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Com as universidades fechadas e a implementação do Ensino Remoto Emergencial, as atividades curriculares ocorreram através de plataformas digitais. O objetivo do presente trabalho foi avaliar a percepção de aprendizagem on-line na disciplina de Biomateriais da Faculdade de Odontologia da Universidade Federal Fluminense no período da pandemia. O questionário COLLES (Constructivist OnLine Learning Environment Survey) foi enviado individualmente por e-mail aos cinquenta alunos, apresentando 24 declarações divididas em seis quesitos: relevância, reflexão crítica, interatividade, apoio dos tutores, apoio entre os colegas e compreensão; e para cada declaração cinco opções de resposta: quase sempre, frequentemente, algumas vezes, raramente e quase nunca. Quarenta e um alunos responderam. A soma das médias obtidas em quase sempre e frequentemente foi de 87,2% para relevância, 70% para reflexão crítica, 33,9% para interatividade, 47,6% para apoio dos tutores, 44,2% para apoio dos colegas e 89,5% para compreensão. Concluiu-se que a relevância, a reflexão crítica e a compreensão apresentaram melhores resultados, enquanto a interatividade, o apoio entre os colegas e o apoio dos tutores demonstraram necessidade de aprimoramento. E apesar das limitações do ERE, a avaliação positiva dos alunos evidenciou esta modalidade de educação on-line como uma solução plausível.

With universities closed and the implementation of Emergency Remote Teaching, curricular activities took place through digital platforms. The objective of this study was to assess the perception of online learning in the Biomaterials course at the Dental School of the Federal Fluminense University during the pandemic. The COLLES questionnaire (Constructivist OnLine Learning Environment Survey) was individually sent via email to fifty students, presenting 24 statements divided into six aspects: relevance, critical reflection, interactivity, tutor support, peer support, and comprehension. For each statement, there were five response options: almost always, often, sometimes, rarely, and almost never. Forty-one students responded. The sum of the averages obtained for almost always and often was 87.2% for relevance, 70% for critical reflection, 33.9% for interactivity, 47.6% for tutor support, 44.2% for peer support, and 89.5% for comprehension. It was concluded that relevance, critical reflection, and comprehension showed better results, while interactivity, peer support, and tutor support demonstrated a need for improvement. Despite the limitations of Emergency Remote Teaching, the positive evaluation from the students highlighted this mode of online education as a plausible solution.

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Introducción. La formación integral de los residentes excede el conocimiento teórico y la técnica operatoria. Frente a la complejidad de la cirugía moderna, su incertidumbre y dinamismo, es necesario redefinir la comprensión de la educación quirúrgica y promover capacidades adaptativas en los futuros cirujanos para manejar efectivamente el entorno. Estos aspectos se refieren a la experticia adaptativa. Métodos. La presente revisión narrativa propone una definición de la educación quirúrgica con énfasis en la experticia adaptativa, y un enfoque para su adopción en la práctica. Resultados. Con base en la literatura disponible, la educación quirúrgica representa un proceso dinámico que se sitúa en la intersección de la complejidad de la cultura quirúrgica, del aprendizaje en el sitio de trabajo y de la calidad en el cuidado de la salud, dirigido a la formación de capacidades cognitivas, manuales y adaptativas en el futuro cirujano, que le permitan proveer cuidado de alto valor en un sistema de trabajo colectivo, mientras se fortalece su identidad profesional. La experticia adaptativa del residente es una capacidad fundamental para maximizar su desempeño frente a estas características de la educación quirúrgica. En la literatura disponible se encuentran seis estrategias para fortalecer esta capacidad. Conclusión. La experticia adaptativa es una capacidad esperada y necesaria en el médico residente de cirugía, para hacer frente a la complejidad de la educación quirúrgica. Existen estrategias prácticas que pueden ayudar a fortalecerla, las cuales deben ser evaluadas en nuevos estudios.

Introduction. The comprehensive training of residents exceeds theoretical knowledge and operative technique. Faced with the complexity of modern surgery, its uncertainty and dynamism, it is necessary to redefine the understanding of surgical education and promote adaptive capabilities in future surgeons for the effective management of the environment. These aspects refer to adaptive expertise. Methods. The present narrative review proposes a definition of surgical education with an emphasis on adaptive expertise, and an approach for its adoption in practice. Results. Based on the available literature, surgical education represents a dynamic process that is situated at the intersection of the complexity of surgical culture, learning in the workplace, and quality in health care, aimed at training of cognitive, manual, and adaptive capacities in the future surgeon, which allow them to provide high-value care in a collective work system, while strengthening their professional identity. Resident's adaptive expertise is a fundamental capacity to maximize his or her performance in the face of these characteristics of surgical education. In the available literature there are six strategies to strengthen this capacity. Conclusion. Adaptive expertise is an expected and necessary capacity in the surgical resident to deal with the complexity of surgical education. There are practical strategies that can help strengthen it, which must be evaluated in new studies.

