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The article discusses various issues related to the journal as of December 2018which include its aims and scope, special issues, topic proposals for future issues, and its acceptance in Scopus Indexing.

Objective: To explore and apply multimodel inference to test the relative contributions of latent genetic, environmental and direct causal factors to the covariation between two variables with data from the classical twin design by estimating model-averaged parameters. Methods: Behavior genetics is concerned with understanding the causes of variation in phenotypes and the causes of covariation between two or more phenotypes. Two variables may correlate as a result of genetic, shared environmental or unique environmental factors contributing to variation in both variables. Two variables may also correlate because one or both directly cause variation in the other. Furthermore, covariation may result from any combination of these sources, leading to 25 different identified structural equation models. OpenMx was used to fit all these models to account for covariation between two variables collected in twins. Multimodel inference and model averaging were used to summarize the key sources of covariation, and estimate the magnitude of these causes of covariance. Extensions of these models to test heterogeneity by sex are discussed. Results: We illustrate the application of multimodel inference by fitting a comprehensive set of bivariate models to twin data from the Virginia Twin Study of Psychiatric and Substance Use Disorders. Analyses of body mass index and tobacco consumption data show sufficient power to reject distinct models, and to estimate the contribution of each of the five potential sources of covariation, irrespective of selecting the best fitting model. Discrimination between models on sample size, type of variable (continuous versus binary or ordinal measures) and the effect size of sources of variance and covariance. Conclusions: We introduce multimodel inference and model averaging approaches to the behavior genetics community, in the context of testing models for the causes of covariation between traits in term of genetic, environmental and causal explanations. [ABSTRACT FROM AUTHOR]

Several letters to the editor are presented in response to articles in previous issues including "A Step Forward" in the March 2014 issue, "Making Connections" in the July 2014 issue, and "Full Steam Ahead" by Mark Edmund in the August 2014 issue.

Alcohol use (i.e., quantity, frequency) and alcohol use disorder (AUD) are common, associated with adverse outcomes, and genetically-influenced. Genome-wide association studies (GWAS) identified genetic loci associated with both. AUD is positively genetically associated with psychopathology, while alcohol use (e.g., drinks per week) is negatively associated or NS related to psychopathology. We wanted to test if these genetic associations extended to life satisfaction, as there is an interest in understanding the associations between psychopathology-related traits and constructs that are not just the absence of psychopathology, but positive outcomes (e.g., well-being variables). Thus, we used Genomic Structural Equation Modeling (gSEM) to analyze summary-level genomic data (i.e., effects of genetic variants on constructs of interest) from large-scale GWAS of European ancestry individuals. Results suggest that the best-fitting model is a Bifactor Model, in which unique alcohol use, unique AUD, and common alcohol factors are extracted. The genetic correlation (rg) between life satisfaction-AUD specific factor was near zero, the rg with the alcohol use specific factor was positive and significant, and the rg with the common alcohol factor was negative and significant. Findings indicate that life satisfaction shares genetic etiology with typical alcohol use and life dissatisfaction shares genetic etiology with heavy alcohol use. [ABSTRACT FROM AUTHOR]

Mdm2 negatively regulates p53 by inhibiting its transcriptional activity and promoting its degradation by functioning as an E3 ubiquitin ligase. The primary p53 binding site on mdm2 is located in its N-terminal domain. Through binding to p53 at its N-terminal transactivation domain, mdm2 directly blocks the transcriptional activation function of p53. To understand how the binding interaction between mdm2 and p53 is related to the mdm2-catalyzed p53 ubiquitination, we compared the ubiquitin ligase activity and binding interactioff of full length mdm2 (mdm2-FL) and various N-terminally truncated mdm2 constructs toward p53. Our results indicate that the interaction between the N-terminal domains of mdm2 and p53 is not required tbr the mdm2-catalyzed p53 ubiquitination. Our results further indicate a second p53 binding site on mdm2 that is critical for its ubiquitin ligase activity toward p53; this site is located between amino acids 211 and 361, a region that includes the acidic domain and the zinc finger region. This second mdm2-p53 interaction site represents an alternative target for small molecule modulators of the mdm2-p53 pathway. [ABSTRACT FROM AUTHOR]

