Fellowship Opportunity Advancing Models of Infectious Disease through Deep Learning and Artificial Intelligence

Brief description of fellowship training environment (1-2 pages)

a. Laboratory research goals, and relevance to translating animal models to human health. Computer-aidedimage processing, analysis, deep learning and artificial intelligence are actively being used in medicalapplications and biomedical research. The field of veterinary anatomic pathology, with support from theAmerican College of Veterinary Pathologists strategic plan, is now exploring and adopting these newtechnologies (“digital pathology”) to improve data extraction from images used research and diagnosticsettings. To our knowledge, this fellowship is the first to actively recruit and support pathologist fellows totrain under veterinary pathologists and computer scientists that are currently developing and using deeplearning and artificial intelligence to investigate animal models of human infectious disease. This fellowshipis open to board-certified veterinary pathologists and board-eligible trainees with a strong interest in learningto use and apply digital pathology to animal models of infectious disease. The goals of this fellowship are to1) Create and use digital pathology tools to understand host-pathogen interactions that cause disease; 2)Extract and quantify image features for quantitative analyses and predictive modelling; 3) Gain an understanding of how machine learning, deep learning, and artificial intelligence apply to modern pathology and biomedical research images; 4) Develop critical thinking, scientific communication, and grant-writing skills. The overarching goal of Dr Beamer’s laboratory is to understand differential host susceptibility to Mycobacterium tuberculosis and to use that knowledge to produce and test accurate prognostic biomarkers.

b. Primary fellowship mentor and prior mentoring experience. The primary mentor is Gillian Beamer, VMD,PhD, DACVP an assistant professor at Tufts University Cummings School of Veterinary Medicine. She is anNIH-funded scientist with a research program on tuberculosis and a board-certified veterinary pathologist.She also collaborates as an experimental pathologist on many other infectious diseases including:cryptosporidiosis, shigellosis, enteropathogenic and enterohemorrhagic E. coli, salmonellosis, andhelminthiasis. Dr Beamer is an active user of digital pathology and engages with computer and imagescientists in academia and industry to create new digital pathology solutions for biomedical researchproblems. Since joining Tufts in 2012, Dr Beamer has mentored (formally and informally) in research projectsand diagnostic pathology: 5 post-doctoral scientists, visiting faculty, veterinarians; 9 residents (anatomicpathology and ophthalmology); 10 veterinary students; 1 medical student; 3 PhD students; 12 Mastersstudents; 5 undergraduates and 2 high school students.

c. Interdisciplinary mentor team, with brief description of collaborative roles in the research program. Theinterdisciplinary mentor team consists of 2 computer scientists and as an option (depending on the interestof the candidate), a second veterinary anatomic pathologist whose focus is diagnostic pathology.

i. Metin Gurcan, PhD is a full professor and Director, Center for Biomedical Informatics at Wake ForestSchool of Medicine in Winston Salem, NC. His research interests are in algorithms, artificial intelligence,biomedical Informatics, image Interpretation, computer-assisted, image processing and automatedpattern recognition. His role would be to supervise work associated with whole slide images.

ii. Dr Bulent Yener, PhD is co-Director of Pervasive Computing and Networking Center, and full professorin the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) in Troy, New York. Hisrole would be to supervise work in data mining and analytics.

iii. Nicholas Robinson, BVSc (Hons), PhD, MACVSc, DACVP is an associate professor and interim-chairin the Department of Biomedical Sciences. His diagnostic and research pathology interests are inendocrine and cardiovascular pathophysiology and medical device pathology. His role is optional anddepends on the interest of the fellow. Dr Robinson would support the fellow diagnostic service (necropsy,biopsy).

d. Programmatic training opportunities available to the fellow. The 3 institutions (Tufts University, Wake Forest,and RPI) offer many different training opportunities. As Tufts is the main site for this fellowship, examplesbelow focus on Tufts.

i. Department of Infectious Disease and Global Health The department sponsors monthly journal clubsand work-in-progress seminars. These are part of existing graduate or signature programs such as theMasters of Infectious Disease and Global Health; Masters of Conservation Medicine, PhD in BiomedicalSciences, and certificate in International Veterinary Medicine.

ii. Cummings School of Veterinary Medicine The school has approximately 100 faculty, of which one thirdare devoted to research. Weekly, monthly, and joint seminars within the Cummings School, at other Tuftscampuses and local institutions (i.e., UMass Medical in Worcester; Harvard and the Forsyth Institute inCambridge) provide a rich intellectual environment that the fellow could participate in depending on his/herspecific research interests. The Cummings School has a robust research program for veterinary students(T35 PI: Anwer) that runs a summer seminar series and an annual Research Day dedicated to the program.If a fellow had a strong clinical interest, there are weekly and monthly seminar series on clinical topics(diagnostic and therapeutic for small and large animals, and wildlife). As part of our anatomic pathologyresidency training program, we offer weekly histopathology rounds; and monthly “grand rounds” with clinicalspecialty services for example, with ophthalmology and neurology.

iii. Tufts University CTSI The university has numerous opportunities for fellows at Grafton, Medford, andBoston campuses. Tufts CTSI, housed on the Boston campus, provides many different opportunities andresources in translational science education and training. The mission of the CTSI is to acceleratetranslation of research into clinical care. The CTSI provide support in research design & analysis, researchcollaboration, clinical studies & trials, informatics, one health and medical devices. They sponsor drop-insessions and events with a calendar here: https://www.tuftsctsi.org/all-events/201906/. The CTSI also hasa K-scholar program at https://www.tuftsctsi.org/education/k-scholar-programs/ and each semester offersinteractive seminars and workshops for new and experienced researchers affiliated with Tufts. Currently,there are at least 28 courses to address the need for investigators to operate across disciplines and stagesin the translational spectrum, and to equip participants with competencies that can truly advance clinicaland translational research.