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Introducción: Las redes académicas adquieren elevada significación para la gestión colaborativa de la interdisciplinariedad en la educación de posgrado, de ahí que se suscite el análisis, para su optimización, en el proceso pedagógico de posgrado. El objetivo del presente estudio consistió en revelar criterios teórico-metodológicos para el empleo de las redes académicas en función de la interdisciplinariedad en la educación de posgrado. Métodos: Investigación cualitativa, comprendida entre noviembre de 2022 a septiembre de 2023. Incluyó el empleo de métodos teóricos como el análisis y síntesis, el histórico y lógico, la sistematización y la modelación. Entre los métodos empíricos se aplicó la revisión de documentos y la consulta a especialistas. También se empleó el análisis porcentual para procesar datos. Resultados: Se revelan criterios teórico-metodológicos que fundamentan el empleo de redes académicas en el posgrado, al considerar elementos asociados a la interdisciplinariedad, el trabajo colaborativo, las relaciones interprofesionales e intersectoriales y las alianzas interinstitucionales, a partir de las exigencias de ese nivel educacional. Este resultado es ampliamente generalizable al diseño y gestión de programas de posgrado en sus dos vertientes: superación profesional y formación académica. Discusión: Investigaciones precedentes evidencian un consenso sobre las potencialidades de las redes académicas para el desarrollo del aprendizaje colaborativo, la gestión de proyectos y la práctica interdisciplinaria. Los resultados de este estudio optimizan su empleo en el proceso pedagógico de posgrado. Los criterios teórico-metodológicos revelados en el presente trabajo, tienen un enfoque holístico con elevada pertinencia, según criterios valorativos de los especialistas que participaron en el estudio.

Introduction: Academic networks acquire high significance for the collaborative management of interdisciplinarity in postgraduate education, hence the analysis arises, for its optimization, in the postgraduate pedagogical process. The objective of this study was to reveal theoretical-methodological criteria for the use of academic networks based on interdisciplinarity in postgraduate education. Methods: Qualitative research, between november 2022 and september 2023. It included the use of theoretical methods such as analysis and synthesis, historical and logical, systematization and modeling. Among the empirical methods, document review and consultation with specialists were applied. Percentage analysis was also used to process data. Results: Theoretical-methodological criteria are revealed that support the use of academic networks in postgraduate studies, when considering elements associated with interdisciplinarity, collaborative work, interprofessional and intersectoral relationships and inter-institutional alliances, based on the demands of that educational level. This result is widely generalizable to the design and management of postgraduate programs in its two aspects: professional development and academic training. Discussion: Previous research shows a consensus on the potential of academic networks for the development of collaborative learning, project management and interdisciplinary practice. The results of this study optimize its use in the postgraduate pedagogical process. The theoretical-methodological criteria revealed in this work have a holistic approach with high relevance, according to the evaluation criteria of the specialists who participated in the study.

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En la Educación Física la lateralidad motriz se debe desarrollar a temprana edad. El objetivo de esta investigación fue analizar de manera integral la lateralidad de estudiantes de décimo año en la clase de Educación Física, a partir de sus necesidades y percepciones, para el diseño de actividades recreativas inclusivas que aborden trastornos de lateralidad. El estudio fue de tipo explicativo y corte transversal con un enfoque mixto y se desarrolló en ocho instituciones educativas particulares de la ciudad de Quito. Se seleccionaron 14 docentes para la entrevista y se evaluaron, con el test validado de Harris, a 688 estudiantes de décimo año, se identificó a 40 de ellos con trastorno de lateralidad, y se les aplicó una encuesta, con lo que se pudo diseñar una propuesta de intervención de actividades recreativas inclusivas que aborden este trastorno. Los datos se analizaron en Excel, luego de haber aplicado una escala de Likert en la encuesta, para comprender las experiencias, percepciones y adaptaciones en profundidad. Esta investigación arroja luz sobre la importancia de considerar la lateralidad en el diseño de actividades recreativas inclusivas, además se evidenció que la adaptación curricular y la personalización son claves para atender las necesidades específicas de estos estudiantes.

