Section 59

EurekaMag Full Text Articles Chapter 58,594


Basques, B.A.; Ibe, I.; Samuel, A.M.; Lukasiewicz, A.M.; Webb, M.L.; Bohl, D.D.; Grauer, J.N. 2017: Predicting Postoperative Morbidity and Readmission for Revision Posterior Lumbar Fusion. Clinical Spine Surgery 30(6): E770-E775
Sieberg, C.B.; Manganella, J.; Manalo, G.; Simons, L.E.; Hresko, M.T. 2017: Predicting Postsurgical Satisfaction in Adolescents with Idiopathic Scoliosis: the Role of Presurgical Functioning and Expectations. Journal of Pediatric Orthopedics 37(8): E548-E551
Bautista, I.J.; Chirosa, I.J.; Tamayo, I.M.ín.; González, A.és.; Robinson, J.E.; Chirosa, L.J.; Robertson, R.J. 2014: Predicting Power Output of Upper Body using the OMNI-RES Scale. Journal of Human Kinetics 44: 161-169
Mehra, L.K.; Cowger, C.; Gross, K.; Ojiambo, P.S. 2016: Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models. Frontiers in Plant Science 7: 390
Tanaka, N.; Ohno, Y.; Hori, M.; Utada, M.; Ito, K.; Suzuki, T.; Furukawa, F. 2015: Predicting Preoperative Hemodynamic Changes Using the Visual Analog Scale. Journal of Perianesthesia Nursing: Official Journal of the American Society of Perianesthesia Nurses 30(6): 460-467
Miller, N.; Frankenfield, D.; Lehman, E.; Maguire, M.; Schirm, V. 2016: Predicting Pressure Ulcer Development in Clinical Practice: Evaluation of Braden Scale Scores and Nutrition Parameters. Journal of Wound Ostomy and Continence Nursing: Official Publication of Wound Ostomy and Continence Nurses Society 43(2): 133-139
Tucker, C.M.; Berrien, K.; Menard, M.Kathryn.; Herring, A.H.; Daniels, J.; Rowley, D.L.; Halpern, C.Tucker. 2015: Predicting Preterm Birth Among Women Screened by North Carolina's Pregnancy Medical Home Program. Maternal and Child Health Journal 19(11): 2438-2452
Georgiou, H.M.; Di Quinzio, M.K.W.; Permezel, M.; Brennecke, S.P. 2015: Predicting Preterm Labour: Current Status and Future Prospects. Disease Markers 2015: 435014
Eggers, S.M.; Mathews, C.; Aarø, L.E.; McClinton-Appollis, T.; Bos, A.E.R.; de Vries, H. 2017: Predicting Primary and Secondary Abstinence Among Adolescent Boys and Girls in the Western Cape, South Africa. Aids and Behavior 21(5): 1417-1428
Dyrba, M.; Barkhof, F.; Fellgiebel, A.; Filippi, M.; Hausner, L.; Hauenstein, K.; Kirste, T.; Teipel, S.J. 2015: Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging 25(5): 738-747
Lauer, M.S.; Danthi, N.S.; Kaltman, J.; Wu, C. 2015: Predicting Productivity Returns on Investment: Thirty Years of Peer Review, Grant Funding, and Publication of Highly Cited Papers at the National Heart, Lung, and Blood Institute. Circulation research 117(3): 239-243
Condon, J.V.; Barefield, A.C. 2016: Predicting Professional Examination Outcomes: a Case of the Registered Health Information Administration Certification Examination. Journal of Allied Health 45(4): 267-273
Waters, D.D.; Arsenault, B.J. 2016: Predicting Prognosis in Acute Coronary Syndromes: Makeover time for TIMi and GRACE?. Canadian Journal of Cardiology 32(11): 1290-1293
Nabhan, C.; Raca, G.; Wang, Y.L. 2015: Predicting Prognosis in Chronic Lymphocytic Leukemia in the Contemporary Era. JAMA Oncology 1(7): 965-974
Der, S.; Zhu, C.-Q.; Brower, S.; Uihlein, A. 2015: Predicting Prognosis of Early-Stage Non-Small Cell Lung Cancer Using the GeneFx® Lung Signature. Plos Currents 7
Korolev, I.O.; Symonds, L.L.; Bozoki, A.C. 2016: Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. Plos one 11(2): E0138866
Kadatz, M.J.; Lee, E.S.; Levin, A. 2016: Predicting Progression in CKD: Perspectives and Precautions. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 67(5): 779-786
Cideciyan, A.V.; Swider, M.; Schwartz, S.B.; Stone, E.M.; Jacobson, S.G. 2015: Predicting Progression of ABCA4-Associated Retinal Degenerations Based on Longitudinal Measurements of the Leading Disease Front. Investigative Ophthalmology and Visual Science 56(10): 5946-5955
Tsai, C.; Latimer, A.A.; Yoo, J.S.; Studt, F.; Abild-Pedersen, F. 2015: Predicting Promoter-Induced Bond Activation on Solid Catalysts Using Elementary Bond Orders. Journal of Physical Chemistry Letters 6(18): 3670-3674
Zhang, K.; Shen, Y.; Zhang, X.; Ma, L.; Wang, H.; An, N.; Guo, A.; Ye, H. 2016: Predicting Prostate Biopsy Outcomes: a Preliminary Investigation on Screening with Ultrahigh B-Value Diffusion-Weighted Imaging as an Innovative Diagnostic Biomarker. Plos one 11(3): E0151176
Albitar, M.; Ma, W.; Lund, L.; Albitar, F.; Diep, K.; Fritsche, H.A.; Shore, N. 2016: Predicting Prostate Biopsy Results Using a Panel of Plasma and Urine Biomarkers Combined in a Scoring System. Journal of Cancer 7(3): 297-303
Jeffers, A.; Sochat, V.; Kattan, M.W.; Yu, C.; Melcon, E.; Yamoah, K.; Rebbeck, T.R.; Whittemore, A.S. 2017: Predicting Prostate Cancer Recurrence After Radical Prostatectomy. Prostate 77(3): 291-298
Yu, G.; Rangwala, H.; Domeniconi, C.; Zhang, G.; Zhang, Z. 2015: Predicting Protein Function Using Multiple Kernels. Ieee/Acm Transactions on Computational Biology and Bioinformatics 12(1): 219-233
Yu, G.; Fu, G.; Wang, J.; Zhu, H. 2016: Predicting Protein Function via Semantic Integration of Multiple Networks. Ieee/Acm Transactions on Computational Biology and Bioinformatics 13(2): 220-232
Deng, X.; Li, J.; Cheng, J. 2013: Predicting Protein Model Quality from Sequence Alignments by Support Vector Machines. Journal of Proteomics and Bioinformatics Suppl. 9
García-Jiménez, B.; Pons, T.; Sanchis, A.; Valencia, A. 2014: Predicting Protein Relationships to Human Pathways through a Relational Learning Approach Based on Simple Sequence Features. Ieee/Acm Transactions on Computational Biology and Bioinformatics 11(4): 753-765
Kandoi, G.; Leelananda, S.P.; Jernigan, R.L.; Sen, T.Z. 2017: Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information. Methods in Molecular Biology 1484: 35-44
Hu, J.; Li, Y.; Zhang, M.; Yang, X.; Shen, H-Bin.; Yu, D-Jun. 2017: Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs. Ieee/Acm Transactions on Computational Biology and Bioinformatics 14(6): 1389-1398
Kuo, T.-H.; Li, K.-B. 2016: Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids. International Journal of Molecular Sciences 17(11)
Sriwastava, B.K.; Basu, S.; Maulik, U. 2015: Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier. IEEE/ACM transactions on computational biology and bioinformatics 12(6): 1394-1404
Almeida, R.M.; Dell'Acqua, S.; Krippahl, L.; Moura, J.é J.G.; Pauleta, S.R. 2016: Predicting Protein-Protein Interactions Using BiGGER: Case Studies. Molecules 21(8)
Keskin, O.; Tuncbag, N.; Gursoy, A. 2016: Predicting Protein-Protein Interactions from the Molecular to the Proteome Level. Chemical Reviews 116(8): 4884-4909
Paschoal, D.; Guerra, C.F.; de Oliveira, M.A.L.; Ramalho, T.C.; Dos Santos, H.F. 2016: Predicting Pt-195 NMR chemical shift using new relativistic all-electron basis set. Journal of Computational Chemistry 37(26): 2360-2373
Heinzerling, L.; Hartmann, R.W.; Frotscher, M.; Neumann, D. 2010: Predicting Putative Inhibitors of 17β-HSD1. Molecular Informatics 29(10): 695-705
Herrera, S.; Reyes-Herrera, P.H.; Shank, T.M. 2015: Predicting RAD-seq Marker Numbers across the Eukaryotic Tree of Life. Genome Biology and Evolution 7(12): 3207-3225
Kerpedjiev, P.; Höner Zu Siederdissen, C.; Hofacker, I.L. 2015: Predicting RNA 3D structure using a coarse-grain helix-centered model. Rna 21(6): 1110-1121
Sakuraba, S.; Asai, K.; Kameda, T. 2015: Predicting RNA Duplex Dimerization Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations. Journal of Physical Chemistry Letters 6(21): 4348-4351
Lorenz, R.; Wolfinger, M.T.; Tanzer, A.; Hofacker, I.L. 2016: Predicting RNA secondary structures from sequence and probing data. Methods 103: 86-98
DiChiacchio, L.; Mathews, D.H. 2016: Predicting RNA-RNA Interactions Using RNAstructure. Methods in Molecular Biology 1490: 51-62
Mehmood, Q.; Sun, A.; Becker, N.; Higgins, J.; Marshall, A.; Le, L.W.; Vines, D.C.; McCloskey, P.; Ford, V.; Clarke, K.; Yap, M.; Bezjak, A.; Bissonnette, J.-P. 2016: Predicting Radiation Esophagitis Using 18F-FDG PET During Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer 11(2): 213-221
Van Oorschot, B.; Hovingh, S.; Dekker, A.; Stalpers, L.J.; Franken, N.A.P. 2016: Predicting Radiosensitivity with Gamma-H2AX Foci Assay after Single High-Dose-Rate and Pulsed Dose-Rate Ionizing Irradiation. Radiation Research 185(2): 190-198
Feng, Z.; Xu, Q.S.; Qin, L.Z.; Li, H.; Han, Z. 2017: Predicting Radiotherapy Necessity in Tongue Cancer Using Lymph Node Yield. Journal of Oral and Maxillofacial Surgery: Official Journal of the American Association of Oral and Maxillofacial Surgeons 75(5): 1062-1070
Chaudhuri, A.A.; Binkley, M.S.; Osmundson, E.C.; Alizadeh, A.A.; Diehn, M. 2015: Predicting Radiotherapy Responses and Treatment Outcomes Through Analysis of Circulating Tumor DNA. Seminars in Radiation Oncology 25(4): 305-312
Osman, S.L. 2016: Predicting Rape Victim Empathy Based on Rape Victimization and Acknowledgment Labeling. Violence Against Women 22(7): 767-779
AbdulHameed, M.D.M.; Ippolito, D.L.; Wallqvist, A. 2016: Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models. Chemical Research in Toxicology 29(10): 1729-1740
