Nuclear imaging and semi-invasive electrocardiography in CRT

Cardiac resynchronisation therapy (CRT) is a promising treatment option in patients with chronic heart failure. In this article the roles of semi-invasive esophageal left-heart electrocardiography and functional cardiac nuclear imaging in the field of CRT are highlighted, as the combination of both could be a favourable diagnostic approach in special cardiac situations. Also original esophageal left heart electrogram data of exemplary CRT patients is presented

Text Entry Performance and Situation Awareness of a Joint Optical See-Through Head-Mounted Display and Smartphone System

Optical see-through head-mounted displays (OST HMDs) are a popular output medium for mobile Augmented Reality (AR) applications. To date, they lack efficient text entry techniques. Smartphones are a major text entry medium in mobile contexts but attentional demands can contribute to accidents while typing on the go. Mobile multi-display ecologies, such as combined OST HMD-smartphone systems, promise performance and situation awareness benefits over single-device use. We study the joint performance of text entry on mobile phones with text output on optical see-through head-mounted displays. A series of five experiments with a total of 86 participants indicate that, as of today, the challenges in such a joint interactive system outweigh the potential benefits.Comment: To appear in IEEE Transactions on Visualization and Computer Graphics On page(s): 1-17 Print ISSN: 1077-2626 Online ISSN: 1077-262

Immunological substrates of depressive symptoms in patients with severe obesity: An exploratory study

In this pilot study, we explored the immune phenotype of patients with severe obesity and comorbid depressive symptoms compared to non-depressed patients with obesity and normal-weight controls. Immune cell subsets were analysed by flow cytometry and depressive symptoms assessed using the Patient Health Questionnaire (PHQ-9). Cell frequencies were correlated with depressive symptom scores and waist-to-hip ratio (WHR). Patients with obesity and comorbid depression showed significantly lower numbers of circulating cytotoxic natural killer cells, dendritic cells and CD8(+) effector memory T cells, compared to normal-weight controls. Regulatory T cells and CD4(+) central memory T cells were increased compared to non-depressed patients with obesity and compared to normal-weight controls, respectively. Frequencies of cytotoxic natural killer cells and CD4(+) central memory T cells significantly correlated with PHQ-9 scores, but not with WHR. Reduced numbers of dendritic cells were observed in both patient groups with obesity and correlated with PHQ-9 scores and WHR. These findings provide evidence for an altered immune composition in comorbid obesity and depression, supporting a pathobiological overlap between the two disorders

Inductive biases in deep learning models for weather prediction

Deep learning has recently gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction (NWP) models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and how they contribute to model performance remains unclear. Here, we review and analyse the inductive biases of six state-of-the-art DLWP models, involving a deeper look at five key design elements: input data, forecasting objective, loss components, layered design of the deep learning architectures, and optimisation methods. We show how the design choices made in each of the five design elements relate to structural assumptions. Given recent developments in the broader DL community, we anticipate that the future of DLWP will likely see a wider use of foundation models -- large models pre-trained on big databases with self-supervised learning -- combined with explicit physics-informed inductive biases that allow the models to provide competitive forecasts even at the more challenging subseasonal-to-seasonal scales

Epigenetic modifications associated with maternal anxiety during pregnancy and children's behavioral measures

Epigenetic changes are associated with altered behavior and neuropsychiatric disorders and they modify the trajectory of aging. Maternal anxiety during pregnancy is a common environmental challenge for the fetus, causing changes in DNA methylation. Here, we determined the mediating role of DNA methylation and the moderating role of offspring sex on the association between maternal anxiety and children’s behavioral measures. In 83 mother–child dyads, maternal anxiety was assessed in each trimester of pregnancy when the child was four years of age. Children’s behavioral measures and children’s buccal DNA methylation levels (NR3C1, IGF2/H19 ICR, and LINE1) were examined. Higher maternal anxiety during the third trimester was associated with more methylation levels of the NR3C1. Moderating effects of sex on the association between maternal anxiety and methylation were found for IGF2/H19 and LINE1 CpGs. Mediation analysis showed that methylation of NR3C1 could buffer the effects of maternal anxiety on children’s behavioral measures, but this effect did not remain significant after controlling for covariates. In conclusion, our data support an association between maternal anxiety during pregnancy and DNA methylation. The results also underscore the importance of sex differences and timing effects. However, DNA methylation as underlying mechanism of the effect of maternal anxiety during pregnancy on offspring’s behavioral measures was not supported