Human spermine oxidase (hSMOX) plays a central role in polyamine catabolism. Due to its association with several pathological processes, including inflammation and cancer, hSMOX has garnered interest as a possible therapeutic target. Therefore, determination of the structure of hSMOX is an important step to enable drug discovery and validate hSMOX as a drug target. Using insights from hydrogen/deuterium exchange mass spectrometry (HDX-MS), we engineered a hSMOX construct to obtain the first crystal structure of hSMOX bound to the known polyamine oxidase inhibitor MDL72527 at 2.4 Å resolution. While the overall fold of hSMOX is similar to its homolog, murine N1-acetylpolyamine oxidase (mPAOX), the two structures contain significant differences, notably in their substrate-binding domains and active site pockets. Subsequently, we employed a sensitive biochemical assay to conduct a high-throughput screen that identified a potent and selective hSMOX inhibitor, JNJ-1289. The co-crystal structure of hSMOX with JNJ-1289 was determined at 2.1 Å resolution, revealing that JNJ-1289 binds to an allosteric site, providing JNJ-1289 with a high degree of selectivity towards hSMOX. These results provide crucial insights into understanding the substrate specificity and enzymatic mechanism of hSMOX, and for the design of highly selective inhibitors. Rational engineering of human spermine oxidase yields crystallizable structures and the design of an allosteric inhibitor. [ABSTRACT FROM AUTHOR]

• A high-fidelity dynamic simulation of a novel mass integrated column configuration. • Regulatory control was developed and implemented to maintain stable process operations. • Advance supervisory control implemented on-top of the regulatory control structure. Process integration in distillation unit operations promises improved process efficiencies. However, to realize these benefits fit for purpose control structures are needed to ensure stable and on-specification operations. In practice, this requires the development of regulatory and advanced process controls accounting for nuances and intricacies of a given design and application. This manuscript focuses on developing a control structure for a novel industrial mass-integrated twin-column distillation configuration, where a multi-component crude methanol feed is refined into AA grade (ultra-high purity) methanol, while meeting many technical and economic requirements. To this end, a Digital Model (a dynamic process model) of the column configuration was developed and used to carry out a thorough dynamic analysis. Based on the understanding gained, a regulatory control layer was introduced to achieve stable operations. An advanced supervisory level control structure and a Model Predictive Control (MPC) structure were then developed and compared on the Digital model where both alternatives achieved on-specification (technical and economic) operations. It was illustrated that a well-designed control structure could achieve stable and on-specification control for this specific application. Further disturbance testing showed the MPC is marginal efficient than a supervisory control but has similar robustness. [Display omitted] [ABSTRACT FROM AUTHOR]

Children's educational outcomes are strongly correlated with their parents' educational attainment. This finding is often attributed to the family environment—assuming, for instance, that parents' behavior and resources affect their children's educational outcomes. However, such inferences of a causal role of the family environment depend on the largely untested assumption that such relationships do not simply reflect genes shared between parent and child. We examine this assumption with an adoptee design in full-population cohorts from Danish administrative data. We test whether parental education predicts children's educational outcomes in both biological and adopted children, looking at four components of the child's educational development: (I) the child's conscientiousness during compulsory schooling, (II) academic performance in those same years, (III) enrollment in academically challenging high schools, and (IV) graduation success. Parental education was a substantial predictor of each of these child outcomes in the full population. However, little intergenerational correlation in education was observed in the absence of genetic similarity between parent and child—that is, among adoptees. Further analysis showed that what links adoptive parents' education did have with later-occurring components such as educational attainment (IV) and enrollment (III) appeared to be largely attributable to effects identifiable earlier in development, namely early academic performance (II). The primary nongenetic mechanisms by which education is transmitted across generations may thus have their effects on children early in their educational development, even as the consequences of those early effects persist throughout the child's educational development. [ABSTRACT FROM AUTHOR]