Section 56

EurekaMag Full Text Articles Chapter 55,100


MacNeil, J.A.M.; Adachi, J.D.; Goltzman, D.; Josse, R.G.; Kovacs, C.S.; Prior, J.C.; Olszynski, W.; Davison, K.S.; Kaiser, S.M. 2012: Predicting fracture using 2D finite element modelling. Medical Engineering and Physics 34(4): 478-484
Sambrook, P.N.; Flahive, J.; Hooven, F.H.; Boonen, S.; Chapurlat, R.; Lindsay, R.; Nguyen, T.V.; Díez-Perez, A.; Pfeilschifter, J.; Greenspan, S.L.; Hosmer, D.; Netelenbos, J.Coen.; Adachi, J.D.; Watts, N.B.; Cooper, C.; Roux, C.; Rossini, M.; Siris, E.S.; Silverman, S.; Saag, K.G.; Compston, J.E.; LaCroix, A.; Gehlbach, S. 2011: Predicting fractures in an international cohort using risk factor algorithms without BMD. Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research 26(11): 2770-2777
Haider, M.K.; Bertrand, H.-O.; Hubbard, R.E. 2011: Predicting fragment binding poses using a combined MCSS MM-GBSA approach. Journal of Chemical Information and Modeling 51(5): 1092-1105
Mathias, J.M. 2010: Predicting frailty risk in older patients. Or Manager 26(11): 23-24
Abramson, D.H.; Folberg, R.; Francis, J.H. 2018: Clinicopathologic Correlation of Choroidal Invasion in Retinoblastoma. Ophthalmology 125(4): 568
Bouarfa, L.; Atallah, L.; Kwasnicki, R.M.; Pettitt, C.; Frost, G.; Yang, G.-Z. 2014: Predicting free-living energy expenditure using a miniaturized ear-worn sensor: an evaluation against doubly labeled water. IEEE Transactions on Bio-Medical Engineering 61(2): 566-575
Bracewell, S.A.; Robinson, L.A.; Firth, L.B.; Knights, A.M. 2013: Predicting free-space occupancy on novel artificial structures by an invasive intertidal barnacle using a removal experiment. Plos one 8(9): E74457
Gale, C.P.; Cattle, B.A.; Simms, A.D.; Greenwood, D.C.; West, R.M. 2009: Predicting freedom from clinical events in non-ST-elevation acute coronary syndromes. Heart 95(16): 1355; Author Reply 1355-6
Brieger, D.; Fox, K.A.A.; Fitzgerald, G.; Eagle, K.A.; Budaj, A.; Avezum, A.; Granger, C.B.; Costa, B.; Anderson, F.A.; Steg, P.G. 2009: Predicting freedom from clinical events in non-ST-elevation acute coronary syndromes: the Global Registry of Acute Coronary Events. Heart 95(11): 888-894
Khrapak, S.A.; Morfill, G.E. 2009: Predicting freezing for some repulsive potentials. Physical Review Letters 103(25): 255003
Feng, G.; Foster, D.H. 2012: Predicting frequency of metamerism in natural scenes by entropy of colors. Journal of the Optical Society of America. a Optics Image Science and Vision 29(2): A200-A208
Bauminger, N.; Solomon, M.; Rogers, S.J. 2010: Predicting friendship quality in autism spectrum disorders and typical development. Journal of Autism and Developmental Disorders 40(6): 751-761
De Vries, H.; Eggers, S.M.; Lechner, L.; van Osch, L.; van Stralen, M.M. 2014: Predicting fruit consumption: the role of habits, previous behavior and mediation effects. Bmc Public Health 14: 730
Chang, S.; Wang, Z.J. 2014: Predicting fruit fly's sensing rate with insect flight simulations. Proceedings of the National Academy of Sciences of the United States of America 111(31): 11246-11251
Lee, S.; Dong, D.X.; Jindal, R.; Maguire, T.; Mitra, B.; Schloss, R.; Yarmush, M. 2014: Predicting full thickness skin sensitization using a support vector machine. Toxicology in Vitro: An International Journal Published in Association with Bibra 28(8): 1413-1423
Greenaway, M.C.; Duncan, N.L.; Hanna, S.; Smith, G.E. 2012: Predicting functional ability in mild cognitive impairment with the Dementia Rating Scale-2. International Psychogeriatrics 24(6): 987-993
De Saint-Hubert, M.; Schoevaerdts, D.; Cornette, P.; D'Hoore, W.; Boland, B.; Swine, C. 2010: Predicting functional adverse outcomes in hospitalized older patients: a systematic review of screening tools. Journal of Nutrition Health and Aging 14(5): 394-399
Lu, H.; Lin, L.; Sato, S.; Xing, Y.; Lee, C.J. 2009: Predicting functional alternative splicing by measuring RNA selection pressure from multigenome alignments. Plos Computational Biology 5(12): E1000608
Veeramani, B.; Bader, J.S. 2010: Predicting functional associations from metabolism using bi-partite network algorithms. Bmc Systems Biology 4: 95
Zhang, T.; Guo, L.; Li, K.; Zhu, D.; Cui, G.; Liu, T. 2011: Predicting functional brain ROIs via fiber shape models. Medical Image Computing and Computer-Assisted Intervention: Miccai . International Conference on Medical Image Computing and Computer-Assisted Intervention 14(Part 2): 42-49
Coleman, A.; Weir, K.; Ware, R.S.; Boyd, R. 2015: Predicting functional communication ability in children with cerebral palsy at school entry. Developmental Medicine and Child Neurology 57(3): 279-285
Josephs, K.A.; Whitwell, J.L.; Weigand, S.D.; Senjem, M.L.; Boeve, B.F.; Knopman, D.S.; Smith, G.E.; Ivnik, R.J.; Jack, C.R.; Petersen, R.C. 2011: Predicting functional decline in behavioural variant frontotemporal dementia. Brain: a Journal of Neurology 134(Part 2): 432-448
Hoogerduijn, J.G.; de Rooij, S.E.; Grobbee, D.E.; Schuurmans, M.J. 2014: Predicting functional decline in older patients undergoing cardiac surgery. Age and Ageing 43(2): 218-221
Chen, L-Yu.; Liu, L-Kuo.; Liu, C-Liang.; Peng, L-Ning.; Lin, M-Hsien.; Chen, L-Kung.; Lan, C-Fu.; Chang, P-Lun. 2013: Predicting functional decline of older men living in veteran homes by minimum data set: implications for disability prevention programs in long term care settings. Journal of the American Medical Directors Association 14(4): 309.E9
Adzhubei, I.; Jordan, D.M.; Sunyaev, S.R. 2013: Predicting functional effect of human missense mutations using PolyPhen-2. Current Protocols in Human Genetics Chapter 7: Unit 7.20
Lindenberg, R.; Zhu, L.L.; Rüber, T.; Schlaug, G. 2012: Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Human Brain Mapping 33(5): 1040-1051
Reid, J.M.; Gubitz, G.J.; Dai, D.; Kydd, D.; Eskes, G.; Reidy, Y.; Christian, C.; Counsell, C.E.; Dennis, M.; Phillips, S.J. 2010: Predicting functional outcome after stroke by modelling baseline clinical and CT variables. Age and Ageing 39(3): 360-366
Nijboer, T.; van de Port, I.; Schepers, V.; Post, M.; Visser-Meily, A. 2013: Predicting functional outcome after stroke: the influence of neglect on basic activities in daily living. Frontiers in Human Neuroscience 7: 182
Simons, M.A.; Ziviani, J.; Copley, J. 2010: Predicting functional outcome for children on admission after burn injury: do parents hold the key?. Journal of Burn Care and Research: Official Publication of the American Burn Association 31(5): 750-765
Li, C.-C.; Chen, Y.-M.; Tsay, S.-L.; Hu, G.-C.; Lin, K.-C. 2010: Predicting functional outcomes in patients suffering from ischaemic stroke using initial admission variables and physiological data: a comparison between tree model and multivariate regression analysis. Disability and Rehabilitation 32(25): 2088-2096
Bade, M.J.; Kittelson, J.M.; Kohrt, W.M.; Stevens-Lapsley, J.E. 2014: Predicting functional performance and range of motion outcomes after total knee arthroplasty. American Journal of Physical Medicine and Rehabilitation 93(7): 579-585
O'Connor, S.R.; Bleakley, C.M.; Tully, M.A.; McDonough, S.M. 2013: Predicting functional recovery after acute ankle sprain. Plos one 8(8): E72124
Matsuzaki, H.; Narisawa, H.; Miwa, H.; Toishi, S. 2009: Predicting functional recovery and return to work after mutilating hand injuries: usefulness of Campbell's Hand Injury Severity Score. Journal of Hand Surgery 34(5): 880-885
Friedrich, M.G. 2013: Predicting functional recovery in acute heart disease using a single snapshot: we have a winner. Journal of the American College of Cardiology 61(1): 64-65
Torkamani, A.; Schork, N.J. 2008: Predicting functional regulatory polymorphisms. Bioinformatics 24(16): 1787-1792
Haddow, C.; Perry, J.; Durrant, M.; Faith, J. 2011: Predicting functional residues of protein sequence alignments as a feature selection task. International Journal of Data Mining and Bioinformatics 5(6): 691-705
Skinner, M.L.; Haggerty, K.P.; Fleming, C.B.; Catalano, R.F. 2009: Predicting functional resilience among young-adult children of opiate-dependent parents. Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine 44(3): 283-290
McKenzie, H.A.; Fung, C.; Becker, T.M.; Irvine, M.; Mann, G.J.; Kefford, R.F.; Rizos, H. 2010: Predicting functional significance of cancer-associated p16(INK4a) mutations in CDKN2A. Human Mutation 31(6): 692-701
Ward, L.D.; Bussemaker, H.J. 2008: Predicting functional transcription factor binding through alignment-free and affinity-based analysis of orthologous promoter sequences. Bioinformatics 24(13): I165-I171
Selpi; Bryant, C.H.; Kemp, G.J.L.; Sarv, J.; Kristiansson, E.; Sunnerhagen, P. 2009: Predicting functional upstream open reading frames in Saccharomyces cerevisiae. Bmc Bioinformatics 10: 451
Ucar, D.; Beyer, A.; Parthasarathy, S.; Workman, C.T. 2009: Predicting functionality of protein-DNA interactions by integrating diverse evidence. Bioinformatics 25(12): I137-I144
Levenstien, M.A.; Klein, R.J. 2011: Predicting functionally important SNP classes based on negative selection. Bmc Bioinformatics 12: 26
Dwyer, R.S.; Ricci, D.P.; Colwell, L.J.; Silhavy, T.J.; Wingreen, N.S. 2013: Predicting functionally informative mutations in Escherichia coli BamA using evolutionary covariance analysis. Genetics 195(2): 443-455
Hu, L.; Huang, T.; Shi, X.; Lu, W.-C.; Cai, Y.-D.; Chou, K.-C. 2011: Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. Plos one 6(1): E14556
Shah, D.A.; Molineros, J.E.; Paul, P.A.; Willyerd, K.T.; Madden, L.V.; De Wolf, E.D. 2013: Predicting fusarium head blight epidemics with weather-driven pre- and post-anthesis logistic regression models. Phytopathology 103(9): 906-919
Dunn, M.; Mazanov, J.; Sitharthan, G. 2009: Predicting future anabolic-androgenic steroid use intentions with current substance use: findings from an internet-based survey. Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine 19(3): 222-227
