Publications - 2016

2016

1. Cheng Lu, Maneesh Dave, Matthew Cohn, Prateek Prasanna, Jeffrey Katz, Rajmohan Paspulati, Anant Madabhushi, Satish Viswanath. Radiomic Features on Diagnostic Magnetic Resonance Enterography Appear to Predict Patient Outcome Following Treatment of Crohn’s Disease: Preliminary Results. Singapore, ISMRM 2016.

2. Matthew Cohn, Cheng Lu, Maneesh Dave, Prateek Prasanna, Jeffrey Katz, Rajmohan Paspulati, Anant Madabhushi, Satish Viswanath. A Machine-Learning Based Risk Score to Predict Response to Therapy in Crohn's Disease via Baseline MRE. Digestive Disease Week (DDW) at the San Diego Convention Center in San Diego, CA, May 21-24, 2016.

3. Ashwood, B.; Pollum, M.; Crespo-Hernández, C. E. "Can a Six-Letter Alphabet Increase the Likelihood of Photochemical Assault to the Genetic Code?", Chem. Eur. J. 2016, 22, 16648-16656.

4. Wei Huang, Krishna M Ravikumar, Marc Parisien, and Sichun Yang, Theoretical modeling of multiprotein complexes by iSPOT: Integration of small-angle X-ray scattering, hydroxyl radical footprinting, andcomputational docking, Journal of Structural Biology 196 (3) 340-349 (2016) Gunalan K, Howell B, Duchin Y, Patriat R, Sapiro G, Harel N, McIntyre CC. Axon pathways activated during therapeutic deep brain stimulation. American Society for Stereotactic and Functional Neurosurgery Biennial Meeting.  2016.  (Oral presentation) 

5. Gunalan K, Howell B, Duchin Y, Patriat R, Sapiro G, Harel N, McIntyre CC. Axon pathways activated during therapeutic deep brain stimulation. Biomedical Engineering Midwest Student Speaker Exchange Program with Purdue University. 2016. (Oral presentation)

6. J. Park and Y.-T. T. Liao: Numerical Study of Upward Flame Spread over Discrete Fuels, Spring Technical Meeting of the Eastern States Section of the Combustion Institute, Princeton, NJ, March 13-16, 2016 

7. C. Li and Y.-T. T. Liao: Numerical Simulation of Flame Splitting Phenomenon in Concurrent Flame Spread over Thin Solid Fuel with Two-Stage Pyrolysis, Spring Technical Meeting of the Eastern States Section of the Combustion Institute, Princeton, NJ, March 13-16, 2016 

8. Ginsburg SB, Algohary A, Pahwa S, Gulani V, Ponsky L, Aronen HJ, Boström PJ, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Taimen P, Villani R, Stricker P, Rastinehad AR, Jambor I, Madabhushi A, “Radiomic Features for Prostate Cancer Detection on MRI Differ Between the Transition and Peripheral Zones: Preliminary Findings from a Multi-Institutional Study”, J Magn Reson Imaging. 2016 Dec 19. doi: 10.1002/jmri.25562. [Epub ahead of print] (PMID: 27990722). 

9. Xu, J, Luo, X, Wang, G, Gilmore, H, Madabhushi, A,”A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images”, Neurocomputing. 2016 May 26;191:214-223. doi: 10.1016/j.neucom.2016.01.034. (PMID: 28154470). 

10. Prasanna, P, Tiwari, P, Madabhushi, A, “Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor”, Sci Rep. 2016 Nov 22;6:37241. doi: 10.1038/srep37241. (PMID: 27872484). 

11. Shiradkar, R, Podder, TK, Algohary, A, Viswanath, S, Ellis, JR, Madabhushi, A, “Radiomics based Targeted Radiotherapy Planning (Rad-TRaP): A computational framework for prostate cancer treatment planning with MRI”, Radiation Oncology, 2016 Nov 10;11(1):148. (PMID: 27829431) 

12. Leo, P, Lee, G, Shih, N, Eliot, R, Feldman, M, Madabhushi, A, “Evaluating Stability of Histomorphometric Features across Scanner and Staining Variations: Prostate Cancer Diagnosis from Whole Slide Images”,       J Med Imaging (Bellingham). 2016 Oct;3(4):047502. (PMID: 27803941). 

13. Prasanna, P, Patel, JB, Partovi, S, Madabhushi, A, Tiwari P, “Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in Glioblastoma: Preliminary Findings”, Eur Radiol. 2016 Oct 24. (PMID: 27778090). 

