Thoracic fat volume is independently associated with coronary vasomotion.

Thoracic fat has been associated with an increased risk of coronary artery disease (CAD). As endothelium-dependent vasoreactivity is a surrogate of cardiovascular events and is impaired early in atherosclerosis, we aimed at assessing the possible relationship between thoracic fat volume (TFV) and endothelium-dependent coronary vasomotion. Fifty healthy volunteers without known CAD or major cardiovascular risk factors (CRFs) prospectively underwent a (82)Rb cardiac PET/CT to quantify myocardial blood flow (MBF) at rest, and MBF response to cold pressor testing (CPT-MBF) and adenosine (i.e., stress-MBF). TFV was measured by a 2D volumetric CT method and common laboratory blood tests (glucose and insulin levels, HOMA-IR, cholesterol, triglyceride, hsCRP) were performed. Relationships between CPT-MBF, TFV and other CRFs were assessed using non-parametric Spearman rank correlation testing and multivariate linear regression analysis. All of the 50 participants (58 ± 10y) had normal stress-MBF (2.7 ± 0.6 mL/min/g; 95 % CI: 2.6-2.9) and myocardial flow reserve (2.8 ± 0.8; 95 % CI: 2.6-3.0) excluding underlying CAD. Univariate analysis revealed a significant inverse relation between absolute CPT-MBF and sex (ρ = -0.47, p = 0.0006), triglyceride (ρ = -0.32, p = 0.024) and insulin levels (ρ = -0.43, p = 0.0024), HOMA-IR (ρ = -0.39, p = 0.007), BMI (ρ = -0.51, p = 0.0002) and TFV (ρ = -0.52, p = 0.0001). MBF response to adenosine was also correlated with TFV (ρ = -0.32, p = 0.026). On multivariate analysis, TFV emerged as the only significant predictor of MBF response to CPT (p = 0.014). TFV is significantly correlated with endothelium-dependent and -independent coronary vasomotion. High TF burden might negatively influence MBF response to CPT and to adenosine stress, even in persons without CAD, suggesting a link between thoracic fat and future cardiovascular events

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

An interactive fluid model of jellyfish for animation

We present an automatic animation system for jellyfish that is based on a physical simulation. We model the thrust of an adult jellyfish, and the organism's morphology in its most active mode of locomotion. We reduce our model by considering only species that are axially symmetric so that we can approximate the full 3D geometry of a jellyfish with a 2D simulation. We simulate the organism's elastic volume with a spring-mass system, and the surrounding sea water using the semi-Lagrangian method. We couple the two representations with the immersed boundary method. We propose a simple open-loop controller to contract the swimming muscles of the jellyfish. A 3D rendering model is extrapolated from our 2D simulation. We add variation to the extrapolated 3D geometry, which is inspired by empirical observations of real jellyfish. The resulting animation system is efficient with an acceptable compromise in physical accuracy