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Structural Covariance of Early Visual Cortex Is Negatively Associated With Posttraumatic Stress Disorder Symptoms:A Mega-Analysis From the ENIGMA PTSD Working Group
No full textBackground: Identifying robust neural signatures of posttraumatic stress disorder (PTSD) symptoms is important to facilitate precision psychiatry and help in understanding and treatment of the disorder. Emergent research suggests that the structural covariance of early visual regions is associated with later PTSD development. However, large-scale analyses are needed in heterogeneous samples of trauma-exposed and trauma-naïve individuals to determine whether such a neural signature is a robust marker of vulnerability. Methods: We analyzed data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-PTSD dataset (N = 2814) and the HCP-YA (Human Connectome Project–Young Adult) dataset (N = 890) to investigate whether the structural covariance of the early visual cortex is associated with either PTSD symptoms or perceived stress. Structural covariance was derived from a multimodal pattern previously identified in recent trauma survivors, and participant loadings on the profile were included in linear mixed effects models to evaluate associations with stress. Results: Early visual cortex covariance loadings were negatively associated with PTSD symptoms in the ENIGMA-PTSD dataset. The relationship persisted when accounting for prior childhood maltreatment; supporting PTSD symptom specificity, no relationship was observed with depressive symptoms, and no association was observed between loadings and perceived stress measures in the HCP-YA dataset. Conclusions: The structural covariance of early visual cortex was robustly associated with PTSD symptoms across an international, heterogeneous sample of trauma survivors. Future studies should aim to identify specific mechanisms that underlie structural alterations in the visual cortex to better understand posttrauma psychopathology.</p
Broadening STEM participation:An intersectional approach to promoting minoritised students’ inclusion
No full textThis dissertation examines the systemic marginalization of LGBTQIA+ individuals (hereafter referred to as queer) in Science, Technology, Engineering, and Mathematics (STEM) education and professional environments, and argues for the urgent need to reimagine STEM through inclusive and justice-centered frameworks. Rather than treating queer lives as exceptions to be accommodated, this research positions them as central to transforming what STEM education is, does, and can become.Grounded in queer theory and intersectionality, the dissertation interrogates how power operates through normative assumptions embedded in science and education. Queer theory is used to challenge binary constructions of gender and sexuality and to disrupt dominant norms, while intersectionality highlights how interlocking systems of oppression—including cisheteronormativity, racism, ableism, classism, and patriarchy—shape lived experiences and access to STEM. Together, these frameworks support a research agenda that centers lived experience, resists assimilation, and seeks systemic transformation.The dissertation consists of five articles. The first presents a systematic review of 24 empirical studies on queer individuals in STEM learning and working environments, revealing persistent patterns of exclusion, harassment, and professional marginalization. The second article is a multiple case study of three queer individuals, using life-history interviews to examine how dominant cultural models in science shape science identity trajectories. The third article foregrounds queer perspectives on transforming STEM education through changes in culture, curriculum, pedagogy, and relationships. The fourth article develops a teacher training course focused on diversity, inclusion, and social justice in STEM, translating theory into practice. The fifth article analyzes instructional materials created by participating teachers, identifying both promising practices and ongoing challenges.Overall, this dissertation contributes theoretical, empirical, and practical tools for advancing more inclusive and liberatory STEM education
Towards understanding cyclic di-AMP metabolism
No full textBacteria rely on small signaling molecules to rapidly adapt to changes in their environment. One such molecule, cyclic di-AMP, has emerged over the past decade as an essential regulator of bacterial physiology. This thesis investigates how cyclic di-AMP is synthesized, broken down, and used by bacteria to maintain their viability in a dynamic external environment.Chapter 1 places cyclic di-AMP in a broader physiological context, showing that its primary role is to control bacterial cell volume. By regulating the movement of ions and small molecules across the cell membrane, cyclic di-AMP allows cells to maintain internal pressure and adapt to osmotic stress. The chapter then explores how other essential cell processes are connected to cyclic di-AMP signaling, for example, cell wall synthesis, DNA replication and the stringent response, and how they are connected to cell volume control.Chapter 2 focuses on the enzyme responsible for cyclic di-AMP synthesis, CdaA. Using a combination of biochemical and structural methods, chapter 2 demonstrates that CdaA may only synthesize cyclic di-AMP efficiently when membrane-embedded, where it forms an active complex.Chapter 3 examines how cyclic di-AMP is broken down. Chapter 3 focuses on NrnA and GdpP, enzymes that are required for breakdown of cyclic di-AMP to pApA (GdpP) and finally from pApA to AMP (NrnA). NrnA is an enzyme essential which maintains bacterial viability, but which does not directly degrade cyclic di-AMP. Instead, NrnA clears the potentially toxic accumulation of dinucleotide breakdown products, like pApA.Together, this thesis presents a clear and concise investigation into cyclic di-AMP signalling, followed by investigation of key cyclic di-AMP metabolic enzymes, and finishes by exploring the implications of cyclic di-AMP research in microbial physiology and future development of cyclic di-AMP-targeted antibacterial therapies
Misfortunes never come singly:Microbial metabolites link heart failure and chronic kidney disease
No full textHeart failure is life threatening and common in chronic kidney disease patients. In this issue,1 Zheng et al. report that toxin-generating E. coli tryptophan metabolism induces myocardial apoptosis, contributing to heart failure risk with kidney dysfunction. The authors show that a probiotic product reduces this risk in preclinical and clinical settings.</p
