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Abstract Background Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual’s sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning. Materials and methods Maxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans. The following machine learning algorithms were used to build the predictive models: Logistic Regression, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron Classifier (MLP), Decision Tree, and Random Forest Classifier. A 10-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and Receiver Operating Characteristic (ROC) curves were constructed. Results Univariate analysis showed statistical significance (p

Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.

Localization plays a significant role in the production of ultrasound localization microscopy images. For instance, detecting more microbubbles reduces the time of acquisition, while localizing them more accurately improves the resolution of the images. Previous approaches to compare the multiple localization algorithms rely on numerical simulation of a single steady microbubble, with or without modeling its nonlinear response. In real-life situations, vessels have a nonconstant velocity profile, which creates relative movement, producing dynamically overlapped microbubbles even at low concentrations. These complexities deteriorate the behavior of the localization algorithms. To incorporate these effects on the characterization of the localization methods, we designed a virtual medium containing four microtubes of different inner diameters, where single-pixel microbubbles were allowed to flow within each microtube with a parabolic velocity profile. A finite difference method was used to simulate the propagation of ultrasound waves to obtain B-mode images that fed four direct microbubbles localization algorithms (i.e., weighted centroid, 2D-spline interpolation, parabolic fitting, and onset detection). The performance of these methods was quantified using the number of microbubbles detected, the microbubbles distribution, the full width at half maximum, the maximum velocity, and the computational time as metrics. Our simulation results suggest that 2D-spline and paraboloid fitting were the best methods, detecting 100% of the microbubbles with an error in their distribution of 249 and 244 microbubbles, respectively. Both methods with a computational time cost of 18% and 7% lower than weighted centroid, respectively. We also present an experimental comparison of these localization methods, finding results similar to the numerical ones.

Pivotal barrier properties of the hydrophobic plant cuticle covering aerial plant surfaces depend on its physicochemical composition. Among plant species and organs, compounds of this boundary layer between the plant interior and the environment vary considerably but cuticle-related studies comparing different organs from the same plant species are still scarce. Thus, this study focused on the cuticle profiles of Physalis peruviana, Physalis ixocarpa, Alkekengi officinarum, and Nicandra physalodes species. Inflated fruiting calyces enveloping fruits make Physalis, Alkekengi, and Nicandra highly recognizable genera among the Solanoideae subfamily. Although the inflation of fruiting calyces is well discussed in the literature still little is known about their post-floral functionalities. Cuticular composition, surface structure, and barrier function were examined and compared in fully expanded amphistomatous leaves, ripe astomatous fruits, and fully inflated hypostomatous fruiting calyces. Species- and organ-specific abundances of non-glandular and glandular trichomes revealed high structural diversity, covering not only abaxial and adaxial leaf surfaces but also fruiting calyx surfaces, whereas fruits were glabrous. Cuticular waxes, which limit non-stomatal transpiration, ranged from

The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.

The search for a universal explanation of the altitudinal limit determined by the alpine treeline has given rise to different hypotheses. In this study, we revisited Michaelis’ hypothesis which proposed that an inadequate “ripening” of the cuticle caused a greater transpiration rate during winter in the treeline. However, few studies with different explanations have investigated the role of passive mechanisms of needles for protecting against water loss during winter in conifers at the treeline. To shed light on this, the cuticular transpiration barrier was studied in the transition from subalpine Pinus uncinata forests to alpine tundra at the upper limit of the species in the Pyrenees. This upper limit of P. uncinata was selected here as an example of the ecotones formed by conifers in the temperate mountains of the northern hemisphere. Our study showed that minimum leaf conductance in needles from upper limit specimens was higher than those measured in specimens living in the lower levels of the sub-alpine forest and also displayed lower cuticle thickness values, which should reinforce the seminal hypothesis by Michaelis. Our study showed clear evidence that supports the inadequate development of needle cuticles as one of the factors that lead to increased transpirational water losses during winter and, consequently, a higher risk of suffering frost drought.