Messner, S.F.; Liu, J.; Zhao, Y. 2018: Predicting Re-Incarceration Status of Prisoners in Contemporary China: Applying Western Criminological Theories. International Journal of Offender Therapy and Comparative Criminology 62(4): 1018-1042
Zhao, M.; Anderson, A.B. 2016: Predicting Reaction Mechanisms and Potentials in Acid and Base from Self-Consistent Quantum Theory: H(ads) and OH(ads) Deposition on the Pt(111) Electrode. Journal of Physical Chemistry Letters 7(4): 711-714
Michaels, J.A.; Dann, B.; Intveld, R.W.; Scherberger, H.ör. 2015: Predicting Reaction time from the Neural State Space of the Premotor and Parietal Grasping Network. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 35(32): 11415-11432
Bigozzi, L.; Tarchi, C.; Caudek, C.; Pinto, G. 2016: Predicting Reading and Spelling Disorders: a 4-Year Prospective Cohort Study. Frontiers in Psychology 7: 337
Pinto, G.; Bigozzi, L.; Tarchi, C.; Vezzani, C.; Accorti Gamannossi, B. 2016: Predicting Reading, Spelling, and Mathematical Skills: a Longitudinal Study from Kindergarten Through first Grade. Psychological Reports 118(2): 413-440
Tabak, Y.P.; Sun, X.; Nunez, C.M.; Gupta, V.; Johannes, R.S. 2017: Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score. Medical Care 55(3): 267-275
Peterson, L.; Jamroz, M.; Kolinski, A.; Kihara, D. 2017: Predicting Real-Valued Protein Residue Fluctuation Using FlexPred. Methods in Molecular Biology 1484: 175-186
Tesoriero, A.J.; Terziotti, S.; Abrams, D.B. 2015: Predicting Redox Conditions in Groundwater at a Regional Scale. Environmental Science and Technology 49(16): 9657-9664
Lowe, J.A.; Crist, B.D.; Pfeiffer, F.; Carson, W.L. 2015: Predicting Reduction in Torsional Strength by Concentric/Eccentric RIA Reaming Normal and Osteoporotic Long Bones (Femurs). Journal of Orthopaedic Trauma 29(10): E371-E379
Mattsson, N.; Insel, P.S.; Donohue, M.; Jagust, W.; Sperling, R.; Aisen, P.; Weiner, M.W. 2015: Predicting Reduction of Cerebrospinal Fluid β-Amyloid 42 in Cognitively Healthy Controls. JAMA Neurology 72(5): 554-560
Almquist, Z.W.; Butts, C.T. 2015: Predicting Regional Self-identification from Spatial Network Models. Geographical Analysis 47(1): 50-72
Tyzack, J.D.; Hunt, P.A.; Segall, M.D. 2016: Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations. Journal of Chemical Information and Modeling 56(11): 2180-2193
Almutairi, K.M. 2016: Predicting Relationship of Smoking Behavior Among Male Saudi Arabian College Students Related to Their Religious Practice. Journal of religion and health 55(2): 469-479
Norouzi, J.; Yadollahpour, A.; Mirbagheri, S.A.; Mazdeh, M.M.; Hosseini, S.A. 2016: Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System. Computational and Mathematical Methods in Medicine 2016: 6080814
Meyer, A.; Woldu, S.L.; Weinberg, A.C.; Thoreson, G.R.; Pierorazio, P.; Matulay, J.T.; Benson, M.C.; DeCastro, G.J.; McKiernan, J.M. 2015: Predicting Renal Parenchymal Loss after Nephron Sparing Surgery. Journal of Urology 194(3): 658-663
Hawkins, N.J.; Fraaije, B.A. 2016: Predicting Resistance by Mutagenesis: Lessons from 45 Years of MBC Resistance. Frontiers in microbiology 7: 1814
Segal, O.; Barayev, E.; Nemet, A.Y.; Mimouni, M. 2016: Predicting Response of Exudative Age-Related Macular Degeneration to Bevacizumab Based on Spectralis Optical Coherence Tomography. Retina 36(2): 259-263
Santacana, Mí.; Arias, Bárbara.; Mitjans, M.; Bonillo, A.; Montoro, Mía.; Rosado, Sílvia.; Guillamat, R.; Vallès, Vç.; Pérez, Víctor.; Forero, C.G.; Fullana, M.A. 2016: Predicting Response Trajectories during Cognitive-Behavioural Therapy for Panic Disorder: No Association with the BDNF Gene or Childhood Maltreatment. Plos one 11(6): E0158224
Geeleher, P.; Loboda, A.; Lenkala, D.; Wang, F.; LaCroix, B.; Karovic, S.; Wang, J.; Nebozhyn, M.; Chisamore, M.; Hardwick, J.; Maitland, M.L.; Huang, R.S. 2015: Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics. Journal of the National Cancer Institute 107(11)
Zuiverloon, T.C.M.; Zwarthoff, E.C. 2016: Predicting Response to Intravesical Bacillus Calmette-Guérin Immunotherapy: Are we Moving Forward?. European Urology 69(2): 201-202
Ypsilantis, P.-P.; Siddique, M.; Sohn, H.-M.; Davies, A.; Cook, G.; Goh, V.; Montana, G. 2015: Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks. Plos one 10(9): E0137036
Gammaitoni, A.R.; Trudeau, J.J.; Radnovich, R.; Galer, B.S.; Jensen, M.P. 2015: Predicting Response to Subacromial Injections and Lidocaine/Tetracaine Patch from Pretreatment Pain Quality in Patients with Shoulder Impingement Syndrome. Pain Medicine 16(7): 1333-1340
Hill, D.A.; Lim, H.-W.; Kim, Y.H.; Ho, W.Y.; Foong, Y.H.; Nelson, V.L.; Nguyen, H.C.B.; Chegireddy, K.; Kim, J.; Habertheuer, A.; Vallabhajosyula, P.; Kambayashi, T.; Won, K.-J.; Lazar, M.A. 2018: Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue. Proceedings of the National Academy of Sciences of the United States of America 115(22): E5096-E5105
Zhang, J.; Zhang, Z.; Zhang, Y.; Wu, Y. 2018: Distinct magnetic resonance imaging features in a patient with novel RARS2 mutations: A case report and review of the literature. Experimental and Therapeutic Medicine 15(1): 1099-1104
Akbulut, V.; Weger, H. 2016: Predicting Responses to Bids for Sexual and Romantic Escalation in Cross-Sex Friendships. Journal of Social Psychology 156(1): 98-114
Tromberg, B.J.; Zhang, Z.; Leproux, A.ïs.; O'Sullivan, T.D.; Cerussi, A.E.; Carpenter, P.M.; Mehta, R.S.; Roblyer, D.; Yang, W.; Paulsen, K.D.; Pogue, B.W.; Jiang, S.; Kaufman, P.A.; Yodh, A.G.; Chung, S.H.; Schnall, M.; Snyder, B.S.; Hylton, N.; Boas, D.A.; Carp, S.A.; Isakoff, S.J.; Mankoff, D. 2016: Predicting Responses to Neoadjuvant Chemotherapy in Breast Cancer: ACRIN 6691 Trial of Diffuse Optical Spectroscopic Imaging. Cancer Research 76(20): 5933-5944
Nozaki, T.; Tasaki, A.; Horiuchi, S.; Ochi, J.; Starkey, J.; Hara, T.; Saida, Y.; Yoshioka, H. 2016: Predicting Retear after Repair of Full-Thickness Rotator Cuff Tear: Two-Point Dixon MR Imaging Quantification of Fatty Muscle Degeneration-Initial Experience with 1-year Follow-up. Radiology 280(2): 500-509
Martin, D.M.; Gálvez, V.òn.; Loo, C.K. 2015: Predicting Retrograde Autobiographical Memory Changes Following Electroconvulsive Therapy: Relationships between Individual, Treatment, and Early Clinical Factors. International Journal of Neuropsychopharmacology 18(12)
Kim, E.; Hou, J.; Han, J.Yeob.; Himelboim, I. 2016: Predicting Retweeting Behavior on Breast Cancer Social Networks: Network and Content Characteristics. Journal of Health Communication 21(4): 479-486
Lancellotti, P.; Moonen, M.; Jerusalem, G. 2016: Predicting Reversibility of Anticancer Drugs-Related Cardiac Dysfunction: a new Piece to the Routine use of Deformation Imaging. Echocardiography 33(4): 504-509
Gaspar, M.P.; Kane, P.M.; Putthiwara, D.; Jacoby, S.M.; Osterman, A.Lee. 2016: Predicting Revision Following In Situ Ulnar Nerve Decompression for Patients With Idiopathic Cubital Tunnel Syndrome. Journal of Hand Surgery 41(3): 427-435
Guo, H. 2017: Predicting Rights-Only Score Distributions from Data Collected Under Formula Score Instructions. Psychometrika 82(1): 1-16
Hachamovitch, R.; Nutter, B.; Menon, V.; Cerqueira, M.D. 2015: Predicting Risk Versus Predicting Potential Survival Benefit Using 123I-mIBG Imaging in Patients with Systolic Dysfunction Eligible for Implantable Cardiac Defibrillator Implantation: Analysis of Data from the Prospective ADMIRE-HF Study. CIRCULATION. Cardiovascular Imaging 8(12)
Seimiya, H. 2015: Predicting Risk at the End of the End: Telomere G-tail as a Biomarker. Ebiomedicine 2(8): 804-805
Brackman, E.H.; Morris, B.W.; Andover, M.S. 2016: Predicting Risk for Suicide: A Preliminary Examination of Non-Suicidal Self-Injury and the Acquired Capability Construct in a College Sample. Archives of suicide research: official journal of the International Academy for Suicide Research 20(4): 663-676
Ramirez, M.E.; Bargman, J. 2015: Predicting Risk in Peritoneal Dialysis: Is Membrane Biology Destiny?. Clinical Journal of the American Society of Nephrology: Cjasn 10(11): 1895-1896
Odeh, R.; Noone, D.; Bowlin, P.R.; Braga, L.H.P.; Lorenzo, A.J. 2016: Predicting Risk of Chronic Kidney Disease in Infants and Young Children at Diagnosis of Posterior Urethral Valves: Initial Ultrasound Kidney Characteristics and Validation of Parenchymal Area as Forecasters of Renal Reserve. Journal of Urology 196(3): 862-868
Ross, S.S. 2016: Predicting Risk of Chronic Renal Disease in Children with Vesicoureteral Reflux-How Good or Bad are we Doing?. Journal of Urology 195(4 Part 1): 829-830
Harrison, S.L.; de Craen, A.J.M.; Kerse, N.; Teh, R.; Granic, A.; Davies, K.; Wesnes, K.A.; den Elzen, W.P.J.; Gussekloo, J.; Kirkwood, T.B.L.; Robinson, L.; Jagger, C.; Siervo, M.; Stephan, B.C.M. 2017: Predicting Risk of Cognitive Decline in very Old Adults Using Three Models: the Framingham Stroke Risk Profile; the Cardiovascular Risk Factors, Aging, and Dementia Model; and Oxi-Inflammatory Biomarkers. Journal of the American Geriatrics Society 65(2): 381-389
Tonolini, M.; Rigiroli, F.; Scorza, D. 2016: Predicting Risk of Contrast-Induced Nephrotoxicity in Hospitalized Patients Undergoing Computed Tomography Using the Mehran Stratification Score. Current Problems in Diagnostic Radiology 45(3): 238-239