Na Educação Física a lateralidade motora deve ser desenvolvida desde cedo. O objetivo desta pesquisa foi analisar de forma abrangente a lateralidade dos alunos do décimo ano das aulas de Educação Física, a partir de suas necessidades e percepções, para o desenho de atividades lúdicas inclusivas que abordem os transtornos de lateralidade. O estudo foi explicativo e transversal com abordagem mista e foi desenvolvido em oito instituições de ensino privadas da cidade de Quito. Foram selecionados 14 professores para a entrevista e 688 alunos do décimo ano foram avaliados com o teste de Harris validado, 40 deles foram identificados com transtorno de lateralidade, e foi aplicado um questionário a eles, para que fosse apresentada uma proposta de intervenção para atividades lúdicas inclusivas que abordassem esse assunto; transtorno. Os dados foram analisados ​​em Excel, após aplicação de escala Likert na pesquisa, para compreender em profundidade as experiências, percepções e adaptações. Esta pesquisa esclarece a importância de considerar a lateralidade na concepção de atividades recreativas inclusivas. Também mostrou que a adaptação curricular e a personalização são fundamentais para atender às necessidades específicas desses alunos.

In Physical Education, motor laterality must be developed at an early age. The objective of this research was to comprehensively analyze the laterality of tenth-year students in Physical Education class, based on their needs and perceptions, for the design of inclusive recreational activities that address laterality disorders. The study was explanatory and cross-sectional with a mixed approach and was developed in eight private educational institutions in the city of Quito. 14 teachers were selected for the interview and 688 tenth-grade students were evaluated with the validated Harris test; 40 of them were identified with laterality disorder, and a survey was applied to them, so that an intervention proposal for inclusive recreational activities that address this disorder. The data was analyzed in Excel, after having applied a Likert scale in the survey, to understand the experiences, perceptions and adaptations in depth. This research sheds light on the importance of considering laterality in the design of inclusive recreational activities. It also showed that curricular adaptation and personalization are key to addressing the specific needs of these students.

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El propósito de esta investigación fue comprender las dificultades y necesidades para el aprendizaje de las ideas principales de la Anatomía Macroscópica Humana AMH. Se investigó un grupo de 90 estudiantes de segundo semestre del programa académico de Medicina y Cirugía de una Universidad pública, quienes se encontraban cursando la asignatura de Anatomía Macroscópica Humana I, para ello se tuvo en cuenta el modelo del conocimiento pedagógico del contenido PCK que incluye el conocimiento de los estudiantes, de su comprensión de la AMH, se realizó una observación participante de las clases teóricas y prácticas durante 16 semanas, llevando a cabo los registros de la observación en diario de campo y se obtuvo material audiovisual. Posteriormente se elaboró un índice analítico, se transcribió la información, todos los documentos fueron analizados por medio del software para análisis ATLAS.ti. Se encontraron aspectos de la enseñanza que dificultan el aprendizaje, como son la metodología de enseñanza, la gran cantidad de contenido abordado en la asignatura, la dificultad en la comprensión de las descripciones y complejidad de la ubicación espacial de las piezas anatómicas, la dificultad para encontrar una metodología de estudio apropiada y la falta de concentración durante las clases. El comprender la complejidad del proceso de aprendizaje puede favorecer la planeación y desarrollo de la enseñanza y la evaluación.

SUMMARY: The purpose of this research was to understand the difficulties and needs for learning the main ideas of Human Macroscopic Anatomy AMH. A group of 90 students from the second semester of the academic program of Medicine and Surgery of a public University were investigated, who were studying the subject of Human Macroscopic Anatomy I, for this the model of pedagogical knowledge of the PCK content that includes the knowledge of the students, their understanding of the AMH, a participant observation of the theoretical and practical classes was carried out for 16 weeks, keeping records of the observation in a field diary, and audiovisual material was obtained. Subsequently, an analytical index was prepared, the information was transcribed, all documents were analyzed using the ATLAS.ti analysis software. Aspects of teaching that hinder learning were found, such as the teaching methodology, the great amount of content addressed in the subject, the difficulty in understanding the descriptions and complexity of the spatial location of the anatomical pieces, the difficulty in finding an appropriate study methodology and the lack of concentration during classes. Understanding the complexity of the learning process can favor the planning and development of teaching and assessment.

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SUMMARY: Since machine learning algorithms give more reliable results, they have been used in the field of health in recent years. The orbital variables give very successful results in classifying sex correctly. This research has focused on sex determination using certain variables obtained from the orbital images of the computerized tomography (CT) by using machine learning algorithms (ML). In this study 12 variables determined on 600 orbital images of 300 individuals (150 men and 150 women) were tested with different ML. Decision tree (DT), K-Nearest Neighbour (KNN), Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), and Naive Bayes (NB) algorithms of ML were used for unsupervised learning. Statistical analyses of the variables were conducted with Minitab® 21.2 (64-bit) program. ACC rate of NB, DT, KNN, and LR algorithms was found as % 83 while the ACC rate of LDA and RFC algorithms was determined as % 85. According to Shap analysis, the variable with the highest degree of effect was found as BOW. The study has determined the sex with high accuracy at the ratios of 0.83 and 0.85 through using the variables of the orbital CT images, and the related morphometric data of the population under question was acquired, emphasizing the racial variation.