Constructions of Subjectivities about Adolescent Motherhood in the Chilean Child Protection Context
No full textThis dissertation examines the construction of subjectivities around adolescent motherhood within the Chilean child protection system, with particular attention to young mothers residing in residential care institutions. Adolescent pregnancy and motherhood have historically been framed as social and public health problems to be eradicated, often accompanied by stigma, moral panic, and deficit-oriented narratives. In contrast, this study approaches adolescent motherhood as a complex socio-cultural and political phenomenon, produced through the interplay of intersecting structures of inequality, institutional logics, and the lived experiences and agency of young mothers
AI-enhanced detection of prostate cancer
No full textProstate cancer poses a pressing public health challenge with an increasing elderly population in whom prostate cancer-specific mortality is highest. For the diagnosis of prostate cancer, magnetic resonance imaging (MRI) scans are recommended, leading to an increasing demand and a need to use current diagnostic resources more efficiently. This thesis explored an AI-enhanced diagnostic pathway to handle the increasing demand for prostate cancer diagnosis. The focus was on evaluating AI-enhanced lesion detection and AI-based reconstruction of accelerated MRI, assessing the effectiveness compared to conventional methods, and discussing the potential impact on clinical practice.This thesis demonstrates the potential of AI to reduce MRI scan time and assist radiologists in assessing prostate MRI. The proposed prostate cancer diagnostic pathway could reduce MRI scan time by up to 71% without significantly compromising diagnostic quality. Furthermore, AI could reduce the radiologists’ workload by up to 20%, which might increase with continued improvements in AI performance. The use of AI for prostate cancer detection was also accepted by patients, especially with AI performances approaching expert level. These findings indicate an AI-enhanced diagnostic pathway that benefits the patient, society, and healthcare professionals. The proposed pathway could potentially handle the increasing demand for prostate cancer diagnosis, but before clinical deployment, prospective studies are needed to validate the generalization of the findings in this thesis
The co-evolution of informal social status and gossip in workplace social networks
No full textThis study examines the co-evolution of informal social status and the three positions in a gossip triad – gossip senders, receivers, and objects – in the workplace. Two different social mechanisms are proposed to explain these interrelationships, suggesting relationships between gossip and informal social status in the opposite direction. First, the social bonding perspective suggests that gossip bonds between actors in a gossip triad shape their informal social status. Second, the social capital perspective indicates that employees’ informal social status leads to their position within gossip triads. The hypotheses are tested in a three-wave social network study among employees in a Dutch childcare organization. Results of stochastic actor-oriented models indicate a co-evolution between informal social status and the receiver's role in a gossip triad, but not with the roles of sender and object. Contrary to what the social capital perspective predicts, employees’ informal social status negatively affects receiving gossip over time. In line with the social bonding perspective, receiving gossip positively affects informal social status over time. The co-evolution process suggests that over time, an equilibrium may emerge where an employee's informal social status stabilizes at a point where enough gossip is received to sustain their social position. We conclude that the previously often neglected receivers of third-party information should be considered when examining the dynamics of workplace gossip.</p
The Effects of the 2020 BLM Protests on Racial Bias in the United States
No full textThe 2020 Black Lives Matter (BLM) protests in response to the murder of George Floyd highlighted the lingering structural inequalities faced by Black people in the United States. In the present research, we investigated whether these protests led to reduced implicit and explicit racial bias among White U.S. Americans. Combining data from Project Implicit, Armed Conflict Location Event Data Project (ACLED), Google Trends, and the American Community survey, we observed rapid drops in implicit and explicit measures of racial bias after the onset of the protests. However, both types of racial bias slowly increased again over time as (attention to) BLM faded. We use directed acyclic graphs to show under which assumptions causal inferences are warranted. We discuss our results in light of situational models of bias, their implications for protest movements, and raise questions about when and how social norms play a role in large-scale attitude change.</p
Longitudinal social network methods for the educational and psychological sciences
No full textSocial network analysis is useful for obtaining a better understanding of antecedents and mechanisms of relationship formation and interactions between individuals in educational and psychological contexts. Research utilising descriptive and cross-sectional applications of network analysis is regularly reported, but longitudinal analyses of networks have received less scrutiny. In this methodological article, we compare three commonly applied approaches for analysing longitudinal social network data: Multiple Regression Quadratic Assignment Procedure (MRQAP), Separable Temporal Exponential Random Graph Models (STERGM), and Stochastic Actor Oriented Modelling (SAOM) with research questions about correlations, social structures and mechanisms, respectively. We highlight advantages and disadvantages of the methods and illustrate differences between these methods by analysing longitudinal peer-communication network data of pre-service teachers. The key considerations by the researcher are summarised as ‘FACTS’ (Focus, Assumptions, Conceptualisation, Time points, and Size) as an aid to researchers in selecting the most appropriate method for the analysis of longitudinal social network data.</p