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Rief W, Asmundson GJG, Bryant RA, Clark DM, Ehlers A, Holmes EA, McNally RJ, Neufeld CB, Wilhelm S, Jaroszewski AC, Berg M, Haberkamp A, Hofmann SG. The future of psychological treatments: The Marburg Declaration. Clin Psychol Rev 2024; 110:102417. [PMID: 38688158 DOI: 10.1016/j.cpr.2024.102417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 05/02/2024]
Abstract
Although psychological treatments are broadly recognized as evidence-based interventions for various mental disorders, challenges remain. For example, a substantial proportion of patients receiving such treatments do not fully recover, and many obstacles hinder the dissemination, implementation, and training of psychological treatments. These problems require those in our field to rethink some of our basic models of mental disorders and their treatments, and question how research and practice in clinical psychology should progress. To answer these questions, a group of experts of clinical psychology convened at a Think-Tank in Marburg, Germany, in August 2022 to review the evidence and analyze barriers for current and future developments. After this event, an overview of the current state-of-the-art was drafted and suggestions for improvements and specific recommendations for research and practice were integrated. Recommendations arising from our meeting cover further improving psychological interventions through translational approaches, improving clinical research methodology, bridging the gap between more nomothetic (group-oriented) studies and idiographic (person-centered) decisions, using network approaches in addition to selecting single mechanisms to embrace the complexity of clinical reality, making use of scalable digital options for assessments and interventions, improving the training and education of future psychotherapists, and accepting the societal responsibilities that clinical psychology has in improving national and global health care. The objective of the Marburg Declaration is to stimulate a significant change regarding our understanding of mental disorders and their treatments, with the aim to trigger a new era of evidence-based psychological interventions.
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Affiliation(s)
- Winfried Rief
- Philipps-University of Marburg, Department of Psychology, Clinical Psychology and Psychotherapy Group, Marburg, Germany.
| | | | - Richard A Bryant
- University of New South Wales, School of Psychology, Sydney, New South Wales, Australia
| | - David M Clark
- University of Oxford, Department of Experimental Psychology, Oxford, UK
| | - Anke Ehlers
- University of Oxford, Department of Experimental Psychology, Oxford, UK
| | - Emily A Holmes
- Uppsala University, Department of Women's and Children's Health, Uppsala, Sweden; Karolinska Institutet, Department of Clinical Neuroscience, Solna, Sweden
| | | | - Carmem B Neufeld
- University of São Paulo, Department of Psychology, Ribeirão Preto, SP, Brazil
| | - Sabine Wilhelm
- Massachusetts General Hospital and Harvard School of Medicine, Boston, USA
| | - Adam C Jaroszewski
- Massachusetts General Hospital and Harvard School of Medicine, Boston, USA
| | - Max Berg
- Philipps-University of Marburg, Department of Psychology, Clinical Psychology and Psychotherapy Group, Marburg, Germany
| | - Anke Haberkamp
- Philipps-University of Marburg, Department of Psychology, Clinical Psychology and Psychotherapy Group, Marburg, Germany
| | - Stefan G Hofmann
- Philipps-University of Marburg, Department of Psychology, Translational Clinical Psychology Group, Marburg, Germany
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2
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Ripley DM, Garner T, Hook SA, Veríssimo A, Grunow B, Moritz T, Clayton P, Shiels HA, Stevens A. Warming during embryogenesis induces a lasting transcriptomic signature in fishes. Sci Total Environ 2023; 902:165954. [PMID: 37536606 DOI: 10.1016/j.scitotenv.2023.165954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/24/2023] [Accepted: 07/30/2023] [Indexed: 08/05/2023]
Abstract
Exposure to elevated temperatures during embryogenesis can influence the plasticity of tissues in later life. Despite these long-term changes in plasticity, few differentially expressed genes are ever identified, suggesting that the developmental programming of later life plasticity may occur through the modulation of other aspects of transcriptomic architecture, such as gene network organisation. Here, we use network modelling approaches to demonstrate that warm temperatures during embryonic development (developmental warming) have consistent effects in later life on the organisation of transcriptomic networks across four diverse species of fishes: Scyliorhinus canicula, Danio rerio, Dicentrarchus labrax, and Gasterosteus aculeatus. The transcriptomes of developmentally warmed fishes are characterised by an increased entropy of their pairwise gene interaction networks, implying a less structured, more 'random' set of gene interactions. We also show that, in zebrafish subject to developmental warming, the entropy of an individual gene within a network is associated with that gene's probability of expression change during temperature acclimation in later life. However, this association is absent in animals reared under 'control' conditions. Thus, the thermal environment experienced during embryogenesis can alter transcriptomic organisation in later life, and these changes may influence an individual's responsiveness to future temperature challenges.