14. Lu, C, Xu, H, Xu, J, Gilmore, H, Mandal, M, Madabhushi, A, “Multi-Pass Adaptive Voting for Nuclei Detection in Histopathlogical Images”, Sci Rep. 2016 Oct 3;6:33985. doi: 10.1038/srep33985. (PMID: 27694950) 

15. Tiwari P, Prasanna P, Wolansky L, Pinho M, Cohen M, Nayate AP, Gupta A, Singh G, Hattanpaa K, Sloan A, Rogers L, Madabhushi A, “Computer-extracted Texture Features to Distinguish Cerebral Radiation necrosis from Recurrent Brain Tumors on Multi-parametric MRI – A Feasibility Study”, AJNR Am J Neuroradiol. 2016 Sep 15. [Epub ahead of print] (PMID: 27633806). Romo-Bucheli, D, Janowczyk, A, Romero, E, Gilmore, H, Madabhushi, A, “Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images”, Sci Rep. 2016 Sep 7;6:32706. doi: 10.1038/srep32706. (PMID: 27599752). 

16. Janowczyk, A, Madabhushi, A, “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”, Journal of Pathology Informatics, 2016 Jul 26;7:29. doi: 10.4103/2153-3539.186902. eCollection 2016. (PMID: 27563488). 

17. Penzias G, Janowczyk A, Singanamalli A, Rusu M, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M, Haynes AM, Ponsky L, Viswanath S, Madabhushi A, “AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments", Nature Sci Rep. 2016 Jul 26;6:29906. doi: 10.1038/srep29906 (PMID: 27457670). 

18. Madabhushi, A, Lee, Ge, “Image Analysis and Machine Learning in Digital Pathology: Challenges and Opportunities”, Med Image Anal. 2016 Jul 4. pii: S1361-8415(16)30114-1. doi: 10.1016/j.media.2016.06.037. [Epub ahead of print] (PMID: 27423409). Bhargava, R and Madabhushi, A, “Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology”, Annu Rev Biomed Eng. 2016 Jul 11;18:387-412. (PMID: 27420575) 

19. Lee, G, Romo-Bucheli, DE, Madabhushi, A, “Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-attribute Biomedical Data”, PLoS One. 2016 Jul 15;11(7):e0159088. doi: 10.1371/journal.pone.0159088. eCollection 2016. (PMID: 27421116) 

20. Janowczyk, A, Basavanhally, A, Madabhushi, A, “Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology”, Comput Med Imaging Graph. 2016 May 16. pii: S0895-6111(16)30040-4. doi: 10.1016/j.compmedimag.2016.05.003. [Epub ahead of print] (PMID: 27373749) 

21. Ginsburg, S, Taimen, P, Merisaari, H, Vainio, P, Boström, PJ, Aronen, HJ, Jambor, I, Madabhushi, A, “Patient-Specific Pharmacokinetic Parameter Estimation on Dynamic Contrast-Enhanced MRI of Prostate: Preliminary Evaluation of a Novel AIF-Free Estimation Method”, Journal of Magnetic Resonance Imaging, 2016 Jun 10. doi: 10.1002/jmri.25330. [Epub ahead of print] (PMID:27285161) 

22. Sparks, R, Madabhushi, A, “Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images”, Nature Sci Rep. 2016 Jun 6;6:27306. doi: 10.1038/srep27306. (PMID: 27264985) 

23. Toth, R, Sperling, D, Madabhushi, A, “Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings”, PLoS One. 2016 Apr 18;11(4):e0150016. doi: 10.1371/journal.pone.0150016. eCollection 2016. (PMID: 27088600) 

24. Antunes, J, Viswanath, S, Rusu, M, Valls, L, Hoimes, C, Avril, N, Madabhushi, A, “Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study”, Translational Oncology, 2016 Apr;9(2):155-62. doi: 10.1016/j.tranon.2016.01.008. (PMID:27084432) Wan, T, Bloch, BN, Plecha, D, Thompson, CL, Gilmore, H, Jaffe, C, Harris, L, and Madabhushi, A, "A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting Oncotype DX Risk Scores", Nature Scientific Reports, 2016 Feb 18;6:21394. doi: 10.1038/srep21394 (PMID:26887643) 

25. Turner, Mark. 2016. “La Integración Conceptual Como Un Programa De Investigación Para La Creatividad Computacional.” In Creatividad Computacional, edited by Rafael Pérez y Pérez. Grupo Editorial Patria. ISBN: 978-607-744-284-4. Pages 33-50.

26. Liqun Zhang, Susmita Borthakur, Matthias Buck “Molecular Simulations of a Dynamic Protein Complex: Role of Polar Interactions and Solvent in the Process of Protein Dissociation”, Biophysical Journal. 110, 877–886. 2016. 

