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Abstract. In psychology and the social sciences, it is often of interest how complex structural relations among variables are moderated by profiles or combinations of persons’ attributes. Some state-of-the-art methods, such as latent class analysis, are well-suited for this purpose. However, they can lead to methodological problems (e.g., convergence issues) or interpretative difficulties (e.g., due to nondistinctive profiles). For these cases, two other approaches combining structural equation modeling with machine learning have been proposed, namely structural equation model (SEM) trees and SubgroupSEM. These approaches allow for exploration of how parameters of a SEM differ depending on combinations of a person's attributes. This can be useful for generating hypotheses for future research. In this paper, we provide an empirical illustration of SubgroupSEM using an example from research on the development of commitment in interdisciplinary study programs in German higher education and identify combinations of vocational interests related to exceptional development.

To align with the target of reducing plant protection measures within the EU Green Deal programme, the utilisation of resistant grape varieties in viticulture becomes indispensable. Consequently, this study investigated the factors that influence consumer acceptance of fungus-resistant grape varieties (FRGV) in Germany. The qualitative research methodology involved conducting semi-structured interviews, including sensory evaluation tests, with focus groups consisting of 48 consumers and evaluating the data using content analysis. The findings revealed that the attractiveness of a grape variety is determined by four distinct attributes: familiarity, designation, association, and image. Furthermore, FRGV can be made more attractive to consumers by using denominations based on established grape varieties or by using and associating them with their sensory patterns. The sensory acceptance of FRGV varies significantly across consumer segments and vinification methods. Providing relevant information about the benefits of resistant grape varieties has a positive impact on consumer acceptance, and the level of interest and relevance varies by target group. The study suggests that it is possible to enhance consumer acceptance by offering attractive grape varieties, targeting group-specific sensory profiles, and engaging in storytelling campaigns that educate consumers about the advantages of resistant grapes.

This study investigated the factors that influence consumer acceptance of fungus-resistant grape varieties (FRGV) in Germany. The qualitative research methodology involved conducting semi-structured interviews including sensory evaluation test with focus groups consisting of 48 consumers, and the data were evaluated using content analysis. The findings revealed that the attractiveness of a grape variety is determined by four distinct attributes. Furthermore, FRGV can be made more attractive to consumers by using denominations based on established grape varieties or by using and associating them with their sensory patterns. The sensory acceptance of FRGV varies significantly across consumer segments and vinification methods. Providing relevant information about the benefits of resistant grape varieties has a positive impact on consumer acceptance, and the level of interest and relevance varies by the target group. The study suggests that it is possible to enhance consumer acceptance by offering attractive grape varieties, targeting group-specific sensory profiles and engaging in storytelling campaigns that educate consumers about the advantages of resistant grapes.

The recruitment of thermogenic brite adipocytes within white adipose tissue attenuates obesity and metabolic comorbidities, arousing interest in understanding the underlying regulatory mechanisms. The molecular network of brite adipogenesis, however, remains largely unresolved. In this light, long noncoding RNAs (lncRNAs) emerged as a versatile class of modulators that control many steps within the differentiation machinery. Leveraging the naturally varying propensities of different inbred mouse strains for white adipose tissue browning, we identify the nuclear lncRNA Ctcflos as a pivotal orchestrator of thermogenic gene expression during brite adipocyte differentiation. Mechanistically, Ctcflos acts as a pleiotropic regulator, being essential for the transcriptional recruitment of the early core thermogenic regulatory program and the modulation of alternative splicing to drive brite adipogenesis. This is showcased by Ctcflos‐regulated gene transcription and splicing of the key browning factor Prdm16 toward the isoform that is specific for the thermogenic gene program. Conclusively, our findings emphasize the mechanistic versatility of lncRNAs acting at several independent levels of gene expression for effective regulation of key differentiation factors to direct cell fate and function., LncRNA Ctcflos functions as an essential regulator in brite adipocyte development and thermogenesis through orchestration of transcriptional and post‐transcriptional alternative splicing programs. These include the modulation of transcript abundance and isoform profiles of Prdm16.

