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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach – CORRIGENDUM
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, JeanMichel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, HsiChung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil TekolaAyele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 221 / Issue 2 / August 2022
- Published online by Cambridge University Press:
- 04 May 2022, p. 494
- Print publication:
- August 2022
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Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach
- Micah Cearns, Azmeraw T. Amare, Klaus Oliver Schubert, Anbupalam Thalamuthu, Joseph Frank, Fabian Streit, Mazda Adli, Nirmala Akula, Kazufumi Akiyama, Raffaella Ardau, Bárbara Arias, Jean-Michel Aubry, Lena Backlund, Abesh Kumar Bhattacharjee, Frank Bellivier, Antonio Benabarre, Susanne Bengesser, Joanna M. Biernacka, Armin Birner, Clara Brichant-Petitjean, Pablo Cervantes, Hsi-Chung Chen, Caterina Chillotti, Sven Cichon, Cristiana Cruceanu, Piotr M. Czerski, Nina Dalkner, Alexandre Dayer, Franziska Degenhardt, Maria Del Zompo, J. Raymond DePaulo, Bruno Étain, Peter Falkai, Andreas J. Forstner, Louise Frisen, Mark A. Frye, Janice M. Fullerton, Sébastien Gard, Julie S. Garnham, Fernando S. Goes, Maria Grigoroiu-Serbanescu, Paul Grof, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Stefan Herms, Per Hoffmann, Andrea Hofmann, Liping Hou, Yi-Hsiang Hsu, Stephane Jamain, Esther Jiménez, Jean-Pierre Kahn, Layla Kassem, Po-Hsiu Kuo, Tadafumi Kato, John Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Barbara König, Ichiro Kusumi, Gonzalo Laje, Mikael Landén, Catharina Lavebratt, Marion Leboyer, Susan G. Leckband, Mario Maj, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Mirko Manchia, Lina Martinsson, Michael J. McCarthy, Susan McElroy, Francesc Colom, Marina Mitjans, Francis M. Mondimore, Palmiero Monteleone, Caroline M. Nievergelt, Markus M. Nöthen, Tomas Novák, Claire O'Donovan, Norio Ozaki, Vincent Millischer, Sergi Papiol, Andrea Pfennig, Claudia Pisanu, James B. Potash, Andreas Reif, Eva Reininghaus, Guy A. Rouleau, Janusz K. Rybakowski, Martin Schalling, Peter R. Schofield, Barbara W. Schweizer, Giovanni Severino, Tatyana Shekhtman, Paul D. Shilling, Katzutaka Shimoda, Christian Simhandl, Claire M. Slaney, Alessio Squassina, Thomas Stamm, Pavla Stopkova, Fasil Tekola-Ayele, Alfonso Tortorella, Gustavo Turecki, Julia Veeh, Eduard Vieta, Stephanie H. Witt, Gloria Roberts, Peter P. Zandi, Martin Alda, Michael Bauer, Francis J. McMahon, Philip B. Mitchell, Thomas G. Schulze, Marcella Rietschel, Scott R. Clark, Bernhard T. Baune
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- Journal:
- The British Journal of Psychiatry / Volume 220 / Issue 4 / April 2022
- Published online by Cambridge University Press:
- 28 February 2022, pp. 219-228
- Print publication:
- April 2022
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Background
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
AimsTo use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
MethodThis study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
ResultsThe best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
ConclusionsUsing PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
Learning to count: A deep learning framework for graphlet count estimation
- Xutong Liu, Yu-Zhen Janice Chen, John C. S. Lui, Konstantin Avrachenkov
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- Journal:
- Network Science / Volume 9 / Issue S1 / October 2021
- Published online by Cambridge University Press:
- 11 September 2020, pp. S23-S60
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Graphlet counting is a widely explored problem in network analysis and has been successfully applied to a variety of applications in many domains, most notatbly bioinformatics, social science, and infrastructure network studies. Efficiently computing graphlet counts remains challenging due to the combinatorial explosion, where a naive enumeration algorithm needs O(Nk) time for k-node graphlets in a network of size N. Recently, many works introduced carefully designed combinatorial and sampling methods with encouraging results. However, the existing methods ignore the fact that graphlet counts and the graph structural information are correlated. They always consider a graph as a new input and repeat the tedious counting procedure on a regular basis even if it is similar or exactly isomorphic to previously studied graphs. This provides an opportunity to speed up the graphlet count estimation procedure by exploiting this correlation via learning methods. In this paper, we raise a novel graphlet count learning (GCL) problem: given a set of historical graphs with known graphlet counts, how to learn to estimate/predict graphlet count for unseen graphs coming from the same (or similar) underlying distribution. We develop a deep learning framework which contains two convolutional neural network models and a series of data preprocessing techniques to solve the GCL problem. Extensive experiments are conducted on three types of synthetic random graphs and three types of real-world graphs for all 3-, 4-, and 5-node graphlets to demonstrate the accuracy, efficiency, and generalizability of our framework. Compared with state-of-the-art exact/sampling methods, our framework shows great potential, which can offer up to two orders of magnitude speedup on synthetic graphs and achieve on par speed on real-world graphs with competitive accuracy.