Introduction and objectives: Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of liver abnormalities including steatosis, steatohepatitis, fibrosis, and cirrhosis. Liver biopsy remains the gold standard method to determine the disease stage in NAFLD but is an invasive and risky procedure. Studies have previously reported that changes in intrahepatic fatty acids (FA) composition are related to the progression of NAFLD, mainly in its early stages. The aim of this study was to characterize the liver FA composition in mice fed a Choline-deficient L-amino-defined (CDAA) diet at different stages of NAFLD using magnetic resonance spectroscopy (MRS). Methods: We used in-vivo MRS to perform a longitudinal characterization of hepatic FA changes in NAFLD mice for 10 weeks. We validated our findings with ex-vivo MRS, gas chromatography-mass spectrometry and histology. Results: In-vivo and ex-vivo results showed that livers from CDAA-fed mice exhibit a significant increase in liver FA content as well as a change in FA composition compared with control mice. After 4 weeks of CDAA diet, a decrease in polyunsaturated and an increase in monounsaturated FA were observed. These changes were associated with the appearance of early stages of steatohepatitis, confirmed by histology (NAFLD Activity Score (NAS) = 4.5). After 10 weeks of CDAA-diet, the liver FA composition remained stable while the NAS increased further to 6 showing a combination of early and late stages of steatohepatitis. Conclusion: Our results suggest that monitoring lipid composition in addition to total water/fat with MRS may yield additional insights that can be translated for non-invasive stratification of high-risk NAFLD patients.

Este estudo trata se de uma revisão sistemática e tem como objetivo analisar como a pesquisa sobre a indústria 4.0 está contribuindo para um maior entendimento dos efeitos da implantação da indústria 4.0 na sustentabilidade. Este estudo foi feito mapeando a pesquisa já existente, utilizando critérios de seleção, elegibilidade e inclusão como eixo norteador da técnica da metodologia PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). A seleção dos artigos nas bases de dados consultadas foi baseada no cruzamento dos termos “Industry 4.0” e “Sustainability”. A pesquisa traz como resultados os principais desafios para a implantação das tecnologias referentes à indústria 4.0, entre eles a falta de infraestrutura tecnológica e de recursos para investimento e quais os possíveis benefícios para a sustentabilidade que essa implantação pode trazer, como a redução no consumo de recursos em todas as fases da produção, bem como nos impactos ambientais gerados. Através deste estudo foi possível concluir que apesar de apresentar fatores que influenciam positivamente na sustentabilidade das empresas, a implantação das novas tecnologias para a adoção do conceito de indústria 4.0, ainda está cercada de riscos e desafios e existe a necessidade de novos estudos que explorem e consolidem os resultados referentes à quarta revolução industrial.

Motherhood is socially seen through an idealized perception in which mothers should unconditionally act in a protective way with their children, being comprehended as insane or evil when they transgress this gender role. This article aims to analyze women that has transgressed that social rule and were claimed as being violent (physically or psychologically) or neglectful. Three interviews were made with women that have been followed up by Children´s Rights System and a content analyses was done. Four main categories were noticeable: “Motherhood”, “Family of Origin”, “Loving Relationship” and “Socioeconomic condition”. It was remarkable that those women were overburden due the absence of the State, the family and the children´s fathers, that are not considered when they abandon or when they do not accomplish parenthood, what can contribute to a violent familiar dynamic against children, aside from a notorious process of psychiatric approach and judicialization against these women.

Abstract Covid-19 is a novel infectious disease whose spectrum of presentation ranges from absence of symptoms to widespread interstitial pneumonia associated with severe acute respiratory syndrome (SARS), leading to significant mortality. Given the systemic pattern of Covid-19, there are many factors that can influence patient's functional capacity after acute infection and the identification of such factors can contribute to the development of specific rehabilitation strategies. Pulmonary impairment is the primary cause of hospitalization due to Covid-19, and can progress to SARS as well as increase length of hospitalization. Moreover, cardiac involvement is observed in approximately 30% of hospitalized patients, with an increased risk of acute myocarditis, myocardial injury, and heart failure, which may compromise functional capacity in the long-term. Thromboembolic complications have also been reported in some patients with Covid-19 and are associated with a poor prognosis. Musculoskeletal complications may result from long periods of hospitalization and immobility, and can include fatigue, muscle weakness and polyneuropathy. Studies that address the functional capacity of patients after Covid-19 infection are still scarce. However, based on knowledge from the multiple systemic complications associated with Covid-19, it is reasonable to suggest that most patients, especially those who underwent prolonged hospitalization, will need a multiprofessional rehabilitation program. Further studies are needed to evaluate the functional impact and the rehabilitation strategies for patients affected by Covid-19.

