Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning

Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience. In self-guided VRET, participants can gradually increase their tolerance to exposure and reduce anxiety-induced arousal and PSA over time. However, creating such a VR environment and determining physiological indices of anxiety-induced arousal or distress is an open challenge. Environment modelling, character creation and animation, psychological state determination and the use of machine learning (ML) models for anxiety or stress detection are equally important, and multi-disciplinary expertise is required. In this work, we have explored a series of ML models with publicly available data sets (using electroencephalogram and heart rate variability) to predict arousal states. If we can detect anxiety-induced arousal, we can trigger calming activities to allow individuals to cope with and overcome distress. Here, we discuss the means of effective selection of ML models and parameters in arousal detection. We propose a pipeline to overcome the model selection problem with different parameter settings in the context of virtual reality exposure therapy. This pipeline can be extended to other domains of interest where arousal detection is crucial. Finally, we have implemented a biofeedback framework for VRET where we successfully provided feedback as a form of heart rate and brain laterality index from our acquired multimodal data for psychological intervention to overcome anxiety

Demographic change and conflict in Northern Ireland: reconciling qualitative and quantitative evidence

Recent large-N quantitative studies have failed to uncover a link between demographic change and conflict. This seems to refute quite powerful qualitative evidence from small-scale case studies that such a relationship exists. This article attempts to reconcile the conflicting evidence by revealing how population change—in this case a major decline in the size of the Protestant majority—matters for violent conflict, but not in a direct way. In addition, relationships differ by level of geography. In the case of Northern Ireland, no significant quantitative association exists between ethnic change and violence. This holds across both geographic units and years. However, there is a significant association between ethnic demography and Protestant mobilization into the Orange Order across counties. This in turn is related to Protestant resistance to reforms aimed at extending civil rights to the Catholic population during the Stormont period. This stance was a major factor in generating Catholic support for IRA violence. Moreover, in specific locations, a direct link between Protestant population decline relative to Catholics and loyalist violence against Catholics is evident. Hence demography matters, but in conjunction with other factors, and several steps upstream from the outbreak of violence

DNL104, a Centrally Penetrant RIPK1 Inhibitor, Inhibits RIP1 Kinase Phosphorylation in a Randomized Phase I Ascending Dose Study in Healthy Volunteers

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) regulates inflammation, cytokine release, and necroptotic cell death and is implicated in pathogenic cellular pathways in amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), and multiple sclerosis. Inhibition of RIPK1 activity protects against inflammation and cell death in multiple animal models. DNL104 is a selective, brain-penetrant inhibitor of RIPK1 phosphorylation in clinical development for AD and ALS. DNL104 was tested in 68 healthy volunteers to investigate safety and tolerability, pharmacokinetic profile in plasma and cerebrospinal fluid, and pharmacodynamic effects of RIPK1 inhibition in peripheral blood mononuclear cells in a first-in-human, placebo-controlled, double-blind, randomized single-ascending dose (SAD) and multiple-ascending dose (MAD) study. DNL104 was well-tolerated in the SAD group and during the dosing period of the MAD group. However, postdose liver toxicity in 37.5% of subjects was observed in the MAD, and assessed to be drug related. We demonstrate that DNL104 leads to RIP1 kinase inhibition, and this is not associated with central nervous system (CNS) toxicities, supporting future development of CNS penetrant RIPK1 inhibitors.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Diverse Brain Myeloid Expression Profiles Reveal Distinct Microglial Activation States and Aspects of Alzheimer’s Disease Not Evident in Mouse Models

Microglia, the CNS-resident immune cells, play important roles in disease, but the spectrum of their possible activation states is not well understood. We derived co-regulated gene modules from transcriptional profiles of CNS myeloid cells of diverse mouse models, including new tauopathy model datasets. Using these modules to interpret single-cell data from an Alzheimer’s disease (AD) model, we identified microglial subsets—distinct from previously reported “disease-associated microglia”—expressing interferon-related or proliferation modules. We then analyzed whole-tissue RNA profiles from human neurodegenerative diseases, including a new AD dataset. Correcting for altered cellular composition of AD tissue, we observed elevated expression of the neurodegeneration-related modules, but also modules not implicated using expression profiles from mouse models alone. We provide a searchable, interactive database for exploring gene expression in all these datasets (http://research-pub.gene.com/BrainMyeloidLandscape). Understanding the dimensions of CNS myeloid cell activation in human disease may reveal opportunities for therapeutic intervention

The Minority-Majority Conundrum in Northern Ireland: An Orange Order Perspective.

Researchers have argued that, depending on the framing of the Northern Ireland conflict, each group could either be a minority or a majority relative to the other. This complicates macrosocial explanations of the conflict which make specific predictions on the basis of minority or majority positions. The present paper argues that this conundrum may have arisen from the inherent variability in microidentity processes that do not fit easily with macroexplanations. In this paper the rhetoric of relative group position is analysed in political speeches delivered by leading members of an influential Protestant institution in Northern Ireland. It is apparent that minority and majority claims are not fixed but are flexibly used to achieve local rhetorical goals. Furthermore, the speeches differ before and after the Good Friday Agreement, with a reactionary “hegemonic” Unionist position giving way to a “majority-rights power sharing” argument and a “pseudo-minority” status giving way to a “disempowered minority” argument. These results suggest a view of the Northern Ireland conflict as a struggle for “symbolic power,” i.e., the ability to flexibly define the intergroup situation to the ingroup’s advantage