Hansel, N.N.; Matsui, E.C.; Rusher, R.; McCormack, M.C.; Curtin-Brosnan, J.; Peng, R.D.; Mazique, D.; Breysse, P.N.; Diette, G.B. 2011: Predicting future asthma morbidity in preschool inner-city children. Journal of Asthma: Official Journal of the Association for the Care of Asthma 48(8): 797-803
Siegenthaler, U.; Oeschger, H. 1978: Predicting future atmospheric carbon dioxide levels. Science 199(4327): 388-395
Tolan, T. 2011: Predicting future behavior. Healthcare Informatics: the Business Magazine for Information and Communication Systems 28(6): 80
Lau, E.H.Y.; He, X.-Q.; Lee, C.-K.; Wu, J.T. 2013: Predicting future blood demand from thalassemia major patients in Hong Kong. Plos one 8(12): E81846
Kuceyeski, A.; Kamel, H.; Navi, B.B.; Raj, A.; Iadecola, C. 2014: Predicting future brain tissue loss from white matter connectivity disruption in ischemic stroke. Stroke 45(3): 717-722
Zhang, D.; Shen, D. 2012: Predicting future clinical changes of MCi patients using longitudinal and multimodal biomarkers. Plos one 7(3): E33182
Bewick, S.; Stuble, K.L.; Lessard, J-Phillipe.; Dunn, R.R.; Adler, F.R.; Sanders, N.J. 2014: Predicting future coexistence in a North American ant community. Ecology and evolution 4(10): 1804-1819
Petrič, I.; Cestnik, B. 2014: Predicting future discoveries from current scientific literature. Methods in Molecular Biology 1159: 159-168
Ordonez-Llanos, J.; Sionis, A. 2013: Predicting future events in patients with stable cardiovascular disease. Will high-sensitivity cardiac troponins be up to the challenge?. Clinical Biochemistry 46(1-2): 10-11
Furukawa, K.; Cologne, J.B.; Shimizu, Y.; Ross, N.Phillip. 2009: Predicting future excess events in risk assessment. Risk Analysis: An Official Publication of the Society for Risk Analysis 29(6): 885-899
Corke, C.; Leeuw, E.d.; Lo, S.K.; George, C. 2009: Predicting future intensive care demand in Australia. Critical Care and Resuscitation: Journal of the Australasian Academy of Critical Care Medicine 11(4): 257-260
De Grey, A.D.N.J. 2014: Predicting future longevity: at long last, extrapolation is on the way out. Rejuvenation Research 17(3): 247-248
Bagci, U.; Yao, J.; Miller-Jaster, K.; Chen, X.; Mollura, D.J. 2013: Predicting future morphological changes of lesions from radiotracer uptake in 18F-FDG-PET images. Plos one 8(2): E57105
Gray, N.S.; Fitzgerald, S.; Taylor, J.; Macculloch, M.J.; Snowden, R.J. 2007: Predicting future reconviction in offenders with intellectual disabilities: the predictive efficacy of VRAG, PCL-SV, and the HCR-20. Psychological Assessment 19(4): 474-479
Curtis, J.R.; Luijtens, K.; Kavanaugh, A. 2012: Predicting future response to certolizumab pegol in rheumatoid arthritis patients: features at 12 weeks associated with low disease activity at 1 year. Arthritis Care and Research 64(5): 658-667
Thamrin, C.; Zindel, J.; Nydegger, R.; Reddel, H.K.; Chanez, P.; Wenzel, S.E.; FitzPatrick, S.; Watt, R.A.; Suki, B.él.; Frey, U. 2011: Predicting future risk of asthma exacerbations using individual conditional probabilities. Journal of Allergy and Clinical Immunology 127(6): 1494-502.E3
Van Voorhees, B.W.; Paunesku, D.; Gollan, J.; Kuwabara, S.; Reinecke, M.; Basu, A. 2008: Predicting future risk of depressive episode in adolescents: the Chicago Adolescent Depression Risk Assessment (CADRA). Annals of Family Medicine 6(6): 503-511
Hermosillo, R.; Ritterband-Rosenbaum, A.; van Donkelaar, P. 2011: Predicting future sensorimotor states influences current temporal decision making. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 31(27): 10019-10022
Kanoupakis, E.M.; Manios, E.G.; Vardas, P.E. 2009: Predicting future shocks in implantable cardioverter defibrillator recipients: the role of biomarkers. Europace: European Pacing Arrhythmias and Cardiac Electrophysiology: Journal of the Working Groups on Cardiac Pacing Arrhythmias and Cardiac Cellular Electrophysiology of the European Society of Cardiology 11(11): 1434-1439
Mitchell, C.C.; Ashley, S.W.; Zinner, M.J.; Moore, F.D. 2007: Predicting future staffing needs at teaching hospitals: use of an analytical program with multiple variables. Archives of Surgery 142(4): 329-334
May, A.M.; Klonsky, E.D.; Klein, D.N. 2012: Predicting future suicide attempts among depressed suicide ideators: a 10-year longitudinal study. Journal of Psychiatric Research 46(7): 946-952
Coid, J.W.; Ullrich, S.; Kallis, C. 2013: Predicting future violence among individuals with psychopathy. British Journal of Psychiatry: the Journal of Mental Science 203(5): 387-388
Liew, T.M.; Luo, N.; Ng, W.Y.; Chionh, H.L.; Goh, J.; Yap, P. 2010: Predicting gains in dementia caregiving. Dementia and Geriatric Cognitive Disorders 29(2): 115-122
Pagani, L.S.; Derevensky, J.L.; Japel, C. 2009: Predicting gambling behavior in sixth grade from kindergarten impulsivity: a tale of developmental continuity. Archives of Pediatrics and Adolescent Medicine 163(3): 238-243
Lu, W.C.; Cheng, C.-F.; Chen, L.H. 2013: Predicting game-attending behavior in amateur athletes: the moderating role of intention stability. Psychological Reports 113(2): 420-434
Wu, B.; Buddensick, T.J.; Ferdosi, H.; Narducci, D.Marie.; Sautter, A.; Setiawan, L.; Shaukat, H.; Siddique, M.; Sulkowski, G.N.; Kamangar, F.; Kowdley, G.C.; Cunningham, S.C. 2014: Predicting gangrenous cholecystitis. Hpb: the Official Journal of the International Hepato Pancreato Biliary Association 16(9): 801-806
Siderius, D.W.; Gelb, L.D. 2009: Predicting gas adsorption in complex microporous and mesoporous materials using a new density functional theory of finely discretized lattice fluids. Langmuir 25(3): 1296-1299
Guerra, D.; Ricciardi, L.; Laborde, J-Claude.; Domenech, S. 2007: Predicting gaseous pollutant dispersion around a workplace. Journal of Occupational and Environmental Hygiene 4(8): 619-633
Li, C.; Xie, H.-B.; Chen, J.; Yang, X.; Zhang, Y.; Qiao, X. 2014: Predicting gaseous reaction rates of short chain chlorinated paraffins with ·OH: overcoming the difficulty in experimental determination. Environmental Science and Technology 48(23): 13808-13816
Paproski, R.J.; Young, J.D.; Cass, C.E. 2010: Predicting gemcitabine transport and toxicity in human pancreatic cancer cell lines with the positron emission tomography tracer 3'-deoxy-3'-fluorothymidine. Biochemical Pharmacology 79(4): 587-595
Joyce, A.R.; Palsson, B.Ø 2008: Predicting gene essentiality using genome-scale in silico models. Methods in Molecular Biology 416: 433-457
Costa, I.G.; Roider, H.G.; do Rego, T.G.; de Carvalho, F.d.A.T. 2011: Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models. Bmc Bioinformatics 12(Suppl 1): S29
Bhosale, R.; Jewell, J.B.; Hollunder, J.; Koo, A.J.K.; Vuylsteke, M.; Michoel, T.; Hilson, P.; Goossens, A.; Howe, G.A.; Browse, J.; Maere, S. 2013: Predicting gene function from uncontrolled expression variation among individual wild-type Arabidopsis plants. Plant Cell 25(8): 2865-2877
Guan, Y.; Myers, C.L.; Hess, D.C.; Barutcuoglu, Z.; Caudy, A.A.; Troyanskaya, O.G. 2008: Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biology 9(Suppl 1): S3
Chen, Y.; Li, Z.; Wang, X.; Feng, J.; Hu, X. 2010: Predicting gene function using few positive examples and unlabeled ones. Bmc Genomics 11(Suppl 2): S11
Phuong, T.; Nhung, N. 2013: Predicting gene function using similarity learning. Bmc Genomics 14(Suppl 4): S4
Cohen, P.E.; Holloway, J.K. 2010: Predicting gene networks in human oocyte meiosis. Biology of Reproduction 82(3): 469-472
Kuppuswamy, U.; Ananthasubramanian, S.; Wang, Y.; Balakrishnan, N.; Ganapathiraju, M.K. 2014: Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions. Algorithms for Molecular Biology: Amb 9(1): 10
Dozmorov, M.G.; Giles, C.B.; Wren, J.D. 2011: Predicting gene ontology from a global meta-analysis of 1-color microarray experiments. Bmc Bioinformatics 12(Suppl 10): S14
Zhu, M.; Dahmen, J.L.; Stacey, G.; Cheng, J. 2013: Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data. Bmc Bioinformatics 14: 278
Cosgrove, E.J.; Zhou, Y.; Gardner, T.S.; Kolaczyk, E.D. 2008: Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia. Bioinformatics 24(21): 2482-2490
Teif, V.B. 2010: Predicting gene-regulation functions: lessons from temperate bacteriophages. Biophysical Journal 98(7): 1247-1256
Rahmani, H.; Blockeel, H.; Bender, A. 2012: Predicting genes involved in human cancer using network contextual information. Journal of Integrative Bioinformatics 9(1): 210
Feng, Y.; Chiu, C.-H. 2014: Predicting genetic and ecological characteristics of bacterial species by comparing the trajectories of dN/dS and dI/dS in bacterial genomes. Molecular Biosystems 10(2): 266-272
Chipman, K.C.; Singh, A.K. 2009: Predicting genetic interactions with random walks on biological networks. Bmc Bioinformatics 10: 17
Schooler, C.; Revell, A.J.; Timpano, K.R.; Wheaton, M.; Murphy, D.L. 2008: Predicting genetic loading from symptom patterns in obsessive- compulsive disorder: a latent variable analysis. Depression and Anxiety 25(8): 680-688
Raadsma, H.W.; Moser, G.; Crump, R.E.; Khatkar, M.S.; Zenger, K.R.; Cavanagh, J.A.L.; Hawken, R.J.; Hobbs, M.; Barris, W.; Solkner, J.; Nicholas, F.W.; Tier, B. 2008: Predicting genetic merit for mastitis and fertility in dairy cattle using genome wide selection and high density SNP screens. Developments in Biologicals 132: 219-223
Lee, I.; Lehner, B.; Vavouri, T.; Shin, J.; Fraser, A.G.; Marcotte, E.M. 2010: Predicting genetic modifier loci using functional gene networks. Genome Research 20(8): 1143-1153
Sahu, S.S.; Weirick, T.; Kaundal, R. 2014: Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interolog-based approaches. Bmc Bioinformatics 15(Suppl 11): S13
Yang, N.; Winkel, L.H.E.; Johannesson, K.H. 2014: Predicting geogenic arsenic contamination in shallow groundwater of south Louisiana, United States. Environmental Science and Technology 48(10): 5660-5666
Sandoval-Ruiz, C.A.; Zumaquero-Rios, J.L.; Rojas-Soto, O.R. 2008: Predicting geographic and ecological distributions of triatomine species in the southern Mexican state of Puebla using ecological niche modeling. Journal of Medical Entomology 45(3): 540-546
Epp, T.Y.; Waldner, C.L.; Berke, O. 2009: Predicting geographical human risk of West Nile virus--Saskatchewan, 2003 and 2007. Canadian Journal of Public Health 100(5): 344-348
Rocha, C.M.; Kruger, E.; Whyman, R.; Tennant, M. 2014: Predicting geographically distributed adult dental decay in the greater Auckland region of New Zealand. Community Dental Health 31(2): 85-90
Carpenter, C.R.; Avidan, M.S.; Wildes, T.; Stark, S.; Fowler, S.A.; Lo, A.X. 2014: Predicting geriatric falls following an episode of emergency department care: a systematic review. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine 21(10): 1069-1082
Reboul, C.F.; Mahmood, K.; Whisstock, J.C.; Dunstone, M.A. 2012: Predicting giant transmembrane β-barrel architecture. Bioinformatics 28(10): 1299-1302
Hamerton, I.; Howlin, B.J.; Kamyszek, G. 2012: Predicting glass transition temperatures of polyarylethersulphones using QSPR methods. Plos one 7(6): E38424
Howard, D.L.; Kim, M.M.; Hartnett, M.E. 2011: Predicting glaucoma diagnosis in an elderly sample: revisiting the established populations for epidemiologic studies of the elderly. Journal of the National Medical Association 103(4): 332-341
Bowd, C.; Lee, I.; Goldbaum, M.H.; Balasubramanian, M.; Medeiros, F.A.; Zangwill, L.M.; Girkin, C.A.; Liebmann, J.M.; Weinreb, R.N. 2012: Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements. Investigative Ophthalmology and Visual Science 53(4): 2382-2389
Xiang, Y.; Zhang, C.-Q.; Huang, K. 2012: Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data. Bmc Bioinformatics 13(Suppl 2): S12
Trautwein, U.; Lüdtke, O. 2007: Predicting global and topic-specific certainty beliefs: domain-specificity and the role of the academic environment. British Journal of Educational Psychology 77(Part 4): 907-934
DeMott, P.J.; Prenni, A.J.; Liu, X.; Kreidenweis, S.M.; Petters, M.D.; Twohy, C.H.; Richardson, M.S.; Eidhammer, T.; Rogers, D.C. 2010: Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proceedings of the National Academy of Sciences of the United States of America 107(25): 11217-11222
Gustafson, E.J.; Shvidenko, A.Z.; Sturtevant, B.R.; Scheller, R.M. 2010: Predicting global change effects on forest biomass and composition in south-central Siberia. Ecological Applications: a Publication of the Ecological Society of America 20(3): 700-715
Senior, K. 2008: Predicting global death patterns not an easy task. Lancet. Infectious Diseases 8(7): 411
Tangri, N.; Alam, A.; Giannetti, N.; Deedwardes, M.B.; Cantarovich, M. 2008: Predicting glomerular filtration rate in heart transplant recipients using serum creatinine-based equations with cimetidine. Journal of Heart and Lung Transplantation: the Official Publication of the International Society for Heart Transplantation 27(8): 905-909
Novak, M.T.; Yuan, F.; Reichert, W.M. 2013: Predicting glucose sensor behavior in blood using transport modeling: relative impacts of protein biofouling and cellular metabolic effects. Journal of Diabetes Science and Technology 7(6): 1547-1560
McDowell, G.S.V.; Blanchard, A.P.; Taylor, G.P.; Figeys, D.; Fai, S.; Bennett, S.A.L. 2014: Predicting glycerophosphoinositol identities in lipidomic datasets using VaLID (Visualization and Phospholipid Identification)--an online bioinformatic search engine. Biomed Research International 2014: 818670
Fluckiger, M.; Brown, M.R.; Ward, L.R.; Moltschaniwskyj, N.A. 2011: Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS). Food Chemistry 126(4): 1817-1820
O'Brien, S.R.; Xue, Y. 2014: Predicting goal achievement during stroke rehabilitation for Medicare beneficiaries. Disability and Rehabilitation 36(15): 1273-1278
Wurm, M.F.; Hrkać, M.; Morikawa, Y.; Schubotz, R.I. 2014: Predicting goals in action episodes attenuates BOLD response in inferior frontal and occipitotemporal cortex. Behavioural Brain Research 274: 108-117
Xu, B.; Madix, R.J.; Friend, C.M. 2014: Predicting gold-mediated catalytic oxidative-coupling reactions from single crystal studies. Accounts of Chemical Research 47(3): 761-772
Robertson, J.M.; Söhn, M.; Yan, D. 2010: Predicting grade 3 acute diarrhea during radiation therapy for rectal cancer using a cutoff-dose logistic regression normal tissue complication probability model. International Journal of Radiation Oncology Biology Physics 77(1): 66-72
Duval, B.D.; Anderson-Teixeira, K.J.; Davis, S.C.; Keogh, C.; Long, S.P.; Parton, W.J.; DeLucia, E.H. 2013: Predicting greenhouse gas emissions and soil carbon from changing pasture to an energy crop. Plos one 8(8): E72019
Magnusson, M.; Bergsten, A.; Ecke, F.; Bodin, O.; Bodin, L.; Hörnfeldt, B. 2013: Predicting grey-sided vole occurrence in northern Sweden at multiple spatial scales. Ecology and Evolution 3(13): 4365-4376
Wasson, P.; Prodoehl, J.; Coombes, S.A.; Corcos, D.M.; Vaillancourt, D.E. 2010: Predicting grip force amplitude involves circuits in the anterior basal ganglia. Neuroimage 49(4): 3230-3238
Mowat, G.; Heard, D.C.; Schwarz, C.J. 2013: Predicting grizzly bear density in western North America. Plos one 8(12): E82757
Peterson, C.B.; Crosby, R.D.; Wonderlich, S.A.; Mitchell, J.E.; Crow, S.J.; Engel, S. 2013: Predicting group cognitive-behavioral therapy outcome of binge eating disorder using empirical classification. Behaviour Research and Therapy 51(9): 526-532
Croon, M.A.; van Veldhoven, M.J.P.M. 2007: Predicting group-level outcome variables from variables measured at the individual level: a latent variable multilevel model. Psychological Methods 12(1): 45-57
Busscher, I.; Wapstra, F.H.; Veldhuizen, A.G. 2010: Predicting growth and curve progression in the individual patient with adolescent idiopathic scoliosis: design of a prospective longitudinal cohort study. Bmc Musculoskeletal Disorders 11: 93
Mesman, J.; Stoel, R.; Bakermans-Kranenburg, M.J.; van IJzendoorn, M.H.; Juffer, F.; Koot, H.M.; Alink, L.R.A. 2009: Predicting growth curves of early childhood externalizing problems: differential susceptibility of children with difficult temperament. Journal of Abnormal Child Psychology 37(5): 625-636
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Mejlholm, O.; Gunvig, A.; Borggaard, C.; Blom-Hanssen, J.; Mellefont, L.; Ross, T.; Leroi, F.; Else, T.; Visser, D.; Dalgaard, P. 2010: Predicting growth rates and growth boundary of Listeria monocytogenes - An international validation study with focus on processed and ready-to-eat meat and seafood. International Journal of Food Microbiology 141(3): 137-150
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Buyuktiryaki, A.B.; Civelek, E.; Can, D.; Orhan, F.ıl.; Aydogan, M.; Reisli, I.; Keskin, O.; Akcay, A.; Yazicioglu, M.; Cokugras, H.; Yuksel, H.; Zeyrek, D.; Kocak, A.K.; Sekerel, B.E. 2013: Predicting hospitalization in children with acute asthma. Journal of Emergency Medicine 44(5): 919-927
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Parker, B.M.; Cywinski, J.B.; Alster, J.M.; Irefin, S.A.; Popovich, M.; Beven, M.; Fung, J.J. 2008: Predicting immunosuppressant dosing in the early postoperative period with noninvasive indocyanine green elimination following orthotopic liver transplantation. Liver Transplantation: Official Publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society 14(1): 46-52
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Fox, N.J.; White, P.C.L.; McClean, C.J.; Marion, G.; Evans, A.; Hutchings, M.R. 2011: Predicting impacts of climate change on Fasciola hepatica risk. Plos one 6(1): E16126
Ma, J.; Hu, Y.; Bu, R.; Chang, Y.; Deng, H.; Qin, Q. 2014: Predicting impacts of climate change on the aboveground carbon sequestration rate of a temperate forest in northeastern China. Plos one 9(4): E96157
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Nieuwenhuijsen, K.; Cornelius, L.R.; de Boer, M.R.; Groothoff, J.W.; Frings-Dresen, M.H.W.; van der Klink, J.J.L.; Brouwer, S. 2014: Predicting improvement of functioning in disability claimants. Journal of Occupational Rehabilitation 24(3): 410-418
Haber, M.G.; Karpur, A.; Deschênes, N.; Clark, H.B. 2008: Predicting improvement of transitioning young people in the partnerships for youth transition initiative: findings from a multisite demonstration. Journal of Behavioral Health Services and Research 35(4): 488-513
Black, E.B.; Mildred, H. 2013: Predicting impulsive self-injurious behavior in a sample of adult women. Journal of Nervous and Mental Disease 201(1): 72-75
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Nielsen, E.I.; Cars, O.; Friberg, L.E. 2011: Predicting in vitro antibacterial efficacy across experimental designs with a semimechanistic pharmacokinetic-pharmacodynamic model. Antimicrobial Agents and ChemoTherapy 55(4): 1571-1579
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Zheng, B.; Tan, L.; Mo, X.; Yu, W.; Wang, Y.; Tucker-Kellogg, L.; Welsch, R.E.; So, P.T.C.; Yu, H. 2011: Predicting in vivo anti-hepatofibrotic drug efficacy based on in vitro high-content analysis. Plos one 6(11): E26230
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Epstein, H.; Rabinovich, L.; Banai, S.; Elazar, V.; Gao, J.; Chorny, M.; Danenebrg, H.D.; Golomb, G. 2008: Predicting in vivo efficacy of potential restenosis therapies by cell culture studies: species-dependent susceptibility of vascular smooth muscle cells. Open Cardiovascular Medicine Journal 2: 60-69
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Stojanac, I.; Drobac, M.; Petrovic, L.; Atanackovic, T. 2012: Predicting in vivo failure of rotary nickel-titanium endodontic instruments under cyclic fatigue. Dental Materials Journal 31(4): 650-655
