Challenges in modelling the random structure correctly in growth mixture models and the impact this has on model mixtures

Lifecourse trajectories of clinical or anthropological attributes are useful for identifying how our early-life experiences influence later-life morbidity and mortality. Researchers often use growth mixture models (GMMs) to estimate such phenomena. It is common to place constrains on the random part of the GMM to improve parsimony or to aid convergence, but this can lead to an autoregressive structure that distorts the nature of the mixtures and subsequent model interpretation. This is especially true if changes in the outcome within individuals are gradual compared with the magnitude of differences between individuals. This is not widely appreciated, nor is its impact well understood. Using repeat measures of body mass index (BMI) for 1528 US adolescents, we estimated GMMs that required variance-covariance constraints to attain convergence. We contrasted constrained models with and without an autocorrelation structure to assess the impact this had on the ideal number of latent classes, their size and composition. We also contrasted model options using simulations. When the GMM variance-covariance structure was constrained, a within-class autocorrelation structure emerged. When not modelled explicitly, this led to poorer model fit and models that differed substantially in the ideal number of latent classes, as well as class size and composition. Failure to carefully consider the random structure of data within a GMM framework may lead to erroneous model inferences, especially for outcomes with greater within-person than between-person homogeneity, such as BMI. It is crucial to reflect on the underlying data generation processes when building such models

SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words’ sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure

From direct to absolute mass measurements: a study of the accuracy of ISOLTRAP

For a detailed study of the accuracy of the Penning trap mass spectrometer ISOLTRAP all expected sources of uncertainty were investigated with respect to their contributions to the uncertainty of the final result. In the course of these investigations, cross-reference measurements with singly charged carbon clusters $^{12}$C$^{+}_{n}$ were carried out. The carbon cluster ions were produced by use of laser-induced desorption, fragmentation, and ionization of C$_{60}$ fullerenes and injected into and stored in the Penning trap system. The comparison of the cyclotron frequencies of different carbon clusters has provided detailed insight into the residual systematic uncertainty of \acro{ISOLTRAP} and yielded a value of $8 \cdot 10^{-9}$. This also represents the current limit of mass accuracy of the apparatus. Since the unified atomic mass unit is defined as 1/12 of the mass of the $^{12}$C atom, it will be possible to carry out absolute mass measurements with \acro{ISOLTRAP} in the future.\\[1\baselineskip] PACS: 07.75.+h (Mass spectrometers and related techniques), 21.10.Dr (Binding energies and masses), 32.10.Bi (Atomic masses, mass spectra, abundances, and isotopes), 36.40.Wa (Charged clusters)

Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions

How to Educate Entrepreneurs?

Entrepreneurship education has two purposes: To improve students’ entrepreneurial skills and to provide impetus to those suited to entrepreneurship while discouraging the rest. While entrepreneurship education helps students to make a vocational decision its effects may conflict for those not suited to entrepreneurship. This study shows that vocational and the skill formation effects of entrepreneurship education can be identified empirically by drawing on the Theory of Planned Behavior. This is embedded in a structural equation model which we estimate and test using a robust 2SLS estimator. We find that the attitudinal factors posited by the Theory of Planned Behavior are positively correlated with students’ entrepreneurial intentions. While conflicting effects of vocational and skill directed course content are observed in some individuals, overall these types of content are complements. This finding contradicts previous results in the literature. We reconcile the conflicting findings and discuss implications for the design of entrepreneurship courses

Children's trust and the development of prosocial behavior

This study examined the role of children’s trust beliefs and trustworthiness in the development of prosocial behavior using data from four waves of a longitudinal study in a large, ethnically diverse sample of children in Switzerland (mean age = 8.11 years at Time 1, N = 1,028). Prosocial behavior directed towards peers was measured at all assessment points by teacher reports. Children’s trust beliefs and their trustworthiness with peers were assessed and calculated by a social relations analysis at the first assessment point using children’s reports of the extent to which classmates kept promises. In addition, teacher reports of children’s trustworthiness were assessed at all four assessment points. Latent growth curve modeling yielded a decrease in prosocial behavior over time. Peer- and teacher-reported trustworthiness predicted higher initial levels of prosocial behavior, and peer-reported trustworthiness predicted less steep decreases in prosocial behavior over time. Autoregressive cross-lagged analysis also revealed bidirectional longitudinal associations between teacher-reported trustworthiness and prosocial behavior. We discuss the implications of the findings for research on the role of trust in the development of children’s prosocial behavior