27. Liqun Zhang, Thomas Centra, Matthias Buck, “Investigation on Plexin Rho GTPase Binding Domain Binding with Small Rho GTPases using Molecular Dynamics Simulations”, Biophysical Society. Los Angeles, CA. 2016 (posters). 

28. Liqun Zhang, Esmaeel Azadian, “Investigation on the Interaction between Human Beta Defensin Type 3 (hBD-3) and Ectodomain of EphA2 Receptor using Molecular Dynamics Simulation Method”, American Association of Cancer Research, New Orlands, LA, 2016 (posters).

29. M. M Brister.; L. E. Piñero-Santiago; M. Morel; R. Arce; C. E. Crespo-Hernández; "The Photochemical Branching Ratio in 1,6-Dinotropyrene Depends on Excitation Energy", J. Phys. Chem. Lett. 2016, 7, 5086-5092

30. F. Santiago; M. M. Brister; C. E. Crespo-Hernández, “Investigation of Ancestral RNA with Computational Chemistry”, ACS Seed Program, Case Western Reserve University, Cleveland, Ohio, July 29, 2016.(Poster Presentation) 

31. M. M. Brister; C. E. Crespo-Hernández, “Investigation of Ancestral Nucleobases”, Astrobiology Graduate Conference, University of Boulder Colorado, Boulder, CO, July 25 to 28, 2016. (Oral Presentation)

32. M. Zhao and A. B. Anderson, Predicting Reversible Potentials in Acid and Base from Self-Consistent Quantum Theory: H(ads) and OH(ads) Deposition on the Pt(111) Electrode, Phys Chem. Lett. 7, 711-714 (2016). F. Santiago; M. M. Brister; C. E. Crespo-Hernández, “Investigation of Ancestral RNA with Computational Chemistry”, ACS Seed Program, Case Western Reserve University, Cleveland, Ohio, July 29, 2016.(Poster Presentation)

33. M. Zhao, L. Yu, R. Akolkar and A. B. Anderson, Mechanism of Electroless Copper Deposition from [CullEDTA]1- Complexes Using Aldehyde-Based Reductants, J. Phys. Chem. C 120, 24789-24793 (2016).

34. Ling-yi Huang and Walter R. L. Lambrecht, Electronic band structure trends of perovskite halides: Beyond Pb and Sn to Ge and Si, Phys. Rev. B 93, 195211 (2016) 

35. Atchara Punya Jaroenjittichai and Walter R. L. Lambrecht, Electronic band structure of Mg−IV−N2 compounds in the quasiparticle-self-consistent GW approximation, Phys. Rev. B 94, 125201 (2016) 

36. Ling-yi Huang and Walter R. L. Lambrecht, Vibrational spectra and nonlinear optical coefficients of rhombohedral CsGeX3 halide compounds with X=I, Br, Cl, Phys. Rev. B 94, 115202 (2016)

37. Aron Lindberg, Nicholas Berente, James Gaskin, Kalle Lyytinen (2016) Coordinating Interdependencies in Online Communities: A Study of an Open Source Software Project. Information Systems Research Published online in Articles in Advance 02 Dec 2016

38. R. DiScipio, C. E. Crespo-Hernández, “Early Events in the Absorption of Ultraviolet Light by Pterin Biomolecules,” Cleveland Section of the American Chemical Society: Meeting in Miniature, Case Western Reserve University, Cleveland, Ohio, March 7, 2016. (O)

39. R. DiScipio, C. E. Crespo-Hernández, “Early Events in the Absorption of Ultraviolet Light by Pterin Biomolecules,” Research ShowCASE: Poster session, Case Western Reserve University, Cleveland, Ohio, April 15, 2016. (P) 

40. D. Taylor*, R. DiScipio, C. E. Crespo-Hernández, “Species and Excited-State Characterization of Pterin,” Project SEED: Poster Session, Case Western Reserve University, July 29, 2016. * Participated as a High School Student. (P)

41. Jiankui Yuan, Simon S. Lo, Yiran Zheng, Jason W. Sohn, Andrew E. Sloan, Rodney Ellis, Mitchell Machtay, Barry Wessels, Development of a Monte Carlo model for treatment planning dose verification of the Leksell Gamma Knife Perfexion radiosurgery system, Journal of Applied Clinical Medical Physics, (2016) 17, 6196.