Lo Re, V.; Kallan, M.J.; Tate, J.P.; Lim, J.K.; Goetz, M.B.; Klein, M.B.; Rimland, D.; Rodriguez-Barradas, M.C.; Butt, A.A.; Gibert, C.L.; Brown, S.T.; Park, L.S.; Dubrow, R.; Reddy, K.R.; Kostman, J.R.; Justice, A.C.; Localio, A.R. 2015: Predicting Risk of End-Stage Liver Disease in Antiretroviral-Treated Human Immunodeficiency Virus/Hepatitis C Virus-Coinfected Patients. Open Forum Infectious Diseases 2(3): Ofv109
Palraj, B.R.; Baddour, L.M.; Hess, E.P.; Steckelberg, J.M.; Wilson, W.R.; Lahr, B.D.; Sohail, M.R. 2015: Predicting Risk of Endocarditis Using a Clinical Tool (PREDICT): Scoring System to Guide use of Echocardiography in the Management of Staphylococcus aureus Bacteremia. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America 61(1): 18-28
Mehran, R.; Baber, U. 2016: Predicting Risk of Ischemic or Bleeding Events After Percutaneous Coronary Intervention. JAMA Cardiology 1(6): 731-732
Gracitelli, C.P.B.; Tatham, A.J.; Boer, E.R.; Abe, R.Y.; Diniz-Filho, A.; Rosen, P.N.; Medeiros, F.A. 2015: Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: a Longitudinal Study. Plos one 10(10): E0138288
Karmakar, C.; Luo, W.; Tran, T.; Berk, M.; Venkatesh, S. 2016: Predicting Risk of Suicide Attempt Using History of Physical Illnesses from Electronic Medical Records. Jmir Mental Health 3(3): E19
Jelovsek, J.Eric.; Hill, A.Jolyn.; Chagin, K.M.; Kattan, M.W.; Barber, M.D. 2016: Predicting Risk of Urinary Incontinence and Adverse Events After Midurethral Sling Surgery in Women. Obstetrics and Gynecology 127(2): 330-340
Anderson, S.L.; Zheng, Y.; McMahon, R.J. 2017: Predicting Risky Sexual Behavior: the Unique and Interactive Roles of Childhood Conduct Disorder Symptoms and Callous-Unemotional Traits. Journal of Abnormal Child Psychology 45(6): 1147-1156
Askitas, N. 2016: Predicting Road Conditions with Internet Search. Plos one 11(8): E0162080
Shannon, C.K.; Price, S.S.; Jackson, J. 2016: Predicting Rural Practice and Service to Indigent Patients: Survey of Dental Students Before and After Rural Community Rotations. Journal of Dental Education 80(10): 1180-1187
Krauskopf, A.L.; Knippel, A.J.; Verde, P.E.; Kozlowski, P. 2016: Predicting SGA neonates using first-trimester screening: influence of previous pregnancy's birthweight and PAPP-A MoM. Journal of Maternal-Fetal and Neonatal Medicine: the Official Journal of the European Association of Perinatal Medicine the Federation of Asia and Oceania Perinatal Societies the International Society of Perinatal Obstetricians 29(18): 2962-2967
Pandey, P.; Cao, W.; Wang, Y.; Vaddella, V. 2016: Predicting Salmonella Typhimurium reductions in poultry ground carcasses. Poultry Science 95(11): 2640-2646
Lee, M.; Huang, Y.; Chong, H.C.; Ning, Y.; Lo, N.N.; Yeo, S.J. 2016: Predicting Satisfaction for Unicompartmental Knee Arthroplasty Patients in an Asian Population. Journal of Arthroplasty 31(8): 1706-1710
Kiware, S.S.; Corliss, G.; Merrill, S.; Lwetoijera, D.W.; Devine, G.; Majambere, S.; Killeen, G.F. 2015: Predicting Scenarios for Successful Autodissemination of Pyriproxyfen by Malaria Vectors from Their Resting Sites to Aquatic Habitats; Description and Simulation Analysis of a Field-Parameterizable Model. Plos one 10(7): E0131835
Shepperd, J.A.; Emanuel, A.S.; Howell, J.L.; Logan, H.L. 2015: Predicting Scheduling and Attending for an Oral Cancer Examination. Annals of Behavioral Medicine: a Publication of the Society of Behavioral Medicine 49(6): 828-838
Keefe, R.S.E.; Reichenberg, A. 2016: Predicting Schizophrenia. JAMA Psychiatry 73(5): 441-442
Hagger, M.S.; Hardcastle, S.J.; Hingley, C.; Strickland, E.; Pang, J.; Watts, G.F. 2016: Predicting Self-Management Behaviors in Familial Hypercholesterolemia Using an Integrated Theoretical Model: the Impact of Beliefs about Illnesses and Beliefs about Behaviors. International Journal of Behavioral Medicine 23(3): 282-294
Böhme, S.; Renneberg, B. 2015: Predicting Self-Rated Health in Diabetes and Chronic Heart Failure - a Multiple Mediation Model. Frontiers in Public Health 3: 266
Thornton, H.R.; Delaney, J.A.; Duthie, G.M.; Scott, B.R.; Chivers, W.J.; Sanctuary, C.E.; Dascombe, B.J. 2016: Predicting Self-Reported Illness for Professional Team-Sport Athletes. International Journal of Sports Physiology and Performance 11(4): 543-550
Schlegl, T.; Waldstein, S.M.; Vogl, W.-D.; Schmidt-Erfurth, U.; Langs, G. 2015: Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks. Information Processing in Medical Imaging: Proceedings of the . Conference 24: 437-448
Olenick, E.M.; Zimbro, K.S.; DʼLima, G.M.; Ver Schneider, P.; Jones, D. 2017: Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record. Journal of Nursing Care Quality 32(1): 25-31
Puranik, S.; Forno, E.; Bush, A.; Celedón, J.C. 2017: Predicting Severe Asthma Exacerbations in Children. American Journal of Respiratory and Critical Care Medicine 195(7): 854-859
Williams, D.J.; Zhu, Y.; Grijalva, C.G.; Self, W.H.; Harrell, F.E.; Reed, C.; Stockmann, C.; Arnold, S.R.; Ampofo, K.K.; Anderson, E.J.; Bramley, A.M.; Wunderink, R.G.; McCullers, J.A.; Pavia, A.T.; Jain, S.; Edwards, K.M. 2016: Predicting Severe Pneumonia Outcomes in Children. Pediatrics 138(4)
Tanigasalam, V.; Bhat, B.V.; Adhisivam, B.; Sridhar, M.G.; Harichandrakumar, K.T. 2016: Predicting Severity of Acute Kidney Injury in Term Neonates with Perinatal Asphyxia Using Urinary Neutrophil Gelatinase Associated Lipocalin. Indian Journal of Pediatrics 83(12-13): 1374-1378
Casey, E.A.; Masters, N.T.; Beadnell, B.; Hoppe, M.J.; Morrison, D.M.; Wells, E.A. 2017: Predicting Sexual Assault Perpetration Among Heterosexually Active Young Men. Violence Against Women 23(1): 3-27
Relyea, M.; Ullman, S.E. 2017: Predicting Sexual Assault Revictimization in a Longitudinal Sample of Women Survivors: Variation by Type of Assault. Violence Against Women 23(12): 1462-1483
Diehl, C.; Rees, J.; Bohner, G. 2018: Predicting Sexual Harassment from Hostile Sexism and Short-Term Mating Orientation: Relative Strength of Predictors Depends on Situational Priming of Power Versus Sex. Violence Against Women 24(2): 123-143
Howes, A.; Duggan, G.B.; Kalidindi, K.; Tseng, Y-Chi.; Lewis, R.L. 2016: Predicting Short-Term Remembering as Boundedly Optimal Strategy Choice. Cognitive Science 40(5): 1192-1223
Basques, B.A.; Chung, S.H.; Lukasiewicz, A.M.; Webb, M.L.; Samuel, A.M.; Bohl, D.D.; Smith, B.G.; Grauer, J.N. 2015: Predicting Short-term Morbidity in Patients Undergoing Posterior Spinal Fusion for Neuromuscular Scoliosis. Spine 40(24): 1910-1917
Diec, J.; Tilia, D.; Naduvilath, T.; Bakaraju, R.C. 2017: Predicting Short-term Performance of Multifocal Contact Lenses. Eye and Contact Lens 43(6): 340-345
Fichtenholtz, A.M.; Camarda, N.D.; Neumann, E.K. 2016: Predicting Significance of Unknown Variants in Glial Tumors through Sub-Class Enrichment. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 21: 297-308
Jiao, Y.; Zhou, L.; Ma, F.; Gao, G.; Kou, L.; Bell, J.; Sanvito, S.; Du, A. 2016: Predicting Single-Layer Technetium Dichalcogenides (TcX₂, X = S, Se) with Promising Applications in Photovoltaics and Photocatalysis. Acs Applied Materials and Interfaces 8(8): 5385-5392
Asghar, K.A.; Rowlands, D.A.; Elliott, J.M.; Squires, A.M. 2015: Predicting Sizes of Hexagonal and Gyroid Metal Nanostructures from Liquid Crystal Templating. Acs Nano 9(11): 10970-10978
Shaw, H.; Ellis, D.A.; Kendrick, L.-R.; Ziegler, F.; Wiseman, R. 2016: Predicting Smartphone Operating System from Personality and Individual Differences. Cyberpsychology Behavior and Social Networking 19(12): 727-732
Hoogendoorn, M.; Berger, T.; Schulz, A.; Stolz, T.; Szolovits, P. 2017: Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations. IEEE Journal of Biomedical and Health Informatics 21(5): 1449-1459
Liu, Y.; Pan, X.; Wang, C.; Li, Y.; Shi, R. 2015: Predicting Soil Salinity with Vis-NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization. Plos one 10(10): E0140688
Hydren, J.R.; Foulis, S.A.; Frykman, P.N.; Warr, B.J.; Redmond, J.E.; Canino, M.C.; Cohen, B.S.; Sharp, M.A.; Zambraski, E.J. 2016: Predicting Soldier Load Carriage Performance: 960 Board #276 June 1, 2: 00 PM - 3: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 274-274
Redmond, J.E.; Foulis, S.A.; Frykman, P.N.; Warr, B.J.; Sharp, M.A.; Zambraski, E.J. 2016: Predicting Soldier Performance of the Casualty Evacuation: 943 Board #259 June 1, 2: 00 PM - 3: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 268-269
Zhang, X.; Li, J.; Wei, D.; Li, B.; Ma, Y. 2015: Predicting Soluble Nickel in Soils Using Soil Properties and Total Nickel. Plos one 10(7): E0133920
Misin, M.; Palmer, D.S.; Fedorov, M.V. 2016: Predicting Solvation Free Energies Using Parameter-Free Solvent Models. Journal of Physical Chemistry. B 120(25): 5724-5731
Osburn, C.L.; Handsel, L.T.; Peierls, B.L.; Paerl, H.W. 2016: Predicting Sources of Dissolved Organic Nitrogen to an Estuary from an Agro-Urban Coastal Watershed. Environmental Science and Technology 50(16): 8473-8484
Bellamy, C.; Altringham, J. 2015: Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. Plos one 10(6): E0128440