Introdução e Objetivo: Apresentar a origem dos gladiadores, seus sistemas de treinamento e vida cotidiana. Procedimentos metodológicos: Realizou-se revisão narrativa, a partir de recuperação de fatos históricos contidos em fontes iconográficas, eletrônicas e bibliográficas. Discussão: Acredita-se que a origem dos jogos de gladiadores seja etrusca, inicialmente realizados como parte de ritos funerais. Após inserido na famiglia gladiatoria, os gladiadores seguiam as leis das escolas de formação (ludus). Gladiadores que haviam combatido durante muito tempo, e sobreviveram a todos os combates, recebiam uma espada de madeira como símbolo de sua aposentadoria, denominada rudis. Conclusão: Por um período de quase 600 anos, a arena foi um dos entretenimentos mais populares do mundo romano. Combates de gladiadores eram um elemento do paganismo que governou Roma até a conversão do imperador Constantino ao cristianismo no século IV, e ocupam um lugar central na percepção popular moderna de comportamento romano.

A recent effort by literary critics to highlight 19th century women playwrights’ work has brought fourth plays by the Portuguese Guiomar Torresão (1844-1898) and Brazilian Júlia Lopes de Almeida (1862-1934), several of which had never been published before. This work analyzes A herança (1908) and Vai raiar o sol (2016) by Lopes de Almeida and O fraco da baronesa (2005) by Torresão in order to understand how ambiguity is used to persuade readers/spectators to support women’s cause for equality. In the case of Lopes de Almeida, who had been neglected by 20th century critics for her lack of support for equality, we find strong female protagonists that are not afraid to challenge their roles in order to find happiness. Torresão’s comedy, on the other hand, brings forward the hypocrisy of upper-class liberals that claimed to support women’s right for equality. All three plays were clearly written with the reader — men — in mind since women counted on their support to entice change.

Background: Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual's sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning. Materials and methods: Maxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans. The following machine learning algorithms were used to build the predictive models: Logistic Regression, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron Classifier (MLP), Decision Tree, and Random Forest Classifier. A 10-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and Receiver Operating Characteristic (ROC) curves were constructed. Results: Univariate analysis showed statistical significance (p < 0.10) for all skeletal and dental variables. Nostril width showed greater importance in two models, while Inter-molar width stood out among dental measurements. The models achieved accuracy values ranging from 0.75 to 0.85 on the test data. Logistic Regression, Random Forest, Decision Tree, and SVM models had the highest AUC values, with SVM showing the smallest disparity between cross-validation and test data for accuracy metrics. Conclusion: Transverse dental arch and maxillary skeletal base measurements exhibited strong predictive capability, achieving high accuracy with machine learning methods. Among the evaluated models, the SVM algorithm exhibited the best performance. This indicates potential usefulness in forensic sex determination. [ABSTRACT FROM AUTHOR]

Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliablymeasuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the prevalence of signs and symptoms related to temporomandibular disorders (TMD) and orofacial pain in patients with indication for orthognathic surgery. Methods: The search was carried out in seven electronic databases and gray literature. Studies that evaluated the frequency of signs and symptoms related to TMD and orofacial pain were included. The risk of bias was assessed using the Joanna Briggs Critical Appraisal tool. A meta-analysis of proportions with a random effect model was performed and the GRADE tool judged the certainty of evidence. Results: After searching the databases, 1859 references were retrieved, 18 of which were selected for synthesis. The prevalence of individuals with at least one TMD symptom was 51% [CI95% = 44–58%], and 44% of the subjects had temporomandibular joint click/crepitus [CI95% = 37–52%]. Additionally, 28% exhibited symptoms related to muscle disorders [CI95% = 22–35%], 34% had disc displacement with or without reduction [CI95% = 25–44%], and 24% had inflammatory joint disorders [CI95% = 13–36%]. The prevalence of headache was 26% [CI95% = 8–51%]. The certainty of evidence was considered very low. Conclusion: Approximately 1 in 2 patients with dentofacial deformity presents some sign and symptom related to TMD. Myofascial pain and headache may be present in approximately a quarter of patients with dentofacial deformity. Clinical relevance: A multidisciplinary treatment is necessary for these patients, involving a professional with expertise in the management of TMD. [ABSTRACT FROM AUTHOR]