Biosketch of primary mentor: Link

Mentor contact information for interested potential trainees:

Gillian Beamer, VMD, PhD, DACVP Tufts University Cummings School of Veterinary Medicine 200 Westborough Road North Grafton, MA, 01536 508-839-7901 gillian.beamer@tufts.edu

BIOGRAPHICAL SKETCH NAME: BEAMER, GILLIAN eRA COMMONS USER NAME: GBEAMER POSITION TITLE: Assistant Professor EDUCATION/TRAINING INSTITUTION AND LOCATION DEGREE Date FIELD OF STUDY The University of Pennsylvania, Phila, PA BA 05/1996 Paleobiology The University of Pennsylvania, Phila, PA The Manhattan Veterinary Group, NYC, NY

VMD (DOCTORATE) Internship

05/2000 096/2001

Veterinary Medicine Medicine/Surgery

The Ohio State University, Columbus, OH PhD 06/2009 Tuberculosis The Ohio State University, Columbus, OH Resident 09/2008 Pathology The Ohio State University, Columbus, OH Research Scientist 11/2011 Tuberculosis A. Personal Statement I am Assistant Professor at Tufts University in the Department of Infectious Disease and Global Health (IDGH), a scientist, veterinarian, and board-certified pathologist with over 10 years focused on host responses to Mycobacterium tuberculosis (M.tb). I am principal Investigator for an R01 “Predicting tuberculosis outcomes using genotypic and biomarker signatures” that directly uses “digital pathology” machine learning, deep learning, and artificial intelligence to analyze tissue sections from experimentally infected mice and further incorporates that information into predictive models. This R01 provides the foundation for a CTSA fellow in digital pathology but the fellow would not be limited to tuberculosis, because Dr Beamer has active collaborations with investigators who use many any models of infectious such as cryptosporidiosis, shigellosis, salmonellosis, E. coli, helminthiasis, leishmania, borreliosis, and other vector borne diseases. As a PI, Dr Beamer uses the Diversity Outbred (DO) population to identify genotypic (alleles at significant QTLs), serum (protein) and lung (protein and granuloma) biomarkers of pulmonary tuberculosis. We test whether combinatorial signatures can accurately predict outcomes. Predictions are tested blindly, and in independent experimental data from mice. The best performing signatures are tested on human samples. Dr Beamer has productive and collegial collaborations with Dr Metin Gurcan (Wake Forest) and Dr Bulent Yener (RPI) who are co-mentors for the CTSA fellow. The most relevant publications are below:

a. Niazi MKK, Beamer G, Gurcan M. A computational framework to detect normal and tuberculosis infected lung from H&E-stained whole slide images. 2017 Proc. Of SPIE Vol. 10140. doi: 10.1117/12.2255627.

b. Niazi MK, Dhulekar N, Schmidt D, Major S, Cooper R, Abeijon C, Gatti DM, Kramnik I, Yener B, Gurcan M, Beamer G. Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice. Dis Model Mech. 2015 Sep;8(9):1141-53.

c. Harrison DE, Astle CM, Niazi MK, Major S, Beamer GL. Genetically diverse mice are novel and valuable models of age-associated susceptibility to Mycobacterium tuberculosis. Immun Ageing. 2014 Dec 16;11(1):24.

d. Niazi MK, Beamer G, Gurcan MN. Detecting and characterizing cellular response to Mycobacterium tuberculosis from histology slides. Cytometry A. 2014F

B. Positions and Honors Positions and Employment 2000 - 2001 Veterinary Intern, VCA Manhattan Veterinary Group, Manhattan, NY 2000 - 2003 Veterinarian (Staff), North Shore Animal League, Port Washington, NY 2002 - 2003 Veterinarian (Relief), Bide-A-Wee Animal Shelter, Manhattan, NY 2003 - 2004 Graduate Research Associate, The Ohio State University, Columbus, OH 2004 - 2007 Post Doctoral Fellow, The Ohio State University, Columbus, OH 2007 - 2011 Senior Research Associate, The Ohio State University, Columbus, OH 2009 - 2011 Clinical Instructor, The Ohio State University, Columbus, OH

2012 – current Assistant Professor, Tufts University, Dept of Infectious Disease and Global Health 2012 – current Assistant Professor, Tufts University, Department of Biomedical Sciences, North Grafton, MA 2015 - current Assistant Professor, Tufts University, Clinical and Translational Science Institute, Boston, MA Other Experience and Professional Memberships 2000 – current # VT1042, Veterinary Licensure ME 2000 – current Member, American Veterinary Medical Association 2000 – 2003 # 008497, Veterinary Licensure NY 2000 – 2003 Member, NY State Veterinary Medical Society 2008 – current Member & Diplomate, American College of Veterinary Pathologists 2010 – 2011 Member, Center for Microbial Interface Biology Honors 1994 Summer Research Student, The Jackson Laboratory 1995 Thuron Undergraduate Research Award, University of Pennsylvania 1998 Summer Pathology Research Fellow, Armed Forces Institute of Pathology 2006 Travel Award Veterinary Research Day, The Ohio State University 2006 Travel Award, American College of Veterinary Pathologists 2007 Travel Award OSUMC Research Day, The Ohio State University 2007 P.E.O. Scholar Award, P.E.O. International 2007 Travel Award Veterinary Research Day, The Ohio State University 2008 Young Investigator Award, American College of Veterinary Pathologists 2008 Travel Award, American College of Veterinary Pathologists 2008 Harold W Casey Scholarship, American College of Veterinary Pathologists 2008 NIH Health Disparities Loan Repayment Award, National Institutes of Health 2008 Roche Applied Sciences Seminar Award, The Ohio State University 2009 Roche Applied Sciences Seminar Award, The Ohio State University 2011 Becoming Faculty Workshop, Burroughs Wellcome Fund 2012 Tufts University CSVM nominee, Ellison Medical Foundation 2012 Tufts University CSVM nominee, Burroughs Wellcome Fund 2013 Tufts University Nominee, Smith Family Foundation 2015 2018

Junior Faculty Research Award, Cummings School of Veterinary Medicine Tufts University Academic Leadership Development Program

C. Contribution to Science 2. We focus on host factors that drive susceptibility to M.tb, using outbred, cross-bred, and inbred strains of

mice. The Diversity Outbred mouse population is our focus here because of its genetic diversity, heterozygosity, and development of a spectrum of responses, including some human-like disease features. Specifically, we seek to identify markers and mechanisms that influence outcomes to M.tb.

a. Smith CM, Proulx MK, Olive AJ, Laddy D, Mishra BB, Moss C, Gutierrez NM, Bellerose MM, Barreira-Silva P, Phuah JY, Baker RE, Behar SM, Kornfeld H, Evans TG, Beamer G, Sassetti CM. Tuberculosis susceptibility and vaccine protection are independently controlled by host genotype. MBio. 2016 Sep 20;7(5).

b. Niazi MK, Dhulekar N, Schmidt D, Major S, Cooper R, Abeijon C, Gatti DM, Kramnik I, Yener B, Gurcan M, Beamer G. Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice. Dis Model Mech. 2015 Sep;8(9):1141-53.

c. Harrison DE, Astle CM, Niazi MK, Major S, Beamer GL. Genetically diverse mice are novel and valuable models of age-associated susceptibility to Mycobacterium tuberculosis. Immun Ageing. 2014 Dec 16;11(1):24.

