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Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space.

Frontiers in physiology

Wong HK, Tiffin PA, Chappell MJ, Nichols TE, Welsh PR, Doyle OM, Lopez-Kolkovska BC, Inglis SK, Coghill D, Shen Y, Tiño P.
PMID: 28443027
Front Physiol. 2017 Apr 11;8:199. doi: 10.3389/fphys.2017.00199. eCollection 2017.

Attention-Deficit Hyperactive Disorder (ADHD) is one of the most common mental health disorders amongst school-aged children with an estimated prevalence of 5% in the global population (American Psychiatric Association, 2013). Stimulants, particularly methylphenidate (MPH), are the first-line option in...

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Wong HK, Tiffin PA, Chappell MJ, et al. Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space. Front Physiol. 2017;8:199doi: 10.3389/fphys.2017.00199.
Wong, H. K., Tiffin, P. A., Chappell, M. J., Nichols, T. E., Welsh, P. R., Doyle, O. M., Lopez-Kolkovska, B. C., Inglis, S. K., Coghill, D., Shen, Y., & Tiño, P. (2017). Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space. Frontiers in physiology, 8199. https://doi.org/10.3389/fphys.2017.00199
Wong, Hin K, et al. "Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space." Frontiers in physiology vol. 8 (2017): 199. doi: https://doi.org/10.3389/fphys.2017.00199
Wong HK, Tiffin PA, Chappell MJ, Nichols TE, Welsh PR, Doyle OM, Lopez-Kolkovska BC, Inglis SK, Coghill D, Shen Y, Tiño P. Personalized Medication Response Prediction for Attention-Deficit Hyperactivity Disorder: Learning in the Model Space vs. Learning in the Data Space. Front Physiol. 2017 Apr 11;8:199. doi: 10.3389/fphys.2017.00199. eCollection 2017. PMID: 28443027; PMCID: PMC5387107.
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Reply to Brown and Behrmann, Cox, et al., and Kessler et al.: Data and code sharing is the way forward for fMRI.

Proceedings of the National Academy of Sciences of the United States of America

Eklund A, Nichols TE, Knutsson H.
PMID: 28420795
Proc Natl Acad Sci U S A. 2017 Apr 25;114(17):E3374-E3375. doi: 10.1073/pnas.1620285114. Epub 2017 Apr 18.

No abstract available.

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Eklund A, Nichols TE, Knutsson H. Reply to Brown and Behrmann, Cox, et al., and Kessler et al.: Data and code sharing is the way forward for fMRI. Proc Natl Acad Sci U S A. 2017;114(17):E3374-E3375doi: 10.1073/pnas.1620285114.
Eklund, A., Nichols, T. E., & Knutsson, H. (2017). Reply to Brown and Behrmann, Cox, et al., and Kessler et al.: Data and code sharing is the way forward for fMRI. Proceedings of the National Academy of Sciences of the United States of America, 114(17), E3374-E3375. https://doi.org/10.1073/pnas.1620285114
Eklund, Anders, et al. "Reply to Brown and Behrmann, Cox, et al., and Kessler et al.: Data and code sharing is the way forward for fMRI." Proceedings of the National Academy of Sciences of the United States of America vol. 114,17 (2017): E3374-E3375. doi: https://doi.org/10.1073/pnas.1620285114
Eklund A, Nichols TE, Knutsson H. Reply to Brown and Behrmann, Cox, et al., and Kessler et al.: Data and code sharing is the way forward for fMRI. Proc Natl Acad Sci U S A. 2017 Apr 25;114(17):E3374-E3375. doi: 10.1073/pnas.1620285114. Epub 2017 Apr 18. PMID: 28420795; PMCID: PMC5410782.
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Flight performance of actively foraging honey bees is reduced by a common pathogen.

Environmental microbiology reports

Wells T, Wolf S, Nicholls E, Groll H, Lim KS, Clark SJ, Swain J, Osborne JL, Haughton AJ.
PMID: 27337097
Environ Microbiol Rep. 2016 Oct;8(5):728-737. doi: 10.1111/1758-2229.12434. Epub 2016 Jul 07.