Akers, W.J.; Berezin, M.Y.; Lee, H.; Achilefu, S. 2008: Predicting in vivo fluorescence lifetime behavior of near-infrared fluorescent contrast agents using in vitro measurements. Journal of Biomedical Optics 13(5): 054042
Péry, A.R.R.; Brochot, C.él.; Desmots, S.; Boize, M.; Sparfel, L.; Fardel, O. 2011: Predicting in vivo gene expression in macrophages after exposure to benzo(a)pyrene based on in vitro assays and toxicokinetic/toxicodynamic models. Toxicology Letters 201(1): 8-14
Wolf, M.T.; Vodovotz, Y.; Tottey, S.; Brown, B.N.; Badylak, S.F. 2015: Predicting in vivo responses to biomaterials via combined in vitro and in silico analysis. Tissue Engineering. Part C Methods 21(2): 148-159
Greene, N.; Song, M. 2011: Predicting in vivo safety characteristics using physiochemical properties and in vitro assays. Future Medicinal Chemistry 3(12): 1503-1511
Doucet, F.J.; White, G.A.; Wulfert, F.; Hill, S.E.; Wiseman, J. 2010: Predicting in vivo starch digestibility coefficients in newly weaned piglets from in vitro assessment of diets using multivariate analysis. British Journal of Nutrition 103(9): 1309-1318
Alghnam, S.; Palta, M.; Hamedani, A.; Alkelya, M.; Remington, P.L.; Durkin, M.S. 2014: Predicting in-hospital death among patients injured in traffic crashes in Saudi Arabia. Injury 45(11): 1693-1699
Ndour, C.; Dossou Gbété, S.; Bru, N.; Abrahamowicz, M.; Fauconnier, A.; Traoré, M.; Diop, A.; Fournier, P.; Dumont, A. 2013: Predicting in-hospital maternal mortality in Senegal and Mali. Plos one 8(5): E64157
Tolenaar, J.L.; Froehlich, W.; Jonker, F.H.W.; Upchurch, G.R.; Rampoldi, V.; Tsai, T.T.; Bossone, E.; Evangelista, A.; O'Gara, P.; Pape, L.; Montgomery, D.; Isselbacher, E.M.; Nienaber, C.A.; Eagle, K.A.; Trimarchi, S. 2014: Predicting in-hospital mortality in acute type B aortic dissection: evidence from International Registry of Acute Aortic Dissection. Circulation 130(11 Suppl. 1): S45-S50
Grendar, J.; Shaheen, A.A.; Myers, R.P.; Parker, R.; Vollmer, C.M.; Ball, C.G.; Quan, M.L.; Kaplan, G.G.; Al-Manasra, T.; Dixon, E. 2012: Predicting in-hospital mortality in patients undergoing complex gastrointestinal surgery: determining the optimal risk adjustment method. Archives of Surgery 147(2): 126-135
Myers, R.P.; Quan, H.; Hubbard, J.N.; Shaheen, A.A.M.; Kaplan, G.G. 2009: Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods. Hepatology 49(2): 568-577
Ribera, A.; Marsal, J.R.; Ferreira-González, I.; Cascant, P.ó; Pons, J.M.V.; Mitjavila, F.; Salas, T.; Permanyer-Miralda, G.à 2008: Predicting in-hospital mortality with coronary bypass surgery using hospital discharge data: comparison with a prospective observational study. Revista Espanola de Cardiologia 61(8): 843-852
Marschollek, M.; Nemitz, G.; Gietzelt, M.; Wolf, K.H.; Meyer Zu Schwabedissen, H.; Haux, R. 2009: Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements. Zeitschrift für Gerontologie und Geriatrie 42(4): 317-321
Xu, M.; Yu, L.; Wan, B.; Yu, L.; Huang, Q. 2011: Predicting inactive conformations of protein kinases using active structures: conformational selection of type-II inhibitors. Plos one 6(7): E22644
Allen, S.; Yeung, P.; Janczewski, M.; Siddique, N. 2008: Predicting inadequate spirometry technique and the use of FEV1/FEV3 as an alternative to FEV1/FVC for patients with mild cognitive impairment. Clinical Respiratory Journal 2(4): 208-213
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Cheng, S.; Wang, T.J. 2009: Predicting incident CKD. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 53(6): 936-939
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Cruz-Cabeza, A.J.; Day, G.M.; Jones, W. 2009: Predicting inclusion behaviour and framework structures in organic crystals. Chemistry 15(47): 13033-13040
Golino, H.Fernandes.; Amaral, L.Souza.de.Brito.; Duarte, S.Fernando.Pimentel.; Gomes, C.Mauro.Assis.; Soares, T.de.Jesus.; Dos Reis, L.Araujo.; Santos, J. 2014: Predicting increased blood pressure using machine learning. Journal of Obesity 2014: 637635
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Jarošík, Věch.; Pyšek, P.; Foxcroft, L.C.; Richardson, D.M.; Rouget, M.; MacFadyen, S. 2011: Predicting incursion of plant invaders into Kruger National Park, South Africa: the interplay of general drivers and species-specific factors. Plos one 6(12): E28711
Corley, C.D.; Mihalcea, R.; Mikler, A.R.; Sanfilippo, A.P. 2011: Predicting individual affect of health interventions to reduce HPV prevalence. Advances in Experimental Medicine and Biology 696: 181-190
Bekker, M.H.J.; Croon, M.A.; van Balkom, E.G.A.; Vermee, J.B.G. 2008: Predicting individual differences in autonomy-connectedness: the role of body awareness, alexithymia, and assertiveness. Journal of Clinical Psychology 64(6): 747-765
Connors, B.L.; Rende, R.; Colton, T.J. 2013: Predicting individual differences in decision-making process from signature movement styles: an illustrative study of leaders. Frontiers in Psychology 4: 658
Nagata, K. 2008: Predicting individual differences in drug-metabolizing enzymes. Drug Metabolism and Pharmacokinetics 23(1): 1
Wager, T.D.; Atlas, L.Y.; Leotti, L.A.; Rilling, J.K. 2011: Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 31(2): 439-452
Harlaar, N.; Cutting, L.; Deater-Deckard, K.; Dethorne, L.S.; Justice, L.M.; Schatschneider, C.; Thompson, L.A.; Petrill, S.A. 2010: Predicting individual differences in reading comprehension: a twin study. Annals of Dyslexia 60(2): 265-288
Chandler, J.F.; Arnold, R.D.; Phillips, J.B.; Turnmire, A.E. 2013: Predicting individual differences in response to sleep loss: application of current techniques. Aviation Space and Environmental Medicine 84(9): 927-937
Zaak, D.; Burger, M.; Otto, W.; Bastian, P.J.; Denzinger, S.; Stief, C.G.; Buchner, H.; Hartmann, A.; Wieland, W.F.; Shariat, S.F.; Fritsche, H.-M. 2010: Predicting individual outcomes after radical cystectomy: an external validation of current nomograms. Bju International 106(3): 342-348
Chen, J.; Zhao, G. 2011: Predicting individual prognosis for patients undergoing resection of colorectal liver metastases. Annals of Surgical Oncology 18(3): 894-895
Van de Pas, N.C.A.; Rullmann, J.A.C.; Woutersen, R.A.; van Ommen, B.; Rietjens, I.M.C.M.; de Graaf, A.A. 2014: Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations. Journal of Pharmacokinetics and Pharmacodynamics 41(4): 351-362
Coderch, J.; Sánchez-Pérez, I.; Ibern, P.; Carreras, M.; Pérez-Berruezo, X.; Inoriza, J.é M. 2014: Predicting individual risk of high healthcare cost to identify complex chronic patients. Gaceta Sanitaria 28(4): 292-300
Lyman, G.H.; Kuderer, N.M.; Crawford, J.; Wolff, D.A.; Culakova, E.; Poniewierski, M.S.; Dale, D.C. 2011: Predicting individual risk of neutropenic complications in patients receiving cancer chemotherapy. Cancer 117(9): 1917-1927
Souto, J.C.; Soria, J.é M. 2012: Predicting individual risk of venous thrombosis. Blood 120(3): 500-501
Lagarde, S.M.; Reitsma, J.B.; Ten Kate, F.J.W.; Busch, O.R.C.; Obertop, H.; Zwinderman, A.H.; Moons, J.; van Lanschot, J.J.B.; Lerut, T. 2008: Predicting individual survival after potentially curative esophagectomy for adenocarcinoma of the esophagus or gastroesophageal junction. Annals of Surgery 248(6): 1006-1013
Cristia, A.; Seidl, A.; Junge, C.; Soderstrom, M.; Hagoort, P. 2014: Predicting individual variation in language from infant speech perception measures. Child Development 85(4): 1330-1345
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Quinn, A.; Tamerius, J.D.; Perzanowski, M.; Jacobson, J.S.; Goldstein, I.; Acosta, L.; Shaman, J. 2014: Predicting indoor heat exposure risk during extreme heat events. Science of the Total Environment 490: 686-693
Martinez-Serrano, J.J.; Diez de Los Rios, A. 2014: Predicting induced activity in the Havar foils of the (18)F production targets of a PET cyclotron and derived radiological risk. Health Physics 107(2): 103-110
Sheu, R.J.; Jiang, S.H. 2010: Predicting induced radioactivity for the accelerator operations at the Taiwan Photon Source. Health Physics 99(6): 788-799
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Carrera, E.; Jones, P.S.; Alawneh, J.A.; Klærke Mikkelsen, I.; Cho, T.-H.; Siemonsen, S.; Guadagno, J.V.; Mouridsen, K.; Ribe, L.; Hjort, N.; Fryer, T.D.; Carpenter, T.A.; Aigbirhio, F.I.; Fiehler, J.; Nighoghossian, N.; Warburton, E.A.; Ostergaard, L.; Baron, J.-C. 2011: Predicting infarction within the diffusion-weighted imaging lesion: does the mean transit time have added value?. Stroke 42(6): 1602-1607
Schley, D.; Burgin, L.; Gloster, J. 2009: Predicting infection risk of airborne foot-and-mouth disease. Journal of the Royal Society Interface 6(34): 455-462
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Jarvie, M.E.; Hand, D.W. 2009: Predicting influent estradiol and estrone concentrations for wastewater treatment facilities. Water Environment Research: a Research Publication of the Water Environment Federation 81(2): 131-139
Keijzers, G.B.; Vossen, C.Nathaniel.Kai-Lik.; Zhang, P.; Macbeth, D.; Derrington, P.; Gerrard, J.Gregory.; Doust, J. 2011: Predicting influenza A and 2009 H1N1 influenza in patients admitted to hospital with acute respiratory illness. Emergency Medicine Journal: Emj 28(6): 500-506
Yu, D.S.F.; Low, L.P.L.; Lee, I.F.K.; Lee, D.T.F.; Ng, W.M. 2014: Predicting influenza vaccination intent among at-risk chinese older adults in Hong Kong. Nursing Research 63(4): 270-277
Janssen, M.; Vereecken, K.M.; Geeraerd, A.H.; Logist, F.; De Visscher, Y.; Cappuyns, A.; Devlieghere, F.; Debevere, J.; Van Impe, J.F. 2003: Predicting inhibition and inactivation of Yersinia enterocolitica through lactic acid production by Lactobacillus sakei. Communications in Agricultural and Applied Biological Sciences 68(2): 449-457