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Affiliation(s)
- Daniel M Ripley
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK.
| | - Terence Garner
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Samantha A Hook
- Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK
| | - Ana Veríssimo
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, 4485-661 Vairão, Portugal; BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, 4485-661 Vairão, Portugal
| | - Bianka Grunow
- Fish Growth Physiology, Research Institute for Farm Animal Biology (FBN), Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany
| | - Timo Moritz
- Deutsches Meeresmuseum, Katharinenberg 14-20, 18439 Stralsund, Germany; Institute of Biological Sciences, University of Rostock, Albert-Einstein-Straße 3, 18059 Rostock, Germany
| | - Peter Clayton
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Holly A Shiels
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
| | - Adam Stevens
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK.
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Bar H, Wells MT. On Graphical Models and Convex Geometry. Comput Stat Data Anal 2023; 187:107800. [PMID: 37396752 PMCID: PMC10310290 DOI: 10.1016/j.csda.2023.107800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
A mixture-model of beta distributions framework is introduced to identify significant correlations among P features when P is large. The method relies on theorems in convex geometry, which are used to show how to control the error rate of edge detection in graphical models. The proposed 'betaMix' method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results hold for a wide class of data-generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions. The results are robust for sufficiently large sample sizes and hold for non-elliptically-symmetric distributions.
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Affiliation(s)
- Haim Bar
- Department of Statistics, University of Connecticut, Room 315, Philip E. Austin Building, Storrs, 06269-4120, CT, USA
| | - Martin T. Wells
- Department of Statistics and Data Science, Cornell University, 1190 Comstock Hall, Ithaca, 14853, NY, USA
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Whiston A, Igou ER, Fortune DG, Semkovska M. Longitudinal interactions between residual symptoms and physiological stress in the remitted symptom network structure of depression. Acta Psychol (Amst) 2023; 241:104078. [PMID: 37944268 DOI: 10.1016/j.actpsy.2023.104078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 10/16/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023] Open
Abstract
Residual symptoms and stress are amongst the most reliable predictors of relapse in remitted depression. Standard methodologies often preclude continuous stress sampling or the evaluation of complex symptom interactions. This limits knowledge acquisition relative to the day-to-day interactions between residual symptoms and stress. The study aims to explore the interactions between physiological stress and residual symptoms network structure in remitted depression. Twenty-two individuals remitted from depression completed baseline, daily diary (DD), and post-DD assessments. Self-reported stress and residual symptoms were measured at baseline and post-DD. Daily diaries required participants to use a wearable electrodermal activity (EDA) device during waking hours and complete residual symptom measures twice daily for 3-weeks. Two-step multilevel vector auto-regression models were used to estimate contemporaneous and dynamic networks. Depressed mood and concentration problems were central across networks. Skin conductance responses (SCRs), suicide, appetite, and sleep problems were central in the temporal and energy loss in the contemporaneous network. Increased SCRs predicted decreased energy loss. Residual symptoms and stress showed bi-directional interactions. Overall, depressed mood and concentration problems were consistently central, thus potentially important intervention targets. Non-obtrusive bio-signal measures should be used to provide the clinical evidence-base for modelling the interactions between depressive residual symptoms and stress. Practical implications are discussed throughout related to focusing on symptom-specific interactions in clinical practice, simultaneously reducing residual symptom and stress occurrences, EDA as pioneering signal for stress detection, and the central role of specific residual symptoms in remitted depression.