Rong, P.; Yunusova, Y.; Wang, J.; Zinman, L.; Pattee, G.L.; Berry, J.D.; Perry, B.; Green, J.R. 2016: Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems. Plos one 11(5): E0154971
Ellis, R.J.; Molander, P.; Rönnberg, J.; Lyxell, B.ör.; Andersson, G.; Lunner, T. 2016: Predicting Speech-in-Noise Recognition from Performance on the Trail Making Test: Results from a Large-Scale Internet Study. Ear and Hearing 37(1): 73-79
Dangle, P.; Ayyash, O.; Shaikh, H.; Stephany, H.A.; Cannon, G.M.; Schneck, F.X.; Ost, M.C. 2016: Predicting Spontaneous Stone Passage in Prepubertal Children: A Single Institution Cohort. Journal of Endourology 30(9): 945-949
Carpentier, Jëlle.; Mageau, Gève.A. 2016: Predicting Sport Experience During Training: The Role of Change-Oriented Feedback in Athletes' Motivation, Self-Confidence and Needs Satisfaction Fluctuations. Journal of Sport and Exercise Psychology 38(1): 45-58
Miller, N.A.; Kirk, A. 2016: Predicting State Investment in Medicaid Home- and Community-Based Services, 2000-2011. Journal of Aging and Social Policy 28(1): 49-64
Rasool, M.Fawad.; Khalil, F.; Läer, S. 2016: Predicting Stereoselective Disposition of Carvedilol in Adult and Pediatric Chronic Heart Failure Patients by Incorporating Pathophysiological Changes in Organ Blood Flows-A Physiologically Based Pharmacokinetic Approach. Drug Metabolism and Disposition: the Biological Fate of Chemicals 44(7): 1103-1115
Manuel, D.G.; Tuna, M.; Perez, R.; Tanuseputro, P.; Hennessy, D.; Bennett, C.; Rosella, L.; Sanmartin, C.; van Walraven, C.; Tu, J.V. 2015: Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT). Plos one 10(12): E0143342
Safder, T.B.; Badgett, R.G. 2016: Predicting Stroke in Patients with Atrial Fibrillation: An Incomplete Picture Without Considering Quality of Anticoagulation. Journal of the American College of Cardiology 67(18): 2192-2193
Fredin, L.A.; Allison, T.C. 2016: Predicting Structures of Ru-Centered Dyes: a Computational Screening Tool. Journal of Physical Chemistry. a 120(13): 2135-2143
Wang, X.; Li, H.; Zhang, Q.; Wang, R. 2016: Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier. Biomed Research International 2016: 1793272
Nielsen, H. 2017: Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms. Current Topics in Microbiology and Immunology 404: 129-158
Schmack, K.; Burk, J.; Haynes, J.-D.; Sterzer, P. 2016: Predicting Subjective Affective Salience from Cortical Responses to Invisible Object Stimuli. Cerebral Cortex 26(8): 3453-3460
Valeri, L.; Patterson-Lomba, O.; Gurmu, Y.; Ablorh, A.; Bobb, J.; Townes, F.William.; Harling, G. 2016: Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators. Plos one 11(10): E0163544
Sahebally, S.M. 2017: Predicting Suboptimal Bowel Preparation: Taking it up a PEG. Digestive Diseases and Sciences 62(2): 289-291
Lester, D. 2016: Predicting Success in Psychological Statistics Courses. Psychological Reports 118(3): 772-777
Hu, D. 2016: Predicting Success in Residency: the Quarterback Problem. Hospital Pharmacy 51(8): 615-617
Hollemans, R.A.; Bollen, T.L.; van Brunschot, S.; Bakker, O.J.; Ahmed Ali, U.; van Goor, H.; Boermeester, M.A.; Gooszen, H.G.; Besselink, M.G.; van Santvoort, H.C. 2016: Predicting Success of Catheter Drainage in Infected Necrotizing Pancreatitis. Annals of Surgery 263(4): 787-792
Al Fayyadh, M.J.; Heller, S.F.; Rajab, T.Konrad.; Gardner, A.K.; Bloom, J.P.; Rawlings, J.A.; Mullen, J.T.; Smink, D.S.; Farley, D.R.; Willis, R.E.; Dent, D.L. 2016: Predicting Success of Preliminary Surgical Residents: A Multi-Institutional Study. Journal of surgical education 73(6): e77-e83
Kawabata, K.; Watanabe, H.; Hara, K.; Bagarinao, E.; Yoneyama, N.; Ogura, A.; Imai, K.; Masuda, M.; Yokoi, T.; Ohdake, R.; Tanaka, Y.; Tsuboi, T.; Nakamura, T.; Hirayama, M.; Ito, M.; Atsuta, N.; Maesawa, S.; Naganawa, S.; Katsuno, M.; Sobue, G. 2018: Distinct manifestation of cognitive deficits associate with different resting-state network disruptions in non-demented patients with Parkinson's disease. Journal of Neurology 265(3): 688-700
Araújo, L.; Ribeiro, O.; Teixeira, L.; Paúl, Cça. 2016: Predicting Successful Aging at One Hundred Years of Age. Research on aging 38(6): 689-709
Barak-Corren, Y.; Castro, V.M.; Javitt, S.; Hoffnagle, A.G.; Dai, Y.; Perlis, R.H.; Nock, M.K.; Smoller, J.W.; Reis, B.Y. 2017: Predicting Suicidal Behavior From Longitudinal Electronic Health Records. American Journal of Psychiatry 174(2): 154-162
Sage, C.C.; Platko, J.V.; Nokela, M. 2014: Predicting Suicidal Behavior In Veterans And Active Military Personnel: Possibilities For Electronic Deployment To Discover A Predictive Assessment. Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research 17(7): A569-A570
Jordan, J.T.; Samuelson, K.W. 2016: Predicting Suicide Intent: the Roles of Experiencing or Committing Violent Acts. Suicide and Life-Threatening Behavior 46(3): 293-300
Sarma, K.; Kumar, A.; Krishna, M.; Medhi, M.; Tripathi, O.P. 2015: Predicting Suitable Habitats for the Vulnerable Eastern Hoolock Gibbon, Hoolock leuconedys, in India Using the MaxEnt Model. Folia Primatologica; International Journal of Primatology 86(4): 387-397
Hutchings, F.; Han, C.E.; Keller, S.S.; Weber, B.; Taylor, P.N.; Kaiser, M. 2015: Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations. Plos Computational Biology 11(12): E1004642
Barrera, J.é E.; Dion, G.R. 2016: Predicting Surgical Response Using Tensiometry in OSA Patients after Genioglossus Advancement with Uvulopalatopharyngoplasty. Otolaryngology--Head and Neck Surgery: Official Journal of American Academy of Otolaryngology-Head and Neck Surgery 154(3): 558-563
Rades, D.; Dziggel, L.; Hakim, S.G.; Rudat, V.; Janssen, S.; Trang, N.T.; Khoa, M.T.; Bartscht, T. 2015: Predicting Survival After Irradiation for Brain Metastases from Head and Neck Cancer. In Vivo 29(5): 525-528
Bolm, L.; Janssen, S.; Käsmann, L.; Wellner, U.; Bartscht, T.; Schild, S.E.; Rades, D. 2015: Predicting Survival After Irradiation of Metastases from Pancreatic Cancer. Anticancer Research 35(7): 4105-4108
Manig, L.; Käsmann, L.; Janssen, S.; Rades, D. 2016: Predicting Survival After Irradiation of Metastases from Transitional Carcinoma of the Bladder. Anticancer Research 36(12): 6663-6665
Glei, D.A.; Goldman, N.; Risques, R.Ana.; Rehkopf, D.H.; Dow, W.H.; Rosero-Bixby, L.; Weinstein, M. 2016: Predicting Survival from Telomere Length versus Conventional Predictors: A Multinational Population-Based Cohort Study. Plos one 11(4): E0152486
Karnik, N.D.; Gupta, A.V. 2016: Predicting Survival in ARDS. Journal of the Association of Physicians of India 63(11): 11-13
Graff-Radford, J.; Lesnick, T.G.; Boeve, B.F.; Przybelski, S.A.; Jones, D.T.; Senjem, M.L.; Gunter, J.L.; Ferman, T.J.; Knopman, D.S.; Murray, M.E.; Dickson, D.W.; Sarro, L.; Jack, C.R.; Petersen, R.C.; Kantarci, K. 2016: Predicting Survival in Dementia with Lewy Bodies with Hippocampal Volumetry. Movement Disorders: Official Journal of the Movement Disorder Society 31(7): 989-994
Albaeni, A.; Eid, S.M.; Vaidya, D.; Chandra-Strobos, N. 2014: Predicting Survival with Good Neurological Outcome Within 24 Hours Following Out of Hospital Cardiac Arrest:The Application and Validation of a Novel Clinical Score. Journal of Neurology and Translational Neuroscience 2(1)
Edwards, A.; Eisenberg, N.; Spinrad, T.L.; Reiser, M.; Eggum-Wilkens, N.D.; Liew, J. 2015: Predicting Sympathy and Prosocial Behavior from Young Children's Dispositional Sadness. Social Development 24(1): 76-94
Novak, D.; Riener, R. 2015: Predicting Targets of Human Reaching Motions with an Arm Rehabilitation Exoskeleton. Biomedical Sciences Instrumentation 51: 385-392
Hagen, G. 2015: Predicting The Future Economic Burden of Hip Fractures In Norway-The Impact of Epidemiological Uncertainty. Value in Health 18(7): A642
Grogan, J.A.; Markelc, B.; Connor, A.J.; Muschel, R.J.; Pitt-Francis, J.M.; Maini, P.K.; Byrne, H.M. 2017: Predicting the Influence of Microvascular Structure on Tumor Response to Radiotherapy. IEEE Transactions on Bio-Medical Engineering 64(3): 504-511
Young, A.S.; Meers, M.R.; Vesco, A.T.; Seidenfeld, A.M.; Arnold, L.Eugene.; Fristad, M.A. 2019: Predicting Therapeutic Effects of Psychodiagnostic Assessment Among Children and Adolescents Participating in Randomized Controlled Trials. Journal of Clinical Child and Adolescent Psychology: the Official Journal for the Society of Clinical Child and Adolescent Psychology American Psychological Association Division 53 48(Sup1): S1-S12
Atreya, R.; Neurath, M.F. 2016: Predicting Therapeutic Response by in vivo Molecular Imaging in Inflammatory Bowel Diseases. Digestive Diseases 34(5): 552-557
Kitadai, N. 2016: Predicting Thermodynamic Behaviors of Non-Protein Amino Acids as a Function of Temperature and pH. Origins of Life and Evolution of the Biosphere: the Journal of the International Society for the Study of the Origin of Life 46(1): 3-18
Xiao, F.; Peng, G.; Nie, C.; Yu, L. 2016: Predicting Thermodynamic Properties of PBXTHs with new Quantum Topological Indexes. Plos one 11(2): E0147126