Dado que los algoritmos de aprendizaje automático dan resultados más fiables, en los últimos años han sido utilizados en el campo de la salud. Las variables orbitales dan resultados muy exitosos a la hora de clasificar correctamente el sexo. Esta investigación se ha centrado en la determinación del sexo utilizando determinadas variables obtenidas a partir de las imágenes orbitales de la tomografía computarizada (TC) mediante el uso de algoritmos de aprendizaje automático (AA). En este estudio se probaron 12 variables determinadas en 600 imágenes orbitales de 300 individuos (150 hombres y 150 mujeres) con diferentes AA. Se utilizaron algoritmos de AA de árbol de decisión (DT), K-Nearest Neighbour, regresión logística (RL), Random Forest (RF), análisis discriminante lineal (ADL) y Naive Bayes (NB) para el aprendizaje no supervisado. Los análisis estadísticos de las variables se realizaron con el programa Minitab® 21.2 (64 bits). La tasa de ACC de los algoritmos NB, DT, KNN y RL se encontró en % 83, mientras que la tasa de ACC de los algoritmos ADL y RFC se determinó en % 85. Según el análisis de Sharp, la variable con el mayor grado de efecto se encontró como BOW. El estudio determinó el sexo con alta precisión en las proporciones de 0,83 y 0,85 mediante el uso de las variables de las imágenes de TC orbitales, y se adquirieron los datos morfométricos relacionados de la población en cuestión, enfatizando la variación racial.

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SUMMARY: The importance and relevance of e-learning courses in medicine and health sciences has increased significantly in the last decade. Despite this, there are few published teaching experiences of e-learning histology courses in the literature worldwide. The histology course we designed was structured on the Moodle platform as a learning management system, and the content was proposed in a synchronous (zoom) and asynchronous (recordings) format. We also included the use of free virtual microscopy tools. This study aimed to investigate the impact of an e-learning histology course on the satisfaction and performance of medical, nursing and midwifery students. The sample included 424 Chilean medical, nursing, and midwifery students from two cohorts. A Likert-type survey was administered at the end of the course. We performed exploratory analysis and ordinary least squares regression. In this study, we present a positive experience of an e-learning histology course. Exploratory factor analysis revealed three main factors related to "e- learning satisfaction", "in-person class activities", and "course design and teaching quality". We also found that there was a positive and significant relationship between students' perceptions of the adaptation of the traditional (face-to-face) histology course into an e-learning format and their academic performance. Our study shows that e-learning histology courses that integrate lectures and practical sessions can be a valuable teaching method for learning histology. Curriculum developers and teachers need to consider the limitations and advantages of this type of teaching and incorporate these three factors into the design and assessment of e-learning histology courses.

La importancia y relevancia de los cursos e-learning en medicina y ciencias de la salud ha aumentado significativamente en la última década. A pesar de ello, existen pocas experiencias docentes publicadas de cursos de histología e-learning en la literatura a nivel mundial. El curso de histología que diseñamos se estructuró en la plataforma Moodle, y los contenidos se propusieron en formato síncrono (zoom) y asíncrono (grabaciones). También incluimos el uso de herramientas gratuitas de microscopía virtual. Este estudio tuvo como objetivo investigar el impacto de un curso de histología e-learning en la satisfacción y el rendimiento de los estudiantes de medicina, enfermería y obstetricia. La muestra incluyó 424 estudiantes chilenos de medicina, enfermería y obstetricia de dos cohortes. Se aplicó una encuesta tipo Likert al final del curso. Se realizó un análisis exploratorio y una regresión por mínimos cuadrados ordinarios. En este estudio, presentamos una experiencia positiva de un curso de e-learning de histología. El análisis factorial exploratorio reveló tres factores principales relacionados con la "satisfacción sobre el aprendizaje e-learning", "clases presenciales versus clases virtuales" y el "diseño del curso y la calidad de la enseñanza". También encontramos que existía una relación positiva y significativa entre las percepciones de los estudiantes sobre la adaptación del curso de histología tradicional (presencial) a un formato e-learning y su rendimiento académico. Nuestro estudio muestra que los cursos de histología e-learning que integran clases teóricas y sesiones prácticas pueden ser una valiosa herramienta de enseñanza. Los responsables de la elaboración de planes de estudios y los profesores de histología deben tener en cuenta las limitaciones y ventajas de este tipo de enseñanza y sugerimos incorporar estos tres factores al diseño y la evaluación de los cursos de histología en línea.

