has since spread into Pennsylvania (arxiv.org).
After collecting the data, the team built a mathematical model that captures the large-scale flow of new words between cities. The model revealed that cities with big African American populations tend to lead the way in linguistic innovation. The team is still working on a more detailed analysis and says it is too early to say which cities are the most influential.
Computer Model of Spread of Dementia Can Predict Future Disease Patterns Years Before They Occur in a Patient
Researchers at Weill Cornell Medical College have developed a computer program that has tracked the manner in which different forms of dementia spread within a human brain. They say their mathematic model can be used to predict where and approximately when an individual patient’s brain will suffer from the spread, neuron to neuron, of “prion-like” toxic proteins — a process they say underlies all forms of dementia.
Their findings, published in the March 22 issue of Neuron, could help patients and their families confirm a diagnosis of dementia and prepare in advance for future cognitive declines over time. In the future — in an era where targeted drugs against dementia exist — the program might also help physicians identify suitable brain targets for therapeutic intervention, says the study’s lead researcher, Ashish Raj, Ph.D., an assistant professor of computer science in radiology at Weill Cornell Medical College.
Brain fiber tracts shown in this image are used to obtain connectivity networks, whose diffusion dynamics model dementias. Colors represent the orientation of fibers. (Credit: Ashish Raj et al./Neuron)
“Think of it as a weather radar system, which shows you a video of weather patterns in your area over the next 48 hours,” says Dr. Raj. “Our model, when applied to the baseline magnetic resonance imaging scan of an individual brain, can similarly produce a future map of degeneration in that person over the next few years or decades.
“This could allow neurologists to predict what the patient’s neuroanatomic and associated cognitive state will be at any given point in the future. They could tell whether and when the patient will develop speech impediments, memory loss, behavioral peculiarities, and so on,” he says. “Knowledge of what the future holds will allow patients to make informed choices regarding their lifestyle and therapeutic interventions.
“At some point we will gain the ability to target and improve the health of specific brain regions and nerve fiber tracts,” Dr. Raj says. “At that point, a good prediction of a subject’s future anatomic state can help identify promising target regions for this intervention. Early detection will be key to preventing and managing dementia.”
TRACKING THE FLOW OF PROTEINS
The computational model, which Dr. Raj developed, is the latest, and one of the most significant, validations of the idea that dementia is caused by proteins that spread through the brain along networks of neurons. It extends findings that were widely reported in February that Alzheimer’s disease starts in a particular brain region, but spreads further via misfolded, toxic “tau” proteins. Those studies, by researchers at Columbia University Medical Center and Massachusetts General Hospital, were conducted in mouse models and focused only on Alzheimer’s disease.
In this study, Dr. Raj details how he developed the mathematical model of the flow of toxic proteins, and then demonstrates that it correctly predicted the patterns of degeneration that results in a number of different forms of dementia.
He says his model is predicated on the recent understanding that all known forms of dementia are accompanied by, and likely caused by, abnormal or “misfolded” proteins. Proteins have a defined shape, depending on their specific function — but proteins that become misshapen can produce unwanted toxic effects. One example is tau, which is found in a misfolded state in the brains of both Alzheimer’s patients and patients with frontal temporal dementia (FTD). Other proteins, such as TDP43 and ubiquitin, are also found in FTD, and alpha synuclein is found in Parkinson’s disease.
These proteins are called “prion-like” because misfolded, or diseased, proteins induce the misfolding of other proteins they touch down a specific neuronal pathway. Prion diseases (such as mad cow disease) that involve transmission of misfolded proteins are thought to be infectious between people. “There is no evidence that Alzheimer’s or other dementias are contagious in that way, which is why their transmission is called prion-like.”
SIMPLE EXPLANATION FOR CLINICALLY OBSERVED PATTERNS OF DEMENTIA
Dr. Raj calls his model of trans-neuronal spread of misfolded proteins “very simple.” It models the same process by which any gas diffuses in air, except that in the case of dementias the diffusion process occurs along connected neural fiber tracts in the brain.