Sudden and severe declines in honey bee (Apis mellifera) colony health in the US and Europe have been attributed, in part, to emergent microbial pathogens, however, the mechanisms behind the impact are unclear. Using roundabout flight mills, we measured...

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Wells T, Wolf S, Nicholls E, et al. Flight performance of actively foraging honey bees is reduced by a common pathogen. Environ Microbiol Rep. 2016;8(5):728-737doi: 10.1111/1758-2229.12434.
Wells, T., Wolf, S., Nicholls, E., Groll, H., Lim, K. S., Clark, S. J., Swain, J., Osborne, J. L., & Haughton, A. J. (2016). Flight performance of actively foraging honey bees is reduced by a common pathogen. Environmental microbiology reports, 8(5), 728-737. https://doi.org/10.1111/1758-2229.12434
Wells, Trish, et al. "Flight performance of actively foraging honey bees is reduced by a common pathogen." Environmental microbiology reports vol. 8,5 (2016): 728-737. doi: https://doi.org/10.1111/1758-2229.12434
Wells T, Wolf S, Nicholls E, Groll H, Lim KS, Clark SJ, Swain J, Osborne JL, Haughton AJ. Flight performance of actively foraging honey bees is reduced by a common pathogen. Environ Microbiol Rep. 2016 Oct;8(5):728-737. doi: 10.1111/1758-2229.12434. Epub 2016 Jul 07. PMID: 27337097; PMCID: PMC5091639.
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Youthful Processing Speed in Older Adults: Genetic, Biological, and Behavioral Predictors of Cognitive Processing Speed Trajectories in Aging.

Frontiers in aging neuroscience

Bott NT, Bettcher BM, Yokoyama JS, Frazier DT, Wynn M, Karydas A, Yaffe K, Kramer JH.
PMID: 28344553
Front Aging Neurosci. 2017 Mar 10;9:55. doi: 10.3389/fnagi.2017.00055. eCollection 2017.

No abstract available.

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Bott NT, Bettcher BM, Yokoyama JS, et al. Youthful Processing Speed in Older Adults: Genetic, Biological, and Behavioral Predictors of Cognitive Processing Speed Trajectories in Aging. Front Aging Neurosci. 2017;9:55doi: 10.3389/fnagi.2017.00055.
Bott, N. T., Bettcher, B. M., Yokoyama, J. S., Frazier, D. T., Wynn, M., Karydas, A., Yaffe, K., & Kramer, J. H. (2017). Youthful Processing Speed in Older Adults: Genetic, Biological, and Behavioral Predictors of Cognitive Processing Speed Trajectories in Aging. Frontiers in aging neuroscience, 955. https://doi.org/10.3389/fnagi.2017.00055
Bott, Nicholas T, et al. "Youthful Processing Speed in Older Adults: Genetic, Biological, and Behavioral Predictors of Cognitive Processing Speed Trajectories in Aging." Frontiers in aging neuroscience vol. 9 (2017): 55. doi: https://doi.org/10.3389/fnagi.2017.00055
Bott NT, Bettcher BM, Yokoyama JS, Frazier DT, Wynn M, Karydas A, Yaffe K, Kramer JH. Youthful Processing Speed in Older Adults: Genetic, Biological, and Behavioral Predictors of Cognitive Processing Speed Trajectories in Aging. Front Aging Neurosci. 2017 Mar 10;9:55. doi: 10.3389/fnagi.2017.00055. eCollection 2017. PMID: 28344553; PMCID: PMC5344896.
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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Scientific data

Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA.
PMID: 27326542
Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain....

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Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044doi: 10.1038/sdata.2016.44.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3160044. https://doi.org/10.1038/sdata.2016.44
Gorgolewski, Krzysztof J, et al. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments." Scientific data vol. 3 (2016): 160044. doi: https://doi.org/10.1038/sdata.2016.44
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44. PMID: 27326542; PMCID: PMC4978148.
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The Next Breakthrough for Organic Photovoltaics?.

The journal of physical chemistry letters

Jackson NE, Savoie BM, Marks TJ, Chen LX, Ratner MA.
PMID: 26263095
J Phys Chem Lett. 2015 Jan 02;6(1):77-84. doi: 10.1021/jz502223t. Epub 2014 Dec 15.