42. H. Zaid, M. H. Berger, D. Jalabert, M. Walls, R. Akrobetu, I. Fongkaew, W. R. L. Lambrecht, N. J. Goble, X. P. A. Gao, P. Berger & A. Sehirlioglu, Atomic-resolved depth profile of strain and cation intermixing around LaAlO3/SrTiO3 interfaces, Scientific Reports 6, Article number: 28118 (2016), doi:10.1038/srep28118

43. John T. Giblin, Jr., James B. Mertens, and Glenn D. Starkman, Departures from the Friedmann-Lemaitre-Robertston-Walker Cosmological Model in an Inhomogeneous Universe: A Numerical Examination, Phys. Rev. Lett. 116, 251301 – Published 24 June 2016

44. James B. Mertens, John T. Giblin, Jr., and Glenn D. Starkman, Integration of inhomogeneous cosmological spacetimes in the BSSN formalism, Phys. Rev. D 93, 124059 – Published 24 June 2016

45. Zhao, X., Liao, Y.-T. T., Johnston, M.C., T’ien, J.S., Ferkul, P.V. and Olson, S.L., Concurrent flame growth, spread and quenching over composite fabric samples in low speed purely forced flow in microgravity, to be presented at the Thirty-Six International Symposium on Combustion, July 31-August 5, 2016, Seoul, Korea

46. In vitro direct and in vivo indirect evidence for the existence of water monomers and the need of chaotic modeling to understand their role in life processes. 9th Chaos Modeling and Simulation International Conference, May 23-26, 2016.

47. Endo, M. (2016). Numerical modeling of flame spread over spherical solid fuel under low speed flow in microgravity: Model development and comparison to space flight experiments. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

48. Pathway analysis by randomization incorporating structure-PARIS: an update. Butkiewicz M, Cooke Bailey JN, Frase A, Dudek S, Yaspan BL, Ritchie MD, Pendergrass SA, Haines JL. Bioinformatics. 2016 Mar 7. pii: btw130. [Epub ahead of print] PMID: 27153576

49. Genetic Association Analysis of Drusen Progression. Hoffman JD, van Grinsven MJ, Li C, Brantley M Jr, McGrath J, Agarwal A, Scott WK, Schwartz SG, Kovach J, Pericak-Vance M, Sanchez CI, Haines JL. Invest Ophthalmol Vis Sci. 2016 Apr 1;57(4):2225-31. doi: 10.1167/iovs.15-18571. PMID: 27116550

50. The Application of Genetic Risk Scores in Age-Related Macular Degeneration: A Review. Cooke Bailey JN, Hoffman JD, Sardell RJ, Scott WK, Pericak-Vance MA, Haines JL. J Clin Med. 2016 Mar 4;5(3). pii: E31. doi: 10.3390/jcm5030031. Review. PMID: 26959068 Free PMC Article 

51. Genome-wide association analysis identifies TXNRD2, ATXN2 and FOXC1 as susceptibility loci for primary open-angle glaucoma. Bailey JN, Loomis SJ, Kang JH, Allingham RR, Gharahkhani P, Khor CC, Burdon KP, Aschard H, Chasman DI, Igo RP Jr, Hysi PG, Glastonbury CA, Ashley-Koch A, Brilliant M, Brown AA, Budenz DL, Buil A, Cheng CY, Choi H, Christen WG, Curhan G, De Vivo I, Fingert JH, Foster PJ, Fuchs C, Gaasterland D, Gaasterland T, Hewitt AW, Hu F, Hunter DJ, Khawaja AP, Lee RK, Li Z, Lichter PR, Mackey DA, McGuffin P, Mitchell P, Moroi SE, Perera SA, Pepper KW, Qi Q, Realini T, Richards JE, Ridker PM, Rimm E, Ritch R, Ritchie M, Schuman JS, Scott WK, Singh K, Sit AJ, Song YE, Tamimi RM, Topouzis F, Viswanathan AC, Verma SS, Vollrath D, Wang JJ, Weisschuh N, Wissinger B, Wollstein G, Wong TY, Yaspan BL, Zack DJ, Zhang K, Study EN; ANZRAG Consortium, Weinreb RN, Pericak-Vance MA, Small K, Hammond CJ, Aung T, Liu Y, Vithana EN, MacGregor S, Craig JE, Kraft P, Howell G, Hauser MA, Pasquale LR, Haines JL, Wiggs JL. Nat Genet. 2016 Feb;48(2):189-94. doi: 10.1038/ng.3482. Epub 2016 Jan 11. PMID: 26752265 

52. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Fritsche LG, Igl W, Bailey JN, Grassmann F, Sengupta S, Bragg-Gresham JL, Burdon KP, Hebbring SJ, Wen C, Gorski M, Kim IK, Cho D, Zack D, Souied E, Scholl HP, Bala E, Lee KE, Hunter DJ, Sardell RJ, Mitchell P, Merriam JE, Cipriani V, Hoffman JD, Schick T, Lechanteur YT, Guymer RH, Johnson MP, Jiang Y, Stanton CM, Buitendijk GH, Zhan X, Kwong AM, Boleda A, Brooks M, Gieser L, Ratnapriya R, Branham KE, Foerster JR, Heckenlively JR, Othman MI, Vote BJ, Liang HH, Souzeau E, McAllister IL, Isaacs T, Hall J, Lake S, Mackey DA, Constable IJ, Craig JE, Kitchner TE, Yang Z, Su Z, Luo H, Chen D, Ouyang H, Flagg K, Lin D, Mao G, Ferreyra H, Stark K, von Strachwitz CN, Wolf A, Brandl C, Rudolph G, Olden M, Morrison MA, Morgan DJ, Schu M, Ahn J, Silvestri G, Tsironi EE, Park KH, Farrer LA, Orlin A, Brucker A, Li M, Curcio CA, Mohand-Saïd S, Sahel JA, Audo I, Benchaboune M, Cree AJ, Rennie CA, Goverdhan SV, Grunin M, Hagbi-Levi S, Campochiaro P, Katsanis N, Holz FG, Blond F, Blanché H, Deleuze JF, Igo RP Jr, Truitt B, Peachey NS, Meuer SM, Myers CE, Moore EL, Klein R, Hauser MA, Postel EA, Courtenay MD, Schwartz SG, Kovach JL, Scott WK, Liew G, Tan AG, Gopinath B, Merriam JC, Smith RT, Khan JC, Shahid H, Moore AT, McGrath JA, Laux R, Brantley MA Jr, Agarwal A, Ersoy L, Caramoy A, Langmann T, Saksens NT, de Jong EK, Hoyng CB, Cain MS, Richardson AJ, Martin TM, Blangero J, Weeks DE, Dhillon B, van Duijn CM, Doheny KF, Romm J, Klaver CC, Hayward C, Gorin MB, Klein ML, Baird PN, den Hollander AI, Fauser S, Yates JR, Allikmets R, Wang JJ, Schaumberg DA, Klein BE, Hagstrom SA, Chowers I, Lotery AJ, Léveillard T, Zhang K, Brilliant MH, Hewitt AW, Swaroop A, Chew EY, Pericak-Vance MA, DeAngelis M, Stambolian D, Haines JL, Iyengar SK, Weber BH, Abecasis GR, Heid IM. Nat Genet. 2016 Feb;48(2):134-43. doi: 10.1038/ng.3448. Epub 2015 Dec 21. PMID: 26691988

53. LICONG CUI, SHIQIANG TAO, GUO-QIANG ZHANG, Biomedical Ontology Quality Assurance Using a Big Data Approach, ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10 Issue 4, May 2016. [link]

54. Howell B and McIntyre CC, Analyzing the tradeoff between electrical complexity and accuracy in patient-specific computational models of deep brain stimulation, J Neural Eng. 2016 May 11;13(3):036023.

55. Murphy BA, Miller JP, Gunalan K, and Ajiboye AB, Contributions of Subsurface Cortical Modulations to Discrimination of Executed and Imagined Grasp Forces through Stereoelectroencephalography, PLoS One. 2016 Mar 10;11(3):e0150359. doi: 10.1371/journal.pone.0150359. eCollection 2016.

56. Brister, M. M.; Pollum, M.; Crespo-Hernández, C. E. "Photochemical Etiology of Promising Ancestors of the RNA Nucleobases", Phys. Chem. Chem. Phys. 2016, DOI: 10.1039/C6CP00639F. Invited Manuscript; submitted as part of the themed issue on the "Prebiotic Chemistry and the Molecular Origins of Life". 

57. M. M. Brister; C. E. Crespo-Hernández, “Photochemistry of Promising Ancestral Nucleobases” ACS Meeting-in Miniature, Case Western Reserve University, Cleveland, Ohio, March 4, 2016.

58. Tong D, Yang S, Lu L (2016) "Accurate optimization of amino acid form factors for computing small-angle X-ray scattering intensity,"

59. Accepted. Journal of Applied Crystallography Meng Zhao and Alfred B. Anderson, Predicting Reversible Potentials in Acid and Base from Self-Consistent Quantum Theory: H(ads) and OH(ads) Deposition on the Pt(111) Electrode, Phys Chem. Lett. 7, 711-714 (2016).

