Research Resources

Office of the Vice President for Research & Economic Development (OVPRED)

Leadership for all administrative research units serving the research enterprise at UAB. OVPRED oversees Core Facilities, Institutional Animal Care and Use Committee, and Institutional Review Board.

Integrated Research Administration Portal (IRAP)

Electronic submission of funding applications and compliance forms for future research initiatives.

UAB Institute for Innovation and Entrepreneurship

The nexus for UAB innovation, entrepreneurial educational models, applied research, and management of intellectual property.

Funding Sources and Grant Opportunities

Presentations and general information related to effective grant writing.

Office of Postdoctoral Education

UAB is committed to the development and success of outstanding postdoctoral scientists.

Conflict of Interest Review Board (CIRB)

Charged with the ongoing development of policies and procedures related to conflicts of interest in sponsored research, review of disclosures of financial interests submitted by investigators, and the development of conflict of interest management plans.

Research News

Crowdsourcing science: How Amazon’s Mechanical Turk is becoming a research tool
Crowdsourcing science: How Amazon’s Mechanical Turk is becoming a research tool
A growing number of researchers, from computer scientists to philosophers, are taking an interest in the "artificial artificial intelligence" offered by Amazon's microwork platform.

crowdsource mixWritten by Matt Windsor

This spring, Chris Callison-Burch, Ph.D., was in town to share an unusual approach to machine learning. This is one of the hottest topics in computer science: It is behind everything from Google’s self-driving cars to Apple’s Siri personal assistant.

Callison-Burch, an assistant professor at the University of Pennsylvania, is building a system that can automatically translate foreign languages into English — especially obscure dialects (from an American point of view) that can be of great interest to national security. He was in Birmingham at the invitation of Steven Bethard, Ph.D., a machine learning researcher and assistant professor in the UAB College of Arts and Sciences Department of Computer and Information Sciences.

In order to teach a computer to do something, Callison-Burch explained, you need to give it examples. Lots of examples. For a French-English translation, there are millions of sample texts available on the Internet. For Urdu, not so much.

Crowdsourced corpus

One way around this problem would be to pay professional translation services thousands of dollars to create the “corpus” of words you would need to train a computer to translate Urdu automatically. Callison-Burch has pioneered another approach: He paid some random folks on the Internet a few bucks at a time to do the work instead.

Callison-Burch is one of a growing number of researchers using Amazon Mechanical Turk, a service of the giant Internet company that bills itself as a “marketplace for work.” Mechanical Turk, or MTurk, as it is known, “has almost become synonymous with crowdsourcing,” Callison-Burch said. Anyone in need of help with a “human intelligence task” (Amazon’s term) can post a job description, and the “reward” they are willing to pay. One recent afternoon, some of the 255,902 tasks available on MTurk included tagging photos on Instagram (4 cents per picture), typing out the text visible in distorted images (1 cent per image) and rating test questions for a biology exam for a researcher at Michigan State University (a penny per question — this is a popular price point).

Callison-Burch started out by giving Turkers and professional translators the same tasks. He encountered some trouble at first — respondents copying and pasting their assigned sentences into Google Translate, for example. “Quality control is a major challenge,” Callison-Burch said. “It is important to design tasks to be simple and easy to understand.”

In order to teach a computer to do something, you need to give it examples. Lots of examples.
That’s where Mechanical Turk can shine.

So he tweaked his assignments to filter out people who weren’t really native speakers, and added in some clever quality control mechanisms, such as getting additional Turkers to pick the best translations out of multiple versions of the same sentence. Callison-Burch was able to get remarkably close to the professional quality, for “approximately an order of magnitude cheaper than the cost of professional translation,” he said.

Turk-powered translation could be particularly helpful in translating regional Arabic dialects, Callison-Burch noted. “Because standard machine translation systems are trained on written text, they don’t handle spoken language well,” he said. In a recent study, Callison-Burch and his collaborators found that “comments on Arabic newspaper websites were written in dialect forms about 50 percent of the time.” A machine learning system trained in these dialects could offer vital clues about where a writer is from in the Middle East, for example, or about “his or her informal relationship with an interlocutor based on word choice.”

Applications from obesity to philosophy

MTurk’s brand of “artificial artificial intelligence” (Amazon’s Turk tagline) could also be applied to other machine learning research at UAB, notes Steven Bethard. “Chris’ work is fascinating,” with applications from medicine to the social sciences, Bethard said.