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Resumo Objetivou-se analisar a percepção de estudantes e egressos sobre a utilização da Aprendizagem Baseada em Problemas (ABP) na formação do enfermeiro. Trata-se de um estudo qualitativo que utiliza a modalidade compreensiva e interpretativa proposta pela Hermenêutica-Dialética. Realizaram-se quatro grupos focais com a participação de 17 estudantes e 16 egressos de uma instituição de ensino superior que aplica a ABP na formação de enfermeiros. A análise dos resultados permitiu a definição de cinco categorias temáticas: dificuldade de adaptação em relação ao método; conquista de autonomia sobre o próprio aprendizado; incentivo ao desenvolvimento do raciocínio clínico; aprimoramento da comunicação e das relações interpessoais e integração entre teoria e prática. Evidencia-se que a utilização da ABP favorece a aproximação com as proposições das diretrizes curriculares para a formação do enfermeiro por meio do desenvolvimento de habilidades e competências como autonomia, comunicação, relações interpessoais e raciocínio clínico mediante práticas integrais e contextualizadas. Entretanto, os estudantes enfrentam dificuldades com as mudanças observadas ao serem inseridos nela ABP, as quais são superadas no decorrer do processo de implementação.

Abstract This study aimed to analyze students' and graduates' perceptions regarding the use of Problem-Based Learning (PBL) in nurse education. This is a qualitative study that employs the comprehensive and interpretative approach proposed by Dialectical Hermeneutics. Four focus groups were conducted with the participation of 17 students and 16 graduates from a higher education institution that implements PBL in nurse education. The analysis of results allowed for the identification of five thematic categories: difficulty in adapting to the method; attainment of autonomy in one's own learning; encouragement of clinical reasoning development; enhancement of communication and interpersonal relationships; and integration between theory and practice. It is evident that the use of PBL promotes alignment with the propositions of curriculum guidelines for nurse education by fostering the development of skills and competencies such as autonomy, communication, interpersonal relationships, and clinical reasoning through comprehensive and contextualized practices. However, students encounter challenges with the changes observed when introduced to PBL, which are overcome during the implementation process.

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Objetivo: O presente trabalho explora a percepção de gestores das áreas de Tecnologia e Inovação de hospitais privados brasileiros acerca do uso da inteligência artificial (IA) na saúde, com foco específico na personalização da experiência do paciente nesses hospitais. Métodos: Este trabalho se caracteriza como uma pesquisa descritiva transversal quantitativa. Foi desenvolvido um questionário com 14 questões que foi distribuído a uma amostra de gestores de tecnologia e inovação em hospitais, com o apoio da Associação Nacional de Hospitais Privados (ANAHP). O questionário foi disponibilizado em versão online à base de 122 hospitais associados à ANAHP. Resultados: Foram obtidas 30 respostas completas (aproximadamente 25% da base total), conquistando percepções sobre as vantagens, desvantagens e desafios éticos e técnicos relacionados ao emprego da IA na área clínica, particularmente em ambientes hospitalares. As respostas coletadas ratificaram o otimismo e a reserva dos profissionais de tecnologia e inovação em hospitais privados quanto ao poder e aos impactos da IA na personalização da experiência do paciente, bem como indicaram a necessidade de treinamento adequado para os funcionários desses hospitais, a fim de maximizar os benefícios da IA como ferramenta de apoio à tomada de decisão. Conclusões: Este trabalho é uma fonte de consulta para instituições de saúde que considerem utilizar a IA na personalização da experiência do paciente e queiram estabelecer treinamentos de pessoal baseados nesses princípios. Desse modo, os resultados aqui obtidos oferecem orientações valiosas para a adoção plena de IA no setor de saúde.

Objective: This study explores the perception of managers in the Technology and Innovation areas of Brazilian private hospitals regarding the use of artificial intelligence (AI) in healthcare, specifically focusing on patient experience personalization in these hospitals. Methods: This study is characterized as a quantitative cross-sectional descriptive research. A questionnaire with 14 questions was developed and distributed to a sample of technology and innovation managers in hospitals, with the support of the National Association of Private Hospitals (NAPH). The questionnaire was made available online to a base of 122 hospitals associated with NAPH. Results: 30 complete responses were obtained (nearly 25% of the total base), capturing perceptions on the advantages, disadvantages, and ethical and technical challenges related to the use of AI in clinical settings, particularly in hospital environments. The collected responses affirmed the optimism and caution of technology and innovation professionals in private hospitals regarding the power and impacts of AI on patient experience personalization, and indicated the need for adequate training for employees in these hospitals to maximize the benefits of AI as a decision support tool. Conclusions: This study serves as a reference for healthcare institutions considering the use of AI in patient experience personalization and aiming to establish personnel training based on these principles. Thus, the results obtained here offer valuable guidance for the full adoption of AI in the healthcare sector.