Moreira, D.M.; Howard, L.E.; Sourbeer, K.N.; Amarasekara, H.S.; Chow, L.C.; Cockrell, D.C.; Pratson, C.L.; Hanyok, B.T.; Aronson, W.J.; Kane, C.J.; Terris, M.K.; Amling, C.L.; Cooperberg, M.R.; Freedland, S.J. 2017: Predicting time from Metastasis to Overall Survival in Castration-Resistant Prostate Cancer: Results from SEARCH. Clinical Genitourinary Cancer 15(1): 60-66.E2
Cooke, M.E.; Nasim, A.; Cho, S.Bin.; Kendler, K.S.; Clark, S.L.; Dick, D.M. 2016: Predicting Tobacco Use across the First Year of College. American journal of health behavior 40(4): 484-495
Basant, N.; Gupta, S.; Singh, K.P. 2015: Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes. Journal of Chemical Information and Modeling 55(7): 1337-1348
Klintström, E.; Klintström, B.; Moreno, R.; Brismar, T.B.; Pahr, D.H.; Smedby, Ör. 2016: Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an in Vitro Study Using Finite Element Analysis. Plos one 11(8): E0161101
Tauber, H.; Streif, W.; Fritz, J.; Ott, H.; Weigel, G.; Loacker, L.; Heinz, A.; Velik-Salchner, C. 2016: Predicting Transfusion Requirements During Extracorporeal Membrane Oxygenation. Journal of Cardiothoracic and Vascular Anesthesia 30(3): 692-701
Ruocco, A.C.; Rodrigo, A.H.; McMain, S.F.; Page-Gould, E.; Ayaz, H.; Links, P.S. 2016: Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: a Preliminary Study. Frontiers in Human Neuroscience 10: 220
Andreescu, C.; Aizenstein, H. 2016: Predicting Treatment Response with Functional Magnetic Resonance Imaging. Biological Psychiatry 79(4): 262-263
Shenk, C.E.; Dorn, L.D.; Kolko, D.J.; Susman, E.J.; Noll, J.G.; Bukstein, O.G. 2012: Predicting Treatment Response for Oppositional Defiant and Conduct Disorder Using Pre-treatment Adrenal and Gonadal Hormones. Journal of Child and Family Studies 21(6): 973-981
Mattek, R.J.; Harris, S.E.; Fox, R.A. 2016: Predicting Treatment Success in Child and Parent Therapy Among Families in Poverty. Journal of Genetic Psychology 177(2): 44-54
Guitar, B.; Kazenski, D.; Howard, A.; Cousins, S.Freddie.; Fader, E.; Haskell, P. 2015: Predicting Treatment Time and Long-Term Outcome of the Lidcombe Program: A Replication and Reanalysis. American Journal of Speech-Language Pathology 24(3): 533-544
Tsai, M.-F.; Hwang, S.-L.; Tsay, S.-L.; Wang, C.-L.; Tsai, F.-C.; Chen, C.-C.; Huang, T.-Y. 2013: Predicting Trends in Dyspnea and Fatigue in Heart Failure Patients' Outcomes. Acta Cardiologica Sinica 29(6): 488-495
Harper, L.K.; Shoaf, A.L.; Bayse, C.A. 2015: Predicting Trigger Bonds in Explosive Materials through Wiberg Bond Index Analysis. Chemphyschem: a European Journal of Chemical Physics and Physical Chemistry 16(18): 3886-3892
Shi, Z.; Zhang, Z.; Kutana, A.; Yakobson, B.I. 2015: Predicting Two-Dimensional Silicon Carbide Monolayers. Acs Nano 9(10): 9802-9809
Vaala, S.E.; Hornik, R.C. 2014: Predicting US Infants' and Toddlers' TV/Video Viewing Rates: Mothers' Cognitions and Structural Life Circumstances. Journal of Children and Media 8(2): 163-182
Khanna, S.; Good, N.; Boyle, J. 2016: Predicting Unpanned Return to Hospital for Chronic Disease Patients. Studies in Health Technology and Informatics 227: 67-73
Rosenbaum, J.E.; Zenilman, J.; Rose, E.; Wingood, G.; DiClemente, R. 2016: Predicting Unprotected Sex and Unplanned Pregnancy among Urban African-American Adolescent Girls Using the Theory of Gender and Power. Journal of urban health: bulletin of the New York Academy of Medicine 93(3): 493-510
Baranowski, T.; Beltran, A.; Chen, T.-A.; Thompson, D.; O'Connor, T.; Hughes, S.; Diep, C.; Baranowski, J.C. 2013: Predicting use of Ineffective Responsive, Structure and Control Vegetable Parenting Practices with the Model of Goal Directed Behavior. Journal of Food Research 2(6): 80-88
Loe, H.; Nes, B.M.; Wisløff, U. 2016: Predicting VO2peak from Submaximal- and Peak Exercise Models: the HUNT 3 Fitness Study, Norway. Plos one 11(1): E0144873
Chen, N-Tsu.Nancy. 2015: Predicting Vaccination Intention and Benefit and Risk Perceptions: The Incorporation of Affect, Trust, and Television Influence in a Dual-Mode Model. Risk Analysis: An Official Publication of the Society for Risk Analysis 35(7): 1268-1280
Hohtari-Kivimäki, U.; Salminen, M.; Vahlberg, T.; Kivelä, S-Liisa. 2016: Predicting Value of Nine-Item Berg Balance Scale Among the Aged: A 3-Year Prospective Follow-Up Study. Experimental Aging Research 42(2): 151-160
Grieb, M.; Burkovski, A.; Sträng, J.E.; Kraus, J.M.; Groß, A.; Palm, G.ün.; Kühl, M.; Kestler, H.A. 2015: Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty. Plos one 10(7): E0131832
Kalakoti, P.; Notarianni, C.; Nanda, A. 2016: Predicting Venous Thromboembolism in Pediatric Trauma Patients. JAMA Surgery 151(9): 881-882
Karaca, O.; Gunes, H.M.; Omaygenc, M.O.; Cakal, B.; Cakal, S.D.; Demir, G.G.; Kizilirmak, F.; Gokdeniz, T.; Barutcu, I.; Boztosun, B.; Kilicaslan, F. 2016: Predicting Ventricular Arrhythmias in Cardiac Resynchronization Therapy: the Impact of Persistent Electrical Dyssynchrony. Pacing and Clinical Electrophysiology: Pace 39(9): 969-977
Kim, Y.B.; Park, N.; Zhang, Q.; Kim, J.G.; Kang, S.J.; Kim, C.H. 2016: Predicting Virtual World User Population Fluctuations with Deep Learning. Plos one 11(12): E0167153
Skrivanova, K.; Anderkova, L.; Brancikova, D.; Jarkovský, J.; Benesova, K.; Elfmarková, N.; Svěrák, T.; Bendová, M.; Peterkova, H.; Nedvěd, J.; Protivánková, M.; Minar, L.; Holoubková, E.; Dusek, L. 2016: Predicting Vitality Change in Older Breast Cancer Survivors after Primary Treatment--an Approach Based on Using Time-related Difference of Pro-inflammatory Marker C-reactive Protein. Klinicka Onkologie: Casopis Ceske a Slovenske Onkologicke Spolecnosti 29(1): 52-58
Guo, H.; Wang, Z.; Zhang, Y. 2016: Predicting Vo2max From A 6-minute Stairs Climbing And Descending Test (6MSCDT): 355 Board #192 June 1, 11: 00 AM - 12: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 93-93
Paulo, A.; Zaal, F.T.J.M.; Fonseca, S.; Araújo, D. 2016: Predicting Volleyball Serve-Reception. Frontiers in Psychology 7: 1694
Cook, P.G.; Miller, A.; Shanafield, M.; Simmons, C.T. 2017: Predicting Water Resource Impacts of Unconventional Gas Using Simple Analytical Equations. Ground Water 55(3): 387-398
Lau, S.L.; Muir, C.; Assur, Y.; Beach, R.; Tran, B.; Bartrop, R.; McLean, M.; Caetano, D. 2016: Predicting Weight Gain in Patients Treated with Clozapine: the Role of Sex, Body Mass Index, and Smoking. Journal of Clinical Psychopharmacology 36(2): 120-124
Shand, L.; Brown, W.M.; Chaves, L.F.; Goldberg, T.L.; Hamer, G.L.; Haramis, L.; Kitron, U.; Walker, E.D.; Ruiz, M.O. 2016: Predicting West Nile Virus Infection Risk from the Synergistic Effects of Rainfall and Temperature. Journal of Medical Entomology 53(4): 935-944
Warren, B.H.; Baudin, R.ém.; Franck, A.; Hugel, S.; Strasberg, D. 2016: Predicting Where a Radiation will Occur: Acoustic and Molecular Surveys Reveal Overlooked Diversity in Indian Ocean Island Crickets (Mogoplistinae: Ornebius). Plos one 11(2): E0148971
Hartjes, T.M. 2015: Predicting which Patients will Benefit from Palliative Care: use of Bundles, Triggers, and Protocols. Critical Care Nursing Clinics of North America 27(3): 307-314
Mareiniss, D.P.; Xu, T.; Pham, J.Cuong.; Hsieh, Y-Hsiang.; Zhao, J.; Nguyen, C.; Nguyen, M.; Winters, B. 2016: Predicting Which Patients will Likely Benefit from Subglottic Secretion Drainage Endotracheal Tubes: A Retrospective Study. Journal of Emergency Medicine 50(3): 385-393
Hancock, P.A.; White, V.L.; Ritchie, S.A.; Hoffmann, A.A.; Godfray, H.C.J. 2016: Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits. Bmc Biology 14(1): 96
Lambrechts, L. 2015: Predicting Wolbachia potential to knock down dengue virus transmission. Annals of Translational Medicine 3(19): 288
Mendoza, K.; Ulloa, A.; Saavedra, N.; Galván, J.; Berenzon, S. 2017: Predicting Women's Utilization of Primary Care Mental Health Services in Mexico City. Journal of Primary Care and Community Health 8(2): 83-88
Van Benschoten, A.H.; Afonine, P.V.; Terwilliger, T.C.; Wall, M.E.; Jackson, C.J.; Sauter, N.K.; Adams, P.D.; Urzhumtsev, A.; Fraser, J.S. 2015: Predicting X-ray diffuse scattering from translation-libration-screw structural ensembles. Acta Crystallographica. Section D Biological Crystallography 71(Pt 8): 1657-1667
Maggs, J.L.; Staff, J.; Kloska, D.D.; Patrick, M.E.; O'Malley, P.M.; Schulenberg, J. 2015: Predicting Young Adult Degree Attainment by Late Adolescent Marijuana Use. Journal of adolescent health: official publication of the Society for Adolescent Medicine 57(2): 205-211
Papp, L.M.; Kouros, C.D. 2017: Predicting young adults' risk for engaging in prescription drug misuse in daily life from individual, partner, and relationship factors. Substance Abuse 38(1): 61-68
Sichimba, F.; Mooya, H.; Mesman, J. 2017: Predicting Zambian Grandmothers' Sensitivity Toward their Grandchildren. International Journal of Aging and Human Development 85(2): 185-203
Cox, B.D.; Stanton, R.A.; Schinazi, R.F. 2015: Predicting Zika virus structural biology: Challenges and opportunities for intervention. Antiviral Chemistry and ChemoTherapy 24(3-4): 118-126
Guan, C.; Niu, X.; Shi, F.; Yang, K.; Li, N. 2015: Predicting a DNA-binding protein using random forest with multiple mathematical features. Bio-Medical Materials and Engineering 26(Suppl 1): S1883-S1889