3. In collaboration with Dr Metin Gurcan at Wake Forest and Dr Bulent Yener at RPI, we apply innovative and cross-disciplinary methods to TB research. Respectively, the development of algorithms that can automatically detect and quantify microscopic visual features from granulomas; and the application of machine learning, data mining, and network analysis develop signatures that can classify and/or predict outcomes to M.tb infection.

a. Kus P, Gurcan M, Beamer G. Automated detection and segmentation of granuloma necrosis in pulmonary tuberculosis: Relative color channel and texture based analysis. Sept 2017. Submitted for publication, manuscript ID # VISI-D-17-00300, in International Journal of Computer Vision.

b. Niazi MKK, Gurcan M, Beamer, G. An application of transfer learning to neutrophil cluster detection for tuberculosis: efficient implementation with nonmetric multidimensional scaling and sampling. Oct 2017, accepted for publication in Proc. Of SPIE, paper 10584.

c. Niazi MKK, Beamer G, Gurcan M. A Computational Framework to Detect Normal and Tuberculosis Infected Lung from H&E-stained Whole Slide Images. 2017 Proc. Of SPIE Vol. 10140. doi: 10.1117/12.2255627.

d. Niazi MKK, Beamer G, Gurcan MN. Detecting and characterizing cellular responses to Mycobacterium tuberculosis from histology slides. Cytometry A. 2014 Feb;85(2):151-61.

4. We have a long-standing interest in understanding and modeling host immune and inflammatory responses associated with differential susceptibility to M.tb. Using inbred mice, we have shown pathways of early, chronic, and disease-associated cytokines, chemokines, and cellular responses depend on host genotype. For example, interleukin (IL)-10 is associated with disease in some (but not all) strains where it suppresses TH1 immunity and thwarts anti-mycobacterial effectors. Similarly, MHCII haplotype shapes immune responses but does not dictate final TB disease outcomes. a. Beamer GL, Cyktor J, Carruthers B, Turner J. H-2 alleles contribute to antigen 85-specific interferon-

gamma responses during Mycobacterium tuberculosis infection. Cell Immunol. 2011;271(1):53-61. b. Beamer GL, Flaherty DK, Assogba BD, Stromberg P, Gonzalez-Juarrero M, de Waal Malefyt R, Vesosky

B, Turner J. Interleukin-10 promotes Mycobacterium tuberculosis disease progression in CBA/J mice. J Immunol. 2008 Oct 15;181(8):5545-50.

c. Beamer GL, Flaherty DK, Vesosky B, Turner J. Peripheral blood gamma interferon release assays predict lung responses and Mycobacterium tuberculosis disease outcome in mice. Clin Vaccine Immunol. 2008 Mar;15(3):474-83.

d. Major S, Turner J, Beamer G. Tuberculosis in CBA/J mice. Vet Pathol. 2013 Nov;50(6):1016-21.

5. Additional scientific contributions are study of host-pathogen relationships which maintain M.tb infection and contribute to reactivation TB. By using the Cornell model we have shown that CD271+ bone marrow mesenchymal stem cells protect viable M.tb bacilli from antibiotic therapy which sterilizes the lung and spleen. This has important ramifications for treatment failure and reactivation TB, whereby stem cells serve as a nidus for persistent bacilli. Elsewhere, we actively seek funding to better understand this unique form of evasion, and to develop therapies that eliminate M.tb infection in bone marrow mesenchymal stem cells. a. Beamer G, Major S, Das B, Campos-Neto A. Bone marrow mesenchymal stem cells provide an

antibiotic-protective niche for persistent viable Mycobacterium tuberculosis that survive antibiotic treatment. Am J Pathol. 2014 Dec;184(12):3170-5.

D. Additional Information: Research Support Ongoing Research Tuberculosis R01 HL145411 Beamer (PI) 01/15/2019-12/31/2023 NIH Predicting tuberculosis outcomes using genotypic and biomarker signatures Role PI, 25% Total $3,252,983 PR0093 Beamer (PI) 08/07/2018-08/06/2019

Vaxil Bio Research Service Agreement to test novel vaccines Role PI, 5% Total $67,775 Immunology & Pathology Research Collaboration PV0722 Shoemaker (PI) 06/01/2017-6/30/2019 Gates Camelid VHH Single Domain Antibodies that Prevent & Treat Infant Diarrheal Diseases Role Co-Investigator, 10% effort Total $1,084,197 HH4158 Widmer (PI) 1/23/2017-12/31/2019 NIH Towards the development of pro- and prebiotics against cryptosporidiosis Role Co-Investigator, 10% effort Total $453,750 Educational/Teaching OSRO/BGD/505/USA Anwer (PI) 11/27/2018-7/31/2019 FAO Technical Support for Development of Pedagogic Skills in Nine Veterinary Schools and a

Veterinary Professional Accreditation System in Bangladesh Role Faculty Participant, 6.8% Total $277,366 Completed Research Tuberculosis R21 AI115038-01 Beamer (PI) 04/15/2015-03/31/2018 Title Genetic-based susceptibility to pulmonary tuberculosis Role: PI RSA015 Beamer (PI) 04/02/1017-03/21/2018 WaveGuide Waveguide Antibody Testing Role PI, 0.3% Total $8,681 R21 AI115038 Beamer (PI) 04/15/2015-04/14/2018 NIH Genetic-based susceptibility to pulmonary tuberculosis Role PI, 12.5% Directs $275,000 349504 Beamer (PI) 07/01/2015-06/30/2017 ALA Novel responses and control of Mycobacterium tuberculosis Role PI, 7.5% Total $80,000 V330432 Mace (PI) 11/01/2015-10/31/2016 Tufts Univ An approach to screen for early tuberculosis using low-cost, paper-based diagnostics Role Co-Investigator, 3.5% Total $45,000 Tufts Institute for Innovation Beamer (PI) 09/01/2014-08/31/2015 Tufts Univ Towards a direct, rapid, highly sensitive point-of-care diagnostic test for TB Role PI, 20% Directs $222,447 R56 AI111823 Beamer & Campos-Neto (MPI) 08/01/2014-07/31/2015 NIH Mesenchymal stem cells as a protective niche for latent M.tb Role PI, 25%effort Directs $415,135