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Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

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BACKGROUND: Computational assessment of the energetics of protein-ligand complexes is a challenge in the early stages of drug discovery. Previous comparative studies on computational methods to calculate the binding affinity showed that targeted scoring functions outperform universal models. OBJECTIVE: The goal here is to review the application of a simple physics-based model to estimate the binding. The focus is on a mass-spring system developed to predict binding affinity against cyclin-dependent kinase. METHOD: Publications in PubMed were searched to find mass-spring models to predict binding affinity. Crystal structures of cyclin-dependent kinases found in the protein data bank and two web servers to calculate affinity based on the atomic coordinates were employed. RESULTS: One recent study showed how a simple physics-based scoring function (named Taba) could contribute to the analysis of protein-ligand interactions. Taba methodology outperforms robust physics-based models implemented in docking programs such as AutoDock4 and Molegro Virtual Docker. Predictive metrics of 27 scoring functions and energy terms highlight the superior performance of the Taba scoring function for cyclin- dependent kinase. CONCLUSION: The recent progress of machine learning methods and the availability of these techniques through free libraries boosted the development of more accurate models to address protein-ligand interactions. Combining a naïve mass-spring system with machine-learning techniques generated a targeted scoring function with superior predictive performance to estimate pKi.

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As the most abundant pollinator insect in crops, Apis mellifera is a sentinel species of the pollinator communities. In these ecosystems, honey bees of different ages and developmental stages are exposed to diverse agrochemicals. However, most toxicological studies analyse the immediate effects during exposure. Late effects during adulthood after early exposure to pollutants during larval development are poorly studied in bees. The herbicide glyphosate (GLY) is the most applied pesticide worldwide. GLY has been detected in honey and beebread from hives near treated crops. Alterations in growth, morphogenesis or organogenesis during pre-imaginal development could induce late adverse effects after the emergence. Previous studies have demonstrated that GLY alters honey bee development, immediately affecting survival, growth and metabolism, followed by late teratogenic effects. The present study aims to determine the late impact on the behaviour and physiology of adult bees after pre-imaginal exposure to GLY. For that, we reared brood in vitro or in the hive with sub-chronic exposure to the herbicide with the average detected concentration in hives. Then, all newly emerged bees were reared in an incubator until maturity and tested when they became nurse-aged bees. Three behavioural responses were assessed as markers of cognitive and physiological impairment. Our results show i) decreased sensitivity to sucrose regardless of the rearing procedure, ii) increased choice latency and locomotor alterations during chemotaxis and iii) impaired associative learning. These late toxicity signs could indicate adverse effects on task performance and colony efficiency.

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In times of lockdown due to the COVID-19 pandemic, it has been detected that some students are unable to dedicate enough time to their education. They present signs of frustration and even apathy towards dropping out of school. In addition, feelings of fear, anxiety, desperation, and depression are now present because society has not yet been able to adapt to the new way of living. Therefore, this article analyzes the feelings that university students of the Instituto Tecnológico Superior de Misantla present when using long distance education tools during COVID-19 pandemic in Mexico. The results suggest that isolation, because of the pandemic situation, generated high levels of anxiety and depression. Moreover, there are connections between feelings generated by lockdown and school performance while using e-learning platforms. The findings of this research reflect the students' feelings, useful information that could lead to the development and implementation of pedagogical strategies that allow improving the students' academic performance results.

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The U.S. Gulf of Mexico contains a complex network of existing, decommissioned, and abandoned oil and gas pipelines, which are susceptible to a number of stressors in the natural-engineered offshore system including corrosion, environmental hazards, and human error. The age of these structures, coupled with extreme weather events increasing in intensity and occurrence from climate change, have resulted in detrimental environmental and operational impacts such as hydrocarbon release events and pipeline damage. To support the evaluation of pipeline infrastructure integrity for reusability, remediation, and risk prevention, the U.S. Gulf of Mexico Pipeline and Reported Incident Datasets were developed and published. These datasets, in addition to supporting advanced analytics, were constructed to inform regulatory, industry, and research stakeholders. They encompass more than 490 attributes relating to structural information, incident reports, environmental loading statistics, seafloor factors, and potential geohazards, all of which have been spatially, and in some cases temporally matched to more than 89,000 oil and gas pipeline locations. Attributes were acquired or derived from publicly available, credible resources, and were processed using a combination of manual efforts and customized scripts, including big data processing using supercomputing resources. The resulting datasets comprise a spatial geodatabase, tabular files, and metadata. These datasets are publicly available through the Energy Data eXchange®, a curated online data and research library and laboratory developed by the U.S. Department of Energy's National Energy Technology Laboratory. This article describes the contents of the datasets, details the methods involved in processing and curation, and suggests application of the data to inform and mitigate risk associated with offshore pipeline infrastructure in the Gulf of Mexico.