Chekmarev, D.; Kholodovych, V.; Kortagere, S.; Welsh, W.J.; Ekins, S. 2009: Predicting inhibitors of acetylcholinesterase by regression and classification machine learning approaches with combinations of molecular descriptors. Pharmaceutical Research 26(9): 2216-2224
Roeg, D.; van de Goor, I.; Garretsen, H. 2015: Predicting initial client engagement with community mental health services by routinely measured data. Community Mental Health Journal 51(1): 71-78
Edmonds, E.W.; Capelo, R.M.; Stearns, P.; Bastrom, T.P.; Wallace, C.D.; Newton, P.O. 2009: Predicting initial treatment failure of fiberglass casts in pediatric distal radius fractures: utility of the second metacarpal-radius angle. Journal of Children's Orthopaedics 3(5): 375-381
Melzer, I.; Kurz, I.; Shahar, D.; Oddsson, L.I.E. 2009: Predicting injury from falls in older adults: comparison of voluntary step reaction times in injured and noninjured fallers--a prospective study. Journal of the American Geriatrics Society 57(4): 743-745
Neufeld, E.; Perlman, C.M.; Hirdes, J.P. 2012: Predicting inpatient aggression using the InterRAi risk of harm to others clinical assessment protocol: a tool for risk assessment and care planning. Journal of Behavioral Health Services and Research 39(4): 472-480
Bekelis, K.; Missios, S.; MacKenzie, T.A.; Desai, A.; Fischer, A.; Labropoulos, N.; Roberts, D.W. 2014: Predicting inpatient complications from cerebral aneurysm clipping: the Nationwide Inpatient Sample 2005-2009. Journal of neurosurgery 120(3): 591-598
Neuner, T.; Schmid, R.; Wolfersdorf, M.; Spiessl, H. 2008: Predicting inpatient suicides and suicide attempts by using clinical routine data?. General Hospital Psychiatry 30(4): 324-330
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Kokras, N.; Kouzoupis, A.V.; Paparrigopoulos, T.; Ferentinos, P.; Karamanakos, P.; Kontoyannis, D.A.; Papadimitriou, G.N. 2011: Predicting insomnia in medical wards: the effect of anxiety, depression and admission diagnosis. General Hospital Psychiatry 33(1): 78-81
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Fitzgerald, S.; Gray, N.S.; Alexander, R.T.; Bagshaw, R.; Chesterman, P.; Huckle, P.; Jones, S.K.; Taylor, J.; Williams, T.; Snowden, R.J. 2013: Predicting institutional violence in offenders with intellectual disabilities: the predictive efficacy of the VRAG and the HCR-20. Journal of Applied Research in Intellectual Disabilities: Jarid 26(5): 384-393
Eum, R.S.; Seel, R.T.; Goldstein, R.; Brown, A.W.; Watanabe, T.K.; Zasler, N.D.; Roth, E.J.; Zafonte, R.D.; Glenn, M.B. 2015: Predicting institutionalization after traumatic brain injury inpatient rehabilitation. Journal of Neurotrauma 32(4): 280-286
Moreira, S.ér.R.; Ferreira, A.P.; Lima, R.M.; Arsa, G.; Campbell, C.S.G.; Simões, H.G.; Pitanga, F.J.G.; França, N.M. 2008: Predicting insulin resistance in children: anthropometric and metabolic indicators. Jornal de Pediatria 84(1): 47-52
Chiang, J.-K.; Lai, N.-S.; Chang, J.-K.; Koo, M. 2011: Predicting insulin resistance using the triglyceride-to-high-density lipoprotein cholesterol ratio in Taiwanese adults. Cardiovascular Diabetology 10: 93
Thornton, S.; Calam, R. 2011: Predicting intention to attend and actual attendance at a universal parent-training programme: a comparison of social cognition models. Clinical child psychology and psychiatry 16(3): 365-383
Astrøm, A.N.; Nasir, E.F. 2009: Predicting intention to treat HIV-infected patients among Tanzanian and Sudanese medical and dental students using the theory of planned behaviour--a cross sectional study. Bmc Health Services Research 9: 213
Abamecha, F.; Godesso, A.; Girma, E. 2013: Predicting intention to use voluntary HIV counseling and testing services among health professionals in Jimma, Ethiopia, using the theory of planned behavior. Journal of Multidisciplinary Healthcare 6: 399-407
Scholz, U.; Klaghofer, R.; Dux, R.; Roellin, M.; Boehler, A.; Muellhaupt, B.; Noll, G.; Wüthrich, R.P.; Goetzmann, L. 2012: Predicting intentions and adherence behavior in the context of organ transplantation: gender differences of provided social support. Journal of Psychosomatic Research 72(3): 214-219
Moan, I.S.øv.; Rise, J. 2011: Predicting intentions not to "drink and drive" using an extended version of the theory of planned behaviour. Accident; Analysis and Prevention 43(4): 1378-1384
Shapiro, M.A.; Porticella, N.; Jiang, L.C.; Gravani, R.B. 2011: Predicting intentions to adopt safe home food handling practices. Applying the theory of planned behavior. Appetite 56(1): 96-103
Bai, Y.; Wunderlich, S.M.; Fly, A.D. 2011: Predicting intentions to continue exclusive breastfeeding for 6 months: a comparison among racial/ethnic groups. Maternal and Child Health Journal 15(8): 1257-1264
Robinson, N.G.; Masser, B.M.; White, K.M.; Hyde, M.K.; Terry, D.J. 2008: Predicting intentions to donate blood among nondonors in Australia: an extended theory of planned behavior. Transfusion 48(12): 2559-2567
Pawlak, R.; Malinauskas, B.; Rivera, D. 2009: Predicting intentions to eat a healthful diet by college baseball players: applying the theory of planned behavior. Journal of nutrition education and behavior 41(5): 334-339
Smith-McLallen, A.; Fishbein, M. 2009: Predicting intentions to engage in cancer prevention and detection behaviors: examining differences between Black and White adults. Psychology Health and Medicine 14(2): 180-189
Groth, G.N. 2011: Predicting intentions to use research evidence for carpal tunnel syndrome treatment decisions among certified hand therapists. Journal of Occupational Rehabilitation 21(4): 559-572
Whitford, T.J.; Kubicki, M.; Ghorashi, S.; Schneiderman, J.S.; Hawley, K.J.; McCarley, R.W.; Shenton, M.E.; Spencer, K.M. 2011: Predicting inter-hemispheric transfer time from the diffusion properties of the corpus callosum in healthy individuals and schizophrenia patients: a combined ERP and DTi study. Neuroimage 54(3): 2318-2329
Pawelczyk, S.; Scott, K.A.; Hamer, R.; Blades, G.; Deane, C.M.; Wadhams, G.H. 2012: Predicting inter-species cross-talk in two-component signalling systems. Plos one 7(5): E37737
Kern, C.; González, A.J.; Liao, L.; Vijay-Shanker, K. 2013: Predicting interacting residues using long-distance information and novel decoding in hidden Markov models. IEEE Transactions on Nanobioscience 12(3): 158-164
Scarabelli, G.; Morra, G.; Colombo, G. 2010: Predicting interaction sites from the energetics of isolated proteins: a new approach to epitope mapping. Biophysical Journal 98(9): 1966-1975
Griffith, G.P.; Fulton, E.A.; Gorton, R.; Richardson, A.J. 2012: Predicting interactions among fishing, ocean warming, and ocean acidification in a marine system with whole-ecosystem models. Conservation Biology: the Journal of the Society for Conservation Biology 26(6): 1145-1152
Roomp, K.; Domingues, F.S. 2011: Predicting interactions between T cell receptors and MHC-peptide complexes. Molecular Immunology 48(4): 553-562
Hajingabo, L.J.; Daakour, S.; Martin, M.; Grausenburger, R.; Panzer-Grümayer, R.; Dequiedt, F.; Simonis, N.; Twizere, J.-C. 2014: Predicting interactome network perturbations in human cancer: application to gene fusions in acute lymphoblastic leukemia. Molecular Biology of the Cell 25(24): 3973-3985
Park, L.E.; Calogero, R.M.; Harwin, M.J.; DiRaddo, A.Marie. 2009: Predicting interest in cosmetic surgery: interactive effects of appearance-based rejection sensitivity and negative appearance comments. Body Image 6(3): 186-193
Jávo, I.á M.ár.á; Pettersen, G.; Rosenvinge, J.H.; Sørlie, T. 2012: Predicting interest in liposuction among women with eating problems: a population-based study. Body Image 9(1): 131-136
Shah, V.; Broseta, D. 2007: Predicting interfacial tension between water and nonpolar fluids from a Cahn-type theory. Langmuir 23(25): 12598-12605
Brasier, A.R.; Victor, S.; Ju, H.; Busse, W.W.; Curran-Everett, D.; Bleecker, E.; Castro, M.; Chung, K.F.; Gaston, B.; Israel, E.; Wenzel, S.E.; Erzurum, S.C.; Jarjour, N.N.; Calhoun, W.J. 2010: Predicting intermediate phenotypes in asthma using bronchoalveolar lavage-derived cytokines. Clinical and Translational Science 3(4): 147-157
Beckstrand, J.; Cirgin Ellett, M.L.; McDaniel, A. 2007: Predicting internal distance to the stomach for positioning nasogastric and orogastric feeding tubes in children. Journal of Advanced Nursing 59(3): 274-289
Mäntymaa, M.; Puura, K.; Luoma, I.; Latva, R.; Salmelin, R.K.; Tamminen, T. 2012: Predicting internalizing and externalizing problems at five years by child and parental factors in infancy and toddlerhood. Child Psychiatry and Human Development 43(2): 153-170
Muhtadie, L.; Zhou, Q.; Eisenberg, N.; Wang, Y. 2013: Predicting internalizing problems in Chinese children: the unique and interactive effects of parenting and child temperament. Development and Psychopathology 25(3): 653-667
Schabort, I.; Mercuri, M.; Grierson, L.E.M. 2014: Predicting international medical graduate success on college certification examinations: responding to the Thomson and Cohl judicial report on IMG selection. Canadian family physician Medecin de famille canadien 60(10): e478-e484
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Carlert, S.; Pålsson, A.; Hanisch, G.; von Corswant, C.; Nilsson, C.; Lindfors, L.; Lennernäs, H.; Abrahamsson, B. 2010: Predicting intestinal precipitation--a case example for a basic BCS class Ii drug. Pharmaceutical Research 27(10): 2119-2130
Hobohm, C.; Fritzsch, D.; Budig, S.; Classen, J.; Hoffmann, K.-T.; Michalski, D. 2014: Predicting intracerebral hemorrhage by baseline magnetic resonance imaging in stroke patients undergoing systemic thrombolysis. Acta Neurologica Scandinavica 130(5): 338-345
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Fabbri, A.; Servadei, F.; Marchesini, G.; Stein, S.C.; Vandelli, A. 2010: Predicting intracranial lesions by antiplatelet agents in subjects with mild head injury. Journal of Neurology Neurosurgery and Psychiatry 81(11): 1275-1279
