BFB conditions on a class of symmetry constrained 3HDM

We study the bounded from below (BFB) conditions on a class of three Higgs doublet models (3HDM) constrained by the symmetry groups U(1)xU(1), U(1)xZ2 and Z2xZ2. These constraints must be implemented on both the neutral (BFB-n) and charged (BFB-c) directions. The exact necessary and sufficient BFB conditions are unknown in the Z2xZ2 case. We develop a general strategy using lower bounds to find sufficient conditions for BFB-n and BFB-c and apply it to these symmetries. In addition, we investigate the concern that the use of safe sufficient conditions can ignore valid points which would yield distinct physical consequences. This is done by performing a full phenomenological simulation of the U(1)xU(1) and U(1)xZ2 models, where exact necessary and sufficient BFB conditions are possible. We look specifically at the points allowed by exact solutions but precluded by safe lower bounds. We found no evidence of remarkable new effects, partly reassuring the use of the lower bounds we propose here, for those potentials where no exact necessary and sufficient BFB conditions are known.Comment: 41 pages, 15 Figures, revtex. arXiv admin note: text overlap with arXiv:2106.1197

Fingerprinting the Type-Z three Higgs doublet models

There has been great interest in a model with three Higgs doublets in which fermions with a particular charge couple to a single and distinct Higgs field. We study the phenomenological differences between the two common incarnations of this so-called Type-Z 3HDM. We point out that the differences between the two models arise from the scalar potential only. Thus we focus on observables that involve the scalar self-couplings. We find it difficult to uncover features that can uniquely set apart the $Z_3$ variant of the model. However, by studying the dependence of the trilinear Higgs couplings on the nonstandard masses, we have been able to isolate some of the exclusive indicators for the $Z_2\times Z_2$ version of the Type-Z 3HDM. This highlights the importance of precision measurements of the trilinear Higgs couplings.Comment: 15 pages, 4 captioned figure

A Pilot Study of a Panel of Ocular Inflammation Biomarkers in Patients with Primary Sjögren’s Syndrome

Ocular diseases have a strong impact on individuals, the effects of which extend from milder visual impairment to blindness. Due to this and to their prevalence, these conditions constitute important health, social and economic challenges. Thus, improvements in their early detection and diagnosis will help dampen the impact of these conditions, both on patients and on healthcare systems alike. In this sense, identifying tear biomarkers could establish better non-invasive approaches to diagnose these diseases and to monitor responses to therapy. With this in mind, we developed a solid phase capture assay, based on antibody microarrays, to quantify S100A6, MMP-9 and CST4 in human tear samples, and we used these arrays to study tear samples from healthy controls and patients with Sjögren’s Syndrome, at times concomitant with rheumatoid arthritis. Our results point out that the detection of S100A6 in tear samples seems to be positively correlated to rheumatoid arthritis, consistent with the systemic nature of this autoinflammatory pathology. Thus, we provide evidence that antibody microarrays may potentially help diagnose certain pathologies, possibly paving the way for significant improvements in the future care of these patients.This research was funded by the Basque Government (BIKAINTEK, grant number 48-AF-W2-2019-00006), by the University of the Basque Country (PIFIND19/02, grant number 201900016247), and by ELKARTEK (KK-2019/00086) and MINECO-Retos (PID2019-111139RB-I00) grants to E.V., as well as by FISS-21-RD21/0002/0041 to A.A

Systematic analysis of the polyphenol metabolome using the Phenol-Explorer database

SCOPE: The Phenol-Explorer web database details 383 polyphenol metabolites identified in human and animal biofluids from 221 publications. Here we exploit these data to characterize and visualize the polyphenol metabolome, the set of all metabolites derived from phenolic food components. METHODS AND RESULTS: Qualitative and quantitative data on 383 polyphenol metabolites as described in 424 human and animal intervention studies were systematically analyzed. Of these metabolites, 301 were identified without prior enzymatic hydrolysis of biofluids, and included glucuronide and sulfate esters, glycosides, aglycones, and O-methyl ethers. Around one third of these compounds are also known as food constituents and corresponded to polyphenols absorbed without further metabolism. Many ring-cleavage metabolites formed by gut microbiota were noted, mostly derived from hydroxycinnamates, flavanols and flavonols. Median maximum plasma concentrations (Cmax ) of all human metabolites were 0.09 μM and 0.32 μM when consumed from foods or dietary supplements respectively. Median time to reach maximum plasma concentration in humans (Tmax ) was 2.18 h. CONCLUSION: These data show the complexity of the polyphenol metabolome and the need to take into account biotransformations to understand in vivo bioactivities and the role of dietary polyphenols in health and disease. This article is protected by copyright. All rights reserved

Phenol-Explorer 2.0: a major update of the Phenol-Explorer database integrating data on polyphenol metabolism and pharmacokinetics in humans and experimental animals