60. Song L, Ravikumar KM, Huang W, Meng. J, Lu L, and Yang S (2016)" SAXS-driven computational modeling of biomolecular complexes," The Wiley Press (Invited and in press)

61. Kuynh KW, Cohen MR, Jiang J, Samanta A, Lodowski DT, Zhou ZH, Moiseenkova-Bell VY, Structure of the full-length TRPV2 channel by cryo-EM, Nat Commun. 2016 Mar 29;7:11130. doi: 10.1038/ncomms11130

62. Stetson LC, Dazard JE, Barnholtz-Sloan JS. Protein markers predict survival in glioma patients. Mol Cell Proteomics. 2016 May 3. pii: mcp.M116.060657. [Epub ahead of print] PubMed PMID: 27143410.

63. Sun X, Elston RC, Barnholtz-Sloan JS, Falk GW, Grady WM, Faulx A, Mittal SK, Canto M, Shaheen NJ, Wang JS, Iyer PG, Abrams JA, Tian YD, Willis JE, Guda K, Markowitz SD, Chandar A, Warfe JM, Brock W, Chak A. Predicting Barrett's esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm. Cancer Epidemiol Biomarkers

64. Prev. 2016 Feb 29. pii: cebp.0832.2015. [Epub ahead of print] PubMed PMID: 26929243.

65. Diaz D.A., Dazard J-E., Rao J.S. Unsupervised Bump Hunting Using Principal Components. In: Ahmed SE, editor. Big and Complex Data Analysis: Methodologies and Applications. Contributions to Statistics, vol.Edited Refereed Volume. Cham Heidelberg New York: Springer (2016 in press). PMCID: pending. 

66. Dazard J-E., Choe M., LeBlanc M., Rao J.S. Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods. Statistical Analysis and Data Mining (2016) 9(1):12-42. PMCID: PMC4809437.

67. Mark Hagemann, Churna Bhandari,  and Walter R.L. Lambrecht, Vibrational modes in the Pmc21 structure of ZnGeN2, Solid State Communications, Volume 233, May 2016, Pages 46–49.

68. Mehta BB, Ma D, Griswold MA: Image Registration and Robust Fitting for Motion Insensitive Magnetic Resonance Fingerprinting. International Society of Magnetic Resonance in Medicine 24th Scientific Meeting, Singapore, 2016.

69. Mehta BB, Ma D, Griswold MA: Reconstruction algorithm for Motion Insensitive Magnetic Resonance Fingerprinting (MRF). International Society of Magnetic Resonance in Medicine Workshop on Data Sampling and Image Reconstruction,Sedona, AZ, USA, 2016. 

70.  Sparks, R, Madabhushi, A, “Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images”, Nature Scientific Reports, In Press. 

71. Janowczyk, A, Basavanhally, A, Madabhushi, A, “Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology”, Computerized Medical Imaging and Graphics, In Press. 

72. Janowczyk, A, Madabhushi, A, “Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases”, Journal of Pathology Informatics, In Press. 

73. Toth, R, Sperling, D, Madabhushi, A, “Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings”, PLOS One, In Press.

74.  Antunes, J, Viswanath, S, Rusu, M, Valls, L, Hoimes, C, Avril, N, Madabhushi, A, “Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study”, Translational Oncology, 2016 Apr;9(2):155-62. doi: 10.1016/j.tranon.2016.01.008. (PMID:27084432)

75.  Xu, J, Luo, X, Wang, G, Gilmore, H, Madabhushi, A,”A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images”, Neurocomputing, vol. 191, pp. 214-223. 

76. Janowczyk, A, Doyle, S, Gilmore, H, Madabhushi, A, “A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images”, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, In Press. 

77. Bhargava, R and Madabhushi, A, “Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology”, Annual Reviews of Biomedical Engineering 2016, In Press. 

78. Wan, T, Bloch, BN, Plecha, D, Thompson, CL, Gilmore, H, Jaffe, C, Harris, L, and Madabhushi, A, "A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting Oncotype DX Risk Scores", Nature Scientific Reports, 2016 Feb 18;6:21394. doi: 10.1038/srep21394 (PMID:26887643) 

79. Mark Turner, “Cyberinfrastructure for the study of multimodal communication—language, gesture, art.” Cyberinfrastructure Conference. Case Western Reserve University. 11 May 2016.

80. Mark Turner, “Hands-on tutorial on new tools from the Distributed Little Red Hen Lab.” International Workshop on Multimodal Construction Grammar. University of Osnabrück, 11 March 2016. 

81. Mark Turner, “Cyber & statistical tools for the study of speech gesture, iconography, and other systems of visual communication.” International Workshop on Multimodal Construction Grammar. University of Osnabrück, 10 March 2016.

