Dataset for the identification of a ultra-low frequency multidirectional energy harvester for wind turbines.

This paper presents a publicly available dataset designed to support the identification (characterization) and performance optimization of an ultra-low-frequency multidirectional vibration energy harvester. The dataset includes detailed measurements from experiments performed to fully characterize its dynamic behaviour. The experimental data encompasses both input (acceleration)-output (energy) relationships, as well as internal system dynamics, measured using a synchronized image processing and signal acquisition system.

The Chilean COVID-19 Genomics Network Biorepository: A Resource for Multi-Omics Studies of COVID-19 and Long COVID in a Latin American Population.

Although a lack of diversity in genetic studies is an acknowledged obstacle for personalized medicine and precision public health, Latin American populations remain particularly understudied despite their heterogeneity and mixed ancestry. This gap extends to COVID-19 despite its variability in susceptibility and clinical course, where ethnic background appears to influence disease severity, with non-Europeans facing higher hospitalization rates. In addition, access to high-quality samples and data is a critical issue for personalized and precision medicine, and it has become clear that the solution lies in biobanks.

Anesthetic management of idiopathic intracranial hypertension during pregnancy. A case report.

Idiopathic intracranial hypertension (IIH) is a condition characterized by elevated intracranial pressure (ICP) of unknown etiology, more prevalent in obese women of childbearing age. The management of IIH during pregnancy represents a multidisciplinary challenge, as medical treatment is contentious due to the foetal teratogenic risk, and the technically challenging placement of a ventriculoperitoneal shunt is hindered by the presence of the pregnant uterus. The goal of anaesthetic management during childbirth is to maintain hemodynamic stability, cerebral perfusion pressure, and cerebral tissue oxygenation, while avoiding abrupt fluctuations in intracranial pressure.

Artificial intelligence for geoscience: Progress, challenges, and perspectives.

This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability.

Long-term saturated fat-enriched diets impair hippocampal learning and memory processes in a sex-dependent manner.

Consumption of saturated fat-enriched diets during adolescence has been closely associated with the reduction of hippocampal synaptic plasticity and the impairment of cognitive function. Nevertheless, the effect of long-term intake of these foods has not yet been studied. In the present study, we have investigated the effect of a treatment, lasting for 40 weeks, with a diet enriched in saturated fat (SOLF) on i) spatial learning and memory, ii) hippocampal synaptic transmission and plasticity, and iii) hippocampal gene expression levels in aged male and female mice.

IgA Anti-β2-Glycoprotein I Antibodies as Markers of Thrombosis and Severity in COVID-19 Patients.

Patients with COVID-19 may develop a hypercoagulable state due to tissue and endothelial injury, produced by an unbalanced immune response. Therefore, an increased number of thromboembolic events has been reported in these patients. The aim of this study is to investigate the presence of antiphospholipid antibodies (aPL) in COVID-19 patients, their role in the development of thrombosis and their relationship with the severity of the disease.

Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics.

Unlabelled: Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems.

Corrigendum: Case report: Unveiling a less severe congenital nephrotic syndrome in a Rapa Nui patient with a Maori founder variant.

[This corrects the article DOI: 10.3389/fneph.2024.

Tomato pomace-derived nitrated fatty acids: Synthesis and antiplatelet activity.

This study investigates the antiplatelet properties of tomato pulp to combat cardiovascular diseases. Notably, it examines the formation of nitrated fatty acids (NO-FA) in tomato pomace, renowned for its potential antiplatelet effects. Through diverse assays, including tandem mass spectrometry, microplate-based platelet aggregation, and flow cytometry, the research identifies NO-OA, NO-LA, and NO-LnA as pivotal antiplatelet compounds.

Comprehensive Characterisation of a Low-Frequency-Vibration Energy Harvester.

In this paper, we describe a measurement procedure to fully characterise a novel vibration energy harvester operating in the ultra-low-frequency range. The procedure, which is more thorough than those usually found in the literature, comprises three main stages: modelling, experimental characterisation and parameter identification. Modelling is accomplished in two alternative ways, a physical model (white box) and a mixed one (black box), which model the magnetic interaction via Fourier series.

Learning Disentangled Priors for Hyperspectral Anomaly Detection: A Coupling Model-Driven and Data-Driven Paradigm.

Accurately distinguishing between background and anomalous objects within hyperspectral images poses a significant challenge. The primary obstacle lies in the inadequate modeling of prior knowledge, leading to a performance bottleneck in hyperspectral anomaly detection (HAD). In response to this challenge, we put forth a groundbreaking coupling paradigm that combines model-driven low-rank representation (LRR) methods with data-driven deep learning techniques by learning disentangled priors (LDP).