Sivashanmugham, T.; Jahirdar, S.Mahamud.; Parthasarathy, S.; Muthurangan, G. 2013: Predicting intraoperative cardiovascular complication in patients with anterior mediastinal mass-role of central venous pressure monitoring. Indian Journal of Anaesthesia 57(4): 406-408
Azabou, E.; Manel, V.ér.; Abelin-Genevois, K.; Andre-Obadia, N.; Cunin, V.; Garin, C.; Kohler, R.; Berard, J.ér.ôm.; Ulkatan, S. 2014: Predicting intraoperative feasibility of combined TES-mMEP and cSSEP monitoring during scoliosis surgery based on preoperative neurophysiological assessment. Spine Journal: Official Journal of the North American Spine Society 14(7): 1214-1220
Jones, J.P.; Korzekwa, K.R. 2013: Predicting intrinsic clearance for drugs and drug candidates metabolized by aldehyde oxidase. Molecular Pharmaceutics 10(4): 1262-1268
Seabloom, E.W.; Borer, E.T.; Buckley, Y.; Cleland, E.E.; Davies, K.; Firn, J.; Harpole, W.S.; Hautier, Y.; Lind, E.; MacDougall, A.; Orrock, J.L.; Prober, S.M.; Adler, P.; Alberti, J.; Anderson, T.M.; Bakker, J.D.; Biederman, L.A.; Blumenthal, D.; Brown, C.S.; Brudvig, L.A.; Caldeira, M.; Chu, C.; Crawley, M.J.; Daleo, P.; Damschen, E.I.; D'Antonio, C.M.; DeCrappeo, N.M.; Dickman, C.R.; Du, G.; Fay, P.A.; Frater, P.; Gruner, D.S.; Hagenah, N.; Hector, A.; Helm, A.; Hillebrand, H.; Hofmockel, K.S.; Humphries, H.C.; Iribarne, O.; Jin, V.L.; Kay, A.; Kirkman, K.P.; Klein, J.A.; Knops, J.M.H.; La Pierre, K.J.; Ladwig, L.M.; Lambrinos, J.G.; Leakey, A.D.B.; Li, Q.; Li, W.; McCulley, R.; Melbourne, B.; Mitchell, C.E.; Moore, J.L.; Morgan, J. 2013: Predicting invasion in grassland ecosystems: is exotic dominance the real embarrassment of richness?. Global Change Biology 19(12): 3677-3687
Ayvaci, M.U.S.; Alagoz, O.; Chhatwal, J.; Munoz del Rio, A.; Sickles, E.A.; Nassif, H.; Kerlikowske, K.; Burnside, E.S. 2014: Predicting invasive breast cancer versus DCIS in different age groups. Bmc Cancer 14: 584
Paini, D.R.; Bianchi, F.J.J.A.; Northfield, T.D.; De Barro, P.J. 2011: Predicting invasive fungal pathogens using invasive pest assemblages: testing model predictions in a virtual world. Plos one 6(10): E25695
Celebiler Cavusoglu, A.; Kilic, Y.; Saydam, S.; Canda, T.; Başkan, Z.; Sevinc, A.I.; Sakizli, M. 2009: Predicting invasive phenotype with CDH1, CDH13, CD44, and TIMP3 gene expression in primary breast cancer. Cancer Science 100(12): 2341-2345
Paterson, R.A.; Dick, J.T.A.; Pritchard, D.W.; Ennis, M.; Hatcher, M.J.; Dunn, A.M. 2015: Predicting invasive species impacts: a community module functional response approach reveals context dependencies. Journal of Animal Ecology 84(2): 453-463
Loranger, J.; Meyer, S.T.; Shipley, B.; Kattge, J.; Loranger, H.; Roscher, C.; Weisser, W.W. 2012: Predicting invertebrate herbivory from plant traits: evidence from 51 grassland species in experimental monocultures. Ecology 93(12): 2674-2682
Loranger, J.; Meyer, S.T.; Shipley, B.; Kattge, J.; Loranger, H.; Roscher, C.; Wirth, C.; Weisser, W.W. 2013: Predicting invertebrate herbivory from plant traits: polycultures show strong nonadditive effects. Ecology 94(7): 1499-1509
Wood, J.L.; Alleyne, E.; Mozova, K.; James, M. 2014: Predicting involvement in prison gang activity: street gang membership, social and psychological factors. Law and human behavior 38(3): 203-211
Tan, Z.-J.; Chen, S.-J. 2010: Predicting ion binding properties for RNA tertiary structures. Biophysical Journal 99(5): 1565-1576
Lin, H.; Ding, H. 2011: Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition. Journal of Theoretical Biology 269(1): 64-69
Parsons, D.F. 2014: Predicting ion specific capacitances of supercapacitors due to quantum ionic interactions. Journal of Colloid and Interface Science 427: 67-72
He, Z.; Chen, S.-J. 2012: Predicting ion-nucleic acid interactions by energy landscape-guided sampling. Journal of Chemical Theory and Computation 8(6): 2095-2101
Olynyk, J.K.; Gan, E.; Tan, T. 2009: Predicting iron overload in hyperferritinemia. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association 7(3): 359-362
Frisch, D.R.; Giedrimas, E.; Mohanavelu, S.; Shui, A.; Ho, K.K.L.; Gibson, C.M.; Josephson, M.E.; Zimetbaum, P.J. 2009: Predicting irreversible left ventricular dysfunction after acute myocardial infarction. American Journal of Cardiology 103(9): 1206-1209
Meurer, W.J.; Sánchez, B.N.; Smith, M.A.; Lisabeth, L.D.; Majersik, J.J.; Brown, D.L.; Uchino, K.; Bonikowski, F.P.; Mendizabal, J.E.; Zahuranec, D.B.; Morgenstern, L.B. 2009: Predicting ischaemic stroke subtype from presenting systolic blood pressure: the BASIC Project. Journal of Internal Medicine 265(3): 388-396
Goodman, P.G.; Mehta, A.R.; Castresana, M.R. 2009: Predicting ischemic brain injury after intraoperative cardiac arrest during cardiac surgery using the BIS monitor. Journal of Clinical Anesthesia 21(8): 609-612
Lian, B.Q.; Keaney, J.F. 2010: Predicting ischemic heart disease in women: the value of endothelial function. Journal of the American College of Cardiology 55(16): 1697-1699
Bellin, M.D.; Blondet, J.J.; Beilman, G.J.; Dunn, T.B.; Balamurugan, A.N.; Thomas, W.; Sutherland, D.E.R.; Moran, A. 2010: Predicting islet yield in pediatric patients undergoing pancreatectomy and autoislet transplantation for chronic pancreatitis. Pediatric Diabetes 11(4): 227-234
Wolken, G.G.; Fossen, B.J.; Noh, A.; Arriaga, E.A. 2013: Predicting isoelectric points of nonfunctional mitochondria from Monte Carlo simulations of surface compositions. Langmuir 29(8): 2700-2707
Wagner, H.; Boström, K.; Rinke, B. 2011: Predicting isometric force from muscular activation using a physiologically inspired model. Biomechanics and Modeling in Mechanobiology 10(6): 955-961
Whittaker, P.B.; Wang, X.; Regenauer-Lieb, K.; Chua, H.T. 2013: Predicting isosteric heats for gas adsorption. Physical Chemistry Chemical Physics: Pccp 15(2): 473-482
Chen, S.-Y.; Doong, S.-H. 2008: Predicting item exposure parameters in computerized adaptive testing. British Journal of Mathematical and Statistical Psychology 61(Part 1): 75-91
Wilford, J.; McMahon, A.D.; Peters, J.; Pickvance, S.; Jackson, A.; Blank, L.; Craig, D.; O'Rourke, A.; Macdonald, E.B. 2008: Predicting job loss in those off sick. Occupational Medicine 58(2): 99-106
Hardin, E.E.; Donaldson, J.R. 2014: Predicting job satisfaction: a new perspective on person-environment fit. Journal of Counseling Psychology 61(4): 634-640
Jung, H.; Lee, J.-E.; Kim, J. 2013: Predicting job stress: a specific case of a female golf caddy in South Korea. Work 45(2): 183-189
McQueen, F.M.; Dalbeth, N. 2009: Predicting joint damage in rheumatoid arthritis using MRi scanning. Arthritis Research and Therapy 11(5): 124
Cipriano, L.E.; Chesworth, B.M.; Anderson, C.K.; Zaric, G.S. 2007: Predicting joint replacement waiting times. Health Care Management Science 10(2): 195-215
Weber, M.; Thompson-Schill, S.L.; Osherson, D.; Haxby, J.; Parsons, L. 2009: Predicting judged similarity of natural categories from their neural representations. Neuropsychologia 47(3): 859-868
Mallett, C.A.; Stoddard Dare, P.; Seck, M.M. 2009: Predicting juvenile delinquency: the nexus of childhood maltreatment, depression and bipolar disorder. Criminal Behaviour and Mental Health: Cbmh 19(4): 235-246
Ang, R.P.; Goh, D.H. 2013: Predicting juvenile offending: a comparison of data mining methods. International Journal of Offender Therapy and Comparative Criminology 57(2): 191-207
Hattori, K.; Wakabayashi, H.; Tamaki, K. 2008: Predicting key example compounds in competitors' patent applications using structural information alone. Journal of Chemical Information and Modeling 48(1): 135-142
Zaza, G. 2012: Predicting kidney graft survival: molecular biology has the key. Giornale Italiano di Nefrologia: Organo Ufficiale Della Societa Italiana di Nefrologia 29(3): 266
Krikov, S.; Khan, A.; Baird, B.C.; Barenbaum, L.L.; Leviatov, A.; Koford, J.K.; Goldfarb-Rumyantzev, A.S. 2007: Predicting kidney transplant survival using tree-based modeling. Asaio Journal 53(5): 592-600
Talreja, H.; Akbari, A.; White, C.A.; Ramsay, T.O.; Hiremath, S.; Knoll, G. 2014: Predicting kidney transplantation outcomes using proteinuria ascertained from spot urine samples versus timed urine collections. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 64(6): 962-968
Bøgebo, R.; Horn, H.; Olsen, J.V.; Gammeltoft, S.; Jensen, L.J.; Hansen, J.L.; Christensen, G.L. 2014: Predicting kinase activity in angiotensin receptor phosphoproteomes based on sequence-motifs and interactions. Plos one 9(4): E94672
Sciabola, S.; Stanton, R.V.; Wittkopp, S.; Wildman, S.; Moshinsky, D.; Potluri, S.; Xi, H. 2008: Predicting kinase selectivity profiles using Free-Wilson QSAR analysis. Journal of Chemical Information and Modeling 48(9): 1851-1867
Lai, A.C.W.; Nguyen Ba, A.N.; Moses, A.M. 2012: Predicting kinase substrates using conservation of local motif density. Bioinformatics 28(7): 962-969
Hoorn, E.J.; Meima, M.E. 2012: Predicting kinase-substrate interactions in the era of proteomics: focus on "Identifying protein kinase target preferences using mass spectrometry". American Journal of Physiology. Cell Physiology 303(7): C711-C712
Al Otaiba, S.; Puranik, C.; Rouby, A.D.; Greulich, L.; Folsom, J.S.; Lee, J. 2010: Predicting kindergartners' end of year spelling ability from their reading, alphabetic, vocabulary, and phonological awareness skills, and prior literacy experiences. Learning Disability Quarterly: Journal of the Division for Children with Learning Disabilities 33(3): 171-183
Bai, H.; Yang, K.; Yu, D.; Zhang, C.; Chen, F.; Lai, L. 2011: Predicting kinetic constants of protein-protein interactions based on structural properties. Proteins 79(3): 720-734
Syaichurrozi, I.; Budiyono; Sumardiono, S. 2013: Predicting kinetic model of biogas production and biodegradability organic materials: biogas production from vinasse at variation of COD/N ratio. Bioresource Technology 149: 390-397