Key role of the REC lobe during CRISPR–Cas9 activation by ‘sensing’, ‘regulating’, and ‘locking’ the catalytic HNH domain
- Giulia Palermo, Janice S. Chen, Clarisse G. Ricci, Ivan Rivalta, Martin Jinek, Victor S. Batista, Jennifer A. Doudna, J. Andrew McCammon
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- Journal:
- Quarterly Reviews of Biophysics / Volume 51 / 2018
- Published online by Cambridge University Press:
- 03 August 2018, e9
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Understanding the conformational dynamics of CRISPR (clustered regularly interspaced short palindromic repeat)–Cas9 is of the utmost importance for improving its genome editing capability. Here, molecular dynamics simulations performed using Anton-2 – a specialized supercomputer capturing micro-to-millisecond biophysical events in real time and at atomic-level resolution – reveal the activation process of the endonuclease Cas9 toward DNA cleavage. Over the unbiased simulation, we observe that the spontaneous approach of the catalytic domain HNH to the DNA cleavage site is accompanied by a remarkable structural remodeling of the recognition (REC) lobe, which exerts a key role for DNA cleavage. Specifically, the significant conformational changes and the collective conformational dynamics of the REC lobe indicate a mechanism by which the REC1–3 regions ‘sense’ nucleic acids, ‘regulate’ the HNH conformational transition, and ultimately ‘lock’ the HNH domain at the cleavage site, contributing to its catalytic competence. By integrating additional independent simulations and existing experimental data, we provide a solid validation of the activated HNH conformation, which had been so far poorly characterized, and we deliver a comprehensive understanding of the role of REC1–3 in the activation process. Considering the importance of the REC lobe in the specificity of Cas9, this study poses the basis for fully understanding how the REC components control the cleavage of off-target sequences, laying the foundation for future engineering efforts toward improved genome editing.
Predicting habits of vegetable parenting practices to facilitate the design of change programmes
- Tom Baranowski, Tzu-An Chen, Teresia M O’Connor, Sheryl O Hughes, Cassandra S Diep, Alicia Beltran, Leah Brand, Theresa Nicklas, Janice Baranowski
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- Journal:
- Public Health Nutrition / Volume 19 / Issue 11 / August 2016
- Published online by Cambridge University Press:
- 04 December 2015, pp. 1976-1982
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Objective
Habit has been defined as the automatic performance of a usual behaviour. The present paper reports the relationships of variables from a Model of Goal Directed Behavior to four scales in regard to parents’ habits when feeding their children: habit of (i) actively involving child in selection of vegetables; (ii) maintaining a positive vegetable environment; (iii) positive communications about vegetables; and (iv) controlling vegetable practices. We tested the hypothesis that the primary predictor of each habit variable would be the measure of the corresponding parenting practice.
DesignInternet survey data from a mostly female sample. Primary analyses employed regression modelling with backward deletion, controlling for demographics and parenting practices behaviour.
SettingHouston, Texas, USA.
SubjectsParents of 307 pre-school (3–5-year-old) children.
ResultsThree of the four models accounted for about 50 % of the variance in the parenting practices habit scales. Each habit scale was primarily predicted by the corresponding parenting practices scale (suggesting validity). The habit of active child involvement in vegetable selection was also most strongly predicted by two barriers and rudimentary self-efficacy; the habit of maintaining a positive vegetable environment by one barrier; the habit of maintaining positive communications about vegetables by an emotional scale; and the habit of controlling vegetable practices by a perceived behavioural control scale.
ConclusionsThe predictiveness of the psychosocial variables beyond parenting practices behaviour was modest. Discontinuing the habit of ineffective controlling parenting practices may require increasing the parent’s perceived control of parenting practices, perhaps through simulated parent–child interactions.