UAB researchers are already putting MTurk to use. Andrew Brown, Ph.D., a research scientist in the Office of Energetics in the School of Public Health, has tested Turkers’ ability to categorize biomedical research studies. “We like to do some creative looks at what’s been published and how,” Brown said. For arecent paper, Brown and colleagues were interested in systematically evaluating nutrition-obesity studies. They wanted to find out whether studies with results that coincide with popular opinion are more likely to draw attention in the scientific community than studies that contradict the conventional wisdom. (They used citations as a proxy for the scientific community’s opinion of a paper.)  

The first step was to identify all the studies of interest. But “the problem is, there are 25 million papers in PubMed, and sometimes the keywords don’t work very well,” Brown said. “It helps to have a human set of eyes take a look at it.” Instead of giving Ph.D.-level scientists the job, the researchers turned to MTurk. The Turkers successfully evaluated abstracts to identify appropriate studies and categorize the studied foods, then gathered citation counts for the studies in Google Scholar. (There was no significant link between public and scientific opinion when it came to the papers.)

“We found it to be useful,” Brown said. “Expecting a perfect rating or an exhaustive rating from microworkers is probably a little premature, but on the other hand even trained scientists make mistakes.” Brown plans to use crowdsourcing for future studies. “This is just one more tool to add to our research toolbox,” he said.

Josh May, Ph.D., an assistant professor in the UAB College of Arts and Sciences Department of Philosophy, has been using MTurk for several years — asking Turkers to solve thorny moral dilemmas. “I present participants with hypothetical scenarios and ask them to provide their opinion about them — ‘Did the person act wrongly?’” May said. “Then I see whether responses change when the scenarios are slightly different, e.g., when a harm is brought about actively versus passively, or as a means to a goal versus a side effect. Statistical analysis can reveal whether the differences are significant — providing evidence about whether the slight changes to the scenarios make a real difference in everyday moral reasoning.”

“Expecting a perfect rating or an exhaustive rating from microworkers is probably a little premature, but on the other hand even trained scientists make mistakes…. This is just one more tool to add to our research toolbox.” —Andrew Brown, Ph.D.

Social justice and microwork

May, Brown and Callison-Burch share an interest in social justice for Turkers as well. “The main ethical issue with MTurk is exploitation,” May said. “The going rate is often around a quarter for a few minutes of work, which typically adds up to less than the federal minimum wage, even when working quickly. This apparently isn’t illegal given certain loopholes, but that doesn’t make it moral. Just because someone will work for pennies doesn’t mean we should withhold a living wage.”

May’s solution for his own research “is to estimate the time it will take most workers to complete the task and then pay them enough so that the rate would amount to at least minimum wage.” Brown takes a similar approach — and when the Turkers work more slowly than expected, which drives down their overall wage, “there are bonus systems in place where you can give them something extra,” he said.

Callison-Burch is using his programming skills to help Turkers earn fair wages. He has created a free browser extension (available at crowd-workers.com) that identifies high-paying jobs and makes it easier to identify job posters who have a large number of complaints.

Crowdsourcing operations such as MTurk represent an untapped resource for scientists of all stripes, Callison-Burch concluded. “Individual researchers now have access to their own data production companies,” he said. “Now we can get the data we need to solve problems.”

NIH awards nearly $34 million to UAB Center for Clinical and Translational Science
NIH awards nearly $34 million to UAB Center for Clinical and Translational Science
This renewing of UAB’s prestigious Center for Translational Science Award will bolster research and workforce development at UAB and throughout its regional partner network in the Southeast.

news CCTS Fostering ResearchWritten by Christina Crowe

The National Institutes of Health has awarded the University of Alabama at Birmingham Center for Clinical and Translational Science $33.59 million over four years to continue the center’s programs advancing translational research.

Since its initial funding in 2008 through Alabama’s only Center for Translational Science Award to work toward innovative discoveries for better health, the UAB CCTS has nurtured UAB research, accelerating the process of translating laboratory discoveries into treatments for patients, training a new generation of clinical and translational researchers, and engaging communities in clinical research efforts.

The CCTS will continue to advance its mission to accelerate the delivery of new drugs, methodologies and practices to patients at UAB and throughout a partner network of 11 institutions in the Southeast.

“We are excited by the capacity to continue to enhance our institution’s and our region’s innovative research and medical care,” said Robert Kimberly, M.D., UAB CCTS director. “Through internal and external partnerships, as well as a robust clinical environment and cutting-edge informatics and clinical trial resources, we look forward to working with our patients over the course of their lifespan.”

Congress launched the CTSA program in 2006, which is overseen by the National Center for Advancing Translational Sciences.

