651 research outputs found
Individuals with type 2 diabetes have higher density of small intestinal neurotensin-expressing cells
Neurotensin (NT) is a gastro-intestinal hormone involved in several pathways that regulate energy and glucose homeostasis. NT was hypothesized to act in synergy with incretin hormones to potentiate its anti-diabetic effects. Additionally, circulating NT levels were shown to rise after bariatric surgery-induced weight loss. Knowledge of NT-secreting cells distribution along the small intestine and its variation according to diabetes status could provide insights on NT role in mediating type 2 diabetes (T2D) improvement after bariatric surgery. So, our aims were to characterize NT-expressing cell distribution along the human small intestine and to compare the relative density of NT-expressing cells in the small intestine of individuals with and without T2D undergoing bariatric surgery for obesity treatment. Autopsy-derived small intestine fragments (n = 30) were obtained at every 20 cm along the entire intestinal length. Additionally, jejunum biopsies (n = 29) were obtained during elective gastric bypass interventions from patients with (n = 10) or without T2D (n = 18). NT-expressing cells were identified by immunohistochemistry and quantified via computerized morphometric analysis. NT-expressing cell density increased along the human small intestine. NT-expressing cell density was significantly higher from 200 cm distal to the duodenojejunal flexure onward, as well as in subjects with T2D when compared to those without T2D. NT-expressing cell density increases along the human small gut, and a higher density is found in individuals with T2D. This finding suggests a potential role for NT in the mechanisms of disease and T2D improvement observed after bariatric surgery. (c) 2023, The Author(s)
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Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions
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Data will be made available on request.Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes
Priority for the Worse Off and the Social Cost of Carbon
The social cost of carbon (SCC) is a monetary measure of the harms from carbon emission. Specifically, it is the reduction in current consumption that produces a loss in social welfare equivalent to that caused by the emission of a ton of CO2. The standard approach is to calculate the SCC using a discounted-utilitarian social welfare function (SWF)—one that simply adds up the well-being numbers (utilities) of individuals, as discounted by a weighting factor that decreases with time. The discounted-utilitarian SWF has been criticized both for ignoring the distribution of well-being, and for including an arbitrary preference for earlier generations. Here, we use a prioritarian SWF, with no time-discount factor, to calculate the SCC in the integrated assessment model RICE. Prioritarianism is a well-developed concept in ethics and theoretical welfare economics, but has been, thus far, little used in climate scholarship. The core idea is to give greater weight to well-being changes affecting worse off individuals. We find substantial differences between the discounted-utilitarian and non-discounted prioritarian SCC
Estimativa do custo do tratamento do câncer de pele tipo não-melanoma no Estado de São Paulo - Brasil
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Demonstration of the event identification capabilities of the NEXT-White detector
In experiments searching for neutrinoless double-beta decay, the possibility of identifying the two emitted electrons is a powerful tool in rejecting background events and therefore improving the overall sensitivity of the experiment. In this paper we present the first measurement of the efficiency of a cut based on the different event signatures of double and single electron tracks, using the data of the NEXT-White detector, the first detector of the NEXT experiment operating underground. Using a 228Th calibration source to produce signal-like and background-like events with energies near 1.6 MeV, a signal efficiency of 71.6 ± 1.5 stat± 0.3 sys% for a background acceptance of 20.6 ± 0.4 stat± 0.3 sys% is found, in good agreement with Monte Carlo simulations. An extrapolation to the energy region of the neutrinoless double beta decay by means of Monte Carlo simulations is also carried out, and the results obtained show an improvement in background rejection over those obtained at lower energies. [Figure not available: see fulltext.
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Radiogenic backgrounds in the NEXT double beta decay experiment
Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity- induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterráneo de Canfranc with xenon depleted in 136Xe are analyzed to derive a total background rate of (0.84±0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT collaboration. A spectral fit to this model yields the specific contributions of 60Co, 40K, 214Bi and 208Tl to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25±0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5σ after 1 year of data taking. The background measurement in a Qββ±100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75±0.12) events. [Figure not available: see fulltext.]
Francisca Praguer Fróes: medicina, gênero e poder nas trajetórias de uma médica baiana (1872-1931)
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Energy calibration of the NEXT-White detector with 1% resolution near Q ββ of 136Xe
Excellent energy resolution is one of the primary advantages of electroluminescent high-pressure xenon TPCs. These detectors are promising tools in searching for rare physics events, such as neutrinoless double-beta decay (ββ0ν), which require precise energy measurements. Using the NEXT-White detector, developed by the NEXT (Neutrino Experiment with a Xenon TPC) collaboration, we show for the first time that an energy resolution of 1% FWHM can be achieved at 2.6 MeV, establishing the present technology as the one with the best energy resolution of all xenon detectors for ββ0ν searches. [Figure not available: see fulltext.
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