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Load recognition remains not comprehensively explored in Home Energy Management Systems (HEMSs). There are gaps in current approaches to load recognition, such as enhancing appliance identification and increasing the overall performance of the load-recognition system through more robust models. To address this issue, we propose a novel approach based on the Analysis of Variance (ANOVA) F-test combined with SelectKBest and gradient-boosting machines (GBMs) for load recognition. The proposed approach improves the feature selection and consequently aids inter-class separability. Further, we optimized GBM models, such as the histogram-based gradient-boosting machine (HistGBM), light gradient-boosting machine (LightGBM), and XGBoost (extreme gradient boosting), to create a more reliable load-recognition system. Our findings reveal that the ANOVA-GBM approach achieves greater efficiency in training time, even when compared to Principal Component Analysis (PCA) and a higher number of features. ANOVA-XGBoost is approximately 4.31 times faster than PCA-XGBoost, ANOVA-LightGBM is about 5.15 times faster than PCA-LightGBM, and ANOVA-HistGBM is 2.27 times faster than PCA-HistGBM. The general performance results expose the impact on the overall performance of the load-recognition system. Some of the key results show that the ANOVA-LightGBM pair reached 96.42% accuracy, 96.27% F1, and a Kappa index of 0.9404; the ANOVA-HistGBM combination achieved 96.64% accuracy, 96.48% F1, and a Kappa index of 0.9434; and the ANOVA-XGBoost pair attained 96.75% accuracy, 96.64% F1, and a Kappa index of 0.9452; such findings overcome rival methods from the literature. In addition, the accuracy gain of the proposed approach is prominent when compared straight to its competitors. The higher accuracy gains were 13.09, 13.31, and 13.42 percentage points (pp) for the pairs ANOVA-LightGBM, ANOVA-HistGBM, and ANOVA-XGBoost, respectively. These significant improvements highlight the effectiveness and refinement of the proposed approach.

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Objectives: The main purpose of this work was to clinically assess the oculomotricity of one hundred Mexican children with poor reading skills but without any specific learning disorder. Methods: The D.E.M. psychometric test was used. Sex and age analyses of the ratio, type, horizontal and vertical performance, and errors were carried out. Results: Our data suggest that 84% of poor readers exhibit oculomotor difficulties. Sex did not significantly influence the results (p > 0.05), whereas age was associated with the horizontal (p = 0.04) and vertical (p = 0.29) performance, as well as the number of errors (p = 0.001). Omissions were the most prevalent error type. Conclusions: This research gives insights into the role of oculomotricity in children with poor reading skills. Our results suggest that oculomotor performance should be included in the evaluation protocol to assess poor readers to identify any influence of the visual system.

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The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980-1600 cm-1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.

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Type 2 diabetes mellitus (T2DM) is one of the most common metabolic diseases in the world and poses a significant public health challenge. Early detection and management of this metabolic disorder is crucial to prevent complications and improve outcomes. This paper aims to find core differences in male and female markers to detect T2DM by their clinic and anthropometric features, seeking out ranges in potential biomarkers identified to provide useful information as a pre-diagnostic tool whie excluding glucose-related biomarkers using machine learning (ML) models. We used a dataset containing clinical and anthropometric variables from patients diagnosed with T2DM and patients without TD2M as control. We applied feature selection with three different techniques to identify relevant biomarker models: an improved recursive feature elimination (RFE) evaluating each set from all the features to one feature with the Akaike information criterion (AIC) to find optimal outputs; Least Absolute Shrinkage and Selection Operator (LASSO) with glmnet; and Genetic Algorithms (GA) with GALGO and forward selection (FS) applied to GALGO output. We then used these for comparison with the AIC to measure the performance of each technique and collect the optimal set of global features. Then, an implementation and comparison of five different ML models was carried out to identify the most accurate and interpretable one, considering the following models: logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), and nearest centroid (Nearcent). The models were then combined in an ensemble to provide a more robust approximation. The results showed that potential biomarkers such as systolic blood pressure (SBP) and triglycerides are together significantly associated with T2DM. This approach also identified triglycerides, cholesterol, and diastolic blood pressure as biomarkers with differences between male and female actors that have not been previously reported in the literature. The most accurate ML model was selection with RFE and random forest (RF) as the estimator improved with the AIC, which achieved an accuracy of 0.8820. In conclusion, this study demonstrates the potential of ML models in identifying potential biomarkers for early detection of T2DM, excluding glucose-related biomarkers as well as differences between male and female anthropometric and clinic profiles. These findings may help to improve early detection and management of the T2DM by accounting for differences between male and female subjects in terms of anthropometric and clinic profiles, potentially reducing healthcare costs and improving personalized patient attention. Further research is needed to validate these potential biomarkers ranges in other populations and clinical settings.