Markopoulos, G.; Grunenberg, J.ör. 2013: Predicting kinetically unstable C-C bonds from the ground-state properties of a molecule. Angewandte Chemie 52(40): 10648-10651
Jørgensen, D.R.; Dam, E.B.; Lillholm, M. 2013: Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps. Computers in Biology and Medicine 43(8): 1045-1052
Zhao, D.; Sakoda, H.; Sawyer, W.G.; Banks, S.A.; Fregly, B.J. 2008: Predicting knee replacement damage in a simulator machine using a computational model with a consistent wear factor. Journal of Biomechanical Engineering 130(1): 011004
Maderbacher, G.ün.; Schaumburger, J.; Baier, C.; Zeman, F.; Springorum, H.-R.; Dornia, C.; Grifka, J.; Keshmiri, A. 2014: Predicting knee rotation by the projection overlap of the proximal fibula and tibia in long-leg radiographs. Knee Surgery Sports Traumatology Arthroscopy: Official Journal of the Esska 22(12): 2982-2988
Schjølberg, S.; Eadie, P.; Zachrisson, H.D.; Oyen, A.-S.; Prior, M. 2011: Predicting language development at age 18 months: data from the Norwegian Mother and Child Cohort Study. Journal of Developmental and Behavioral Pediatrics: Jdbp 32(5): 375-383
Payabvash, S.; Kamalian, S.; Fung, S.; Wang, Y.; Passanese, J.; Kamalian, S.; Souza, L.C.S.; Kemmling, A.; Harris, G.J.; Halpern, E.F.; González, R.G.; Furie, K.L.; Lev, M.H. 2010: Predicting language improvement in acute stroke patients presenting with aphasia: a multivariate logistic model using location-weighted atlas-based analysis of admission CT perfusion scans. AJNR. American Journal of Neuroradiology 31(9): 1661-1668
Josse, G.; Kherif, F.; Flandin, G.; Seghier, M.L.; Price, C.J. 2009: Predicting language lateralization from gray matter. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 29(43): 13516-13523
Reilly, S.; Wake, M.; Ukoumunne, O.C.; Bavin, E.; Prior, M.; Cini, E.; Conway, L.; Eadie, P.; Bretherton, L. 2010: Predicting language outcomes at 4 years of age: findings from Early Language in Victoria Study. Pediatrics 126(6): e1530-e1537
Brady, N.C.; Thiemann-Bourque, K.; Fleming, K.; Matthews, K. 2013: Predicting language outcomes for children learning augmentative and alternative communication: child and environmental factors. Journal of Speech Language and Hearing Research: Jslhr 56(5): 1595-1612
Ortendahl, M. 2007: Predicting lapse when stopping smoking among pregnant and non-pregnant women. Journal of Obstetrics and Gynaecology: the Journal of the Institute of Obstetrics and Gynaecology 27(2): 138-143
Kim, J.; Lin, L.-C.; Swisher, J.A.; Haranczyk, M.; Smit, B. 2012: Predicting large CO2 adsorption in aluminosilicate zeolites for postcombustion carbon dioxide capture. Journal of the American Chemical Society 134(46): 18940-18943
Grochola, G.; Snook, I.K.; Russo, S.P. 2014: Predicting large area surface reconstructions using molecular dynamics methods. Journal of Chemical Physics 140(5): 054701
Zhang, J.; Kim, S.; Grewal, J.; Albert, P.S. 2012: Predicting large fetuses at birth: do multiple ultrasound examinations and longitudinal statistical modelling improve prediction?. Paediatric and Perinatal Epidemiology 26(3): 199-207
Palmer, D.S.; Jensen, F. 2011: Predicting large-scale conformational changes in proteins using energy-weighted normal modes. Proteins 79(10): 2778-2793
Liao, Z.M.; Spaeth, M.L.; Manes, K.; Adams, J.J.; Carr, C.W. 2010: Predicting laser-induced bulk damage and conditioning for deuterated potassium dihydrogen phosphate crystals using an absorption distribution model. Optics Letters 35(15): 2538-2540
Wedlake, L.J.; Thomas, K.; Lalji, A.; Blake, P.; Khoo, V.S.; Tait, D.; Andreyev, H.J.N. 2010: Predicting late effects of pelvic radiotherapy: is there a better approach?. International Journal of Radiation Oncology Biology Physics 78(4): 1163-1170
Larose, E.; Rodés-Cabau, J.; Pibarot, P.; Rinfret, Séphane.; Proulx, G.; Nguyen, C.M.; Déry, J-Pierre.; Gleeton, O.; Roy, L.; Noël, B.; Barbeau, Gérald.; Rouleau, J.; Boudreault, J-Rock.; Amyot, M.; De Larochellière, R.; Bertrand, O.F. 2010: Predicting late myocardial recovery and outcomes in the early hours of ST-segment elevation myocardial infarction traditional measures compared with microvascular obstruction, salvaged myocardium, and necrosis characteristics by cardiovascular magnetic resonance. Journal of the American College of Cardiology 55(22): 2459-2469
Barbot, M.; Albiger, N.; Koutroumpi, S.; Ceccato, F.; Frigo, A.C.; Manara, R.; Fassina, A.; Gardiman, M.P.; Scanarini, M.; Mantero, F.; Scaroni, C. 2013: Predicting late recurrence in surgically treated patients with Cushing's disease. Clinical Endocrinology 79(3): 394-401
Pesco, D.; O'Neill, D.K. 2012: Predicting later language outcomes from the Language use Inventory. Journal of Speech Language and Hearing Research: Jslhr 55(2): 421-434
Boekelheide, K.; Blumberg, B.; Chapin, R.E.; Cote, I.; Graziano, J.H.; Janesick, A.; Lane, R.; Lillycrop, K.; Myatt, L.; States, J.C.; Thayer, K.A.; Waalkes, M.P.; Rogers, J.M. 2012: Predicting later-life outcomes of early-life exposures. Environmental Health Perspectives 120(10): 1353-1361
Lowmaster, S.E.; Morey, L.C. 2012: Predicting law enforcement officer job performance with the Personality Assessment Inventory. Journal of Personality Assessment 94(3): 254-261
Cheng, T.; Rivard, B.; Sánchez-Azofeifa, A.G.; Féret, J.-B.; Jacquemoud, S.; Ustin, S.L. 2012: Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. Journal of Plant Physiology 169(12): 1134-1142
Roelofsen, H.D.; van Bodegom, P.M.; Kooistra, L.; Witte, J.-P.M. 2014: Predicting leaf traits of herbaceous species from their spectral characteristics. Ecology and Evolution 4(6): 706-719
Tipple, B.J.; Berke, M.A.; Hambach, B.; Roden, J.S.; Ehleringer, J.R. 2015: Predicting leaf wax n-alkane 2H/1H ratios: controlled water source and humidity experiments with hydroponically grown trees confirm predictions of Craig-Gordon model. Plant Cell and Environment 38(6): 1035-1047
Wysokinski, W.E.; Ammash, N.; Sobande, F.; Kalsi, H.; Hodge, D.; McBane, R.D. 2010: Predicting left atrial thrombi in atrial fibrillation. American Heart Journal 159(4): 665-671
Tandon, H.; LaSala, A.F. 2007: Predicting left ventricular end-diastolic pressure by echocardiography. American Journal of Cardiology 99(12): 1776
Koshy, S.K.G.; George, L.K. 2015: Predicting left ventricular function recovery after reperfusion in ST elevation myocardial infarction: can we balance cost and accuracy?. Echocardiography 32(4): 613-614
Fujishiro, T.; Nishiyama, T.; Hayashi, S.; Hashimoto, S.; Kurosaka, M.; Kanno, T.; Masuda, T. 2012: Predicting leg-length change after total hip arthroplasty by measuring preoperative hip flexion under general anaesthesia. Journal of Orthopaedic Surgery 20(3): 327-330
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Konstorum, A.; Sprowl, S.A.; Waterman, M.L.; Lander, A.D.; Lowengrub, J.S. 2013: Predicting mechanism of biphasic growth factor action on tumor growth using a multi-species model with feedback control. Journal of Coupled Systems and Multiscale Dynamics 1(4): 459-467
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Hanazono, K.; Fukumoto, S.; Hirayama, K.; Takashima, K.; Yamane, Y.; Natsuhori, M.; Kadosawa, T.; Uchide, T. 2012: Predicting metastatic potential of gastrointestinal stromal tumors in dog by ultrasonography. Journal of Veterinary Medical Science 74(11): 1477-1482
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Johnston, B.A.; Coghill, D.; Matthews, K.; Steele, J.D. 2015: Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study. Journal of Psychopharmacology 29(1): 24-30
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Lin, K.; Qian, Z.; Lu, L.; Lu, L.; Lai, L.; Gu, J.; Zeng, Z.; Li, H.; Cai, Y. 2010: Predicting miRNA's target from primary structure by the nearest neighbor algorithm. Molecular Diversity 14(4): 719-729
Song, X.; Cheng, L.; Zhou, T.; Guo, X.; Zhang, X.; Chen, Y.-p.P.; Han, P.; Sha, J. 2012: Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features. Computers in Biology and Medicine 42(1): 1-7
Albertini, M.C.; Olivieri, F.; Lazzarini, R.; Pilolli, F.; Galli, F.; Spada, G.; Accorsi, A.; Rippo, M.R.; Procopio, A.D. 2011: Predicting microRNA modulation in human prostate cancer using a simple String IDentifier (SID1.0). Journal of Biomedical Informatics 44(4): 615-620
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Parker, B.J.; Wen, J. 2009: Predicting microRNA targets in time-series microarray experiments via functional data analysis. Bmc Bioinformatics 10(Suppl 1): S32
Van de Leemput, I.A.; Veraart, A.J.; Dakos, V.; de Klein, J.J.M.; Strous, M.; Scheffer, M. 2011: Predicting microbial nitrogen pathways from basic principles. Environmental Microbiology 13(6): 1477-1487
Hampson, D.; Crowther, J.; Bateman, I.; Kay, D.; Posen, P.; Stapleton, C.; Wyer, M.; Fezzi, C.; Jones, P.; Tzanopoulos, J. 2010: Predicting microbial pollution concentrations in UK rivers in response to land use change. Water Research 44(16): 4748-4759
Galyean, M.L.; Tedeschi, L.O. 2014: Predicting microbial protein synthesis in beef cattle: relationship to intakes of total digestible nutrients and crude protein. Journal of Animal Science 92(11): 5099-5111
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Neizel, M.; Futterer, S.; Steen, H.; Giannitsis, E.; Reinhardt, L.; Lossnitzer, D.; Lehrke, S.; Jaffe, A.S.; Katus, H.A. 2009: Predicting microvascular obstruction with cardiac troponin T after acute myocardial infarction: a correlative study with contrast-enhanced magnetic resonance imaging. Clinical Research in Cardiology: Official Journal of the German Cardiac Society 98(9): 555-562