While the intense focus on energy level tuning in organic photovoltaic materials has afforded large gains in device performance, we argue here that strategies based on microstructural/morphological control are at least as promising in any rational design strategy. In...

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Jackson NE, Savoie BM, Marks TJ, et al. The Next Breakthrough for Organic Photovoltaics?. J Phys Chem Lett. 2014;6(1):77-84doi: 10.1021/jz502223t.
Jackson, N. E., Savoie, B. M., Marks, T. J., Chen, L. X., & Ratner, M. A. (2015). The Next Breakthrough for Organic Photovoltaics?. The journal of physical chemistry letters, 6(1), 77-84. https://doi.org/10.1021/jz502223t
Jackson, Nicholas E, et al. "The Next Breakthrough for Organic Photovoltaics?." The journal of physical chemistry letters vol. 6,1 (2015): 77-84. doi: https://doi.org/10.1021/jz502223t
Jackson NE, Savoie BM, Marks TJ, Chen LX, Ratner MA. The Next Breakthrough for Organic Photovoltaics?. J Phys Chem Lett. 2015 Jan 02;6(1):77-84. doi: 10.1021/jz502223t. Epub 2014 Dec 15. PMID: 26263095.
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Distance-dependent aposematism and camouflage in the cinnabar moth caterpillar (.

Royal Society open science

Barnett JB, Cuthill IC, Scott-Samuel NE.
PMID: 29515858
R Soc Open Sci. 2018 Feb 21;5(2):171396. doi: 10.1098/rsos.171396. eCollection 2018 Feb.

Defended prey often use distinctive, conspicuous, colours to advertise their unprofitability to potential predators (aposematism). These warning signals are frequently made up of salient, high contrast, stripes which have been hypothesized to increase the speed and accuracy of predator...

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Barnett JB, Cuthill IC, Scott-Samuel NE. Distance-dependent aposematism and camouflage in the cinnabar moth caterpillar (. R Soc Open Sci. 2018;5(2):171396doi: 10.1098/rsos.171396.
Barnett, J. B., Cuthill, I. C., & Scott-Samuel, N. E. (2018). Distance-dependent aposematism and camouflage in the cinnabar moth caterpillar (. Royal Society open science, 5(2), 171396. https://doi.org/10.1098/rsos.171396
Barnett, James B, et al. "Distance-dependent aposematism and camouflage in the cinnabar moth caterpillar (." Royal Society open science vol. 5,2 (2018): 171396. doi: https://doi.org/10.1098/rsos.171396
Barnett JB, Cuthill IC, Scott-Samuel NE. Distance-dependent aposematism and camouflage in the cinnabar moth caterpillar (. R Soc Open Sci. 2018 Feb 21;5(2):171396. doi: 10.1098/rsos.171396. eCollection 2018 Feb. PMID: 29515858; PMCID: PMC5830747.
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Integrated Transcriptome and Proteome Analyses Reveal Organ-Specific Proteome Deterioration in Old Rats.

Cell systems

Ori A, Toyama BH, Harris MS, Bock T, Iskar M, Bork P, Ingolia NT, Hetzer MW, Beck M.
PMID: 27135913
Cell Syst. 2015 Sep 23;1(3):224-37. doi: 10.1016/j.cels.2015.08.012. Epub 2015 Sep 17.

Aging is associated with the decline of protein, cell, and organ function. Here, we use an integrated approach to characterize gene expression, bulk translation, and cell biology in the brains and livers of young and old rats. We identify...