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BACKGROUND: Identifying mosquito vectors is crucial for controlling diseases. Automated identification studies using the convolutional neural network (CNN) have been conducted for some urban mosquito vectors but not yet for sylvatic mosquito vectors that transmit the yellow fever. We evaluated the ability of the AlexNet CNN to identify four mosquito species: Aedes serratus, Aedes scapularis, Haemagogus leucocelaenus and Sabethes albiprivus and whether there is variation in AlexNet's ability to classify mosquitoes based on pictures of four different body regions. METHODS: The specimens were photographed using a cell phone connected to a stereoscope. Photographs were taken of the full-body, pronotum and lateral view of the thorax, which were pre-processed to train the AlexNet algorithm. The evaluation was based on the confusion matrix, the accuracy (ten pseudo-replicates) and the confidence interval for each experiment. RESULTS: Our study found that the AlexNet can accurately identify mosquito pictures of the genus Aedes, Sabethes and Haemagogus with over 90% accuracy. Furthermore, the algorithm performance did not change according to the body regions submitted. It is worth noting that the state of preservation of the mosquitoes, which were often damaged, may have affected the network's ability to differentiate between these species and thus accuracy rates could have been even higher. CONCLUSIONS: Our results support the idea of applying CNNs for artificial intelligence (AI)-driven identification of mosquito vectors of tropical diseases. This approach can potentially be used in the surveillance of yellow fever vectors by health services and the population as well.

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In recent years, there has been significant growth in the development of Machine Learning (ML) models across various fields, such as image and sound recognition and natural language processing. They need to be trained with a large enough data set, ensuring predictions or results are as accurate as possible. When it comes to models for audio recognition, specifically the detection of car horns, the datasets are generally not built considering the specificities of the different scenarios that may exist in real traffic, being limited to collections of random horns, whose sources are sometimes collected from audio streaming sites. There are benefits associated with a ML model trained on data tailored for horn detection. One notable advantage is the potential implementation of horn detection in smartphones and smartwatches equipped with embedded models to aid hearing-impaired individuals while driving and alert them in potentially hazardous situations, thus promoting social inclusion. Given these considerations, we developed a dataset specifically for car horns. This dataset has 1,080 one-second-long .wav audio files categorized into two classes: horn and not horn. The data collection followed a carefully established protocol designed to encompass different scenarios in a real traffic environment, considering diverse relative positions between the involved vehicles. The protocol defines ten distinct scenarios, incorporating variables within the car receiving the horn, including the presence of internal conversations, music, open or closed windows, engine status (on or off), and whether the car is stationary or in motion. Additionally, there are variations in scenarios associated with the vehicle emitting the horn, such as its relative position-behind, alongside, or in front of the receiving vehicle-and the types of horns used, which may include a short honk, a prolonged one, or a rhythmic pattern of three quick honks. The data collection process started with simultaneous audio recordings on two smartphones positioned inside the receiving vehicle, capturing all scenarios in a single audio file on each device. A 400-meter route was defined in a controlled area, so the audio recordings could be carried out safely. For each established scenario, the route was covered with emissions of different types of horns in distinct positions between the vehicles, and then the route was restarted in the next scenario. After the collection phase, the data preprocessing involved manually cutting each horn sound in multiple one-second windowing profiles, saving them in PCM stereo .wav files with a 16-bit depth and a 44.1 kHz sampling rate. For each horn clipping, a corresponding non-horn clipping in close proximity was performed, ensuring a balanced model. This dataset was designed for utilization in various machine learning algorithms, whether for detecting horns with the binary labels, or classifying different patterns of horns by rearranging labels considering the file nomenclature. In technical validation, classifications were performed using a convolutional neural network trained with spectrograms from the dataset's audio, achieving an average accuracy of 89% across 100 trained models.