1,721,016 research outputs found
Decreased whole body lipolysis as a mechanism of the lipid-lowering effect of pioglitazone in type 2 diabetic patients
No full textGastaldelli A, Casolaro A, Ciociaro D, Frascerra S, Nannipieri M, Buzzigoli E, Ferrannini E. Decreased whole body lipolysis as a mechanism of the lipid-lowering effect of pioglitazone in type 2 diabetic patients. Am J Physiol Endocrinol Metab 297: E225-E230, 2009. First published May 5, 2009; doi: 10.1152/ajpendo.90960.2008.-Pioglitazone has been shown to reduce fasting triglyceride levels. The mechanisms of this effect have not been fully elucidated, but decreased lipolysis may contribute to blunt the hypertriglyceridemic response to a meal. To test this hypothesis, we studied 27 type 2 diabetes mellitus (T2DM) patients and 7 sex-, age-, and body mass index-matched nondiabetic controls. Patients were randomized to pioglitazone (45 mg/day) or placebo for 16 wk. Whole body lipolysis was measured [as the [(2)H(5)] glycerol rate of appearance (R(a))] in the fasting state and for 6 h following a mixed meal. Compared with controls, T2DM had higher postprandial profiles of plasma triglycerides, free fatty acid (FFA), and beta-hydroxybutyrate, and a decreased suppression of glycerol R(a) (P < 0.04) despite higher insulin levels [268 (156) vs. 190 (123) pmol/l, median (interquartile range)]. Following pioglitazone, triglycerides and FFA were reduced (P = 0.05 and P < 0.04, respectively), and glycerol R(a) was more suppressed [-40 (137) vs. +7 (202) mu mol/min of placebo, P < 0.05] despite a greater fall in insulin [-85 (176) vs. -20 (58) pmol/l, P = 0.05]. We conclude that, in well-controlled T2DM patients, whole body lipolysis is insulin resistant, and pioglitazone improves the insulin sensitivity of lipolysis
Spatio-temporal prediction using graph neural networks: A survey
No full textThe analysis of spatial time series is increasingly relevant as spatio-temporal data are becoming widespread due to the ever-growing diffusion of data acquisition devices. Spatio-temporal prediction is crucial for grasping insights on spatio-temporal dynamics in diverse domains. In many cases, spatio-temporal data can be effectively represented using graphs, thus making Graph Neural Networks the most sounding deep learning architecture for the modelling of spatio-temporal series. The aim of the work is to provide a self-consistent and thorough overview on Graph Neural Networks for spatio-temporal prediction, giving a taxonomy of the diverse approaches proposed in the literature. Moreover, attention is paid to the description of the most used benchmarks and metrics in different real-world spatio-temporal domains and to the discussion of the main drawbacks of spatio-temporal Graph Neural Networks. Furthermore, unlike other similar works on deep learning, statistical methods for spatio-temporal modelling are briefly surveyed in this work. Finally, insights on future developments of Graph Neural Networks for spatio-temporal prediction are suggested
Predicting ground-level nitrogen dioxide concentrations using the BaYesian attention-based deep neural network
No full textNitrogen dioxide pollution is an ongoing and growing environmental issue that affects human health in developed Western countries. This study introduced a Bayesian attention-based deep neural network model for predicting ground-level nitrogen dioxide concentrations. The proposed model integrates the principles of the Bayesian neural network and the attention mechanism, enabling it to produce predicted values and their associated uncertainties, expressed as standard deviations. The proposed model was validated using 2020 data collected from 520 European Environmental Agency stations, located in Italy. The performance of the model was assessed using the mean absolute error
Manifold learning by a deep Gaussian process autoencoder
No full textThe paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian process autoencoder algorithm has the following two main characteristics. The former is a bottleneck structure, borrowed by variational autoencoders and the latter is based on the so-called doubly stochastic variational inference for deep Gaussian processes architecture (DSVI). The main novelties of the paper consist in DGPA algorithm and the experimental protocol for evaluating it. In fact, to the best of our knowledge, deep Gaussian processes algorithms have not been applied to manifold learning, yet. Besides, an experimental protocol is introduced, the so-called manifold learning performance protocol (MLPP), to compare quantitatively the geometric preserved properties of manifold learning projections of the proposed deep Gaussian process autoencoder with the ones of state-of-the-art manifold learning algorithms. Extensive experimental tests on eleven synthetic and five real datasets show that deep Gaussian process autoencoder compares favorably with the other manifold learning competitors
PERCUTANEOUS ETHANOL INJECTION THERAPY OF BENIGN THYROID NODULES: IS ALWAYS AN EFFECTIVE AND SAFE ALTERNATIVE? RESULTS OF TEN YEARS? EXPERIENCE
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TERAPIA DEI NODULI TIROIDEI FREDDI BENIGNI MEDIANTE ALCOLIZZAZIONE PERCUTANEA ECOGUIDATA
No full textDynamic Beam Steering with Reconfigurable Metagratings
No full textMetagratings have recently shown promising features for wavefront manipulation, overcoming the efficiency limitations of gradient metasurfaces and their demanding fabrication requirements. Extreme functionalities, such as perfect anomalous reflection and refraction, focusing, and holography, have been proposed and tested. However, most of the developed analytical models concern the manipulation of the reflected field. In this article, we present a theoretical formulation that allows a complete manipulation of both reflected and transmitted fields through a metagrating consisting of arrays of capacitively loaded strips. In addition, the proposed solution enables the design of electronically reconfigurable metagratings for the dynamic control of the diffraction pattern at microwave frequencies. The theoretical formulation is numerically validated and a possible practical implementation of the metagrating is also discussed, as well as the effects of losses and parasitic reactances
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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