The amount of this award, more than double its previous funding awarded in 2008 and one of the largest at UAB, reflects an unmatched enthusiasm for the CCTS and its affiliated programs. It includes funding for 10 annual pre-doctoral training awards, 10 summer training awards, and eight career development awards for senior postdoctoral fellows or faculty-level candidates.

“Our training programs continue to foster a culture of responsible, ethical practice among students, faculty and clinicians conducting human subjects research,” Kimberly said. “The NIH’s support of our expansive partner network, encompassing 11 regional academic and medical institutions throughout Alabama, Louisiana and Mississippi, will allow us to further grow our scope of practices and research resources as we look to tackle health disparities in the Southeast.”

Through One Great Community, the CCTS’ community engagement enterprise, and the Community Health Innovation Awards, the CCTS engages Greater Birmingham­­-area residents in innovative programs designed by community members to improve their neighborhoods.

“UAB is fully committed to the goals of the CCTS and to its continued development as a hub for clinical and translational research in the Southeast,” said UAB President Ray L. Watts. “This significant renewal speaks to the tremendous work and vision of our CCTS leadership and team, as well as our clinical infrastructure, scientific strengths, informatics expertise, training programs, and biostatistical and research design assistance.

“The CCTS touches researchers in all UAB schools and across the partner network, and we are thrilled that this important work will continue with the confidence and support of the NIH.”

news CCTS Next GenerationClick to enlargeState and regional impact

“The growth of the Center for Clinical and Translational Science at UAB will foster economic development in the state and throughout the region,” said Senator Richard Shelby. “With a history of providing optimal clinical care and innovation in human health, UAB’s receipt of this prestigious award enables the continued development of the workforce that is necessary to meet the needs of future research advancement.”

Alabama Governor Robert Bentley, himself a physician, voiced his appreciation for the CCTS’ initiatives. “The center has been highly effective in providing assistance in the state’s efforts to eliminate the health disparities seen throughout our region,” Bentley said. “Whether across the life course or in underserved groups disproportionately affected by cancer, stroke, heart conditions and other diseases prevalent in our state, the center has been exemplary in reaching out to our citizens.”

UAB Vice President, Research and Economic Development Richard Marchase, Ph.D., says he is particularly pleased that the CCTS is building on UAB’s history of serving populations burdened by health disparities through its partnerships with other state and regional institutions committed to advancing health through translational research. “It is through this culture of commitment and collaboration,” he said, “that we have become a national leader in biomedical research.”

When computers learn to understand doctors' notes, the world will be a better place
When computers learn to understand doctors' notes, the world will be a better place
By training computers to pick out timing clues in medical records, UAB machine learning expert Steven Bethard, Ph.D., aims to help individual physicians visualize patient histories, and researchers recruit for clinical trials.

teaching laptop row of laptopsWritten by Matt Windsor

Train a computer to read medical records, and you could do a world of good. Doctors could use it to look for dangerous trends in their patients’ health. Researchers could speed drugs to market by quickly finding appropriate patients for clinical trials. They could also find previously overlooked associations. By keeping track of data points across tens of thousands, or millions, of medical records, computer models could find patterns that would never occur to individual researchers. Maybe Asian women in their 40s with type 2 diabetes respond well to a certain combination of medications, while white men in their 60s do not, for example.

Machine learning, in particular a branch called natural language processing, has had plenty of successes recently. It’s the secret sauce behind IBM’s “Jeopardy”-winning Watson computer and Apple’s Siri personal assistant, for instance. But computers still have a tough time following medical narratives.

“We take it for granted how easy it is for us to understand language,” said Steven Bethard, Ph.D., a machine learning expert and linguist in the UAB College of Arts and Sciences Department of Computer and Information Sciences. “When I’m having a conversation, I can use all kinds of crazy constructions and pauses between words, and you would still understand me. All these things make language very difficult for computers, however. They like rules and an order that is followed every time, but languages aren’t like that.”

