Impact of Long‐Term Alcohol Consumption and Relapse on Genome‐Wide DNA Methylation Changes in Alcohol‐Dependent Subjects: A Longitudinal Study

Background: Genetic factors play an important role in the development and maintenance of alcohol use disorder (AUD). Significant and widespread differences in methylation levels of multiple regions within the genome have been reported between AUD patients and healthy controls in large epigenome-wide association studies (EWASs). Also, within patient populations, methylation changes over time (both during and after withdrawal) have been identified as sensitive indicators for disease activity. The detection of changes in methylation levels is a powerful tool to further explore and understand the biological correlates and underpinnings of AUD. Although there is strong and convincing evidence for differences in methylation of various sites between AUD patients and controls, only few studies assessed changes within patients over longer periods of time while taking into account alcohol consumption, relapse, and abstinence. So far, the longest period assessed as a within-subject design using EWASs was 4 weeks. Methods: Here, we investigated changes in whole-genome methylation levels within a sample of 69 detoxified AUD patients over a period as long as 12 months for the first time, comparing patients that relapsed within the follow-up period to those that remained abstinent. Results: Whole-genome methylation patterns of individual CpG sites over time did not differ between abstinent and relapsing patients. However, there was a negative association between global mean methylation at the 12-month follow-up and alcohol consumption within our sample. Conclusion: Although the present study represents the largest study of methylation levels in a sample of AUD patients with a follow-up period of 1 year and accounting for alcohol consumption and relapse to date, the sample size might still not be large enough to detect genome-wide significant effects. Therefore, large-scale, long-term studies with AUD subjects are needed to determine the utility of DNA methylation for the assessment and monitoring of persons with alcohol use disorders

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

Double parton distributions in the nucleon from lattice QCD

We evaluate nucleon four-point functions in the framework of lattice QCD in order to extract the first Mellin moment of double parton distributions (DPDs) in the unpolarized proton. In this first study, we employ an nf = 2+1 ensemble with pseudoscalar masses of mπ = 355 MeV and mK = 441 MeV. The results are converted to the scale μ = 2 GeV. Our calculation includes all Wick contractions, and for almost all of them a good statistical signal is obtained. We analyze the dependence of the DPD Mellin moments on the quark flavor and the quark polarization. Furthermore, the validity of frequently used factorization assumptions is investigated

Exact Gap Computation for Code Coverage Metrics in ISO-C

Test generation and test data selection are difficult tasks for model based testing. Tests for a program can be meld to a test suite. A lot of research is done to quantify the quality and improve a test suite. Code coverage metrics estimate the quality of a test suite. This quality is fine, if the code coverage value is high or 100%. Unfortunately it might be impossible to achieve 100% code coverage because of dead code for example. There is a gap between the feasible and theoretical maximal possible code coverage value. Our review of the research indicates, none of current research is concerned with exact gap computation. This paper presents a framework to compute such gaps exactly in an ISO-C compatible semantic and similar languages. We describe an efficient approximation of the gap in all the other cases. Thus, a tester can decide if more tests might be able or necessary to achieve better coverage.Comment: In Proceedings MBT 2012, arXiv:1202.582

Interference effects in the photorecombination of argonlike Sc3+ ions: Storage-ring experiment and theory

Absolute total electron-ion recombination rate coefficients of argonlike Sc3+(3s2 3p6) ions have been measured for relative energies between electrons and ions ranging from 0 to 45 eV. This energy range comprises all dielectronic recombination resonances attached to 3p -> 3d and 3p -> 4s excitations. A broad resonance with an experimental width of 0.89 +- 0.07 eV due to the 3p5 3d2 2F intermediate state is found at 12.31 +- 0.03 eV with a small experimental evidence for an asymmetric line shape. From R-Matrix and perturbative calculations we infer that the asymmetric line shape may not only be due to quantum mechanical interference between direct and resonant recombination channels as predicted by Gorczyca et al. [Phys. Rev. A 56, 4742 (1997)], but may partly also be due to the interaction with an adjacent overlapping DR resonance of the same symmetry. The overall agreement between theory and experiment is poor. Differences between our experimental and our theoretical resonance positions are as large as 1.4 eV. This illustrates the difficulty to accurately describe the structure of an atomic system with an open 3d-shell with state-of-the-art theoretical methods. Furthermore, we find that a relativistic theoretical treatment of the system under study is mandatory since the existence of experimentally observed strong 3p5 3d2 2D and 3p5 3d 4s 2D resonances can only be explained when calculations beyond LS-coupling are carried out.Comment: 11 pages, 7 figures, 3 tables, Phys. Rev. A (in print), see also: http://www.strz.uni-giessen.de/~k