Tufts Start-Up Beamer (PI) 01/01/2012-12/31/2014 Role PI Total $425,000 K08 AI071111 Beamer (PI) 08/01/2007-08/31/2012 NIH Chemokines and their receptors in the pathogenesis of tuberculosis Role PI, 75% Total $359,352 R01 AI064522 Turner (PI) 04/01/2007-03/31/2012 NIH Immune correlates of reactivation tuberculosis Role Graduate Student Total $2,000,755 Not available Turner (PI) 07/01/2005-06/30/2008 ALA In vitro predictors of susceptibility for reactivation tuberculosis Role Graduate Student, The Ohio State University Total $80,000 T32 RR07073 Rosol & Lairmore (MPIs) 07/01/2002-06/30/2007 NIH Mouse Pathobiology: Models of Human Disease Role Post-Doctoral Fellow 07/01/2004-06/30/2007, The Ohio State University Total Not available Immunology & Pathology Research Collaboration NIH010 Telford (PI) 05/01/2018-04/30/2019 NIH Emergence of tick-borne encephalitis in North America Role Co-Investigator, 5% effort Total $536,707 HS9040 Tzipori (PI) 01/08/2018-01/07/2019 NIH Refinement of the Hamster Model of C. difficile Disease for Testing of Traditional and

Nontraditional Therapeutics Role Co-Investigator, 5% effort Total $504,569 PV0626 Tzipori (PI) 5/18/2016-5/17/2018 Merck Investigating the relative contribution of TcdA and TcdB to C. difficile infection and

disease in the piglet diarrhea model Role Co-Investigator, 5% effort Total $255,600 PV0551 Tzipori (PI) 9/24/2015-4/30/2018 Gates Development of a pig model for Cryptosporidium hominins Role Co-Investigator, 2% effort Total $713,494 SU0245 Tzipori (PI) 03/01/2014-02/28/2019 NIH Non-typhoidal Salmonella vaccines Role Co-Investigator, 5% effort Total $407,286 SU0174 Tzipori (PI) 03/01/2014-02/28/2019 NIH Development of strategies to achieve immunoprotection against Cryptosporidiosis Role Co-Investigator, 10% effort

Total $3,440,607 SU0173 Tzipori (PI) 03/01/2014-02/28/2019 NIH Shigella live vector-based multivalent vaccine Role Co-Investigator, 5% effort Total $688,660 SU0172 Tzipori (PI) 3/01/2014-02/28/2019 NIH Novel Immunoprophylaxes against Clostridium difficile infection Role Co-Investigator, 5% Total $292,660 Seed Grant Nephew (PI) 06/01/2015-05/31/2016 TCSVM Transgenerational effects of chronic social stress on peripheral cytokines Role Co-Investigator, No salary support (10% effort) Total $10,000 Collaborates! Nephew (PI) 07/01/2014-06/30/2015 Tufts Univ Transgenerational effects of stress on the inflammatory response system Role Co-Investigator, 2.5% Total $21,955 PV0171 Tzipori (PI) 03/09/2012-03/08/2014 Merck Rate and site of excretion of TcdA and TcdB specific antibody in healthy vs. damaged

gut mucosa due to C. difficile infection (CDI) in piglets Role Co-Investigator, 25% Total $114,417 R15 ES020993 Beamer, Celine (PI) 05/01/2012-04/30/2014 NIH Fate and effects of nanomaterials in the gastrointestinal tract Role Co-Investigator, 10% Directs $300,000 R56 AI094459 Tzipori (PI) 9/30/2011-9/29/2013 NIH Evaluation of a new class of antimicrobial agents against Clostridium difficile Role Co-Investigator, 25% Total $896,901 Educational/Teaching Not available Anwer (PI) 02/20/2017-12/31/2017 FAO Technical support for development of pedagogic skills in nine Veterinary Schools & a

Veterinary Professional Accreditation System in Bangladesh Role Faculty Participant, 5% Total $314,445 SUB748 Tzipori (PI) 10/1/2009-9/29/2014 USAID Respond: (VET) The development of outbreak investigation and response training

merging animal and human health dynamics into a comprehensive capacity for disease detection and control

Role Technical Advisor, 4% Total $12,478,000

OMB No. 0925-0001 and 0925-0002 (Rev. 10/15 Approved Through 10/31/2018)

BIOGRAPHICAL SKETCH Provide the following information for the Senior/key personnel and other significant contributors.

Follow this format for each person. DO NOT EXCEED FIVE PAGES.

NAME: Gurcan, Metin N., Ph.D. eRA COMMONS USER NAME: MGURCAN01 POSITION TITLE: Director, Center for Biomedical Informatics and Professor, Internal Medicine EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.)

INSTITUTION AND LOCATION DEGREE

Completion Date

FIELD OF STUDY

Bilkent University, Ankara, Turkey B.Sc. 06/1991 Electrical and Electronics Engineering

UMIST, Manchester, England M.Sc. 10/1994 Digital Systems Engineering

Bilkent University, Ankara, Turkey Ph.D. 07/1999 Electrical and Electronics Engineering

University of Michigan, Ann Arbor, MI Postdoctoral Training 09/2001 Radiology, Computer-

aided Diagnosis A. Personal Statement I am an image analysis scientist with experience both in industry and academia in the past 20 years on various image analysis projects in radiology, dermatology, and histopathology. Recently, I was recruited to be the Director of Center for Biomedical Informatics and Professor of Internal Medicine at Wake Forest School of Medicine. Previously, I founded the Clinical Image Analysis Lab at The Ohio State University was its director. As PI and co-investigator of projects funded by the National Institutes of Health, American Cancer Society and Department of Defense and several foundations, I have led multi-disciplinary, multi-institutional teams in designing, developing, and validating various image analysis systems. I have coauthored over 150 peer-reviewed publications. Leveraging my background and experience in developing image analysis systems, my lab has been developing an algorithmic and computational infrastructure for processing and analyzing histopathological images of various cancers including breast, prostate, and follicular lymphoma, as well as radiological images of osteoarthritis, breast and cervical cancers. I am a senior member of IEEE, SPIE and AMIA. I frequently lecture at different prominent conferences and have organized several meetings related to medical image analysis. I co-chair the Digital Pathology Conference at the SPIE Medical Imaging Symposium, and chair the Histopathological Image Analysis (HIMA) workshop as part of the Pathology Informatics Meeting. In recognition of my work, I have received the British Foreign and Commonwealth Organization Award, the NCI caBIG Embodying the Vision Award, NIH Exceptional, Unconventional Research Enabling Knowledge Acceleration (EUREKA) Award, the Children’s Neuroblastoma Cancer Foundation Young Investigator Award, and the OSU Cancer Center REAP and Pelotonia Awards. The goal of the proposed project is to discover new genes and mechanisms that drive pathologic responses to Mycobacterium tuberculosis, the bacillus which causes tuberculosis (TB) disease in susceptible hosts. The foundation for discovery is Diversity Outbred mice that are known to express a wide range of quantifiable phenotypes of susceptibility and resistance which model the variability in human responses to natural infection with M. tuberculosis. I have been collaborating with the PI, Dr. Beamer, for nearly four years. We have received an R21 award and an R56 award; jointly published two articles, a conference proceeding and two poster abstracts; submitted another manuscript to The Journal of Microscopy; and are now in the process of