Kasir, R.; Zakko, S.; Zakko, P.; Adler, M.; Lee, A.; Dhingra, S.; Guttermuth, C. 2016: Predicting a Response to Antibiotics in Patients with the Irritable Bowel Syndrome. Digestive Diseases and Sciences 61(3): 846-851
Roberts, A.M.I.; Tansey, C.; Smithers, R.J.; Phillimore, A.B. 2015: Predicting a change in the order of spring phenology in temperate forests. Global Change Biology 21(7): 2603-2611
Petersen, T.; Christensen, R.; Juhl, C. 2015: Predicting a clinically important outcome in patients with low back pain following McKenzie therapy or spinal manipulation: a stratified analysis in a randomized controlled trial. Bmc Musculoskeletal Disorders 16: 74
Scior, T.; Paiz-Candia, B.; Islas, Án.A.; Sánchez-Solano, A.; Millan-Perez Peña, L.; Mancilla-Simbro, C.; Salinas-Stefanon, E.M. 2015: Predicting a double mutant in the twilight zone of low homology modeling for the skeletal muscle voltage-gated sodium channel subunit beta-1 (Nav1.4 β1). Computational and Structural Biotechnology Journal 13: 229-240
Wang, P.S.; Ren, W.; Bellaiche, L.; Xiang, H.J. 2015: Predicting a ferrimagnetic phase of Zn(2)FeOsO(6) with strong magnetoelectric coupling. Physical Review Letters 114(14): 147204
Schwab, S.; Gebhardt, M.; Hessels, M.G.P.; Nusser, L. 2016: Predicting a high rate of self-assessed and parent-assessed peer problems--Is it typical for students with disabilities?. Research in Developmental Disabilities 49-50: 196-204
Ma, F.; Gao, G.; Jiao, Y.; Gu, Y.; Bilic, A.; Zhang, H.; Chen, Z.; Du, A. 2016: Predicting a new phase (T'') of two-dimensional transition metal di-chalcogenides and strain-controlled topological phase transition. Nanoscale 8(9): 4969-4975
Huerta, S. 2016: Predicting a pathological complete response in rectal cancer. Anti-Cancer Drugs 27(8): 709-710
Shamid, S.M.; Allender, D.W.; Selinger, J.V. 2014: Predicting a polar analog of chiral blue phases in liquid crystals. Physical Review Letters 113(23): 237801
Nipp, R.D.; Ryan, D.P. 2015: Predicting a response to FOLFIRINOX in pancreatic cancer. Journal of the National Cancer Institute 107(8)
Ljungquist, K.L.; Agnew, S.P.; Huang, J.I. 2015: Predicting a safe screw length for volar plate fixation of distal radius fractures: lunate depth as a marker for distal radius depth. Journal of Hand Surgery 40(5): 940-944
Lee, C.-H.; Lee, C.-C.; Hsieh, C.-C.; Hong, M.-Y.; Chi, C.-H. 2016: Predicting abscesses in adults with community-onset monomicrobial Enterobacteriaceae bacteremia: microorganisms matters. Diagnostic Microbiology and Infectious Disease 84(1): 74-79
Schouteten, R. 2017: Predicting absenteeism: screening for work ability or burnout. Occupational Medicine 67(1): 52-57
Adegbija, O.; Hoy, W.; Wang, Z. 2015: Predicting absolute risk of type 2 diabetes using age and waist circumference values in an aboriginal Australian community. Plos one 10(4): E0123788
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Ladabaum, U.; Patel, A.; Mannalithara, A.; Sundaram, V.; Mitani, A.; Desai, M. 2016: Predicting advanced neoplasia at colonoscopy in a diverse population with the National Cancer Institute colorectal cancer risk-assessment tool. Cancer 122(17): 2663-2670
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Sha, S.J.; Ghosh, P.M.; Lee, S.E.; Corbetta-Rastelli, C.; Jagust, W.J.; Kornak, J.; Rankin, K.P.; Grinberg, L.T.; Vinters, H.V.; Mendez, M.F.; Dickson, D.W.; Seeley, W.W.; Gorno-Tempini, M.; Kramer, J.; Miller, B.L.; Boxer, A.L.; Rabinovici, G.D. 2015: Predicting amyloid status in corticobasal syndrome using modified clinical criteria, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography. Alzheimer's Research and Therapy 7(1): 8
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Chen, T.-T. 2016: Predicting analysis times in randomized clinical trials with cancer immunotherapy. Bmc Medical Research Methodology 16: 12
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Shpak, A.A.; Shkvorchenko, D.O.; Sharafetdinov, I.K.; Yukhanova, O.A. 2016: Predicting anatomical results of surgical treatment of idiopathic macular hole. International Journal of Ophthalmology 9(2): 253-257
Sabir, S.; Ahrar, K.; Matin, S.F. 2016: Predicting and Determining the Success of Percutaneous Ablation. Journal of Urology 196(1): 7-8
Zhu, L.; Zhu, L.; Wang, H.; Yan, J.; Liu, B.; Chen, W.; He, J.; Zhou, Z.; Yang, X. 2017: Predicting and Early Monitoring Treatment Efficiency of Cervical Cancer Under Concurrent Chemoradiotherapy by Intravoxel Incoherent Motion Magnetic Resonance Imaging. Journal of Computer Assisted Tomography 41(3): 422-429
Guo, Z.; Xiao, D.; Li, D.; Wang, X.; Wang, Y.; Yan, T.; Wang, Z. 2016: Predicting and Evaluating the Epidemic Trend of Ebola Virus Disease in the 2014-2015 Outbreak and the Effects of Intervention Measures. PloS one 11(4): e0152438
Reilly, J.P. 2016: Predicting and Eventually Preventing the Future: Sepsis Risk in Community-Dwelling Adults. Critical Care Medicine 44(7): 1425-1426
Fukuda, K.; Woodman, G.F. 2015: Predicting and Improving Recognition Memory Using Multiple Electrophysiological Signals in Real time. Psychological Science 26(7): 1026-1037
Juhász, C. 2016: Predicting and Preventing Epilepsy in Sturge-Weber Syndrome?. Pediatric Neurology Briefs 30(11): 43
Erickson, B.J.; Chalmers, P.N.; Bush-Joseph, C.A.; Romeo, A.A. 2016: Predicting and Preventing Injury in Major League Baseball. American Journal of Orthopedics 45(3): 152-156
Shi, C.; Yu, C.-H.; Zhang, W. 2016: Predicting and Screening Dielectric Transitions in a Series of Hybrid Organic-Inorganic Double Perovskites via an Extended Tolerance Factor Approach. Angewandte Chemie 55(19): 5798-5802
Chow, M.L.; Troussicot, L.; Martin, M.; Doumèche, B.; Guillière, F.; Lancelin, J-Marc. 2016: Predicting and Understanding the Enzymatic Inhibition of Human Peroxiredoxin 5 by 4-Substituted Pyrocatechols by Combining Funnel Metadynamics, Solution NMR, and Steady-State Kinetics. Biochemistry 55(24): 3469-3480
Mehmandar, M.; Soori, H.; Mehrabi, Y. 2016: Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. International Journal of Critical Illness and Injury Science 6(2): 74-78
Michielsen, A.; Kalantari, Z.; Lyon, S.W.; Liljegren, E. 2016: Predicting and communicating flood risk of transport infrastructure based on watershed characteristics. Journal of Environmental Management 182: 505-518
Ratelle, J.T.; Kelm, D.J.; Halvorsen, A.J.; West, C.P.; Oxentenko, A.S. 2015: Predicting and communicating risk of clinical deterioration: an observational cohort study of internal medicine residents. Journal of general internal medicine 30(4): 448-453
Wang, D.-P.; Lai, J.-C.; Lai, H.-Y.; Mo, S.-R.; Zeng, K.-Y.; Li, C.-H.; Zuo, J.-L. 2018: Distinct Mechanical and Self-Healing Properties in two Polydimethylsiloxane Coordination Polymers with Fine-Tuned Bond Strength. Inorganic Chemistry 57(6): 3232-3242
Cai, H.; Lilburn, T.G.; Hong, C.; Gu, J.; Kuang, R.; Wang, Y. 2015: Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments. Bmc Systems Biology 9(Suppl 4): S1
Lee-Dadswell, G.R. 2015: Predicting and identifying finite-size effects in current spectra of one-dimensional oscillator chains. Physical Review. e Statistical Nonlinear and Soft Matter Physics 91(1): 012138
Yue, C. 2015: Predicting and influencing training success: spatial abilities and instructional design. Medical Education 49(11): 1054-1055
Van Leer, E.; Connor, N.P. 2015: Predicting and influencing voice therapy adherence using social-cognitive factors and mobile video. American Journal of Speech-Language Pathology 24(2): 164-176
Mirzabeigi, M.N.; Wilson, A.J.; Fischer, J.P.; Basta, M.; Kanchwala, S.; Kovach, S.J.; Serletti, J.M.; Wu, L.C. 2015: Predicting and managing donor-site wound complications in abdominally based free flap breast reconstruction: improved outcomes with early reoperative closure. Plastic and Reconstructive Surgery 135(1): 14-23
Smith, A.N.; Christian, M.P.; Firebaugh, S.L.; Cooper, G.W.; Jamieson, B.G. 2015: Predicting and managing heat dissipation from a neural probe. Biomedical Microdevices 17(4): 81
Miller, A.; Mandeville, J. 2016: Predicting and measuring fluid responsiveness with echocardiography. Echo Research and Practice 3(2): G1-G12
Janowski, M.; Walczak, P.; Pearl, M.S. 2016: Predicting and optimizing the territory of blood-brain barrier opening by superselective intra-arterial cerebral infusion under dynamic susceptibility contrast MRi guidance. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism 36(3): 569-575
Linder, K.E.; Nicolau, D.P.; Nailor, M.D. 2016: Predicting and preventing antimicrobial resistance utilizing pharmacodynamics: Part I gram positive bacteria. Expert Opinion on Drug Metabolism and Toxicology 12(3): 267-280
Rippey, J.Cr.; Carr, P.J.; Cooke, M.; Higgins, N.; Rickard, C.M. 2016: Predicting and preventing peripheral intravenous cannula insertion failure in the emergency department: Clinician 'gestalt' wins again. Emergency Medicine Australasia: Ema 28(6): 658-665
Covert, K.L.; Fleming, J.N.; Staino, C.; Casale, J.P.; Boyle, K.M.; Pilch, N.A.; Meadows, H.B.; Mardis, C.R.; McGillicuddy, J.W.; Nadig, S.; Bratton, C.F.; Chavin, K.D.; Baliga, P.K.; Taber, D.J. 2016: Predicting and preventing readmissions in kidney transplant recipients. Clinical Transplantation 30(7): 779-786