Case report: Unveiling a less severe congenital nephrotic syndrome in a Rapa Nui patient with a Maori founder variant.

Background: Congenital nephrotic syndrome (CNS) is a severe kidney disorder characterized by edema, massive proteinuria, and hypoalbuminemia that manifests or within three months after birth. CNS affects 1-3 per 100,000 children, primarily associated with genetic variants and occasionally with infections. Genetic analysis is the first-line method for diagnosis.

Integrated Health and Social Home Care Services in Catalonia: Professionals' Perception of its Implementation, Barriers, and Facilitators.

Introduction: This study aimed to assess the implementation of integrated social and health home care services (HCS) offered by the Government of Catalonia, and to identify the main barriers and facilitators of integrated HCS.

Methods: Analysis of the degree of implementation of integrated social and health HCS perceived by social care services (SCS) and primary health care centers (PHCs) between December 2020 and June 2021 in two phases. First, the perception of integration by social workers within SCS and PHCs was assessed using a screening questionnaire.

A Past Genetic Bottleneck from Argentine Beans and a Selective Sweep Led to the Race Chile of the Common Bean ( L.).

The domestication process of the common bean gave rise to six different races which come from the two ancestral genetic pools, the Mesoamerican (Durango, Jalisco, and Mesoamerica races) and the Andean (New Granada, Peru, and Chile races). In this study, a collection of 281 common bean landraces from Chile was analyzed using a 12K-SNP microarray. Additionally, 401 accessions representing the rest of the five common bean races were analyzed.

Nutritional Characterization of Chilean Landraces of Common Bean.

Common bean ( L.) is the primary grain legume cultivated worldwide for direct human consumption due to the high nutritional value of its seeds and pods. The high protein content of common beans highlights it as the most promising source of plant-based protein for the food industry.

SpectralGPT: Spectral Remote Sensing Foundation Model.

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT).

Excess mortality among older adults institutionalized in long-term care facilities during the COVID-19 pandemic: a population-based analysis in Catalonia.

Objectives: To assess excess mortality among older adults institutionalized in nursing homes within the successive waves of the COVID-19 pandemic in Catalonia (north-east Spain).

Design: Observational, retrospective analysis of population-based central healthcare registries.

Setting And Participants: Individuals aged >65 years admitted in any nursing home in Catalonia between January 1, 2015, and April 1, 2022.

Graph Evolution-Based Vertex Extraction for Hyperspectral Anomaly Detection.

Anomaly detection is a fundamental task in hyperspectral image (HSI) processing. However, most existing methods rely on pixel feature vectors and overlook the relational structure information between pixels, limiting the detection performance. In this article, we propose a novel approach to hyperspectral anomaly detection that characterizes the HSI data using a vertex-and edge-weighted graph with the pixels as vertices.

COVID-19 mortality and risk factors in patients with cancer in Ecuador: A retrospective, non-randomized, controlled study.

The 2019 coronavirus disease (COVID-19) pandemic has affected different human populations since March 2020 and challenged healthcare systems, especially in chronic non-communicable diseases such as cancer. The present study aimed to evaluate the mortality, risk factors, and symptoms of cancer patients and control subjects, diagnosed with COVID-19 and admitted to intensive care unit (ICU). This retrospective, observational, non-randomized, controlled study of patients admitted to ICU was conducted between March and August 2020 in an Ecuadorian oncology center.

Peripheral modulation of antidepressant targets MAO-B and GABAAR by harmol induces mitohormesis and delays aging in preclinical models.

Reversible and sub-lethal stresses to the mitochondria elicit a program of compensatory responses that ultimately improve mitochondrial function, a conserved anti-aging mechanism termed mitohormesis. Here, we show that harmol, a member of the beta-carbolines family with anti-depressant properties, improves mitochondrial function and metabolic parameters, and extends healthspan. Treatment with harmol induces a transient mitochondrial depolarization, a strong mitophagy response, and the AMPK compensatory pathway both in cultured C2C12 myotubes and in male mouse liver, brown adipose tissue and muscle, even though harmol crosses poorly the blood-brain barrier.

Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis.

Most existing techniques consider hyperspectral anomaly detection (HAD) as background modeling and anomaly search problems in the spatial domain. In this article, we model the background in the frequency domain and treat anomaly detection as a frequency-domain analysis problem. We illustrate that spikes in the amplitude spectrum correspond to the background, and a Gaussian low-pass filter performing on the amplitude spectrum is equivalent to an anomaly detector.