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Ori A, Toyama BH, Harris MS, et al. Integrated Transcriptome and Proteome Analyses Reveal Organ-Specific Proteome Deterioration in Old Rats. Cell Syst. 2015;1(3):224-37doi: 10.1016/j.cels.2015.08.012.
Ori, A., Toyama, B. H., Harris, M. S., Bock, T., Iskar, M., Bork, P., Ingolia, N. T., Hetzer, M. W., & Beck, M. (2015). Integrated Transcriptome and Proteome Analyses Reveal Organ-Specific Proteome Deterioration in Old Rats. Cell systems, 1(3), 224-37. https://doi.org/10.1016/j.cels.2015.08.012
Ori, Alessandro, et al. "Integrated Transcriptome and Proteome Analyses Reveal Organ-Specific Proteome Deterioration in Old Rats." Cell systems vol. 1,3 (2015): 224-37. doi: https://doi.org/10.1016/j.cels.2015.08.012
Ori A, Toyama BH, Harris MS, Bock T, Iskar M, Bork P, Ingolia NT, Hetzer MW, Beck M. Integrated Transcriptome and Proteome Analyses Reveal Organ-Specific Proteome Deterioration in Old Rats. Cell Syst. 2015 Sep 23;1(3):224-37. doi: 10.1016/j.cels.2015.08.012. Epub 2015 Sep 17. PMID: 27135913; PMCID: PMC4802414.
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Prospective Analysis of Lipid Composition Changes with Antiretroviral Therapy and Immune Activation in Persons Living with HIV.

Pathogens & immunity

Belury MA, Bowman E, Gabriel J, Snyder B, Kulkarni M, Palettas M, Mo X, Lake JE, Zidar D, Sieg SF, Rodriguez B, Playford MP, Andrade A, Kuritzkes DR, Mehta NN, Lederman MM, Funderburg NT.
PMID: 29098203
Pathog Immun. 2017;2(3):376-403. doi: 10.20411/pai.v2i3.218. Epub 2017 Oct 06.

BACKGROUND: Lipid profiles are altered by HIV infection and antiretroviral therapy (ART). Among HIV-uninfected (HIV-) populations the concentrations of various lipid classes (ie, lyso-phosphatidylcholine, LPC) and their saturated (SaFA), mono-unsaturated (MUFA), and polyunsaturated fatty acid (PUFA) composition are related...

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Belury MA, Bowman E, Gabriel J, et al. Prospective Analysis of Lipid Composition Changes with Antiretroviral Therapy and Immune Activation in Persons Living with HIV. Pathog Immun. 2017;2(3):376-403doi: 10.20411/pai.v2i3.218.
Belury, M. A., Bowman, E., Gabriel, J., Snyder, B., Kulkarni, M., Palettas, M., Mo, X., Lake, J. E., Zidar, D., Sieg, S. F., Rodriguez, B., Playford, M. P., Andrade, A., Kuritzkes, D. R., Mehta, N. N., Lederman, M. M., & Funderburg, N. T. (2017). Prospective Analysis of Lipid Composition Changes with Antiretroviral Therapy and Immune Activation in Persons Living with HIV. Pathogens & immunity, 2(3), 376-403. https://doi.org/10.20411/pai.v2i3.218
Belury, Martha A, et al. "Prospective Analysis of Lipid Composition Changes with Antiretroviral Therapy and Immune Activation in Persons Living with HIV." Pathogens & immunity vol. 2,3 (2017): 376-403. doi: https://doi.org/10.20411/pai.v2i3.218
Belury MA, Bowman E, Gabriel J, Snyder B, Kulkarni M, Palettas M, Mo X, Lake JE, Zidar D, Sieg SF, Rodriguez B, Playford MP, Andrade A, Kuritzkes DR, Mehta NN, Lederman MM, Funderburg NT. Prospective Analysis of Lipid Composition Changes with Antiretroviral Therapy and Immune Activation in Persons Living with HIV. Pathog Immun. 2017;2(3):376-403. doi: 10.20411/pai.v2i3.218. Epub 2017 Oct 06. PMID: 29098203; PMCID: PMC5663243.
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Ethylene-regulated gene transcription in carrot roots.

Plant molecular biology

Nichols SE, Laties GG.
PMID: 24310573
Plant Mol Biol. 1984 Nov;3(6):393-401. doi: 10.1007/BF00033387.

The plant hormone ethylene elicits many biochemical changes in target tissues. To investigate ethylene effects on expression of genetic information, cDNA clones corresponding to ethylene-induced carrot root mRNAs were constructed and isolated. RNA dot blot analysis showed that for...