Raza, K.; Klareskog, L.; Holers, V.Michael. 2016: Predicting and preventing the development of rheumatoid arthritis. Rheumatology 55(1): 1-3
Gizzi, M.; Oberic, L.; Massard, C.; Poterie, A.; Le Teuff, G.; Loriot, Y.; Albiges, L.; Baciarello, G.; Michels, J.; Bossi, A.; Blanchard, P.; Escudier, B.; Fizazi, K. 2016: Predicting and preventing thromboembolic events in patients receiving cisplatin-based chemotherapy for germ cell tumours. European Journal of Cancer 69: 151-157
Kim, M.; Chu, A.; Khan, Y.; Malik, S. 2015: Predicting and preventing vascular complications following percutaneous coronary intervention in women. Expert Review of Cardiovascular Therapy 13(2): 163-172
Kimchi, G.; Stlylianou, P.; Wohl, A.; Hadani, M.; Cohen, Z.R.; Zauberman, J.; Feldman, Z.; Spiegelmann, R.; Nissim, O.; Zivly, Z.; Penn, M.; Harnof, S. 2016: Predicting and reducing cranioplasty infections by clinical, radiographic and operative parameters - a historical cohort study. Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia 34: 182-186
Turner, S. 2016: Predicting and reducing risk of exacerbations in children with asthma in the primary care setting: current perspectives. Pragmatic and Observational Research 7: 33-39
Mehta, V.K. 2016: Predicting and reducing risk of radiation pneumonitis in patients with non-small cell lung cancer: A literature review. Journal of Clinical Oncology 23(16_Suppl): 7344-7344
Deary, I.J.; Brett, C.E. 2015: Predicting and retrodicting intelligence between childhood and old age in the 6-Day Sample of the Scottish Mental Survey 1947. Intelligence 50: 1-9
Peñaranda, D.A.; Simonetti, J.A. 2015: Predicting and setting conservation priorities for Bolivian mammals based on biological correlates of the risk of decline. Conservation Biology: the Journal of the Society for Conservation Biology 29(3): 834-843
Becker, C.; Bouvier, E.; Ghestem, A.; Siyoucef, S.; Claverie, D.; Camus, F.ço.; Bartolomei, F.; Benoliel, J.-J.; Bernard, C. 2015: Predicting and treating stress-induced vulnerability to epilepsy and depression. Annals of Neurology 78(1): 128-136
Breed, G.A.; Golson, E.A.; Tinker, M.T. 2017: Predicting animal home-range structure and transitions using a multistate Ornstein-Uhlenbeck biased random walk. Ecology 98(1): 32-47
Progiou, A.G.; Ziomas, I.C. 2015: Predicting annual average particulate concentration in urban areas. Science of the Total Environment 532: 353-359
Peleg, M.; Kim, A.D.; Normand, M.D. 2015: Predicting anthocyanins' isothermal and non-isothermal degradation with the endpoints method. Food Chemistry 187: 537-544
Yang, X.; Liu, H.; Yang, Q.; Liu, J.; Chen, J.; Shi, L. 2016: Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships. Chemosphere 163: 373-381
Gágyor, I.ó; Haasenritter, J.ör.; Bleidorn, J.; McIsaac, W.; Schmiemann, G.; Hummers-Pradier, E.; Himmel, W. 2016: Predicting antibiotic prescription after symptomatic treatment for urinary tract infection: development of a model using data from an RCT in general practice. British Journal of General Practice: the Journal of the Royal College of General Practitioners 66(645): E234-E240
Bianchi, R.; Schonfeld, I.S.; Laurent, E. 2015: Predicting antidepressant treatment without controlling for depression is ill-advised. Journal of Psychiatric Research 69: 180-181
Garcia-Domenech, R.; Zanni, R.; Galvez-Llompart, M.; Galvez, J. 2015: Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology. Molecular Diversity 19(2): 357-366
Tseilikman, O.B.; Kozochkin, D.A.; Manukhina, E.B.; Downey, H.Fred.; Misharina, M.E.; Komelkova, M.V.; Nikitina, A.A.; Golodnii, S.V.; Dodohova, M.A.; Tseilikman, V.E. 2016: Predicting anxiety responses to halogenated glucocorticoid drugs using the hexobarbital sleep time test. Stress 19(4): 390-394
Yourganov, G.; Smith, K.G.; Fridriksson, J.; Rorden, C. 2015: Predicting aphasia type from brain damage measured with structural MRi. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 73: 203-215
Scafoglieri, A.; Clarys, J.P.; Bauer, J.ür.M.; Verlaan, S.; Van Malderen, L.; Vantieghem, S.; Cederholm, T.; Sieber, C.C.; Mets, T.; Bautmans, I. 2017: Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline - the PROVIDE study. Clinical Nutrition 36(3): 869-875
Valle, A.; Pan, I.; Regueiro, B.; Suárez, N.; Tuero, E.án.; Nunes, A.R. 2015: Predicting approach to homework in Primary school students. Psicothema 27(4): 334-340
Basant, N.; Gupta, S.; Singh, K.P. 2015: Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches. Chemosphere 139: 246-255
Templeman, M.A.; Kingsford, M.J. 2015: Predicting aqueous copper and zinc accumulation in the upside-down jellyfish Cassiopea maremetens through the use of biokinetic models. Environmental Monitoring and Assessment 187(7): 416
Ho, S.H.; Speldewinde, P.; Cook, A. 2017: Predicting arboviral disease emergence using Bayesian networks: a case study of dengue virus in Western Australia. Epidemiology and Infection 145(1): 54-66
Boulain, T.; Garot, D.; Vignon, P.; Lascarrou, J.-B.; Benzekri-Lefevre, D.; Dequin, P.-F. 2016: Predicting arterial blood gas and lactate from central venous blood analysis in critically ill patients: a multicentre, prospective, diagnostic accuracy study. British Journal of Anaesthesia 117(3): 341-349
Zheng, T.; Zhang, X.; Wang, Y.; Yu, X. 2016: Predicting associations between micro RNAs and target genes in breast cancer by bioinformatics analyses. Oncology Letters 12(2): 1067-1073
Luo, G.; Stone, B.L.; Fassl, B.; Maloney, C.G.; Gesteland, P.H.; Yerram, S.R.; Nkoy, F.L. 2015: Predicting asthma control deterioration in children. Bmc Medical Informatics and Decision Making 15: 84
Killane, I.; Sulaiman, I.; MacHale, E.; Breathnach, A.; Taylor, T.E.; Holmes, M.S.; Reilly, R.B.; Costello, R.W. 2016: Predicting asthma exacerbations employing remotely monitored adherence. Healthcare Technology Letters 3(1): 51-55
Konradsen, J.R.; Skantz, E.; Nordlund, B.ör.; Lidegran, M.; James, A.; Ono, J.; Ohta, S.; Izuhara, K.; Dahlén, S.-E.; Alving, K.; Hedlin, G. 2015: Predicting asthma morbidity in children using proposed markers of Th2-type inflammation. Pediatric Allergy and Immunology: Official Publication of the European Society of Pediatric Allergy and Immunology 26(8): 772-779
Sears, M.R. 2015: Predicting asthma outcomes. Journal of Allergy and Clinical Immunology 136(4): 829
Ram, S.; Zhang, W.; Williams, M.; Pengetnze, Y. 2015: Predicting asthma-related emergency department visits using big data. IEEE Journal of Biomedical and Health Informatics 19(4): 1216-1223
Ruiz, M.C.; Haapanen, S.; Tolvanen, A.; Robazza, C.; Duda, J.L. 2017: Predicting athletes' functional and dysfunctional emotions: The role of the motivational climate and motivation regulations. Journal of Sports Sciences 35(16): 1598-1606
Winkle, R.A.; Jarman, J.W.E.; Mead, R.Hardwin.; Engel, G.; Kong, M.H.; Fleming, W.; Patrawala, R.A. 2016: Predicting atrial fibrillation ablation outcome: The CAAP-AF score. Heart rhythm 13(11): 2119-2125
Freitas-Murrell, B.; Swift, J.K. 2015: Predicting attitudes toward seeking professional psychological help among Alaska Natives. American Indian and Alaska Native Mental Health Research 22(3): 21-35
Mendonça, C.; Escher, A.; van de Par, S.; Colonius, H. 2015: Predicting auditory space calibration from recent multisensory experience. Experimental Brain Research 233(7): 1983-1991
Eliakim-Raz, N.; Bates, D.W.; Leibovici, L. 2015: Predicting bacteraemia in validated models--a systematic review. Clinical Microbiology and Infection: the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases 21(4): 295-301
José, R.J.; Brown, J.S. 2016: Predicting bacteraemia or rapid identification of the causative pathogen in community acquired pneumonia: where should the priority lie?. European Respiratory Journal 48(3): 619-622
Larsen, P.; Dai, Y.; Collart, F.R. 2015: Predicting bacterial community assemblages using an artificial neural network approach. Methods in Molecular Biology 1260: 33-43
Galarz, L.A.; Fonseca, G.G.; Prentice, C. 2016: Predicting bacterial growth in raw, salted, and cooked chicken breast fillets during storage. Food Science and Technology International 22(6): 461-474
Ding, H.; Liang, Z-Yong.; Guo, F-Biao.; Huang, J.; Chen, W.; Lin, H. 2016: Predicting bacteriophage proteins located in host cell with feature selection technique. Computers in Biology and Medicine 71: 156-161
Wikstrom, E.A.; McKeon, P.O. 2017: Predicting balance improvements following STARS treatments in chronic ankle instability participants. Journal of Science and Medicine in Sport 20(4): 356-361
Meyer, A.D.; Stevens, D.F.; Blackwood, J.C. 2016: Predicting bat colony survival under controls targeting multiple transmission routes of white-nose syndrome. Journal of Theoretical Biology 409: 60-69
Ye, Z.; Rae, C.L.; Nombela, C.; Ham, T.; Rittman, T.; Jones, P.S.; Rodríguez, P.V.áz.; Coyle-Gilchrist, I.; Regenthal, R.; Altena, E.; Housden, C.R.; Maxwell, H.; Sahakian, B.J.; Barker, R.A.; Robbins, T.W.; Rowe, J.B. 2016: Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures. Human Brain Mapping 37(3): 1026-1037