Pavlovian-to-Instrumental Transfer in Alcohol Dependence: A Pilot Study

This publication is with permission of the rights owner freely accessible due to an alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Background: Pavlovian processes are thought to play an important role in the development, maintenance and relapse of alcohol dependence, possibly by influencing and usurping ongoing thought and behavior. The influence of pavlovian stimuli on ongoing behavior is paradigmatically measured by pavlovian-to-instrumental transfer (PIT) tasks. These involve multiple stages and are complex. Whether increased PIT is involved in human alcohol dependence is uncertain. We therefore aimed to establish and validate a modified PIT paradigm that would be robust, consistent and tolerated by healthy controls as well as by patients suffering from alcohol dependence, and to explore whether alcohol dependence is associated with enhanced PIT. Methods: Thirty-two recently detoxified alcohol-dependent patients and 32 age- and gender-matched healthy controls performed a PIT task with instrumental go/no-go approach behaviors. The task involved both pavlovian stimuli associated with monetary rewards and losses, and images of drinks. Results: Both patients and healthy controls showed a robust and temporally stable PIT effect. Strengths of PIT effects to drug-related and monetary conditioned stimuli were highly correlated. Patients more frequently showed a PIT effect, and the effect was stronger in response to aversively conditioned CSs (conditioned suppression), but there was no group difference in response to appetitive CSs. Conclusion: The implementation of PIT has favorably robust properties in chronic alcohol-dependent patients and in healthy controls. It shows internal consistency between monetary and drug-related cues. The findings support an association of alcohol dependence with an increased propensity towards PIT.Peer Reviewe

Double parton distributions in the pion from lattice QCD

We perform a lattice study of double parton distributions in the pion, using the relationship between their Mellin moments and pion matrix elements of two local currents. A good statistical signal is obtained for almost all relevant Wick contractions. We investigate correlations in the spatial distribution of two partons in the pion, as well as correlations involving the parton polarisation. The patterns we observe depend significantly on the quark mass. We investigate the assumption that double parton distributions approximately factorise into a convolution of single parton distributions

Association of the OPRM1 A118G polymorphism and Pavlovian-to-instrumental transfer: Clinical relevance for alcohol dependence

Background: Pavlovian-to-instrumental transfer (PIT) quantifies the extent to which a stimulus that has been associated with reward or punishment alters operant behaviour. In alcohol dependence (AD), the PIT effect serves as a paradigmatic model of cue-induced relapse. Preclinical studies have suggested a critical role of the opioid system in modulating Pavlovian–instrumental interactions. The A118G polymorphism of the OPRM1 gene affects opioid receptor availability and function. Furthermore, this polymorphism interacts with cue-induced approach behaviour and is a potential biomarker for pharmacological treatment response in AD. In this study, we tested whether the OPRM1 polymorphism is associated with the PIT effect and relapse in AD. Methods: Using a PIT task, we examined three independent samples: young healthy subjects ( N = 161), detoxified alcohol-dependent patients ( N = 186) and age-matched healthy controls ( N = 105). We used data from a larger study designed to assess the role of learning mechanisms in the development and maintenance of AD. Subjects were genotyped for the A118G (rs1799971) polymorphism of the OPRM1 gene. Relapse was assessed after three months. Results: In all three samples, participants with the minor OPRM1 G-Allele (G+ carriers) showed increased expression of the PIT effect in the absence of learning differences. Relapse was not associated with the OPRM1 polymorphism. Instead, G+ carriers displaying increased PIT effects were particularly prone to relapse. Conclusion: These results support a role for the opioid system in incentive salience motivation. Furthermore, they inform a mechanistic model of aberrant salience processing and are in line with the pharmacological potential of opioid receptor targets in the treatment of AD

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence