Spatiotemporal fluctuations of olfactory stimuli and its detection by an optical method

Olfactory processing in the mammalian brain is a highly dynamic process, yet most of the olfaction experiments have been studied primarily with static stimuli. Odors in the natural environment are transported by turbulent flow of air or water. Natural odorants have fluctuations in concentration and it changes rapidly with time. These rapid fluctuations may pose some challenges to identifying an odor; on the other hand, the variation itself may provide important clues about the odor source. The goal of this thesis project was to create a similar odorant environment like the rapid odor fluctuations encountered in nature – to meet this goal; we built an odor delivery and optical odor detection system. We combine visible smoke with invisible odorant to make the odorant detectable using two high sensitivity CCD line cameras. Initial tests of the system were carried out to determine the plausibility of its use in future experiments. Based on observed and quantified fluctuations of smoke and odorants, we conclude that the system is a promising tool for studying olfaction with naturalistic odorant fluctuations

Lower Bounds for Shoreline Searching With 2 or More Robots

Searching for a line on the plane with $n$ unit speed robots is a classic online problem that dates back to the 50's, and for which competitive ratio upper bounds are known for every $n\geq 1$. In this work we improve the best lower bound known for $n=2$ robots from 1.5993 to 3. Moreover we prove that the competitive ratio is at least $\sqrt{3}$ for $n=3$ robots, and at least $1/\cos(\pi/n)$ for $n\geq 4$ robots. Our lower bounds match the best upper bounds known for $n\geq 4$, hence resolving these cases. To the best of our knowledge, these are the first lower bounds proven for the cases $n\geq 3$ of this several decades old problem.Comment: This is an updated version of the paper with the same title which will appear in the proceedings of the 23rd International Conference on Principles of Distributed Systems (OPODIS 2019) Neuchatel, Switzerland, July 17-19, 201

Integration of metabolomics, lipidomics and clinical data using a machine learning method.

BACKGROUND: The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, -γ, and -δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry. RESULTS: In order to integrate an extensive set of nine different multivariate metabolic and lipidomics datasets with classical toxicological parameters we developed a hypotheses free, data driven machine learning approach. From the data analysis, we examined how the nine datasets were able to model dose and clinical chemistry results, with the different datasets having very different information content. CONCLUSIONS: We found lipidomics (Direct Infusion-Mass Spectrometry) data the most predictive for different dose responses. In addition, associations with the metabolic and lipidomic data with aspartate amino transaminase (AST), a hepatic leakage enzyme to assess organ damage, and albumin, indicative of altered liver synthetic function, were established. Furthermore, by establishing correlations and network connections between eicosanoids, phospholipids and triacylglycerols, we provide evidence that these lipids function as a key link between inflammatory processes and intermediary metabolism

Update on ranolazine in the management of angina.

Mortality rates attributable to coronary heart disease have declined in recent years, possibly related to changes in clinical presentation patterns and use of proven secondary prevention strategies. Chronic stable angina (CSA) remains prevalent, and the goal of treatment is control of symptoms and reduction in cardiovascular events. Ranolazine is a selective inhibitor of the late sodium current in myocytes with anti-ischemic and metabolic properties. It was approved by the US Food and Drug Administration in 2006 for use in patients with CSA. Multiple, randomized, placebo-controlled trials have shown that ranolazine improves functional capacity and decreases anginal episodes in CSA patients, despite a lack of a significant hemodynamic effect. Ranolazine did not improve cardiovascular mortality or affect incidence of myocardial infarction in the MERLIN (Metabolic Efficiency with Ranolazine for Less Ischemia in Non-ST-Elevation Acute Coronary Syndrome)-TIMI (Thrombolysis In Myocardial Infarction) 36 trial, but significantly decreased the incidence of recurrent angina. More recently, ranolazine has been shown to have beneficial and potent antiarrhythmic effects, both on supraventricular and ventricular tachyarrhythmias, largely due to its inhibition of the late sodium current. Randomized controlled trials testing these effects are underway. Lastly, ranolazine appears to be cost-effective due to its ability to decrease angina-related hospitalizations and improve quality of life

Parametric uncertainty analysis of pulse wave propagation in a model of a human arterial network

Accepted versio

Taurine as a biomarker for aging: A new avenue for translational research

The physiologic and irreversible process of ageing is accompanied by a wide range of structural and functional shifts at multiple different levels. It is also suggested that variations in the blood concentrations of metabolites, hormones, and micronutrients may play a role in the ageing process. Recently, Singh et al. 1,2 investigated a study on Taurine shortage as a driver and biomarker of ageing and its impact on a healthy lifespan.2 They further proposed that functional abnormalities in numerous organs associated with age-related illnesses have been linked to early-life Taurine insufficiency. Taurine deficiency in the elderly and the possible benefits of Taurine supplements One of the reasons for decreasing Taurine concentration is the loss of endogenous synthesis, which may contribute to the decrease in Taurine levels seen in the elderly. While it was previously believed that the liver was responsible for most Taurine synthesis in humans, new research suggests that other organs or common intermediates may play a larger role. The authors experimented with and analysed a life-span examination of various organisms, for example, mice to assess the impacts of Taurine supplementation. They also analysed after the administration of oral Taurine supplementation in conjunction with other interventions using multi-omics data sets (RNA sequencing, metabolomics etc.) across different species