preparing another journal article. The current application will benefit from my expertise in medical image analysis and imaging informatics, as well as the computational infrastructure at the Clinical Image Analysis Lab. The development and application of these tools to tuberculosis research are novel and innovative. 1. Trimboli A, Cantemir-Stone CZ, Li F, Wallace JA, Merchant A, Creasap N, Thompson JC, Caserta E, Wang

H, Chong J-L, Naidu S, Wei G, Sharma SM, Stephens JA, Fernandez SA, Gurcan MN, Weinstein MB, Barsky SH, Yee L, Rosol TJ, Stromberg PC, Robinson ML, Pepin F, Hallett M, Park M, Ostrowski MC, Leone G, Pten in Stromal Fibroblasts Suppresses Mammary Epithelial Tumors, Nature, 461, pp. 1084-1091, 22 October 2009.

2. Das H, Wang Z, Niazi K, Aggarwal R, Lu J, Kanji S, Das M, Joseph M, Gurcan MN, Cristini V, “Impact of Diffusion Barriers to Small Cytotoxic Molecules on the Efficacy of Immunotherapy in Breast Cancer,” PLOS ONE, vol. 8, no. 4, e61398, 2013.

3. Niazi K, Beamer G, Gurcan MN. Detecting and characterizing cellular responses to Mycobacterium tuberculosis from histology slides. Cytometry A, 2014; 85(2):151–161.

4. Niazi M, Dhulekar N, Schmidt D, Major S, Cooper R, Abeijon C, Gatti D, Kramnik I, Yener B, Gurcan MN, Beamer M. Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune competent mice. Dis Model Mech, 2015; 8(9):1141-1153. PMCID: PMC4582107.

B. Positions and Honors Positions and Employment 1991-1992 Research Assistant, Dept. of Electrical and Electronics Eng., Bilkent Uni., Ankara, Turkey 1993-1999 Research Assistant, Dept. of Electrical and Electronics Eng., Bilkent Uni., Ankara, Turkey 1995-1996 Visiting Researcher, Dept. of Electrical and Computer Eng., University of Minnesota,

Minneapolis, MN 1996-1997 Visiting Researcher, Dept. of Electrical and Computer Eng., University of Minnesota,

Minneapolis, MN 1998 Instructor, Computer Engineering Department, Bilkent University, Ankara, Turkey 1999-2001 Research Fellow/Investigator, Dept. of Radiology, University of Michigan, Ann Arbor, MI 2001-2001 Senior Electrical Engineer, Qualia Computing, Inc., Beavercreek, OH 2002-2003 CT Product Director, CADx Systems, Inc., Beavercreek, OH 2004-2005 Senior Algorithmic Engineer, iCAD, Inc., Beavercreek, OH 2005-2006 Research Scientist, Biomedical Informatics Dept., Ohio State University, Columbus, OH 2006-2007 Research Assistant Professor, Biomedical Informatics Dept., Ohio State Univ., Columbus, OH 2007-2010 Assistant Professor, Biomedical Informatics Dept., Ohio State Univ., Columbus, OH 2010-2017 Associate Professor, Biomedical Informatics Dept., Ohio State Univ., Columbus, OH 2015-2017 Associate Professor, Department of Pathology, Ohio State Univ., Columbus, OH 2017-2017 Professor, Biomedical Informatics and Pathology Depts., Ohio State Univ., Columbus, OH 2017-Present Professor, Internal Medicine and Director, Center for Biomedical Informatics, Wake Forest

School of Medicine, Winston-Salem, NC Other Experience and Professional Memberships 1990 – 2006 Member, Institute of Electrical and Electronics Engineering (IEEE) 1994 – Present Member, IEEE Signal Processing Society 1994 – Present Member, IEEE Engineering in Medicine and Biology Society 1998 – 1999 Electronics Communication Development Officer, IEEE Region 8 2000 – Present Member, Radiological Society of North America (RSNA) 2001 – Present Member, The International Society for Optical Engineering (SPIE) 2006 – Present Senior Member, Institute of Electrical and Electronics Engineering (IEEE) 2017 – Present Member, American Medical Informatics Association (AMIA) Honors 1992 British Foreign and Commonwealth Organization Award 2007 National Cancer Institute caBIG™ Embodying the Vision Award 2008 Children’s Neuroblastoma Cancer Foundation Young Investigator Award

2009 NIH Exceptional, Unconventional Research Enabling Knowledge Acceleration (EUREKA) Award 2009-2017 The Ohio State University Comprehensive Cancer Center REAP Award C. Contribution to Science

1. Computer-aided diagnosis of breast cancer. My initial contributions to science were in computer-aided detection and diagnosis of breast cancer in radiology. I developed higher-order statistical methods and optimization techniques to detect signs of breast cancer from mammograms. This field continued to receive increased interest in academia, resulting in a large number of publications from several other researchers. Eventually, a few companies sought to develop commercial products based on my techniques. I worked at one that received FDA approval for a product.

a. Gurcan MN, Yardimci Y, Cetin AE, Ansari R, “Detection of Microcalcifications in Mammograms Using Higher Order Statistics,'' IEEE Signal Processing Letters, 4(8): 213-216, August 1997.

