Nicotinamide riboside or IL-17A signaling blockers to prevent liver disorders

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Machine learning discovery of optimal quadrature rules for isogeometric analysis

We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially consider 1D spline spaces of arbitrary degree spanned over uniform and non-uniform knot sequences, and then the generated optimal rules are used for integration over higher-dimensional spaces using tensor products. The quadrature rule search is posed as an optimization problem and solved by a machine learning strategy based on adaptive gradient-descent. However, since the optimization space is highly non-convex, the success of the search strongly depends on the number of quadrature points and the parameter initialization. Thus, we use a dynamic programming strategy that initializes the parameters from the optimal solution over the spline space with a lower number of knots. With this method, we found optimal quadrature rules for spline spaces when using IGA discretizations with up to 50 uniform elements and polynomial degrees up to 8, showing the generality of the approach in this scenario. For non-uniform partitions, the method also finds an optimal rule in a reasonable number of test cases. We also assess the generated optimal rules in two practical case studies, namely, the eigenvalue problem of the Laplace operator and the eigenfrequency analysis of freeform curved beams, where the latter problem shows the applicability of the method to curved geometries. In particular, the proposed method results in savings with respect to traditional Gaussian integration of up to 44% in 1D, 68% in 2D, and 82% in 3D spaces.Euskampus Foundation through the ORLEG-IA project in the Misiones Euskampus 2.0 program. RYC2021-032853-I/MCIN/AEI/10.13039/501100011033 funded by the Spanish Ministry of Science and Innovation and by the European Union NextGenerationEU/PRTR

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.RYC2021-032853-

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase remains a challenge, particularly for biomedical wearable systems. Traditional training algorithms might not be suitable for these applications due to the substantial memory requirements and high computational costs associated with processing the large number of bits involved in neural network operations. In this paper, we introduce a novel learning procedure specifically designed for low-power wearable systems, dubbed Bio-BPfree (deep neural network training without backpropagation for low-power wearable systems). Using a two-class classification task, Bio-BPfree replaces conventional forward and backward backpropagation passes with four forward passes, two for data of the positive class and two for data of the negative class. Each layer is equipped with a unique objective function aimed at minimizing the distance between data points within the same class while maximizing the distance between data points from different classes. Our experimental results, which were obtained by conducting rigorous evaluations on the MIT-BIH dataset that features electrocardiogram (ECG) signals, effectively demonstrate the superior performance and suitability of Bio-BPfree for two-class classification tasks, particularly within the challenging environment of low-power wearable systems designed for continuous health monitoring and assessment.RYC2021-032853-

Terrestrial Megafauna Response to Drone Noise Levels in Ex Situ Areas

Drone use has significantly grown in recent years, and there is a knowledge gap on how the noise produced by these systems may affect animals. We investigated how 18 species of megafauna reacted to drone sound pressure levels at different frequencies. The sound pressure level on the low frequency generated by the drone did not change the studied species’ behavior, except for the Asian elephant. All other studied species showed higher noise sensitivity at medium and high frequencies. The Asian elephant was the most sensitive species to drone noise, mainly at low frequencies. Felines supported the highest sound pressure level before showing behavioral reactions. Our results suggest that drone sound pressure levels in different frequencies cause behavioral changes that differ among species, which is relevant to assessing drone disturbances in ex situ environments. The findings presented here can help to reduce drone impact for target species and serve as an experimental study for future drone use guidelines.M.M.P. contract is funded by the European Union “NextGenerationEU” Programa María Zambrano, Ministerio de Universidades, Spain. Fundación Barcelona Zoo, 310557 Project (Ayuntamiento de Barcelona)

Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109

Inmunohistochemical Profile of Solid Cell Nest of Thyroid Gland

It is widely held that solid cell nests (SCN) of the thyroid are ultimobranchial body remnants. SCNs are composed of main cells and C cells. It has been suggested that main cells might be pluripotent cells contributing to the histogenesis of C cells and follicular cells, as well as to the formation of certain thyroid tumors. The present study sought to analyze the immunohistochemical profile of SCN and to investigate the potential stem cell role of SCN main cells. Tissue sections from ten cases of nodular hyperplasia (non-tumor goiter) with SCNs were retrieved from the files of the Hospital Infanta Luisa (Seville, Spain). Parathormone (PTH), calcitonin (CT), thyroglobulin (TG), thyroid transcription factor (TTF-1), galectin 3 (GAL3), cytokeratin 19 (CK 19), p63, bcl-2, OCT4, and SALL4 expression were evaluated by immunohistochemistry. Patient clinical data were collected, and tissue sections were stained with hematoxylin–eosin for histological examination. Most cells stained negative for PTH, CT, TG, and TTF-1. Some cells staining positive for TTF-1 and CT required discussion. However, bcl-2, p63, GAL3, and CK 19 protein expression was detected in main cells. OCT4 protein expression was detected in only two cases, and SALL4 expression in none. Positive staining for bcl-2 and p63, and negative staining for PTH, CT, and TG in SCN main cells are both consistent with the widely accepted minimalist definition of stem cells, thus supporting the hypothesis that they may play a stem cell role in the thyroid gland, although further research will be required into stem cell markers. Furthermore, p63 and GAL-3 staining provides a much more sensitive means of detecting SCNs than staining for carcinoembryonic antigen, calcitonin, or other markers; this may help to distinguish SCNs from their mimics