Phenol-Explorer, launched in 2009, is the only comprehensive web-based database on the content in foods of polyphenols, a major class of food bioactives that receive considerable attention due to their role in the prevention of diseases. Polyphenols are rarely absorbed and excreted in their ingested forms, but extensively metabolized in the body, and until now, no database has allowed the recall of identities and concentrations of polyphenol metabolites in biofluids after the consumption of polyphenol-rich sources. Knowledge of these metabolites is essential in the planning of experiments whose aim is to elucidate the effects of polyphenols on health. Release 2.0 is the first major update of the database, allowing the rapid retrieval of data on the biotransformations and pharmacokinetics of dietary polyphenols. Data on 375 polyphenol metabolites identified in urine and plasma were collected from 236 peer-reviewed publications on polyphenol metabolism in humans and experimental animals and added to the database by means of an extended relational design. Pharmacokinetic parameters have been collected and can be retrieved in both tabular and graphical form. The web interface has been enhanced and now allows the filtering of information according to various criteria. Phenol-Explorer 2.0, which will be periodically updated, should prove to be an even more useful and capable resource for polyphenol scientists because bioactivities and health effects of polyphenols are dependent on the nature and concentrations of metabolites reaching the target tissues. The Phenol-Explorer database is publicly available and can be found online at http://www.phenol-explorer.eu. Database URL: http://www.phenol-explorer.eu

BURNOUT SYNDROME: PROFILE OF ESTRESS IN TEACHERS WORKING IN HIGHER EDUCATION INSTITUTIONS OF THE BAIXADA FLUMINENSE, RJ

Objetivo: analisar o perfil do estresse relacionado ao trabalho em professores atuantes na área de Ciências da Saúde. Métodos: estudo observacional, do tipo transversal, tendo como população alvo os professores da área de Ciências da Saúde. A amostra foi constituída por 80 professores que trabalhavam em duas Universidades da Região da Baixada Fluminense, RJ. O modelo foi analisado com base na criação de um banco de dados informatizados. Resultados: os dados analisados revelam que as mulheres casadas obtiveram um elevado grau de comprometimento das dimensões da Síndrome; que quanto maior a jornada semanal de trabalho, maior a frequência das alterações decorrentes da Síndrome de Burnout, e que quanto maior a frequência semanal de atividades físicas, menor a probabilidade de apresentação do fenômeno. Conclusão: o presente estudou possibilitou destacar as reações físicas, psicológicas e sociais relacionadas com a exposição crônica ao estresse laboral contínuo. Descritores: Esgotamento profissional; Saúde do trabalhador; Docentes

Os nacionalismos ibéricos nos estudos sobre o romanceiro tradicional

1 As bases do problema. – 2 Contornos do(s) nacionalismo(s) ibérico(s). – 2.1 O caso português. – 2.2 O caso catalão. – 2.3 O caso galego. – 3 O comparatismo como metodologia. – 4 Que pan-hispanismo? Alguns equívocos e desconhecimentos. – 4.1 Fontes documentais e estudos críticos. – 4.2 Na atividade editorial. – 5 Palavras finaisinfo:eu-repo/semantics/publishedVersio

Bibliografía especializada de traducción sobre interpretación: el Proyecto Hermēneus de publicaciones de traducción e interpretación de la Facultad de Traducción e Interpretación de Soria - Universidad de Valladolid (1999-2011)

Producción CientíficaFrom 1999 up to the present, the Faculty of Translation of Interpreting at the University of Valladolid, Spain, has published three closely related series on Translation and Interpreting, the so-called Proyecto Hermēneus (Hermēneus Project): Hermēneus Journal, the Vertere Monographs, and Disbabelia, a collection of unknown translations. This paper seeks to make some of their features known as well as offer their full bibliographical data.La Facultad de Traducción e Interpretación de Soria, perteneciente a la Universidad de Valladolid, publica desde el año 1999 hasta la fecha tres series relacionadas entre sí dedicadas a la traducción y la interpretación, el llamado Proyecto Hermēneus compuesto por la revista Hermēneus, los monográficos Vertere y las traducciones ignotas Disbabelia. El presente trabajo comentará algunos rasgos de dichas colecciones, y recogerá los datos bibliográficos completos de las mismas

Development of a deep learning-based algorithm to predict pneumonia cases fram chest X-ray images