b. Gurcan MN, Sahiner B, Chan H-P, Hadjiiski LM, Petrick N, “Selection of an Optimal Neural Network Architecture for Computer-aided Detection of Microcalcifications – Comparison of Automated Optimization Techniques,” Medical Physics, 28(9):1937-1948, September 2001. PMID: 11585225

c. Sahiner B, Petrick N, Chan H-P, Hadjiiski LM, Paramagul C, Helvie, MA, Gurcan MN, “Computer-Aided Characterization of Mammographic Masses: Accuracy of Mass Segmentation and its Effects on Characterization,” IEEE Transactions on Medical Imaging, 20(12):1275-1284, 2001. PMID: 11811827

d. Gurcan MN, Chan H-P, Sahiner B, Hadjiiski L, Petrick N, Helvie MA, "Optimal neural network architecture selection: Improvement in computerized detection of microcalcifications," Academic Radiology, 9(4): 420-429, 2002. PMID: 11942656

2. Temporal and 3D image analysis. I expanded my efforts from two-dimensional medical image

analysis to three-dimensional and temporal radiological image analysis, and developed novel image analysis methods for lung, colon, and cervical cancers. These image analysis techniques were all developed using features with clinical relevance and the algorithms were designed to be informed by the current clinical knowledge. My efforts in these areas, mostly in industry, received recognition and I obtained two patents.

a. Gurcan MN, Sahiner B, Petrick N, Chan H-P, Kazerooni EA, Cascade PN, Hadjiiski LM, “Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system,” Medical Physics, 29(11):2552-2558, 2002. PMID: 12462722

b. Gurcan MN, “Computer-aided detection methods in volumetric imagery,” Patent, issued Jun 26, 2007, Patent No. US# 7, 236, 620.

c. Gurcan, MN, Hardie, RC, Rogers, SK, “Shape estimates and temporal registration of lesions and nodules,” Patent, issued February 3, 2009, Patent No. US# 7,486,812.

d. Prescott J, Zhang D, Wang JZ, Mayr NA, Yuh WTC, Saltz J, Gurcan MN, “Temporal Analysis of Tumor Heterogeneity and Volume for Cervical Cancer Treatment Outcome Prediction: Preliminary Evaluation,” Journal of Digital Imaging, vol. 23, no. 3, pp. 342-357, 2010. PMCID: PMC3046647.

3. Histopathological image analysis. Leveraging my background and experience in developing

radiological image analysis systems, my group has been developing an algorithmic and computational infrastructure for processing and analyzing histopathological images, initially of neuroblastoma and follicular lymphoma cancers, followed by breast and prostate cancers. Several of the techniques we developed in whole-slide imaging (e.g. multi-resolution processing and the use of GPUs), were later adopted by other research groups. I also contributed to the development of this field by organizing more than 15 meetings and workshops. I started and co-chaired a conference and a workshop that still continues today: SPIE Digital Pathology Conference and Pathology Informatics Conference HIMA Workshop. I have been the PI, co-PI and investigator of several projects in this area.

a. Gurcan MN, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B, “Histopathological Image Analysis: A review,” IEEE Reviews in Biomedical Engineering, vol. 2, pp 147-171, 2009. PMCID: PMC2910932

b. Sertel O, Kong J, Shimada H, Catalyurek U, Saltz JH, Gurcan MN, “Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development,” Pattern Recognition, vol. 42, no. 6, pp. 1093-1103, 2009. PMCID: PMC2678741.

c. Dundar M, Badve S, Bilgin G, Jain R, Sertel O, Gurcan MN, “Computerized classification of intraductal breast lesions using histopathological images,” IEEE Transactions on Biomedical Imaging (TBME), vol. 58, no. 7, pp. 1977-1984, 2011. PMCID: PMC3328096.

d. Kong H, Gurcan MN, Belkacem-Boussaid K, “Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting,” IEEE Transactions on Medical Imaging, vol. 30, no. 9, pp. 1661-1677, 2011. PMCID: PMC3165069.

4. Image analysis with mathematical modeling. Our efforts in histopathological image analysis were

also translated into microscopic image analysis for cancer, muscle biology, and skin image analysis. These studies also incorporate the results of image analysis with complementary mathematical models, which inform the underlying disease characteristics. Our efforts in tuberculosis image analysis are the culmination of our previous efforts in medical image analysis and benefit from our experience in color image processing, use of novel computing architectures in image analysis, registration and temporal analysis, as well as up-to-date machine learning techniques.

a. Sertel O, Dogtas B, Chiu CS, Gurcan MN, “Microscopic image analysis for quantitative characterization of muscle fiber type composition,” Computerized Medical Imaging and Graphics, vol. 35, no. 7-8, pp. 616-628, 2011. PMID: 21342753

b. Mahoney E, Lucas D, Gupta S, Wagner A, Herman S, Smith L, Yeh Y, Andritsos L, Jones J, Flynn J, Blum K, Zhang X, Lehman A, Kong H, Gurcan MN, Greever M, Johnson A, Byrd J, "ER stress and autophagy: new players in the mechanism of action and drug resistance of the cyclin-dependent kinase inhibitor flavopiridol," Blood, vol. 120, no. 6, pp. 1262-1273, 2012. PMCID: PMC3418721.

c. Das H, Wang Z, Niazi K, Aggarwal R, Lu J, Kanji S, Das M, Joseph M, Gurcan MN, Cristini V, “Impact of Diffusion Barriers to Small Cytotoxic Molecules on the Efficacy of Immunotherapy in Breast Cancer,” PLOS ONE, vol. 8, no. 4, e61398, 2013. PMCID: PMC3631240.

d. Fauzi M, Khansa I, Catignani K, Gordillo G, Sen C, Gurcan MN, “Computerized Segmentation and Measurement of Chronic Wound Images,” Computers in Biology and Medicine, vol. 60, pp. 74-85, 2015. PMID: 25756704

Complete List of Published Work (Over 200 peer-reviewed publications): http://www.ncbi.nlm.nih.gov/myncbi/browse/collection/40338393/?sort=date&direction=descending

D. Research Support. Ongoing Research Support R01 CA134451 Gurcan (PI) 05/01/2009-05/31/2018 Computer-Based Assessment of Tumor Microenvironment (TME) in Follicular Lymphoma The overall goals of this proposal are to: 1) Measure the prognostic impact of histologic grade (without and with computer assistance) of follicular lymphoma cases by comparing it with outcome measures; 2) Develop a computer-assisted image analysis system to quantitatively assess the follicular lymphoma TME; and 3) Compare the effectiveness of a combined prognostic measure incorporating grade (without and with computer assistance), TME parameters, and existing FLIPI score. Overlap: None. Role: PI R21 AI115038 Beamer (PI) 04/15/2015 –03/31/2018 Genetic-based susceptibility to pulmonary tuberculosis Our objectives are to identify genes associated with granuloma-specific phenotypes and to develop genetic-based integrative models of pulmonary TB. As a multi-disciplinary team, our long-term goals are to understand the genetic, molecular, and cellular mechanisms that drive pulmonary TB, and to identify signatures that will predict outcomes following M.tb infection, treatment, or prevention. Role: Co-Investigator