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Nichols SE, Laties GG. Ethylene-regulated gene transcription in carrot roots. Plant Mol Biol. 1984;3(6):393-401doi: 10.1007/BF00033387.
Nichols, S. E., & Laties, G. G. (1984). Ethylene-regulated gene transcription in carrot roots. Plant molecular biology, 3(6), 393-401. https://doi.org/10.1007/BF00033387
Nichols, S E, and Laties, G G. "Ethylene-regulated gene transcription in carrot roots." Plant molecular biology vol. 3,6 (1984): 393-401. doi: https://doi.org/10.1007/BF00033387
Nichols SE, Laties GG. Ethylene-regulated gene transcription in carrot roots. Plant Mol Biol. 1984 Nov;3(6):393-401. doi: 10.1007/BF00033387. PMID: 24310573.
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Correction: changes to serum sample tube and processing methodology does not cause inter-individual variation in automated whole serum N-glycan profiling in health and disease.

PloS one

Ventham NT, Gardner RA, Kennedy NA, Shubhakar A, Kalla R, Nimmo ER, Fernandes DL, Satsangi J, Spencer DI.
PMID: 26030157
PLoS One. 2015 Jun 01;10(6):e0129335. doi: 10.1371/journal.pone.0129335. eCollection 2015.

No abstract available.

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Ventham NT, Gardner RA, Kennedy NA, et al. Correction: changes to serum sample tube and processing methodology does not cause inter-individual variation in automated whole serum N-glycan profiling in health and disease. PLoS One. 2015;10(6):e0129335doi: 10.1371/journal.pone.0129335.
Ventham, N. T., Gardner, R. A., Kennedy, N. A., Shubhakar, A., Kalla, R., Nimmo, E. R., Fernandes, D. L., Satsangi, J., & Spencer, D. I. (2015). Correction: changes to serum sample tube and processing methodology does not cause inter-individual variation in automated whole serum N-glycan profiling in health and disease. PloS one, 10(6), e0129335. https://doi.org/10.1371/journal.pone.0129335
Ventham, Nicholas T, et al. "Correction: changes to serum sample tube and processing methodology does not cause inter-individual variation in automated whole serum N-glycan profiling in health and disease." PloS one vol. 10,6 (2015): e0129335. doi: https://doi.org/10.1371/journal.pone.0129335
Ventham NT, Gardner RA, Kennedy NA, Shubhakar A, Kalla R, Nimmo ER, Fernandes DL, Satsangi J, Spencer DI. Correction: changes to serum sample tube and processing methodology does not cause inter-individual variation in automated whole serum N-glycan profiling in health and disease. PLoS One. 2015 Jun 01;10(6):e0129335. doi: 10.1371/journal.pone.0129335. eCollection 2015. PMID: 26030157; PMCID: PMC4451143.
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Multitrophic diversity effects of network degradation.

Ecology and evolution

Nichols E, Peres CA, Hawes JE, Naeem S.
PMID: 27547324
Ecol Evol. 2016 Jun 21;6(14):4936-46. doi: 10.1002/ece3.2253. eCollection 2016 Jul.

Predicting the functional consequences of biodiversity loss in realistic, multitrophic communities remains a challenge. No existing biodiversity-ecosystem function study to date has simultaneously incorporated information on species traits, network topology, and extinction across multiple trophic levels, while all three...

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Nichols E, Peres CA, Hawes JE, et al. Multitrophic diversity effects of network degradation. Ecol Evol. 2016;6(14):4936-46doi: 10.1002/ece3.2253.
Nichols, E., Peres, C. A., Hawes, J. E., & Naeem, S. (2016). Multitrophic diversity effects of network degradation. Ecology and evolution, 6(14), 4936-46. https://doi.org/10.1002/ece3.2253
Nichols, Elizabeth, et al. "Multitrophic diversity effects of network degradation." Ecology and evolution vol. 6,14 (2016): 4936-46. doi: https://doi.org/10.1002/ece3.2253
Nichols E, Peres CA, Hawes JE, Naeem S. Multitrophic diversity effects of network degradation. Ecol Evol. 2016 Jun 21;6(14):4936-46. doi: 10.1002/ece3.2253. eCollection 2016 Jul. PMID: 27547324; PMCID: PMC4979718.
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Showing 1 to 12 of 1072 entries