82. Mark Turner, “New Big Data Tools for Gesture Research.” Free University, Berlin. 4 March 2016.

83. Mark Turner, “Red Hen Methods for Film Research.” Cinepoetics Institute, Free University, Berlin. 4 March 2016.  INVITED 

84. M. D. Glidden et al., “Application of Engineered Viral Nanoparticles in Materials and Medicine,” in Chemoselective and Bioorthogonal Ligation Reactions: Concepts and Applications, Wiley-VCH, 2016

85. Yu Wang, Timothy J. Peshek, Yang Hu, Mohamed Elseaiti, and Roger H. French, “Massive Data Analytics from Real-World Power Plants Cross- Correlated with Laboratory Applied Aging Conditions,” presented at the NREL PVModule Reliability Workshop 2016, Corolado, 23-Feb-2016

86. Yu Wang, Laura Bruckman, and R. H. French, “Backsheets Correlation of Long-Term Field Reliability with Accelerated Laboratary Testing,” presented at the NREL PV Module Reliabilty Workshop 2016, Corolado, 24-Feb-2016.

87. S. Yiyang, L. Donghui, B. Laura, and F. Roger, “Data Analysis of FTIR Method in the Study of Lifetime and Degradation Science of Polyacrylate Coatings,” presented at the Summer Undergraduate Research in Energy and Sustainability, Cleveland, OH, USA, Aug-2016

88. Yang Hu, Erdmut Schnabel, Michael Kӧhl, Roger H. French, and Timothy J. Peshek, “Detecting heterogeneity in PV modules from massive real-world ‘step’ I-V curves: A machine learning approach,” presented at the IEEE PVSC 2016, Portland OR, Jun-2016

89. T. Noah, L. Donghui, M. Emily, B. Laura, and F. Roger, “A Data Science Approach to Studying the Lifetime and Degradation Science of Polyacrylate Coatings,” presented at the Summer Undergraduate Research in Energy and Sustainability, Cleveland, OH, USA, Aug-2016

90. Nicholas R. Wheeler et al., “Lifetime and Degradation Science Study of EVA Encapsulant in PV Modules Under Damp Heat Exposure Combining Confocal Raman, EL, and I-V Curve Measurements,” presented at the Polymer Initiative of Northeast Ohio (PiNO) Poster Competition, Spring-2016

91. Nicholas R. Wheeler et al., “Lifetime and Degradation Science: Network Modeling the Degradation of Materials Technology Systems,” AMS Cleveland Chapter Meeting, 21-Oct-2016.

92. Nicholas R. Wheeler et al., “Combining Multiple Data Types for Lifetime and Degradation Science of PV Modules,” Engineering Week Banquet, 25-Feb-2016

93. Mohammad A. Hossain and Ethan M. Pickering, “Virtual Energy Diagnostics for Buildings,” Engineering Week Banquet, Case Western Reserve University, 25-Feb-2016

94. Li Donghui, Morris Emily, Tietsort Noah, Sheng Yiyang, Bruckman Laura, and French Roger, “The durability studies on waterborne acrylic coatings,” presented at the THE CLEVELAND COATINGS SOCIETY TECHNICAL SYMPOSIUM, Cleveland, OH, USA, 07-Jun-2016.

95. Li Donghui, Morris Emily, Sahlani Marc, Tisort Noah, Bruckman Laura S., and French Roger H., “Lifetime and Degradation Science Studies of Exterior Coatings,” presented at the Service Life Prediction of Polymeric Materials: Over the Horizon, Santa Fe, NM, USA, 23-Mar-2016

96. Ina T. Martin, Tricia M. Oyster, Lorelle M Mansfield, Roger H French, and Timothy J Peshek, “Interfacial Modifiers for Enhanced Stability and Reduced Degradation of Cu(In,Ga)Se2 Devices,” presented at the IEEE PVSC 2016, Portland OR, Jun-2016

97. D. A. Gordon, “Degradation Science of Fly Ash Filled Polyurethane for Outdoor Applications,” presented at the Case Western Reserve University Engineers Week Banquet, Cleveland, OH, 25-Feb-2016

98. Ethan M. Pickering, Mohammad A. Hossain, and Gabrielle A. Zadina, “Data Analytics for Building Energy Audits,” Research ShowCASE 2016, 15-Apr-2016.

99. Ethan M. Pickering and Mohammad A. Hossain, “Data Analytics for Building Energy Disaggregation,” SPE Visit 2016, 14-Mar-2016

100. Ethan M. Pickering, “EDIFES v0.4: Scalable Data Analytics for Commercial Building Virtual Energy Audits,” Case Western Reserve University, 06-Jun-2016.

101. L. Donghui, T. Noah, J. Adam, B. Laura, and F. Roger, “The Durability Study and Predictive Models of the Waterborne Acrylic Coating,” presented at the IEEE EnergyTech 2016, Power & Energy Forum and Exposition, Cleveland, Ohio, 28-Nov-2016

102. Devin A. Gordon et al., “Characterizing the Weathering Induced Haze Formation and Gloss Loss of Poly(ethylene-terephthalate) via MaPd:RTS Spectroscopy,” presented at the SPIE Optics + Photonics for Sustainable Energy, San Diego, CA, USA, 29-Aug-2016.

103. “Crafting a Minor to Produce T-Shaped Graduates,” presented at the T-SUMMIT 2016: Transformational Approaches to Creating T-shaped Professionals, National Academies, Washington DC, 21-Mar-2016 

104. David C. Miller et al., “Degradation in PV Encapsulant Strength of Attachment:  An Interlaboratory Study Towards a Climate-Specific Test,” presented at the IEEE PVSC 2016, Portland OR, Jun-2016

105. L. S. Bruckman et al., “Cross-correlation of backsheet degradation between real-world exposed modules and accelerated exposures of backsheet materials (Conference Presentation),” 02-Nov-2016

106. Arash Khalilnejad, “Data Analytics for Building Energy Audits,” presented at the American Society of Material (ASM), Cleveland, Ohio, 21-Nov-2016

107. Amit Verma, Mohamed Elsaeiti, Laura  Bruckman, Roger French, et al., “Statistical analysis of heritage data of 9Cr-Steels, using a robust, open-source, data analytics design approach,” presented at the MS&T 16, Salt Lake City, Utah, 23-Oct-2016.

108. Amit Verma, Roger H. French, Jennifer W. Carter, and Steven Claves, “Data-analytics approach for studying Structure-Property relationships in gradient aluminum composite,” presented at the MS&T 16, Salt Lake City, Utah, 23-Oct-2016

109. D. C. Miller et al., “Degradation in PV encapsulant strength of attachment: An interlaboratory study towards a climate-specific test,” in 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), 2016, pp. 0095-0100.

110. I. T. Martin et al., “Interfacial modifiers for enhanced stability and reduced degradation of Cu(In, Ga)Se2 devices,” in 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), 2016, pp. 1489–1493

111. Y. Hu, E. Schnabel, M. Kohl, R. H. French, and T. J. Peshek, “Detecting heterogeneity in PV modules from massive real-world ‘step’ I-V curves: A machine learning approach,” in 2016 IEEE 43rd Photovoltaic Specialists Conference (PVSC), 2016, pp. 0279–0285

112. Devin A. Gordon et al., “Characterizing the weathering induced haze formation and gloss loss of poly(ethylene-terephthalate) via MaPd:RTS spectroscopy,” 2016, vol. 9938, p. 99380I–99380I–12

113. L. S. Bruckman et al., “Cross-correlation of backsheet degradation between real-world exposed modules and accelerated exposures of backsheet materials (Conference Presentation),” 02-Nov-2016

114. Timothy J. Peshek et al., “Insights into metastability of photovoltaic materials at the mesoscale through massive I–V analytics,” Journal of Vacuum Science & Technology B, vol. 34, no. 5, p. 050801, Sep. 2016.

115. Selcuk Yasar et al., “X-ray characterization of mesophases of human telomeric G-quadruplexes and other DNA analogues,” Scientific Reports, vol. 6, p. 27079, Jun. 2016

116. S. Rafique et al., “Heteroepitaxy of N-type β-Ga2O3 thin films on sapphire substrate by low pressure chemical vapor deposition,” Applied Physics Letters, vol. 109, no. 13, p. 132103, Sep. 2016

117. Lokendra Poudel, Nicole F. Steinmetz, Roger H. French, V. Adrian Parsegian, Rudolf Podgornik, and Wai-Yim Ching, “Implication of the solvent effect, metal ions and topology in the electronic structure and hydrogen bonding of human telomeric G-quadruplex DNA,” Phys. Chem. Chem. Phys., Jul. 2016

118. Jay Eifler, Rudi Podgornik, Nicole F. Steinmetz, Roger H. French, V. Adrian Parsegian, and Wai-Yim Ching, “Charge distribution and hydrogen bonding of a collagen α2-chain in vacuum, hydrated, neutral, and charged structural models,” Int. J. Quantum Chem., p. n/a-n/a, Jan. 2016

119. J. S. Fada, N. R. Wheeler, D. Zabiyaka, N. Goel, T. J. Peshek, and R. H. French, “Democratizing an electroluminescence imaging apparatus and analytics project for widespread data acquisition in photovoltaic materials,” Review of Scientific Instruments, vol. 87, no. 8, p. 085109, Aug. 2016