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Affiliation(s)
- Aoife Whiston
- Department of Psychology, University of Limerick, Co., Limerick, Ireland.
| | - Eric R Igou
- Department of Psychology, University of Limerick, Co., Limerick, Ireland
| | - Dònal G Fortune
- Department of Psychology, University of Limerick, Co., Limerick, Ireland
| | - Maria Semkovska
- DeFREE Research Unit, Department of Psychology, University of Southern Denmark, Denmark
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Ait Rai K, Machkour M, Antari J. Influential nodes identification in complex networks: a comprehensive literature review. Beni Suef Univ J Basic Appl Sci 2023; 12:18. [PMID: 36819294 PMCID: PMC9927061 DOI: 10.1186/s43088-023-00357-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/01/2023] [Indexed: 02/16/2023] Open
Abstract
Researchers have paid a lot of attention to complex networks in recent decades. Due to their rapid evolution, they turn into a major scientific and innovative field. Several studies on complex networks are carried out, and other subjects are evolving every day such as the challenge of detecting influential nodes. In this study, we provide a brief overview of complex networks, as well as several concepts key related to measurements, the structure of complex network and social influence, an important state of the art on complex networks including basic metrics on complex networks, the evolution of their topology over the years as well as the dynamic of networks. A detailed literature about influential finding approaches is also provided to indicate their strength and shortcomings. We aim that our contribution of literature can be an interesting base of information for beginners' scientists in this field. At the end of this paper, some conclusions are drawn and some future perspectives are mentioned to be studied as new directions in the future. More detailed references are provided to go further and deep in this area.
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Affiliation(s)
- Khaoula Ait Rai
- grid.417651.00000 0001 2156 6183Computer System and Vision Laboratory, Faculty of Sciences Agadir BP8106, Ibn Zohr University, Agadir, Morocco
| | - Mustapha Machkour
- grid.417651.00000 0001 2156 6183Computer System and Vision Laboratory, Faculty of Sciences Agadir BP8106, Ibn Zohr University, Agadir, Morocco
| | - Jilali Antari
- grid.417651.00000 0001 2156 6183Laboratory of Computer Systems Engineering, Mathematics and Applications, Polydisciplinary Faculty of Taroudant, Ibn Zohr University, B.P. 8106, Agadir, Morocco
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Abstract
Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.
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Affiliation(s)
- Jonas M B Haslbeck
- Psychological Methods Group, University of Amsterdam, Amsterdam, Netherlands.
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Lingjærde C, Lien TG, Borgan Ø, Bergholtz H, Glad IK. Tailored graphical lasso for data integration in gene network reconstruction. BMC Bioinformatics 2021; 22:498. [PMID: 34654363 PMCID: PMC8518261 DOI: 10.1186/s12859-021-04413-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 09/30/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using [Formula: see text]-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. RESULTS We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks. CONCLUSIONS The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading.
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Affiliation(s)
- Camilla Lingjærde
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK.
| | - Tonje G Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70, 0310, Oslo, Norway
| | - Ørnulf Borgan
- Department of Mathematics, University of Oslo, PO Box 1053 Blindern, 0316, Oslo, Norway
| | - Helga Bergholtz
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Ullernchausseen 70, 0310, Oslo, Norway
| | - Ingrid K Glad
- Department of Mathematics, University of Oslo, PO Box 1053 Blindern, 0316, Oslo, Norway
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8
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Abstract
Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.
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9
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Komarova NL, Azizi A, Wodarz D. Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic. Epidemics 2021; 35:100463. [PMID: 34000693 PMCID: PMC8105306 DOI: 10.1016/j.epidem.2021.100463] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 11/23/2020] [Accepted: 04/30/2021] [Indexed: 12/23/2022] Open
Abstract
Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection “corridors”, resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a “peak and decay” pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.
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Affiliation(s)
- Natalia L Komarova
- Department of Mathematics, University of California Irvine, Irvine, CA 92697, United States
| | - Asma Azizi
- Department of Mathematics, University of California Irvine, Irvine, CA 92697, United States
| | - Dominik Wodarz
- Department of Population Health and Disease Prevention, Program in Public Health, Susan and Henry Samueli College of Health Science, University of California Irvine, Irvine, CA, 92697, United States.