Structural equation modeling is one of the most popular statistical frameworks in the social and behavioral sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied researchers, for example, when investigating differential item functioning for a mental ability test or examining children with exceptional educational trajectories. In the present article, we present a new approach combining subgroup discovery-a well-established toolkit of supervised learning algorithms and techniques from the field of computer science-with structural equation models termed SubgroupSEM. We provide an overview and comparison of three approaches to modeling and detecting heterogeneous groups in structural equation models, namely, finite mixture models, SEM trees, and SubgroupSEM. We provide a step-by-step guide to applying subgroup discovery techniques for structural equation models, followed by a detailed and illustrated presentation of pruning strategies and four subgroup discovery algorithms. Finally, the SubgroupSEM approach will be illustrated on two real data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied by examples of the R package subgroupsem, which is a viable implementation of our approach for applied researchers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The effects of a treatment or an intervention on a count outcome are often of interest in applied research. When controlling for additional covariates, a negative binomial regression model is usually applied to estimate conditional expectations of the count outcome. The difference in conditional expectations under treatment and under control is then defined as the (conditional) treatment effect. While traditionally aggregates of these conditional treatment effects (e.g., average treatment effects) are computed by averaging over the empirical distribution, a recently proposed moment-based approach allows for computing aggregate effects as a function of distribution parameters. The moment-based approach makes it possible to control for (latent) multivariate normally distributed covariates and provides more reliable inferences under certain conditions. In this paper we propose three different ways to account for non-normally distributed continuous covariates in this approach: an alternative, known non-normal distribution; a plausible factorization of the joint distribution; and an approximation using finite Gaussian mixtures. A saturated model is used for categorical covariates, making a distributional assumption obsolete. We further extend the moment-based approach to allow for multiple treatment conditions and the computation of conditional effects for categorical covariates. An illustrative example highlighting the key features of our extension is provided.

Researchers often use regressions with a logarithmic link function to evaluate the effects of a treatment on a count variable. In order to judge the average effectiveness of the treatment on the original count scale, they compute average treatment effects, which are defined as the average difference between the expected outcomes under treatment and under control. Current practice is to evaluate the expected differences at every observation and use the sample mean of these differences as a point estimate of the average effect. The standard error for this average effect estimate is based on the implicit assumption that covariate values are fixed, i.e., do not vary across different samples. In this paper, we present a new way of analytically computing average effects based on regressions with log link using stochastic covariates and develop new formulas to obtain standard errors for the average effect. In a simulation study, we evaluate the statistical performance of our new estimator and compare it with the traditional approach. Our findings suggest that the new approach gives unbiased effect estimates and standard errors and outperforms the traditional approach when strong interaction and/or a skewed covariate is present.

The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this paper, we propose a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and manifest covariates. We extend the previously presented moment-based approach in several aspects: Building on a multigroup SEM framework for count variables instead of the generalized linear model, we allow for latent covariates and multiple covariates. We provide an illustrative example to explain the application and estimation in structural equation modeling software.

Cross-classified models accommodate data structures that have more than one cluster variable, which are not nested in each other but overlap. They simultaneously consider all clustering variables. This allows one to study effects on several levels at once. Cross-classified data structures are common in various field of applied research (e.g., research on teams, career paths, interventions). The present article demonstrates modeling options and specifications for cross-classified data to be used within these different research strands. For more specific demonstration purposes, we use a data set on rater variance in assessment centers. Commonly, raters observe only a subset of participants and hence ratings are both nested in participants and raters, but participants and raters are not nested in each other. Using cross-classified models allows studying sources of rater variance (e.g., professional expertise) and interactions between cluster level variables, for example, interactions between participants' and raters' personality and sociodemographic characteristics (e.g., gender). From a practical research point of view and to ease application, we deliver a step-by-step overview of modeling procedures and power analysis including software code.