Gireud-Goss, M.; Reyes, S.; Wilson, M.; Farley, M.; Memarzadeh, K.; Srinivasan, S.; Sirisaengtaksin, N.; Yamashita, S.; Tsunoda, S.; Lang, F.F.; Waxham, M.N.; Bean, A.J. 2018: Distinct mechanisms enable inward or outward budding from late endosomes/multivesicular bodies. Experimental Cell Research 372(1): 1-15
Regan, M.M. 2015: Predicting benefit of endocrine therapy for early breast cancer. Breast 24(Suppl 2): S129-S131
Horowitz-Kraus, T.; Eaton, K.; Farah, R.; Hajinazarian, A.; Vannest, J.; Holland, S.K. 2015: Predicting better performance on a college preparedness test from narrative comprehension at the age of 6 years: An fMRI study. Brain research 1629: 54-62
Klein, B.P.; Paffen, C.L.E.; Pas, S.F.T.; Dumoulin, S.O. 2016: Predicting bias in perceived position using attention field models. Journal of Vision 16(7): 15
Grainger, K.; Dodson, Z.; Korff, T. 2017: Predicting bicycle setup for children based on anthropometrics and comfort. Applied Ergonomics 59(Part A): 449-459
Yang, J.; Pak, Y.E.; Lee, T.-R. 2016: Predicting bifurcation angle effect on blood flow in the microvasculature. Microvascular Research 108: 22-28
Plauška, A.; Borst, J.G.; van der Heijden, M. 2016: Predicting binaural responses from monaural responses in the gerbil medial superior olive. Journal of Neurophysiology 115(6): 2950-2963
Chabot-Leclerc, A.; MacDonald, E.N.; Dau, T. 2016: Predicting binaural speech intelligibility using the signal-to-noise ratio in the envelope power spectrum domain. Journal of the Acoustical Society of America 140(1): 192
Basant, N.; Gupta, S.; Singh, K.P. 2016: Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches. Sar and Qsar in Environmental Research 27(1): 67-85
Grudinin, S.; Kadukova, M.; Eisenbarth, A.; Marillet, S.; Cazals, F.éd.ér. 2016: Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. Journal of Computer-Aided Molecular Design 30(9): 791-804
Fernandez, L.A.; Gschwend, P.M. 2015: Predicting bioaccumulation of polycyclic aromatic hydrocarbons in soft-shelled clams (Mya arenaria) using field deployments of polyethylene passive samplers. Environmental Toxicology and Chemistry 34(5): 993-1000
Anighoro, A.; de la Vega de León, A.; Bajorath, J.ür. 2016: Predicting bioactive conformations and binding modes of macrocycles. Journal of Computer-Aided Molecular Design 30(10): 841-849
Lin, S.; Wang, X.; Chao, Y.; He, Y.; Liu, M. 2016: Predicting biofilm thickness and biofilm viability based on the concentration of carbon-nitrogen-phosphorus by support vector regression. Environmental Science and Pollution Research International 23(1): 418-425
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Kouda, K.; Ohara, K.; Nakamura, H.; Fujita, Y.; Iki, M. 2017: Predicting bone mineral acquisition during puberty: data from a 3-year follow-up study in Hamamatsu, Japan. Journal of Bone and Mineral Metabolism 35(2): 185-191
Marini, C.; Fossa, F.; Paoli, C.; Bellingeri, M.; Gnone, G.; Vassallo, P. 2015: Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors. Journal of Environmental Management 150: 9-20
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Achiron, A.; Haddad, F.; Gerra, M.; Bartov, E.; Burgansky-Eliash, Z. 2016: Predicting cataract surgery time based on preoperative risk assessment. European Journal of Ophthalmology 26(3): 226-229
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Kikuchi, M.; Nakagawa, M.; Tone, S.; Saito, H.; Niino, T.; Nagasawa, N.; Sawai, J. 2016: Predicting changes in aquatic toxicity of chemicals resulting from solvent or dispersant use as vehicle. Chemosphere 154: 34-39
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Tawara, D.; Nagura, K. 2017: Predicting changes in mechanical properties of trabecular bone by adaptive remodeling. Computer Methods in Biomechanics and Biomedical Engineering 20(4): 415-425
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Fenerty, K.E.; Folio, L.R.; Patronas, N.J.; Marté, J.L.; Gulley, J.L.; Heery, C.R. 2016: Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST. Bmc Cancer 16(1): 672
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Chappell, T.M.; Kennedy, G.G.; Walgenbach, J.F. 2015: Predicting codling moth (Cydia pomonella) phenology in North Carolina on the basis of temperature and improved generation turnover estimates. Pest Management Science 71(10): 1425-1432
Bolandzadeh, N.; Kording, K.; Salowitz, N.; Davis, J.C.; Hsu, L.; Chan, A.; Sharma, D.; Blohm, G.; Liu-Ambrose, T. 2015: Predicting cognitive function from clinical measures of physical function and health status in older adults. Plos one 10(3): E0119075
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Lopez, M.J.; Schuckers, M. 2017: Predicting coin flips: using resampling and hierarchical models to help untangle the NHL's shoot-out. Journal of Sports Sciences 35(9): 888-897
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Cabral, R.G.; Chapman, C.E.; Aragona, K.M.; Clark, E.; Lunak, M.; Erickson, P.S. 2016: Predicting colostrum quality from performance in the previous lactation and environmental changes. Journal of Dairy Science 99(5): 4048-4055
Cadotte, M.W.; Arnillas, C.A.; Livingstone, S.W.; Yasui, S.-L.E. 2015: Predicting communities from functional traits. Trends in Ecology and Evolution 30(9): 510-511
Suttiwong, J.; Vongsirinavarat, M.; Chaiyawat, P.; Vachalathiti, R. 2015: Predicting community participation after spinal cord injury in Thailand. Journal of Rehabilitation Medicine 47(4): 325-329
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Koch, S.; Benndorf, D.; Fronk, K.; Reichl, U.; Klamt, S. 2016: Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process. Biotechnology for Biofuels 9: 17
Franquet-Griell, H.; Gómez-Canela, C.; Ventura, F.; Lacorte, S. 2015: Predicting concentrations of cytostatic drugs in sewage effluents and surface waters of Catalonia (NE Spain). Environmental Research 138: 161-172
Jiang, G.; Keller, J.; Bond, P.L.; Yuan, Z. 2016: Predicting concrete corrosion of sewers using artificial neural network. Water Research 92: 52-60
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Platikanov, S.; Hernández, A.; González, S.; Luis Cortina, J.; Tauler, R.; Devesa, R. 2017: Predicting consumer preferences for mineral composition of bottled and tap water. Talanta 162: 1-9
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Cabral, C.; Morgado, P.M.; Campos Costa, D.; Silveira, M. 2015: Predicting conversion from MCi to AD with FDG-PET brain images at different prodromal stages. Computers in Biology and Medicine 58: 101-109
Goonawardena, J.; Gunnarsson, R.; de Costa, A. 2015: Predicting conversion from laparoscopic to open cholecystectomy presented as a probability nomogram based on preoperative patient risk factors. American Journal of Surgery 210(3): 492-500
McGuinness, B.; Barrett, S.L.; McIlvenna, J.; Passmore, A.P.; Shorter, G.W. 2015: Predicting conversion to dementia in a memory clinic: a standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping. Alzheimer's and Dementia 1(4): 447-454
Kader, M.; Lamb, D.T.; Wang, L.; Megharaj, M.; Naidu, R. 2016: Predicting copper phytotoxicity based on pore-water pCu. Ecotoxicology 25(3): 481-490
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Boscardin, C.K.; Gonzales, R.; Bradley, K.L.; Raven, M.C. 2015: Predicting cost of care using self-reported health status data. Bmc Health Services Research 15: 406
Kwak, H.-C.; Kho, S. 2016: Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data. Accident; Analysis and Prevention 88: 9-19
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Braun, A.C.; Ilko, D.; Merget, B.; Gieseler, H.; Germershaus, O.; Holzgrabe, U.; Meinel, L. 2015: Predicting critical micelle concentration and micelle molecular weight of polysorbate 80 using compendial methods. European Journal of Pharmaceutics and Biopharmaceutics: Official Journal of Arbeitsgemeinschaft für Pharmazeutische Verfahrenstechnik E.V 94: 559-568
Weiss, V.C. 2015: Predicting critical temperatures of ionic and non-ionic fluids from thermophysical data obtained near the melting point. Journal of Chemical Physics 143(14): 144503
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Barlett, C.; Chamberlin, K.; Witkower, Z. 2017: Predicting cyberbullying perpetration in emerging adults: a theoretical test of the Barlett Gentile Cyberbullying Model. Aggressive Behavior 43(2): 147-154
Diaz-Rodriguez, S.; Bozada, S.M.; Phifer, J.R.; Paluch, A.S. 2016: Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model. Journal of Computer-Aided Molecular Design 30(11): 1007-1017
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Tian, D.; Zheng, W.; He, G.; Zheng, Y.; Andersen, M.E.; Tan, H.; Qu, W. 2015: Predicting cytotoxicity of complex mixtures in high cancer incidence regions of the Huai River Basin based on GC-MS spectrum with partial least squares regression. Environmental Research 137: 391-397
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O'Dowd, E.L.; Lüchtenborg, M.; Baldwin, D.R.; McKeever, T.M.; Powell, H.A.; Møller, H.; Jakobsen, E.; Hubbard, R.B. 2016: Predicting death from surgery for lung cancer: a comparison of two scoring systems in two European countries. Lung Cancer 95: 88-93
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