Timing is everything

So Bethard, the director of UAB’s Computational Representation and Analysis of Language Lab, builds models that help computers catch our drift. In one ongoing project, he is working with colleagues at the Mayo Clinic and Boston Children’s Hospital “to extract timelines from clinical work,” Bethard said. Using text from clinical notes taken at the Mayo Clinic, “we’re working to find all the clinical events mentioned in those notes — things like ‘asthma’ and ‘CT scan,’ for example — and link them to the proper time,” he said. If the computer sees “the patient has a history of asthma,” it should know that’s in the past. If it sees “planning a CT scan,” that’s in the future. “Sometimes you have explicit dates, such as ‘on Sept. 15, the patient had a colonoscopy,’” Bethard said. “But the computer still has to figure out whether that means Sept. 15, 2014, or Sept. 15, 2015.’”

timeline figure cropThe diagram above illustrates how a computer could extract timeline information out of an entry in a medical record.A system like this would help individual doctors keep track of their patients’ progress. “If you have had a patient for 15 years, you see so many things,” Bethard said. “Looking at a visual of all the conditions and procedures over that time is extremely useful.” The system could also identify patients for clinical trials. “Say you wanted to find someone who had liver toxicity after they started taking methotrexate,” Bethard said. “The sequence of events is important; you only want to find people who have taken the drug and had liver toxicity in the appropriate order.” Another use: finding new associations between drugs or procedures and adverse events. “If you have a large number of patients, you can say, ‘How often do you see a certain side effect?’ for example,” Bethard said. “You can generate new hypotheses about causality.”

Learning to spot cancer

One of Bethard’s graduate students, John David Osborne, has built a machine-learning model that is already having an impact on the practice of health care at UAB. By day, Osborne is a research associate in the biomedical informatics group of UAB’s Center for Clinical and Translational Science. He and his colleagues were called in to help UAB’s Cancer Registry with a Big Data challenge: tracking and cataloguing cancer diagnoses and treatment outcomes.

Every hospital is responsible for reporting new cases of many different types of diseases to the federal government. “Cancer is one of those diseases, but not all cancers are reportable,” Osborne said. “Lots of skin cancers aren’t, but melanoma is; anything malignant or in the central nervous system is reportable.” Identifying and tracking these cases in pathology reports — and determining whether they are or are not reportable — can be quite challenging at a health care system as large as UAB, Osborne notes. A year and a half ago, the biomedical informatics team at the CCTS created the Cancer Registry Control Panel, which uses natural language processing to detect possible cancer cases in the pathology reports. As an additional research project, Osborne recently designed a machine-learning algorithm that provides additional assistance to the human registrars. “It scans through the records and says, ‘This is a likely case, and here’s why I think that,’” Osborne said. “Humans are still going through every record, but you can speed it up and show them where to look.”

computer learning in class 2Language matters

Bethard and Osborne build their models using the Unstructured Information Management Architecture — an open-source version of the code IBM used to create Watson.

The first step in building a machine-learning model is to decide what kind of training material to use. “The machine-learning models we create for health information extraction look at gold-standard models that humans have created,” Bethard said. “They say, ‘I see all these patterns in the human timelines, so this is what I’ll look for.’”

Some of these decisions are relatively simple. “Cancer is always a condition of interest,” Bethard said. “Anything related to cancer is something you want to include. The harder pattern to learn is how to link together time and events. A date and then a colon tells you they are describing something that happened on that date. Verb phrases, noun phrases and linguistic structure in time can be very predictive.”

As that description makes clear, natural language processing requires a deep knowledge of English grammar as well as computer code. “The most successful people in this field are hybrids,” adept at linguistics and computer science, Bethard said. He has a bachelor’s degree in linguistics. He shares his interest in language with his wife, who is now completing a postdoctoral fellowship in the cognitive neuroscience of language at the University of South Carolina.

Bethard came to Birmingham in 2013, attracted by ongoing research in natural language processing in UAB’s computer science department. “For me, it makes a lot of sense to be at a place with a major medical school,” Bethard said. He is looking forward to collaborations with James Cimino, M.D., Ph.D., the inaugural director of the School of Medicine’s new Informatics Institute and a renowned expert in the creation and manipulation of electronic medical records. “He’s famous, very well-known and well-respected,” Bethard said of Cimino. “He knows about all the range of problems: getting information from the text that doctors write, how to input this data, how to store it — the whole spectrum.”

Teaching computers to navigate the ambiguity of the English language can be trying, but the opportunities at UAB are exciting, Bethard says. “There is plenty of data available here, and clear challenges for these models to address.”

HITECH Act did not speed up electronic health record adoption as hoped, study shows
HITECH Act did not speed up electronic health record adoption as hoped, study shows
Despite financial incentives, the HITECH Act, signed into law in 2009, had a weak impact on the uptake of EHRs.

stethoscope tabletThe Health Information Technology for Economic and Clinical Health (HITECH) Act was signed into law Feb. 17, 2009, to promote the adoption and meaningful use of health information technology, improve the quality of health care, prevent medical errors, reduce health care costs, increase administrative efficiencies, decrease paperwork, and expand access to affordable health care.

