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Nonlinear wave mixing has been explored in space plasma. Here the authors report beat-wave Brillouin scattering experiment at EISCAT, in which two radio waves at different transmitted frequencies are driving ion acoustic waves in the ionosphere, leading to stimulated Brillouin emissions escaping the plasma.

Abstract Achilles tendons are mechanosensitive, and their complex hierarchical structure is in part the result of the mechanical stimulation conveyed by the muscles. To fully understand how their microstructure responds to mechanical loading a non-invasive approach for 3D high resolution imaging suitable for soft tissue is required. Here we propose a protocol that can capture the complex 3D organization of the Achilles tendon microstructure, using phase-contrast enhanced synchrotron micro-tomography (SR-PhC-μCT). We investigate the effects that sample preparation and imaging conditions have on the resulting image quality, by considering four types of sample preparations and two imaging setups (sub-micrometric and micrometric final pixel sizes). The image quality is assessed using four quantitative parameters. The results show that for studying tendon collagen fibers, conventional invasive sample preparations such as fixation and embedding are not necessary or advantageous. Instead, fresh frozen samples result in high-quality images that capture the complex 3D organization of tendon fibers in conditions as close as possible to natural. The comprehensive nature of this innovative study by SR-PhC-μCT breaks ground for future studies of soft complex biological tissue in 3D with high resolution in close to natural conditions, which could be further used for in situ characterization of how soft tissue responds to mechanical stimuli on a microscopic level.

Abstract The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwent extensive clinical, biochemical and magnetic resonance imaging phenotyping, and we also investigated non-targeted serum metabolites. We used a multi-modal machine learning approach to evaluate which serum metabolomic compounds predicted markers of glucose and insulin metabolism, adipose tissue morphology and distribution. Fasting glucose was associated with metabolites of intracellular insulin action and beta-cell dysfunction, namely cysteine-s-sulphate and n-acetylgarginine, whereas fasting insulin was predicted by myristoleoylcarnitine, propionylcarnitine and other metabolites of beta-oxidation of fatty acids. OGTT-glucose levels at 30 min were predicted by 7-Hoca, a microbiota derived metabolite, as well as eugenol, a fatty acid. Both insulin clamp and HOMA-IR were predicted by metabolites involved in beta-oxidation of fatty acids and biodegradation of triacylglycerol, namely tartrate and 3-phosphoglycerate, as well as pyruvate, xanthine and liver fat. OGTT glucose area under curve (AUC) and OGTT insulin AUC, was associated with bile acid metabolites, subcutaneous adipocyte cell size, liver fat and fatty chain acids and derivates, such as isovalerylcarnitine. Finally, subcutaneous adipocyte size was associated with long chain fatty acids, markers of sphingolipid metabolism, increasing liver fat and dopamine-sulfate 1. Ectopic liver fat was predicted by methylmalonate, adipocyte cell size, glutathione derived metabolites and fatty chain acids. Ectopic heart fat was predicted visceral fat, gamma-glutamyl tyrosine and 2-acetamidophenol sulfate. Adipocyte cell size, age, alpha-tocopherol and blood pressure were associated with visceral fat. We identified several biomarkers associated with adipose tissue pathophysiology and insulin and glucose metabolism using a multi-modal machine learning approach. Our approach demonstrated the relative importance of serum metabolites and they outperformed traditional clinical and biochemical variables for most endpoints.

Type 2 diabetes (T2D) is caused by insufficient insulin secretion from pancreatic [beta] cells. To identify candidate genes contributing to T2D pathophysiology, we studied human pancreatic islets from approximately 300 individuals. We found 395 differentially expressed genes (DEGs) in islets from individuals with T2D, including, to our knowledge, novel (OPRD1, PAX5, TET1) and previously identified (CHL1, GLRA1, IAPP) candidates. A third of the identified expression changes in islets may predispose to diabetes, as expression of these genes associated with HbA1c in individuals not previously diagnosed with T2D. Most DEGs were expressed in human [beta] cells, based on single-cell RNA-Seq data. Additionally, DEGs displayed alterations in open chromatin and associated with T2D SNPs. Mouse KO strains demonstrated that the identified T2D-associated candidate genes regulate glucose homeostasis and body composition in vivo. Functional validation showed that mimicking T2D-associated changes for OPRD1, PAX5, and SLC2A2 impaired insulin secretion. Impairments in Pax5-overexpressing [beta] cells were due to severe mitochondrial dysfunction. Finally, we discovered PAX5 as a potential transcriptional regulator of many T2D-associated DEGs in human islets. Overall, we have identified molecular alterations in human pancreatic islets that contribute to [beta] cell dysfunction in T2D pathophysiology., Introduction Type 2 diabetes (T2D) is characterized by chronic hyperglycemia due to insufficient insulin secretion from pancreatic islets, often in combination with insulin resistance in target cells. The number of [...]

Out-of-field patient doses in proton therapy are dominated by neutrons. Currently, they are not taken into account by treatment planning systems. There is an increasing need to include out-of-field doses in the dose calculation, especially when treating children, pregnant patients, and patients with implants. In response to this demand, this work presents the first steps towards a tool for the prediction of out-of-field neutron doses in pencil beam scanning proton therapy facilities. As a first step, a general Monte Carlo radiation transport model for simulation of out-of-field neutron doses was set up and successfully verified by comparison of simulated and measured ambient neutron dose equivalent and neutron fluence energy spectra around a solid water phantom irradiated with a variation of different treatment plan parameters. Simulations with the verified model enabled a detailed study of the variation of the neutron ambient dose equivalent with field size, range, modulation width, use of a range shifter, and position inside the treatment room. For future work, it is planned to use this verified model to simulate out-of-field neutron doses inside the phantom and to verify the simulation results by comparison with previous in-phantom measurement campaigns. Eventually, these verified simulations will be used to build a library and a corresponding tool to allow assessment of out-of-field neutron doses at pencil beam scanning proton therapy facilities.