The recruitment of thermogenic brite adipocytes within white adipose tissue attenuates obesity and metabolic comorbidities, arousing great interest in understanding the intricate regulatory mechanisms underlying this process. Brite adipogenesis demands complex spatial and temporal orchestration of multiple regulatory factors. The underlying molecular network, however, remains largely unresolved. In this light, long noncoding RNAs (lncRNAs) are emerging as a versatile class of modulators able to control many steps within the differentiation machinery including chromatin accessibility, gene transcription, splicing and translation. Leveraging the naturally varying propensities of different inbred mouse strains for white adipose tissue browning, we here identify the nuclear lncRNA Ctcflos as a pivotal orchestrator of thermogenic gene expression during brite adipocyte differentiation. Mechanistically, Ctcflos acts as a bi-functional regulator, being essential for the transcriptional recruitment of the early core thermogenic regulatory program and the modulation of alternative splicing to drive brite adipogenesis. This is showcased by Ctcflos regulated gene transcription and splicing of the key browning factor Prdm16: Ctcflos controls its general expression levels as well as a shift towards the isoform that is specific for the thermogenic gene program. Beyond Prdm16, our transcriptome wide search for splice variants identified Ctcflos-dependent alternative splicing of multiple further factors that together might boost brite adipogenesis. Conclusively, our findings emphasize the mechanistic versatility of lncRNAs acting at several independent levels of gene expression for effective regulation of key differentiation factors to direct cell fate and function.

One of the most important decisions researchers face when analyzing the evolution of software systems is the choice of a proper data analysis/exchange format. Most existing formats have to be processed with special programs written specifically for that purpose and are not easily extendible. Most scientists, therefore, use their own database( s) requiring each of them to repeat the work of writing the import/export programs to their format. We present EvoOnt, a software repository data exchange format based on the Web Ontology Language (OWL). EvoOnt includes software, release, and bug-related information. Since OWL describes the semantics of the data, EvoOnt is (1) easily extendible, (2) comes with many existing tools, and (3) allows to derive assertions through its inherent Description Logic reasoning capabilities. The paper also shows iSPARQL -- our SPARQL-based Semantic Web query engine containing similarity joins. Together with EvoOnt, iSPARQL can accomplish a sizable number of tasks sought in software repository mining projects, such as an assessment of the amount of change between versions or the detection of bad code smells. To illustrate the usefulness of EvoOnt (and iSPARQL), we perform a series of experiments with a real-world Java project. These show that a number of software analyses can be reduced to simple iSPARQL queries on an EvoOnt dataset.

Background Transcranial direct current stimulation (tDCS) is increasingly discussed as a new option to support the cognitive rehabilitation in neuropsychiatric disorders. However, the therapeutic impact of tDCS is limited by high inter-individual variability. Genetic factors most likely contribute to this variability by modulating the effects of tDCS. Objectives We aimed to investigate the influence of the COMT Val(108/158)Met polymorphism on cathodal tDCS effects on executive functioning. Methods Cathodal tDCS was applied to the left dorsolateral prefrontal cortex (dlPFC) during the performance of a parametric Go/No-Go test. Results We demonstrate an impairing effect of cathodal tDCS to the dlPFC on response inhibition. This effect was only found in individuals homozygous for the Val-allele of the COMT Val(108/158)Met polymorphism. No effects of stimulation on executive functions in Met-allele carriers were detected. Conclusion Our data indicate that i) cathodal, excitability reducing tDCS, interferes with inhibitory cognitive control, ii) the left dlPFC is critically involved in the neuronal network underlying the control of response inhibition, and iii) the COMT Val(108/158)Met polymorphism modulates the impact of cathodal tDCS on inhibitory control. Together with our previous finding that anodal tDCS selectively impairs set-shifting abilities in COMT Met/Met homozygous individuals, these results indicate that genetic factors modulate effects of tDCS on cognitive performance. Therefore, future tDCS research should account for genetic variability in the design and analysis of neurocognitive as well as therapeutic applications to reduce the variability of results and facilitate individualized neurostimulation approaches.

The Horizon2020 project AURES II aims at ensuring the effective implementation of auctions for renewable energies in the EU Member States (MS). In recent years, auction schemes for the allocation of support for renewable electricity sources (RES) have been advancing rapidly across Europe. Auctions are considered to have brought down support levels and increased planning capability for RES deployment and state budgets. In some unfortunate cases, they have, however, also resulted in delayed or unrealised projects and increased uncertainty for project developers. A variety of auction designs are still being tested and introduced in EU MS, as well as foreseen by European legislation. Therefore, there is still a need for further assessment and improvement of national auction design and implementation to ensure the future success of RES auctions in Europe. Applying different qualitative and quantitative methods in the various work packages (WPs), the AURES II project partners have already drafted and published a large number of reports and studies. This article aims at comprehensively presenting these results and provide a first overview.