Mentor:  Dr. Joanne E. Murphy-Ullrich, Professor of Pathology, Co-Director, BioMatrix Engineering and Regenerative Medicine Center, VH G001, 1720 2nd Avenue South, Birmingham, AL 35294-0019, Email: murphy@uab.edu

A Postdoctoral position is available in the laboratory of Joanne Murphy-Ullrich, Ph.D., in the Department of Pathology at the Alabama at Birmingham. Our research examines the role of the endoplasmic reticulum stress protein calreticulin in regulating TGF-beta signaling and fibrosis through control of calcium-dependent signaling. This position is funded by a new grant to study the role of calreticulin in TGF-beta signaling in the kidney proximal tubule under diabetic conditions. The applicant will be expected to examine in vitro mechanisms of calreticulin-TGF-beta regulation under high glucose and oxidant conditions and also to perform in vivo studies using several novel mouse models of diabetic nephropathy.

Candidates must have a recent Ph.D. and/or M.D., or equivalent. Priority will be given to qualified candidates with a strong background in cell culture, molecular biology, biochemistry, animal models, and diabetes-related research. The candidate will be expected to write manuscripts and present his/her work at scientific meetings and assist with training of graduate level personnel in the lab. Salary (with benefits) will follow NIH guidelines commensurate with training and experience. Competitive applicants should have a proven track record with publications and the potential for career development. UAB is a highly collegial and interactive environment that has an active Office of Post-doctoral Education which provides mentoring and career guidance in addition to that provided by the mentor.

If interested, please send a letter describing your research experience/interest/future career goals, your CV, and contact information for three references electronically to murphy@uab.edu.

Postdocs in UAB News

  • 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.

    Written 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.”

    Click 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
    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.

    Written 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.’”

    The 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.”

    Language 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.”

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UAB Research News

  • Intellectual sweat lands major NIH fund to study exercise
    Researchers will seek molecular answers to how exercise delivers benefits and compete for a UAB center.

    This summer, the NIH Common Fund announced a five-year, $170 million effort to reveal — in molecular terms — how exercise delivers its many benefits throughout the body.

    Marcas Bamman, Ph.D., of the University of Alabama at Birmingham was one of the key investigators who helped NIH staff do the research for their successful application to the Common Fund. Now UAB will compete to help lead the research program after Requests for Applications are released in September, says Bamman, a professor of cell, developmental and integrative biology and director of the UAB Center for Exercise Medicine.

    This new Common Fund, “Molecular Transducers of Physical Activity in Humans,” targets a huge gap in understanding. While it is clear that exercise, or physical activity (PA), improves health outcomes and prevents disease — benefits that include better musculoskeletal function during aging, retaining heart wellness, improving cognition, and preventing cardiovascular disease, neurological diseases, diabetes, osteoporosis and cancer, the mechanisms of how PA does this are little understood.

    “This program will … advance our understanding of how activity improves and preserves health,” NIH Director Francis Collins, M.D., Ph.D., said this summer. “Armed with this knowledge, researchers and clinicians may one day be able to define optimal physical activity recommendations for people at various stages of life, as well as develop precisely targeted regimens for individuals with particular health needs.”

    The path leading to this Common Fund took years. As Bamman and other exercise medicine researchers served on various NIH study panels, they would always emphasize that a lot of work was needed to discover the mechanisms that link PA and prescriptive exercise to those beneficial outcomes.

    Definition of exercise:
    “Exercise is dedicated time for structured physical activity (PA), of sufficient intensity and volume, to achieve a physiological goal.”
    —Marcas Bamman

    “It was a major leap forward when several NIH program officers from multiple institutes came together and recognized the need to apply to the Common Fund,” Bamman said. The Common Fund is a yearly strategic planning effort by NIH to spot emerging scientific opportunities or pressing challenges in biomedical research. It usually covers research that doesn’t fit neatly into any single NIH institute or center (for example, the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute on Aging, National Heart, Lung and Blood Institute, or the National Cancer Institute).

    Unlike grants awarded to university researchers, applications to the Common Fund come from the NIH staff; but those staffers rely on the help and expertise of university researchers to discern a need and craft a proposal.

    For Molecular Transducers, the NIH issued a Request for Information in December 2013. Bamman and fellow experts in exercise medicine put together a concerted response that identified key knowledge gaps. Five working groups were then organized by early spring 2014, to look at: 1) Tools to facilitate clinical trials research to probe the mechanisms of PA, 2) Integrated physiological mechanisms of how PA benefits tissues and organ systems, 3) The role of tissue stress in the benefits of PA, 4) The role of mitochondria in the mechanisms underlying PA benefits, and 5) Tools to discover and identify circulation and tissue signals that bring about the effects of PA.

    Bamman and John Jakicic, Ph.D., of the University of Pittsburgh co-chaired the clinical trials working group, which had four academics and about eight NIH program officers. “We had weekly phone calls and a lot of interactive writing,” Bamman said.

    Gaps in knowledge regarding the health benefits of PA:
    • Constructing a network model that guides and informs PA research.
    • How do all cells/tissues respond to exercise?
    • How are the responses to exercise communicated and coordinated among tissues?
    • How do acute responses to exercise translate over time to training adaptations and benefits?
    • What are the dose/response relationships that maximize specific health benefits?
    • What biological and environmental factors likely mitigate the acute and adaptive responses to PA?
    See “Understanding the Cellular and Molecular Mechanisms of Physical Activity-Induced Health Benefits” in Cell Metabolism, July 2015, for more details.

    The five working groups culminated in a two-day NIH Workshop in Bethesda, Maryland, in October 2014 titled “Understanding the Cellular and Molecular Mechanisms of PA-induced Health Benefits.” The workshop had 18 academics and about 60 NIH staff. Bamman and Jakicic presented recommendations of the clinical trials group.

    Using all this groundwork, the NIH program officers wrote a Common Fund application that they submitted last February. They requested $108 million over five years; NIH funded the program with $170 million.

    “That tells me the NIH director anticipates substantial impact from this program,” Bamman said.

    Now the competition begins

    The Molecular Transducers Common Fund is expected to conduct a large-scale exercise clinical trial with at least 3,000 participants across at least seven centers beginning FY2017, Bamman says. Also included will be one coordination center and a handful of specialized centers that focus on bioinformatics, genomics and transcriptomics, proteomics and metabolomics, and animal studies. Competition will be fierce when the NIH announces Requests for Applications (RFAs) this fall.

    UAB may have a head start in the race for the coordination center, due to its leadership of a 3-year-old, grassroots, multicenter effort that was launched without any funding to improve research in exercise medicine.

    “I figured it was time for a national, coordinated network to lead large-scale multisite trials,” Bamman said, “so I surveyed all of the CTSAs (the NIH-funded centers that perform clinical and translational science) about a national effort. We received a lot of interest.”

    The result is the National Exercise Clinical Trials Network, or NExTNet. This network has grown to 60 member universities today, and UAB is the coordinating center.

    Resources and research needed to potentiate the discovery of the mechanisms for the health benefits of PA:
    • Controlled clinical trials with standardized PA interventions and measures
    • Discovery of the molecular transducers of adaptations to PA: Role of “-omics” technologies.
    • Mechanistic research in animal and cell models.
    • Exercise physiologists trained in integrative biology and interdisciplinary teams.
    See “Understanding the Cellular and Molecular Mechanisms of Physical Activity-Induced Health Benefits” in Cell Metabolism, July 2015, for more details.

    NExTNet members have a shared database listing all of the physical and research capabilities of each member site, in order to facilitate collaboration. NExTNet is also building a database of aggregated data from previous studies and a list of existing sample inventories retained from earlier research that might aid a new study. NExTNet members work to standardize exercise training and testing procedures, whether aerobic or strength, in terms of intensity, volume and the amount of supervision. They also are standardizing the collection procedures for biological samples, such as blood and muscle, and the use of single sites for sample analysis, to get reliable, reproducible results.

    “The RFAs will be an opportunity for UAB,” Bamman said. “I am hopeful that UAB can compete successfully to serve as the coordinating center and as one of the clinical sites.”

    From humans to animals

    The Common Fund money for “Molecular Transducers of Physical Activity in Humans” will allow a national, coordinated research effort.

    “The Fund program will generate data and biospecimens from a large cohort of untrained people — most likely 3,000-plus, who lack chronic disease. The goal is to finally reveal the molecular mechanisms by which exercise works,” Bamman said. “This is a big, big program, with a lot of moving parts.”

    “We know that exercise has clinical value; but we don’t know what dose is optimal, and we don’t fully understand the molecular biology of it,” Bamman said. “Exercise activates a process in every tissue, and in every cell. One of the important questions is, what’s exercise doing to the brain?”

    Early results from the human studies will guide mechanistic animal studies, especially for critical tissues affected by exercise that are not easily studied in humans, such as lung, liver, brain and heart.

    “We certainly want to be part of the effort that identifies those mechanisms,” Bamman said.

  • 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.

    Written 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.”

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