A monument to the player: Preserving a landscape of socio-cultural capital in the transitional MMORPG

This is the pre-print version of the Article. The official published version can be accessed from the links below - Copyright @ 2012 Taylor & Francis LtdMassively multiplayer online role-playing games (MMORPGs) produce dynamic socio-ludic worlds that nurture both culture and gameplay to shape experiences. Despite the persistent nature of these games, however, the virtual spaces that anchor these worlds may not always be able to exist in perpetuity. Encouraging a community to migrate from one space to another is a challenge now facing some game developers. This paper examines the case of Guild Wars® and its “Hall of Monuments”, a feature that bridges the accomplishments of players from the current game to the forthcoming sequel. Two factor analyses describe the perspectives of 105 and 187 self-selected participants. The results reveal four factors affecting attitudes towards the feature, but they do not strongly correlate with existing motivational frameworks, and significant differences were found between different cultures within the game. This informs a discussion about the implications and facilitation of such transitions, investigating themes of capital, value perception and assumptive worlds. It is concluded that the way subcultures produce meaning needs to be considered when attempting to preserve the socio-cultural landscape

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table

Protein Phosphatase 1 Dephosphorylates Profilin-1 at Ser-137

Profilin-1 (PFN1) plays an important role in the control of actin dynamics, and could represent an important therapeutic target in several diseases. We previously identified PFN1 as a huntingtin aggregation inhibitor, and others have implicated it as a tumor-suppressor. Rho-associated kinase (ROCK) directly phosphorylates PFN1 at Ser-137 to prevent its binding to polyproline sequences. This negatively regulates its anti-aggregation activity. However, the phosphatase that dephosphorylates PFN1 at Ser-137, and thus activates it, is unknown. Using a phospho-specific antibody against Ser-137 of PFN1, we characterized PFN1 dephosphorylation in cultured cells based on immunocytochemistry and a quantitative plate reader-based assay. Both okadaic acid and endothall increased pS137-PFN1 levels at concentrations more consistent with their known IC50s for protein phosphatase 1 (PP1) than protein phosphatase 2A (PP2A). Knockdown of the catalytic subunit of PP1 (PP1Cα), but not PP2A (PP2ACα), increased pS137-PFN1 levels. PP1Cα binds PFN1 in cultured cells, and this interaction was increased by a phosphomimetic mutation of PFN1 at Ser-137 (S137D). Together, these data define PP1 as the principal phosphatase for Ser-137 of PFN1, and provide mechanistic insights into PFN1 regulation by phosphorylation

Electromyographic Activity in the EEG in Alzheimer's Disease: Noise or Signal?

Many efforts have been directed at negating the influence of electromyographic (EMG) activity on the EEG, especially in elderly demented patients. We wondered whether these “artifacts” might reflect cognitive and behavioural aspects of dementia. In this pilot study, 11 patients with probable Alzheimer's disease (AD), 13 with amnestic mild cognitive impairment (MCI) and 13 controls underwent EEG registration. As EMG measures, we used frontal and temporal 50–70 Hz activity. We found that the EEGs of AD patients displayed more theta activity, less alpha reactivity, and more frontal EMG than controls. Interestingly, increased EMG activity indicated more cognitive impairment and more depressive complaints. EEG variables on the whole distinguished better between groups than EMG variables, but an EMG variable was best for the distinction between MCI and controls. Our results suggest that EMG activity in the EEG could be more than noise; it differs systematically between groups and may reflect different cerebral functions than the EEG