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Ortiz, A.él.U.; Springuel-Huet, M.-A.; Coudert, F.ço.-X.; Fuchs, A.H.; Boutin, A. 2012: Predicting mixture coadsorption in soft porous crystals: experimental and theoretical Study of CO2/CH4 in MIL-53(Al). Langmuir 28(1): 494-498
Kalu, C.A.; Umeora, O.U.; Egwuatu, E.V.; Okwor, A. 2012: Predicting mode of delivery using mid-pregnancy ultrasonographic measurement of cervical length. Nigerian Journal of Clinical Practice 15(3): 338-343
Annett, R.D.; Bender, B.G.; Skipper, B.; Allen, C. 2010: Predicting moderate improvement and decline in pediatric asthma quality of life over 24 months. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment Care and Rehabilitation 19(10): 1517-1527
Russom, C.L.; Bradbury, S.P.; Broderius, S.J.; Hammermeister, D.J.; Drummond, R.A.; Veith, G.D. 2013: Predicting modes of toxic action from chemical structure. Environmental Toxicology and Chemistry 32(7): 1441-1442
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McGrath, S.P.; Micó, C.; Zhao, F.J.; Stroud, J.L.; Zhang, H.; Fozard, S. 2010: Predicting molybdenum toxicity to higher plants: estimation of toxicity threshold values. Environmental Pollution 158(10): 3085-3094
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Buford, W.L.; Andersen, C.R. 2006: Predicting moment arms in diarthroidal joints - 3D computer simulation capability and muscle-tendon model validation. Conference Proceedings: . Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2006: 3407-3410
Hsu, K-Yu.; Chau, G-Yang.; Lui, W-Yiu.; Tsay, S-Haw.; King, K-Liang.; Wu, C-Wun. 2009: Predicting morbidity and mortality after hepatic resection in patients with hepatocellular carcinoma: the role of Model for End-Stage Liver Disease score. World Journal of Surgery 33(11): 2412-2419
Kouijzer, S.; Michels, J.J.; van den Berg, M.; Gevaerts, V.S.; Turbiez, M.; Wienk, M.M.; Janssen, R.é A.J. 2013: Predicting morphologies of solution processed polymer:fullerene blends. Journal of the American Chemical Society 135(32): 12057-12067
Murphy, B.; Rogerson, M.; Worcester, M.; Elliott, P.; Higgins, R.; Le Grande, M.; Turner, A.; Goble, A. 2013: Predicting mortality 12 years after an acute cardiac event: comparison between inhospital and 2-month assessment of depressive symptoms in women. Journal of Cardiopulmonary Rehabilitation and Prevention 33(3): 160-167
Iyer, K.S.; Iyer, P.U. 2015: Predicting mortality after congenital heart surgeries: evaluation of the Aristotle and Risk Adjustment in Congenital Heart Surgery-1 risk prediction scoring systems: a retrospective single center analysis of 1150 patients. Annals of Cardiac Anaesthesia 17(4): 271-272
Matchett, S.C.; Castaldo, J.; Wasser, T.E.; Baker, K.; Mathiesen, C.; Rodgers, J. 2006: Predicting mortality after intracerebral hemorrhage: comparison of scoring systems and influence of withdrawal of care. Journal of Stroke and Cerebrovascular Diseases: the Official Journal of National Stroke Association 15(4): 144-150
Barnes, S.; Gott, M.; Payne, S.; Parker, C.; Seamark, D.; Gariballa, S.; Small, N. 2008: Predicting mortality among a general practice-based sample of older people with heart failure. Chronic Illness 4(1): 5-12
Emukule, G.O.; McMorrow, M.; Ulloa, C.; Khagayi, S.; Njuguna, H.N.; Burton, D.; Montgomery, J.M.; Muthoka, P.; Katz, M.A.; Breiman, R.F.; Mott, J.A. 2014: Predicting mortality among hospitalized children with respiratory illness in Western Kenya, 2009-2012. PloS one 9(3): e92968
Abisheganaden, J.; Ding, Y.Yoong.; Chong, W-Fung.; Heng, B-Hoon.; Lim, T.Keang. 2012: Predicting mortality among older adults hospitalized for community-acquired pneumonia: an enhanced confusion, urea, respiratory rate and blood pressure score compared with pneumonia severity index. Respirology 17(6): 969-975
Kerbauy, F.áb.R.; Morelli, L.R.; de Andrade, C.áu.T.; Lisboa, L.F.; Cendoroglo Neto, M.; Hamerschlak, N. 2012: Predicting mortality and cost of hematopoietic stem-cell transplantation. Einstein 10(1): 82-85
Gale, C.P.; White, J.E.S.; Hunter, A.; Owen, J.; Allen, J.; Watson, J.; Holbrook, I.; Durham, N.P.; Pye, M.P. 2011: Predicting mortality and hospital admission in patients with COPD: significance of NT pro-BNP, clinical and echocardiographic assessment. Journal of Cardiovascular Medicine 12(9): 613-618
Pepler, P.T.; Uys, D.W.; Nel, D.G. 2012: Predicting mortality and length-of-stay for neonatal admissions to private hospital neonatal intensive care units: a Southern African retrospective study. African Health Sciences 12(2): 166-173
Schoe, A.; Schippers, E.F.; Ebmeyer, S.; Struck, J.; Klautz, R.J.M.; de Jonge, E.; van Dissel, J.T. 2014: Predicting mortality and morbidity after elective cardiac surgery using vasoactive and inflammatory biomarkers with and without the EuroSCORE model. Chest 146(5): 1310-1318
Conway, B.; Webster, A.; Ramsay, G.; Morgan, N.; Neary, J.; Whitworth, C.; Harty, J. 2009: Predicting mortality and uptake of renal replacement therapy in patients with stage 4 chronic kidney disease. Nephrology Dialysis Transplantation: Official Publication of the European Dialysis and Transplant Association - European Renal Association 24(6): 1930-1937
Wildman, M.J.; Sanderson, C.; Groves, J.; Reeves, B.C.; Ayres, J.; Harrison, D.; Young, D.; Rowan, K. 2009: Predicting mortality for patients with exacerbations of COPD and Asthma in the COPD and Asthma Outcome Study (CAOS). Qjm: Monthly Journal of the Association of Physicians 102(6): 389-399
Fei, M.W.; Kim, E.J.; Sant, C.A.; Jarlsberg, L.G.; Davis, J.L.; Swartzman, A.; Huang, L. 2009: Predicting mortality from HIV-associated Pneumocystis pneumonia at illness presentation: an observational cohort study. Thorax 64(12): 1070-1076
Taylor, S.L.; Lawless, M.; Curri, T.; Sen, S.; Greenhalgh, D.G.; Palmieri, T.L. 2014: Predicting mortality from burns: the need for age-group specific models. Burns: Journal of the International Society for Burn Injuries 40(6): 1106-1115
Rodríguez, M. 2013: Predicting mortality from head injury: experience of Sancti Spíritus Province, Cuba. Medicc Review 15(3): 30-33
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Nugent, J.; Edmonds, A.; Lusiama, J.; Thompson, D.; Behets, F. 2014: Predicting mortality in HIV-infected children initiating highly active antiretroviral therapy in a resource-deprived setting. Pediatric Infectious Disease Journal 33(11): 1148-1155
Cárdenas, Aés.; Ginès, P. 2008: Predicting mortality in cirrhosis--serum sodium helps. New England Journal of Medicine 359(10): 1060-1062
Mohan, A.; Bollineni, S. 2007: Predicting mortality in critically ill obstetric patients requiring intensive care unit admission in India. Indian Journal of Medical Sciences 61(4): 175-177
Timmermans, J.; Nicol, A.; Kairinos, N.; Teijink, J.; Prins, M.; Navsaria, P. 2010: Predicting mortality in damage control surgery for major abdominal trauma. South African Journal of Surgery. Suid-Afrikaanse Tydskrif Vir Chirurgie 48(1): 6-9
Riquelme, R.; Jiménez, P.; Videla, A.J.; Lopez, H.; Chalmers, J.; Singanayagam, A.; Riquelme, M.; Peyrani, P.; Wiemken, T.; Arbo, G.; Benchetrit, G.; Rioseco, M.L.; Ayesu, K.; Klotchko, A.; Marzoratti, L.; Raya, M.; Figueroa, S.; Saavedra, F.; Pryluka, D.; Inzunza, C.; Torres, A.; Alvare, P.; Fernandez, P.; Barros, M.; Gomez, Y.; Contreras, C.; Rello, J.; Bordon, J.; Feldman, C.; Arnold, F.; Nakamatsu, R.; Riquelme, J.; Blasi, F.; Aliberti, S.; Cosentini, R.; Lopardo, G.; Gnoni, M.; Welte, T.; Saad, M.; Guardiola, J.; Ramirez, J. 2011: Predicting mortality in hospitalized patients with 2009 H1N1 influenza pneumonia. International Journal of Tuberculosis and Lung Disease: the Official Journal of the International Union Against Tuberculosis and Lung Disease 15(4): 542-546
Wagner, M.; Ansell, D.; Kent, D.M.; Griffith, J.L.; Naimark, D.; Wanner, C.; Tangri, N. 2011: Predicting mortality in incident dialysis patients: an analysis of the United Kingdom Renal Registry. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 57(6): 894-902
Nakwan, N.; Nakwan, N.; Wannaro, J. 2011: Predicting mortality in infants with persistent pulmonary hypertension of the newborn with the Score for Neonatal Acute Physiology-Version Ii (SNAP-II) in Thai neonates. Journal of Perinatal Medicine 39(3): 311-315
Wang, Q.; Mou, S.; Xu, W.; Qi, C.; Ni, Z. 2013: Predicting mortality in microscopic polyangiitis with renal involvement: a survival analysis based on 64 patients. Renal Failure 35(1): 82-87
Garcia, L.; Messaris, E. 2013: Predicting mortality in patients admitted with sepsis using endotoxin activity: will it help us see through the crystal ball?*. Critical Care Medicine 41(6): 1575-1576
Budinger, G.R.S.; Walley, K.R. 2011: Predicting mortality in patients with acute lung injury. American Journal of Respiratory and Critical Care Medicine 184(4): 394-395
Chiu, P.W.Y.; Ng, E.K.W.; Cheung, F.K.Y.; Chan, F.K.L.; Leung, W.K.; Wu, J.C.Y.; Wong, V.W.S.; Yung, M.Y.; Tsoi, K.; Lau, J.Y.W.; Sung, J.J.Y.; Chung, S.S.C. 2009: Predicting mortality in patients with bleeding peptic ulcers after therapeutic endoscopy. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association 7(3): 311-316; Quiz 253
Ronan, D.; Nathwani, D.; Davey, P.; Barlow, G. 2010: Predicting mortality in patients with community-acquired pneumonia and low CURB-65 scores. European Journal of Clinical Microbiology and Infectious Diseases: Official Publication of the European Society of Clinical Microbiology 29(9): 1117-1124
Van Diepen, M.; Schroijen, M.A.; Dekkers, O.M.; Rotmans, J.I.; Krediet, R.T.; Boeschoten, E.W.; Dekker, F.W. 2014: Predicting mortality in patients with diabetes starting dialysis. Plos one 9(3): E89744
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Oxlade, O.; Schwartzman, K.; Pai, M.; Heymann, J.; Benedetti, A.; Royce, S.; Menzies, D. 2010: Predicting outcomes and drug resistance with standardised treatment of active tuberculosis. European Respiratory Journal 36(4): 870-877
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