Overlap: None

U01 CA198945 Bilgin (PI) 06/01/2015 –05/31/2018 Task-Specific Compression for Biomedical Big Data The volume of data that is expected to be generated by a fully digital pathology practice is enormous. The goal of this proposal is to solve this challenging problem by significantly improving the presentation of digital pathology images for accurate diagnoses by designing intelligent image compression schemes. Role: Co-Investigator Overlap: None U24 CA199374 Gurcan/Madabhushi/Martel (MPI) 09/17/2015 –08/31/2020 Pathology Image Informatics Platform for Visualization, Analysis and Management This multi-PI U24 proposal seeks to expand on an existing, freely available pathology image viewer (Sedeen Image Viewer) to create a pathology informatics platform (PIIP) for managing, annotating, sharing, and quantitatively analyzing digital pathology imaging data. Overlap: None Role: MPI

Completed Research Support

R56 AI111823 Campos-Neto/Beamer (MPI) 08/01/2014 –07/31/2016 Mesenchymal Stem Cells as a Protective Niche for Latent M.tb Using a mouse model of tuberculosis, the project will a) dissect the mechanisms by which M.tb can successfully reside in a latent stage inside bone marrow mesenchymal stem cells; b) evaluate how reactivation of the latent infection may occur, including under immunodeficiency conditions that mimic those caused by HIV infection in humans; and c) evaluate how this unique host/pathogen relationship protects the microbe from therapeutic drugs. Role: Site PI Overlap: None

OMB No. 0925-0001 and 0925-0002 (Rev. 11/16 Approved Through 10/31/2018)

BIOGRAPHICAL SKETCH Provide the following information for the Senior/key personnel and other significant contributors.

Follow this format for each person. DO NOT EXCEED FIVE PAGES.

NAME: Bulent Yener eRA COMMONS USER NAME (credential, e.g., agency login): YENERB POSITION TITLE: Director of Data Science Research Center, Professor of Computer Science, Rensselaer Polytechnic Institute EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.)

INSTITUTION AND LOCATION

DEGREE (if

applicable)

Completion Date

MM/YYYY

FIELD OF STUDY

Columbia University New York, NY PhD 1994 Computer Science Columbia University New York, NY MS 1987 Computer Science Technical University of Istanbul Turkey MS 1984 Industrial Engineering Technical University of Istanbul Turkey B.S. 1982 Management Engineering

A. Personal Statement As one of the co-investigators of this proposal, I have the expertise, leadership and motivation necessary to successfully carry out the proposed work. I have a broad background in computer and computational sciences, with specific training and expertise in key research areas for this proposal. I invented the cell-graph approach for tissue modeling which advances Voronoi graph based approaches to allow more general relationships between cells. As a PI on several NIH grants, I have enhanced the original cell-graph paradigm to model different type of cells and ECM in various tissue samples. I have modeled and analyzed complex data from diverse domains e.g., from internet chatroom communication to epilepsy modeling to proteomics using multiway modeling techniques and tensor decompositions. I have developed modeling techniques to understand branching morphogenesis by combining data and physics based approaches. I am the founding Director of Data Science Research Center (DSRC) at RPI, where a team of more than 20 faculty from six different departments collaborate on problems ranging from biology, chemistry and physics to computer and network security. Within DSRC, I have initiated several Big Data research projects, funded by NIH R01s, in biomedical imaging and structure-function relationship analysis. In each of these, I have led a team of medical experts, biologists, mathematicians, and computer scientists to address important clinical and scientific questions in a rigorous, data-driven manner, with the results providing a combination of scientific insights, published algorithms, and software. My educational and work background enables me to integrate different disciples both in science and engineering. After completing my graduate studies at Columbia University, I briefly worked in academia (NJIT) and then joined Bell Labs in Murray Hill. I joined RPI in August 2002, where I am now a Full Professor in Computer Science Department (with a joint appointment in Mathematical Science Department) with research interests in data science and analytics. B. Positions and Honors

Positions and Employment 1995—2002 Visiting Faculty, Department of Computer Science, Columbia University, New York, NY.

1997—2001 Member of Technical Staff, Information Sciences Research Center, Bell Laboratories, Lucent Technologies, Murray Hill, NJ. 2002—2008 Associate Professor Dept of Computer Science, Rensselaer Polytechnic Institute, Troy, NY. 2008—Present Professor, Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY. 2010—Present Director of Data Science Research Center, Rensselaer Polytechnic Institute, Troy, NY.

Other Experience and Professional Memberships 2003—Present (Fellow) the Institute for Electrical and Electronic Engineers (IEEE). 2004—Present (Senior Member) the Association for Computing Machinery (ACM). 2006—2008 Chair of Technical Committee on Computer Communications (TCCC) IEEE Computer Society, 2007—2008 Technical Area Coordinator for Networking Cluster, IEEE Computer Society. Honors 2006—2007 University Bolzano-Innsbruck-Trento (BIT) School Visiting Faculty Award. 2009—2010 European Union (EU) Marie-Currie Fellow. Host: Technical University of Berlin. C. Contributions to Science Dr. Yener has close to 60 journal articles, more than 80 conference publications, 8 book monograms, 10 patents. Dr. Yener developed a graph theoretical method called cell-graphs (Yener, B. “Cell-Graphs: Image-Driven Modeling of Structure-Function Relationship”, Communications of the ACM, Vol. 60 No. 1, Pages 74-84) for modeling of complex biological systems. In this method, cells are segmented from a 2D or 3D tissue images and constitute the vertices of a graph. The edges are established based on a pairwise relationship between two vertices. The cell-graph approach provides a rich and precise set of graph-theoretical features to model/quantify the structural organization of underlying tissue samples. This work generalizes the Voronoi graphs to arbitrary edge functions and has high clinical relevance: (1) Automated cancer diagnosis:

x Gunduz C., Gultekin S. H. , Yener B., ``The Cell-Graphs of Cancer,'' BIOINFORMATICS Vol. 20 Suppl. 2004, pp i145-i151.

x Demir C., Gultekin S. H., and Yener B., ``Augmented cell-graphs for automated cancer diagnosis,'' BIOINFORMATICS Vol. 21 Suppl. 2 2005 pp 7--12., 2005.

x Gunduz C., Gultekin S. H., Yener B., ``Learning the topological properties of brain tumors,'' in IEEE/ACM Transactions on Computational Biology and Bioinformatics July-September 2005 (Vol. 2 No: 3) pp.: 262-270.

x Bilgin CC, Bullough P, Plopper GE, Yener B. "ECM-Aware cell-graph mining for bone tissue modeling and classification". Journal of Data Mining and Knowledge Discovery, 20(3): 416-438 (2010).