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10
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Abstract
Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research, this is often implausible. In this article, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct MNMs and propose an ℓ1-regularized nodewise regression approach to estimate them. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification.
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Abstract
The Ising model is a model for pairwise interactions between binary variables that has become popular in the psychological sciences. It has been first introduced as a theoretical model for the alignment between positive (1) and negative (-1) atom spins. In many psychological applications, however, the Ising model is defined on the domain {0, 1} instead of the classical domain {-1,1}. While it is possible to transform the parameters of the Ising model in one domain to obtain a statistically equivalent model in the other domain, the parameters in the two versions of the Ising model lend themselves to different interpretations and imply different dynamics, when studying the Ising model as a dynamical system. In this tutorial paper, we provide an accessible discussion of the interpretation of threshold and interaction parameters in the two domains and show how the dynamics of the Ising model depends on the choice of domain. Finally, we provide a transformation that allows one to transform the parameters in an Ising model in one domain into a statistically equivalent Ising model in the other domain.
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Affiliation(s)
| | - Sacha Epskamp
- Psychological Methods Group, University of Amsterdam
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12
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Di Luca A, Kaila VRI. Molecular strain in the active/deactive-transition modulates domain coupling in respiratory complex I. Biochim Biophys Acta Bioenerg 2021; 1862:148382. [PMID: 33513365 DOI: 10.1016/j.bbabio.2021.148382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/08/2021] [Accepted: 01/21/2021] [Indexed: 12/14/2022]
Abstract
Complex I functions as a primary redox-driven proton pump in aerobic respiratory chains, establishing a proton motive force that powers ATP synthesis and active transport. Recent cryo-electron microscopy (cryo-EM) experiments have resolved the mammalian complex I in the biomedically relevant active (A) and deactive (D) states (Zhu et al., 2016; Fiedorczuk et al., 2016; Agip et al., 2018 [1-3]) that could regulate enzyme turnover, but it still remains unclear how the conformational state and activity are linked. We show here how global motion along the A/D transition accumulates molecular strain at specific coupling regions important for both redox chemistry and proton pumping. Our data suggest that the A/D motion modulates force propagation pathways between the substrate-binding site and the proton pumping machinery that could alter electrostatic and conformational coupling across large distances. Our findings provide a molecular basis to understand how global protein dynamics can modulate the biological activity of large molecular complexes.
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Affiliation(s)
- Andrea Di Luca
- Department of Biochemistry and Biophysics, Stockholm University, 10691 Stockholm, Sweden
| | - Ville R I Kaila
- Department of Biochemistry and Biophysics, Stockholm University, 10691 Stockholm, Sweden.
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Abstract
BACKGROUND The transitions between epithelial (E) and mesenchymal (M) cell phenotypes are essential in many biological processes like tissue development and cancer metastasis. Previous studies, both modeling and experimental, suggested that in addition to E and M states, the network responsible for these phenotypes exhibits intermediate phenotypes between E and M states. The number and importance of such states is subject to intense discussion in the epithelial-mesenchymal transition (EMT) community. RESULTS Previous modeling efforts used traditional bifurcation analysis to explore the number of the steady states that correspond to E, M and intermediate states by varying one or two parameters at a time. Since the system has dozens of parameters that are largely unknown, it remains a challenging problem to fully describe the potential set of states and their relationship across all parameters. We use the computational tool DSGRN (Dynamic Signatures Generated by Regulatory Networks) to explore the intermediate states of an EMT model network by computing summaries of the dynamics across all of parameter space. We find that the only attractors in the system are equilibria, that E and M states dominate across parameter space, but that bistability and multistability are common. Even at extreme levels of some of the known inducers of the transition, there is a certain proportion of the parameter space at which an E or an M state co-exists with other stable steady states. CONCLUSIONS Our results suggest that the multistability is broadly present in the EMT network across parameters and thus response of cells to signals may strongly depend on the particular cell line and genetic background.