Predicting use of effective vegetable parenting practices with the Model of Goal Directed Behavior
- Cassandra S Diep, Alicia Beltran, Tzu-An Chen, Debbe Thompson, Teresia O’Connor, Sheryl Hughes, Janice Baranowski, Tom Baranowski
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- Journal:
- Public Health Nutrition / Volume 18 / Issue 8 / June 2015
- Published online by Cambridge University Press:
- 19 September 2014, pp. 1389-1396
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Objective
To model effective vegetable parenting practices using the Model of Goal Directed Vegetable Parenting Practices construct scales.
DesignAn Internet survey was conducted with parents of pre-school children to assess their agreement with effective vegetable parenting practices and Model of Goal Directed Vegetable Parenting Practices items. Block regression modelling was conducted using the composite score of effective vegetable parenting practices scales as the outcome variable and the Model of Goal Directed Vegetable Parenting Practices constructs as predictors in separate and sequential blocks: demographics, intention, desire (intrinsic motivation), perceived barriers, autonomy, relatedness, self-efficacy, habit, anticipated emotions, perceived behavioural control, attitudes and lastly norms. Backward deletion was employed at the end for any variable not significant at P<0·05.
SettingHouston, TX, USA.
SubjectsThree hundred and seven parents (mostly mothers) of pre-school children.
ResultsSignificant predictors in the final model in order of relationship strength included habit of active child involvement in vegetable selection, habit of positive vegetable communications, respondent not liking vegetables, habit of keeping a positive vegetable environment and perceived behavioural control of having a positive influence on child’s vegetable consumption. The final model’s adjusted R2 was 0·486.
ConclusionsThis was the first study to test scales from a behavioural model to predict effective vegetable parenting practices. Further research needs to assess these Model of Goal Directed Vegetable Parenting Practices scales for their (i) predictiveness of child consumption of vegetables in longitudinal samples and (ii) utility in guiding design of vegetable parenting practices interventions.
66 - Cardiovascular gene therapy: implications for platelet vessel wall interactions
- from PART IV - PHARMOLOGY
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- By Pierre Zoldhelyi, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA, Harold Eichstaedt, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA, Thomas Jax, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA, Janice M. McNatt, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA, Zhi Qiang Chen, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA, Harnath S. Shelat, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA, Harris Rose, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA, James T. Willerson, Wafic Said Molecular Cardiology and Gene Therapy Research Laboratory and Cullen Research Laboratory, Texas Heart Institute, and Department of Medicine (Divisions of Cardiology and of Clinical Pharmacology), The University of Texas Medical School at Houston, USA
- Edited by Paolo Gresele, Università degli Studi di Perugia, Italy, Clive P. Page, Valentin Fuster, Jos Vermylen, Universiteitsbibliotheek-K.U., Leuven
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- Book:
- Platelets in Thrombotic and Non-Thrombotic Disorders
- Published online:
- 10 May 2010
- Print publication:
- 30 May 2002, pp 978-990
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Summary
Introduction
Evidence for the occurrence of coronary thrombosis early after percutaneous coronary interventions in humans was provided by serial angioscopic studies that demonstrated the development of varying degrees of thrombosis in over 90% of cases within 60 min after successful coronary balloon angioplasty. Furthermore, studies on the effectiveness of acute PTCA during unstable angina have indicated not only an increased number of periprocedural complications but a relatively high incidence of restenosis. Based on experimental and clinical evidence, it has been suggested that arterial thrombosis promotes the fibroproliferative response during later restenosis and progression of atherosclerosis. The platelet IIb/IIIa and αv/β3 integrin receptor blocker, abciximab (ReoPro), reduced the need for recurrent clinical revascularization only in the EPIC trial, which enrolled patients with unstable angina (due, in general, to coronary thrombosis). In contrast, the later EPILOG trial in patients without acute coronary thrombosis failed to show a reduction in the need for recurrent revascularization, consistent with a role of early acute thrombosis in the pathogenesis of clinical restenosis. The incidence of periprocedural complications and ‘clinically apparent’ thrombosis after PTCA and stenting is sharply decreased by the administration of antiplatelet regimens, including the platelet IIb/IIIa receptor blockers and aspirin/ticlopidine or plavix. The efficacy of such interventions for the long-term prevention of ‘subclinical’ thrombosis and platelet deposition is uncertain, especially after these agents are withdrawn.