A new study led by the University of Alabama at Birmingham, “Impact of the HITECH Act on Physicians’ Adoption of Electronic Health Records,” published online in July 2015 in the Journal of the American Medical Informatics Association, finds that the financial incentives offered to physicians through meaningful use programs actually had a weak impact on the uptake of EHRs.

“The models suggest that adoption was driven largely by ‘imitation’ effects as physicians mimic their peers’ technology use or respond to mandates,” said lead study author Stephen T. Mennemeyer, Ph.D., professor in the Department of Health Care Organization and Policy. “Small and often insignificant ‘innovation’ effects were found, suggesting little enthusiasm by physicians who are leaders in technology adoption.”

Mennemeyer and co-authors suggest further research be completed to better understand the dynamics of physician adoption patterns.

Southern-style eating strikes again: Study finds diet pattern increases heart disease risk
Southern-style eating strikes again: Study finds diet pattern increases heart disease risk
Southern favorites like fried chicken and bacon may taste great when consumed, but they can have negative effects on heart health, according to UAB researchers.

Previous research from the University of Alabama at Birmingham has shown regularly consuming the “Southern-style” diet of fried foods, processed meats, foods high in fat and sugar-sweetened beverages, can lead to an increased risk of stroke and an increased risk of death for chronic kidney disease patients.

The latest research, published in Circulation, an American Heart Association journal, finds regularly consuming the “Southern-style” diet could raise your risk of heart disease — including heart attack and heart disease-related death.

Heart disease is the leading cause of death for both men and women in the United States, according to the Centers for Disease Control and Prevention, and the food you eat, along with the amount, is a risk factor.

Using data from the Reasons for Geographic and Racial Differences in Stroke, or REGARDS, study, a national, population-based, longitudinal study of white and black adults, the research team derived five dietary patterns using data from 17,418 participants: convenience, plant-based, sweets, Southern, alcohol and salad.

southern dietParticipants were placed into categories of adherence to these dietary patterns, and then comparisons were made between those who consumed the pattern the most to those who consumed each pattern the least. The Southern-style pattern saw the biggest increase to risk of heart disease.

“People who most often ate foods conforming to the Southern-style dietary pattern had a 56 percent higher risk of heart disease compared to those who ate it less frequently,” said study lead author James M. Shikany, Dr.P.H., professor in the Division of Preventive Medicine.

Shikany says no other dietary pattern was associated with heart disease risk.

“I’m not surprised regular consumption of a Southern-style diet impacts heart disease, but the magnitude of the increased risk for heart disease was surprising,” Shikany said. “However, I was more surprised we didn’t see a protective effect of the plant-based dietary pattern.”

Participants with a higher consumption of the Southern dietary pattern were typically younger than 65 years, male and a resident of the Stroke Belt (Alabama, Arkansas, Georgia, Louisiana, Mississippi, North Carolina, South Carolina and Tennessee).

“For anyone eating a lot of the main components of the Southern dietary pattern, I’d recommend they scale back on their consumption,” Shikany said. “If you’re eating bacon every morning, maybe cut back to only two or three days per week, or if you’re drinking four glasses of sweet tea or several sugar-sweetened soft drinks per day, maybe reduce that to one a day and replace those with non-sweetened beverages.”

Shikany says making smaller dietary changes — rather than going all in all at once — are more likely to be adhered to.

“I don’t like to recommend people completely eliminate foods because people don’t like that, and because of that, they won’t do it,” Shikany said. “So I advise gradual changes and not completely eliminating things that people enjoy eating. I think there’s plenty of room here for people to make changes and not completely eliminate a food item, while still improving their heart health.”

UAB Awards Calendar



The Awards Calendar gives an overview of the cycle for accepting nominations to celebrating recognitions.

 
Month Award Dates by which Nominations are Due*
January
  • President's Award for Excellence in Teaching
  • Odessa Woolfolk Commuity Service Award
mid-January 
February
  • Caroline P. & Charles W. Ireland Prize for Scholarly Distinction 
mid-February 
March
  • Faculty Awards Convocation 
(Scheduled by UAB Events) 
April
May
  • Ellen Gregg Ingalls/ UAB National Alumni Society Award for Lifetime Achievement in Teaching
 mid-April 
June
July
  • Distinguished Faculty Lecturer
  Nominations due by
mid-July

















 



*For specific dates and deadlines, contact Linda Piteo at lapiteo@uab.edu  or call 934-9438.