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Affiliation(s)
- Ying Xin
- Department of Ophthalmology (Wilmer Eye Institute), Johns Hopkins University School of Medicine, Baltimore, USA
| | - Bree Cummins
- Department of Mathematical Sciences, Montana State University, Bozeman, USA
| | - Tomáš Gedeon
- Department of Mathematical Sciences, Montana State University, Bozeman, USA
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Martin F, Gubian S, Talikka M, Hoeng J, Peitsch MC. NPA: an R package for computing network perturbation amplitudes using gene expression data and two-layer networks. BMC Bioinformatics 2019; 20:451. [PMID: 31481014 PMCID: PMC6724309 DOI: 10.1186/s12859-019-3016-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 07/31/2019] [Indexed: 02/06/2023] Open
Abstract
Background High-throughput gene expression technologies provide complex datasets reflecting mechanisms perturbed in an experiment, typically in a treatment versus control design. Analysis of these information-rich data can be guided based on a priori knowledge, such as networks of related proteins or genes. Assessing the response of a specific mechanism and investigating its biological basis is extremely important in systems toxicology; as compounds or treatment need to be assessed with respect to a predefined set of key mechanisms that could lead to toxicity. Two-layer networks are suitable for this task, and a robust computational methodology specifically addressing those needs was previously published. The NPA package (https://github.com/philipmorrisintl/NPA) implements the algorithm, and a data package of eight two-layer networks representing key mechanisms, such as xenobiotic metabolism, apoptosis, or epithelial immune innate activation, is provided. Results Gene expression data from an animal study are analyzed using the package and its network models. The functionalities are implemented using R6 classes, making the use of the package seamless and intuitive. The various network responses are analyzed using the leading node analysis, and an overall perturbation, called the Biological Impact Factor, is computed. Conclusions The NPA package implements the published network perturbation amplitude methodology and provides a set of two-layer networks encoded in the Biological Expression Language.
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Affiliation(s)
- Florian Martin
- PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland.
| | - Sylvain Gubian
- PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Marja Talikka
- PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A, Quai Jeanrenaud 5, CH-2000, Neuchâtel, Switzerland
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15
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Abstract
An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg.
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Affiliation(s)
- Donald R Williams
- Department of Psychology, University of California , Davis , CA , USA
| | - Mijke Rhemtulla
- Department of Psychology, University of California , Davis , CA , USA
| | - Anna C Wysocki
- Department of Psychology, University of California , Davis , CA , USA
| | - Philippe Rast
- Department of Psychology, University of California , Davis , CA , USA
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16
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McCulloch K, Roberts MG, Laing CR. Generalised probability mass function for the final epidemic size of an SIR model on a line of triangles network. Math Biosci 2019; 311:49-61. [PMID: 30844380 DOI: 10.1016/j.mbs.2019.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 12/07/2018] [Accepted: 02/11/2019] [Indexed: 11/17/2022]
Abstract
We explore the feasibility of deriving generalised expressions for the probability mass function (PMF) of the final epidemic size of a Susceptible - Infected - Recovered (SIR) model on a finite network of an arbitrary number of nodes. Expressions for the probability that the infection progresses along a given pathway in a line of triangles (LoT) network are presented. Deriving expressions for the probability that the infection ends at any given node allows us to determine the corresponding final size of the epidemic, and hence produce PMFs of the final epidemic size. We illustrate how we can use the results from small networks derived in a previous study to describe how an infection spreads through a LoT network. The key here is to correctly decompose the larger network into an appropriate assemblage of small networks.
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Affiliation(s)
- Karen McCulloch
- Institute of Natural & Mathematical Sciences, Massey University, Albany 0745, New Zealand.
| | - Mick G Roberts
- Institute of Natural & Mathematical Sciences, Massey University, Albany 0745, New Zealand.
| | - Carlo R Laing
- Institute of Natural & Mathematical Sciences, Massey University, Albany 0745, New Zealand.