(2) Stem Cell Differentiation modeling:

x Lund, A.W., Stegemann, J.P., Yener, B., and Plopper, G.E. The Natural and Engineered 3D Microenvironment as a Regulatory Cue during Stem Cell Fate Determination. Tissue Engineering Part B, 15(3):371-80, 2009.

x Lund AW, Bilgin C, Al Hasan M, McKeen L, Stegemann JP, Yener B, Zaki M, Plopper GE. "Quantification of spatial parameters in 3D cellular constructs using graph theory". Journal of Biomedicine and Biotechnology, 2009 doi:10.1155/2009/928286.

x Bilgin CC, Lund AW, Can A, Plopper GE, Yener B,. Quantication of Three-Dimensional Cell-Mediated Collagen Remodeling Using Graph Theory. 2010. PLoS ONE. 5(9): e12783. doi:10.1371/journal.pone.0012783.

x McKeen-Polizzotti L., Henderson K., Oztan B., Bilgin C. C. , Yener B., and Plopper G. E., Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states, in BMC Medical Imaging, July 2011.

(3) Dr. Yener also extended the use of graph theory to hypergraphs, and their algebraic representations such as multiway (tensor) data models which are successfully applied to the localization of epileptic focus (origin of a seizure) in epilepsy patients. Application of graph theory and network algorithms to biomedical computing is truly novel.

x Acar E., Aykut-Bingol C., Bingol H. , Bro R., Yener B., "Multiway Analysis of Epilepsy Tensors", BIOINFORMATICS 2007 23: i10-i18.

x Acar A.,Yener B. "Unsupervised Multiway data Analysis: A Literature Survey", IEEE Transactions on Knowledge and Data Engineering. vol. 21 pp. 6-20, 2009.

x Yener, B., Acar, E., Agius, P., Vandenberg, S.L., Bennett , K.P.,and Plopper, G.E. Multiway modelling and analysis in stem cell systems biology. BMC-Systems Biology, Jul 14;2(1):63, 2008.

x Yener B., Acar E., Plopper G.E.,Coupled Analysis of in Vitro and Histology Tissue Samples to Quantify Structure-Function Relationship. PLoS One. 2012;7(3):e32227. Epub 2012 Mar 30. http://www.ncbi.nlm.nih.gov/pubmed/22479315.

(4) Modeling of morphogenesis:

x Bilgin C. C., Shayoni R., Daley W. P., Baydil B., Sequeira S. J., Yener B., Larsen M.. Cell-graph modeling of salivary gland morphology. 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Micro 2010c.

x Bilgin C. C., Shayoni R., Daley W. P., Baydil B., Larsen M., Yener B. Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis. PLoS One. 2012;7(3):e32906. Epub 2012 Mar 5. http://www.ncbi.nlm.nih.gov/pubmed/22403724.

x Larsen M., Ray S., Yuan D., Dhulekar N., Bhaskaran A., Oztan B., Yener B., Cell-based multi-parametric model of cleft progression during submandibular salivary gland branching morphogenesis. PLoS Computational Biology 9 (11), e1003319. September 2013.

(5) Mycobacterium tuberculosis

x Ozcaglar, C., Shabbeer, A., Vandenberg, S., Yener, B., & Bennett, K. P. (2011). Sublineage structure analysis of Mycobacterium tuberculosis complex strains using multiple-biomarker tensors. BMC genomics, 12(2), S1.

x Shabbeer, A., Cowan, L. S., Ozcaglar, C., Rastogi, N., Vandenberg, S. L., Yener, B., & Bennett, K. P. (2012). TB-Lineage: an online tool for classification and analysis of strains of Mycobacterium tuberculosis complex. Infection, Genetics and Evolution, 12(4), 789-797.

x Ozcaglar, C., Shabbeer, A., Vandenberg, S. L., Yener, B., & Bennett, K. P. (2012). Epidemiological models of Mycobacterium tuberculosis complex infections. Mathematical Biosciences, 236(2), 77-96.

x Muhammad K. K. Niazi, Nimit Dhulekar, Diane Schmidt, Samuel Major, Rachel Cooper, Claudia Abeijon, Daniel M. Gatti, Igor Kramnik, Bulent Yener, Metin Gurcan, Gillian Beamer, Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice., “Disease Models and Mechanisms”, 2015 8: 1141-1153; doi: 10.1242/dmm.020867.

D. Additional Information: Research Support and/or Scholastic Performance

Ongoing Research Support NIH 1R21AI115038-01 PI: Gillian Beamer 04/15/2015-03/31/2017. “Genetic-based susceptibility to pulmonary tuberculosis”. The goal of this project is to capture, quantify, and analyze biologically relevant disease features of pulmonary TB. Role: Subcontractor NSF IIS:Medium #1302231, PI: Petros Drineas 07/16/2013-06/1/2017. “Mining petabytes of data using cloud computing and a massively parallel cyberinstrument”. The goal of this project is to develop fast algorithms that can operate on Petabyte size data sets. Role: Co-PI

Completed Recent Research Support NIH R01DE019244-02 (MPI) PI: Melinda Larsen 08/01/2009-08/30/2015 “Modeling Dynamics of Salivary Gland Branching Morphogenesis”. The Major goals of this project is to quantify and predict the morphogenesis process. Role: Co- Principal Investigator. NIH 1R01EB008016-01A1 and 3R01EB008016-02S1 (ARRA Supplement) PI: Yener 07/15/2008 – 07/30/2014 “Computational Approach to Close the Gap Between Tissue Structure and Function”. The major goals of this project are to discover the fundamental principles of how cells organize and define a distinct functional state. Role: Principal Investigator. NIH 1R01LM009731-01 and NIH 3-R01-LM009731-02S1 (ARRA supplement) (MPI) PI: Bennett 09/01/2008 – 08/31/2013 “Discovering Hidden Groups Accross Tuberculosis Patient and Pathogen Genotype Data”. The major goals of this project are to discover hidden TB relationships using machine learning and data mining techniques. Role: Co- Principal Investigator.