| 1. |
- Munn-Chernoff, M. A., et al.
(författare)
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Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies
- 2021
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Ingår i: Addiction Biology. - : Wiley. - 1355-6215 .- 1369-1600. ; 26:1
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Tidskriftsartikel (refereegranskat)abstract
- Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [r(g)], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from similar to 2400 to similar to 537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (r(g) = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (r(g) = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (r(g) = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (r(gs) = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.
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| 2. |
- Watson, H. J., et al.
(författare)
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Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa
- 2019
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 51:8
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Tidskriftsartikel (refereegranskat)abstract
- Characterized primarily by a low body-mass index, anorexia nervosa is a complex and serious illness(1), affecting 0.9-4% of women and 0.3% of men(2-4), with twin-based heritability estimates of 50-60%(5). Mortality rates are higher than those in other psychiatric disorders(6), and outcomes are unacceptably poor(7). Here we combine data from the Anorexia Nervosa Genetics Initiative (ANGI)(8,9) and the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) and conduct a genome-wide association study of 16,992 cases of anorexia nervosa and 55,525 controls, identifying eight significant loci. The genetic architecture of anorexia nervosa mirrors its clinical presentation, showing significant genetic correlations with psychiatric disorders, physical activity, and metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of common variants associated with body-mass index. These results further encourage a reconceptualization of anorexia nervosa as a metabo-psychiatric disorder. Elucidating the metabolic component is a critical direction for future research, and paying attention to both psychiatric and metabolic components may be key to improving outcomes.
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| 3. |
- Chang, H., et al.
(författare)
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The protocadherin 17 gene affects cognition, personality, amygdala structure and function, synapse development and risk of major mood disorders
- 2018
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Ingår i: Molecular Psychiatry. - : Springer Science and Business Media LLC. - 1359-4184 .- 1476-5578. ; 23:2, s. 400-412
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Tidskriftsartikel (refereegranskat)abstract
- Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes.
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| 4. |
- Tuttle, Jacob F., et al.
(författare)
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A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling
- 2021
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Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 292
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Tidskriftsartikel (refereegranskat)abstract
- Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant's NO emission rates is performed, directly comparing each modeling method's performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. These efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method's performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.
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| 5. |
- Karanti, Alina (Aikaterini), et al.
(författare)
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Gender differences in the treatment of patients with bipolar disorder: A study of 7354 patients
- 2015
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Ingår i: Journal of Affective Disorders. - : Elsevier BV. - 0165-0327 .- 1573-2517. ; 174, s. 303-309
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Tidskriftsartikel (refereegranskat)abstract
- Background: Gender differences in treatment that are not supported by empirical evidence have been reported in several areas of medicine. Here, the aim was to evaluate potential gender differences in the treatment for bipolar disorder. Methods: Data was collected from the Swedish National Quality Assurance Register for bipolar disorder (BipolaR). Baseline registrations from the period 2004-2011 of 7354 patients were analyzed. Multiple logistic regression analysis was used to study the impact of gender on interventions. Results: Women were more often treated with antidepressants, lamotrigine, electroconvulsive therapy, benzodiazepines, and psychotherapy. Men were more often treated with lithium. There were no gender differences in treatment with mood stabilizers as a group, neuroleptics, or valproate. Subgroup analyses revealed that ECT was more common in women only in the bipolar l subgroup. Contrariwise, lamotrigine was more common in women only in the bipolar II subgroup. Limitations: As BipolaR contains data on outpatient treatment of persons with bipolar disorder in Sweden, it is unclear if these Findings translate to inpatient care and to outpatient treatment in other countries. Conclusions: Men and women with bipolar disorder receive different treatments in routine clinical settings in Sweden. Gender differences in level of functioning, bipolar subtype, or severity of bipolar disorder could not explain the higher prevalence of pharmacological treatment, electroconvulsive therapy, and psychotherapy in women. Our results suggest that clinicians' treatment decisions are to some extent unduly influenced by patients' gender. (C) 2014 Elsevier B.V. All rights reserved.
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| 6. |
- Blackburn, Landen D., et al.
(författare)
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Dynamic machine learning-based optimization algorithm to improve boiler efficiency
- 2022
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Ingår i: Journal of Process Control. - : Elsevier BV. - 0959-1524. ; 120, s. 129-149
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Forskningsöversikt (refereegranskat)abstract
- With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. The best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.
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| 7. |
- Börjesson, Stefan, 1979-, et al.
(författare)
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Characterization of plasmid-mediated AmpC-producing E. coli from Swedish broilers and association with human clinical isolates
- 2013
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Ingår i: Clinical Microbiology and Infection. - : European Society of Clinical Microbiology and Infectious Diseases (ESCMID). - 1198-743X .- 1469-0691. ; 19:7, s. E309-E311
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Tidskriftsartikel (refereegranskat)abstract
- A selection of plasmid-mediated AmpC-producing Escherichia coli isolates carrying blaCMY-2 from Swedish broilers were characterized to establish their relatedness to and a possible overlap with human clinical E. coli isolates. The results showed diversity among the E. coli isolated from broilers, indicating that the spread in the population was not due to one strain. However, only one type of plasmid belonging to replicon type incK was identified. Furthermore, there were no indications of spread of blaCMY-2 E. coli isolates from broilers to human clinical settings, although Swedish broilers may be a source of blaCMY-2 and/or the plasmid carrying blaCMY-2 .
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