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17
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Carrà G, Crocamo C, Angermeyer M, Brugha T, Toumi M, Bebbington P. Positive and negative symptoms in schizophrenia: A longitudinal analysis using latent variable structural equation modelling. Schizophr Res 2019; 204:58-64. [PMID: 30177344 DOI: 10.1016/j.schres.2018.08.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Revised: 07/06/2018] [Accepted: 08/13/2018] [Indexed: 01/29/2023]
Abstract
BACKGROUND Recent network models of schizophrenia propose it is the consequence of mutual interaction between its symptoms. While cross-sectional associations between negative and positive symptoms are consistent with this idea, they may merely reflect their involvement in the diagnostic process. Longitudinal analyses however may allow the identification of possible causal relationships. The European Schizophrenia Cohort (EuroSC) provides data suitable for this purpose. METHODS EuroSC includes 1208 patients randomly sampled from outpatient services in France, Germany and the UK. Initial measures were repeated after 12 and 24 months. Latent variable structural equation modelling was used to investigate the direction of effect between positive and negative symptoms assessed with the Positive and Negative Syndrome Scale, controlling for the effects of depressed mood and antipsychotic medication. RESULTS The structural model provided acceptable overall fit [χ2 (953) = 2444.32, P < 0.001; CFI = 0.909; RMSEA = 0.046 (90% CI: 0.043, 0.048); SRMR = 0.052]. Both positive and negative symptoms were persistent, and strongly auto-correlated. There were also persistent cross-sectional associations between positive and negative symptoms. While the path from latent positive to negative symptoms from T1 to T2 approached conventional levels of statistical significance (P = 0.051), that from T2 to T3 did not (P = 0.546). Pathways in the reverse direction were uniformly non-significant. CONCLUSIONS There was no evidence that negative symptoms predict later positive symptoms. The prediction of negative symptoms by positive symptoms was ambiguous. We discuss implications for conceptualization of schizophrenic processes.
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Affiliation(s)
- Giuseppe Carrà
- Division of Psychiatry, University College London, 149 Tottenham Court Road, London W1T 7NF, UK; Department of Medicine and Surgery, University of Milano Bicocca, Via Cadore 48, Monza 20900, Italy
| | - Cristina Crocamo
- Department of Medicine and Surgery, University of Milano Bicocca, Via Cadore 48, Monza 20900, Italy.
| | - Matthias Angermeyer
- Department of Psychiatry, University of Leipzig, Johannisallee 20, 04137 Leipzig, Germany
| | - Traolach Brugha
- Department of Health Sciences, College of Life Sciences, University of Leicester, University Road, Leicester LE1 7RH, UK
| | - Mondher Toumi
- Laboratoire de Santé Publique, Université de la Méditerranée, Marseille, France
| | - Paul Bebbington
- Division of Psychiatry, University College London, 149 Tottenham Court Road, London W1T 7NF, UK
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18
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Gross F, Kranke N, Meunier R. Pluralization through epistemic competition: scientific change in times of data-intensive biology. Hist Philos Life Sci 2019; 41:1. [PMID: 30603778 DOI: 10.1007/s40656-018-0239-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 12/17/2018] [Indexed: 06/09/2023]
Abstract
We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call "epistemic competition" that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe-contrary to many other accounts of scientific change-that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape.
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Affiliation(s)
- Fridolin Gross
- Institut für Philosophie, Universität Kassel, Henschelstr. 2, 34127, Kassel, Germany
| | - Nina Kranke
- Philosophisches Seminar, Westfälische Wilhelms-Universität Münster, Domplatz 23, 48143, Münster, Germany.
| | - Robert Meunier
- Institut für Philosophie, Universität Kassel, Henschelstr. 2, 34127, Kassel, Germany
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19
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Abstract
Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.
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Affiliation(s)
- Stephen G Oliver
- Department of Biochemistry, University of Cambridge, Cambridge, UK.
- Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.
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20
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Suryanarayana SM, Hellgren Kotaleski J, Grillner S, Gurney KN. Roles for globus pallidus externa revealed in a computational model of action selection in the basal ganglia. Neural Netw 2018; 109:113-136. [PMID: 30414556 DOI: 10.1016/j.neunet.2018.10.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 08/28/2018] [Accepted: 10/09/2018] [Indexed: 01/12/2023]
Abstract
The basal ganglia are considered vital to action selection - a hypothesis supported by several biologically plausible computational models. Of the several subnuclei of the basal ganglia, the globus pallidus externa (GPe) has been thought of largely as a relay nucleus, and its intrinsic connectivity has not been incorporated in significant detail, in any model thus far. Here, we incorporate newly revealed subgroups of neurons within the GPe into an existing computational model of the basal ganglia, and investigate their role in action selection. Three main results ensued. First, using previously used metrics for selection, the new extended connectivity improved the action selection performance of the model. Second, low frequency theta oscillations were observed in the subpopulation of the GPe (the TA or 'arkypallidal' neurons) which project exclusively to the striatum. These oscillations were suppressed by increased dopamine activity - revealing a possible link with symptoms of Parkinson's disease. Third, a new phenomenon was observed in which the usual monotonic relationship between input to the basal ganglia and its output within an action 'channel' was, under some circumstances, reversed. Thus, at high levels of input, further increase of this input to the channel could cause an increase of the corresponding output rather than the more usually observed decrease. Moreover, this phenomenon was associated with the prevention of multiple channel selection, thereby assisting in optimal action selection. Examination of the mechanistic origin of our results showed the so-called 'prototypical' GPe neurons to be the principal subpopulation influencing action selection. They control the striatum via the arkypallidal neurons and are also able to regulate the output nuclei directly. Taken together, our results highlight the role of the GPe as a major control hub of the basal ganglia, and provide a mechanistic account for its control function.
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Affiliation(s)
| | - Jeanette Hellgren Kotaleski
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Sten Grillner
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Kevin N Gurney
- Department of Psychology, University of Sheffield, Sheffield, UK.
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21
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Palowitch J, Bhamidi S, Nobel AB. Significance-based community detection in weighted networks. J Mach Learn Res 2018; 18:188. [PMID: 30853860 PMCID: PMC6402789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Community detection is the process of grouping strongly connected nodes in a network. Many community detection methods for un-weighted networks have a theoretical basis in a null model. Communities discovered by these methods therefore have interpretations in terms of statistical significance. In this paper, we introduce a null for weighted networks called the continuous configuration model. First, we propose a community extraction algorithm for weighted networks which incorporates iterative hypothesis testing under the null. We prove a central limit theorem for edge-weight sums and asymptotic consistency of the algorithm under a weighted stochastic block model. We then incorporate the algorithm in a community detection method called CCME. To benchmark the method, we provide a simulation framework involving the null to plant "background" nodes in weighted networks with communities. We show that the empirical performance of CCME on these simulations is competitive with existing methods, particularly when overlapping communities and background nodes are present. To further validate the method, we present two real-world networks with potential background nodes and analyze them with CCME, yielding results that reveal macro-features of the corresponding systems.
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Affiliation(s)
- John Palowitch
- Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill, NC 27599
| | - Shankar Bhamidi
- Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill, NC 27599
| | - Andrew B Nobel
- Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill, NC 27599
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22
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Abstract
Network models are an increasingly popular way to abstract complex psychological phenomena. While studying the structure of network models has led to many important insights, little attention has been paid to how well they predict observations. This is despite the fact that predictability is crucial for judging the practical relevance of edges: for instance in clinical practice, predictability of a symptom indicates whether an intervention on that symptom through the symptom network is promising. We close this methodological gap by introducing nodewise predictability, which quantifies how well a given node can be predicted by all other nodes it is connected to in the network. In addition, we provide fully reproducible code examples of how to compute and visualize nodewise predictability both for cross-sectional and time series data.
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Affiliation(s)
- Jonas M B Haslbeck
- Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018, WT, Amsterdam, Netherlands.
| | - Lourens J Waldorp
- Psychological Methods, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018, WT, Amsterdam, Netherlands
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23
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González OC, Shiri Z, Krishnan GP, Myers TL, Williams S, Avoli M, Bazhenov M. Role of KCC2-dependent potassium efflux in 4-Aminopyridine-induced Epileptiform synchronization. Neurobiol Dis 2018; 109:137-147. [PMID: 29045814 PMCID: PMC5710807 DOI: 10.1016/j.nbd.2017.10.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/09/2017] [Accepted: 10/13/2017] [Indexed: 01/23/2023] Open
Abstract
A balance between excitation and inhibition is necessary to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be limited, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the KCC2 co-transporter in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices, of either sex, revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). We developed a biophysically realistic network model implementing dynamics of ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of the inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Our model predicts that interneuron stimulation triggered an increase of interneuron firing, which was accompanied by an increase in the intracellular chloride concentration and a subsequent KCC2-dependent gradual accumulation of the extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.
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Affiliation(s)
- Oscar C González
- Neurosciences Graduate Program, University of California, San Diego, CA, United States; Department of Medicine, University of California, San Diego, CA, United States
| | - Zahra Shiri
- Montreal Neurological Institute, McGill University, Montréal, H4H 1R3 Québec, Canada
| | - Giri P Krishnan
- Department of Medicine, University of California, San Diego, CA, United States
| | - Timothy L Myers
- Neuroscience Graduate Program, University of California, Riverside, CA, United States; Department of Cell Biology and Neuroscience, University of California, Riverside, CA, United States
| | - Sylvain Williams
- Douglas Mental Health University Institute, McGill University, Montréal, H4H 1R3 Québec, Canada
| | - Massimo Avoli
- Montreal Neurological Institute, McGill University, Montréal, H4H 1R3 Québec, Canada; Department of Physiology, McGill University, Montréal, H4H 1R3 Québec, Canada
| | - Maxim Bazhenov
- Neurosciences Graduate Program, University of California, San Diego, CA, United States; Department of Medicine, University of California, San Diego, CA, United States.
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24
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Zhang SW, Yan XY. Some Remarks on Prediction of Drug-Target Interaction with Network Models. Curr Top Med Chem 2017; 17:2456-2468. [PMID: 28413948 DOI: 10.2174/1568026617666170414145015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Revised: 12/30/2016] [Accepted: 01/03/2017] [Indexed: 11/22/2022]
Abstract
System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors.
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Affiliation(s)
- Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Xiao-Ying Yan
- College of Computer Science, Xi'an Shiyou University, Xi'an 710065, China
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25
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Khanna A, Goodreau SM, Wohlfeiler D, Daar E, Little S, Gorbach PM. Individualized diagnosis interventions can add significant effectiveness in reducing human immunodeficiency virus incidence among men who have sex with men: insights from Southern California. Ann Epidemiol 2014; 25:1-6. [PMID: 25453725 DOI: 10.1016/j.annepidem.2014.09.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Revised: 09/24/2014] [Accepted: 09/28/2014] [Indexed: 01/04/2023]
Abstract
PURPOSE In this article, we examine the effectiveness of a variety of HIV diagnosis interventions in recently HIV-diagnosed men who have sex with men (MSM). These interventions use the preventive potential of postdiagnosis behavior change (PDBC), as measured by the reduction in the number of new infections. Empirical evidence for PDBC was presented in the behavioral substudy of the Southern California Acute Infection and Early Disease Research Program. In previous modeling work, we demonstrated the existing preventive effects of PDBC. However, a large proportion of new infections among MSM are either undiagnosed or diagnosed late, and the preventive potential of PDBC is not fully utilized. METHODS We derive empirical, stochastic, network-based models to examine the effectiveness of several diagnosis interventions that account for PDBC among MSM over a 10-year period. These interventions involve tests with shorter detection windows, more frequent testing, and individualized testing regimens. RESULTS We find that individualized testing interventions (i.e., testing individuals every three partners or 3 months, whichever is first, or every six partners or 6 months, whichever is first) result in significantly fewer new HIV infections than the generalized interventions we consider. CONCLUSIONS This work highlights the potential of individualized interventions for new public health policies in HIV prevention.
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Affiliation(s)
- Aditya Khanna
- Department of Medicine, University of Chicago, Chicago, IL.
| | | | - Dan Wohlfeiler
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, CA
| | - Eric Daar
- David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Susan Little
- Department of Medicine, University of California, San Diego, CA
| | - Pamina M Gorbach
- Fielding School of Public Health, David Geffen School of Medicine, University of California, Los Angeles, CA
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