Dissertação de mestrado em BioinformáticaInterstitial lung diseases (ILD) are defined as a set of more than 200 pulmonary disorders. Among these, the ones broadly termed as pneumonia represent a major cause of morbidity and mortality in the world. The chest radiograph (CXR) was the first x-ray based lung imaging technique to emerge and is still widely used as a diagnostic method for pneumonia and other lung diseases. However, correct interpretation of CXR requires analysis by experts and stays vulnerable to errors and observer-related variation. To counteract these problems, artificial intelligence (Al) methods have been applied for the automated analysis of CXR and other medical images. The deep learning (DL) branch of AI and in the particular the methods based on convolutional neural networks (CNN), recently obtained impressive results in these tasks. This dissertation presents a DL approach to classify pneumonia from medical CXR image datasets. Two different models based on the development of CNN were trained from a preprocessed dataset of CXR images obtained from 8562 individuals classified as normal (n=7214) or with pneumonia (n=1348) (Dataset XP1’). Model 1 applied a normal cross entropy loss function, and model 2 an alternative loss function aiming at counteracting the unbalance in normal/pneumonia class frequency. For performance enhancing both models underwent a hyper optimization procedure. The optimized model 1 and 2 were tested on a test set from PI'. To better understand the predictability and generalization potential we then tested both models on an unrelated test set of 624 images (Dataset XP2). Interestingly, model 1 obtained better performance when tested on XP2 than in XP1', scoring an accuracy of 85%, recall of 93% and precision of 85% for the detection of the pneumonia class. The higher homogeneity present on dataset XP2 compared with dataset XP1' could be a plausible justification. As for model 2, it correctly predicted more pneumonia cases an test set XP1' than model 1. However, on test set XP2 the results were poor, predicting most cases as pneumonia and scoring a recall value of only 26% for the pneumonia class. Testing the DL models on unseen data is a relevant but not always performed validation. Overall, the higher accuracy, recall and precision levels of model 1 in XP2 suggests it has a higher potential to be applied for real-word application although its performance should be further improved and evaluated. This work opened promising new lines of research for the future development of a high-performance CNN-based automated method to classify CXR and assist in the diagnostic of pneumonia.Doenças intersticiais pulmonares são definidas como um conjunto de mais de 200 doenças pulmonares. Dentro deste grupo de doenças, as doenças denominadas como pneumonia ou pneumonite representam uma condição inflamatória que afecta o interstício pulmonar e representam uma das principais causas de morbidade e mortalidade no mundo. A radiografia torácica foi a primeira técnica de imagiologia pulmonar baseada em raios-x a surgir sendo, ainda amplamente utilizada como método de diagnóstico de pneumonia e outras doenças pulmonares. No entanto, a correcta interpretação de radiografias torácicas requer uma análise de pessoal especializado e encontra-se vulnerável a erros e variações relacionadas com o observador. De modo a contrariar estes problemas, métodos de inteligência artificial têm sido aplicados na análise automatizada de radiografias torácicas e outro tipo de imagens médicas. Métodos de "Deep Iearning" e em particular, métodos baseados em redes neuronais convolucionais, obtiveram recentemente resultados impressionantes quando aplicados nesta área de estudo. Esta dissertação apresenta uma abordagem de "deep learning" que permite classificar imagens de pneumonia a partir de "datasets" de radiografias torácicas. Dois modelos diferentes baseados no desenvolvimento de redes neuronais convolucionais foram treinados a partir de um "dataset" preprocessado de radiografias torácicas obtido a partir de 8562 indivíduos classificados como normais (n=7214) ou como doentes de pneumonia (n=1348) ("Dataset" XP1'). Ao modelo 1 foi aplicada uma função "loss" de entropia cruzada normal, e ao modelo 2 foi aplicada urna função "loss" alternativa que visa contrariar o desbalanceamento entre casos normais e de pneumonia presente no "dataset XP1’”. Para melhorar o desempenho, ambos os modelos foram submetidos a um procedimento de hiper optimização. Os modelos 1 e 2 otimizados foram de seguida testados no conjunto de teste do "dataset XP1’. Para entender melhor a capacidade de previsão e generalização, os dois modelos foram também testados num conjunto de teste não relacionado de 624 imagens (Dataset XP2). Curiosamente, o modelo 1 obteve melhor desempenho quando testado no XP2 do que no XP1', obtendo uma "accuracy" de 85%, sensibilidade de 93% e precisão de 85% durante a deteção de casos de pneumonia. A maior homogeneidade de informação presente no XP2 em comparação com o XP1', é vista como a justificação mais plausível. Quanto ao modelo 2, ele previu correctamente mais casos de pneumonia no XP1 do que o modelo 1. No entanto, quando testado no XP2, os resultados ficaram abaixo das expectativas, prevendo a maioria dos casos como pneumonia e obtendo um valor de sensibilidade de apenas 26% para a classe de pneumonia. Testar os modelos de "deep learning" em dados não relacionados é uma técnica de validação relevante, no entanto nem sempre é realizada. Os níveis elevados de precisão, sensibilidade e "accuracy" do modelo 1 quando aplicado no XP2 sugerem que possuí um grande potencial de utilização em aplicações de carácter real, no entanto, o seu desempenho pode ainda ser melhorado. Este trabalho abriu novas e promissoras vias de pesquisa para o desenvolvimento futuro de um método automatizado baseado em CNN de alto desempenho que sela capaz de classificar radiografias torácicas e auxiliar no diagnóstico de pneumonia