Abstract The growing use of contemporary models for predicting carbonation fronts, such as artificial neural networks, requires greater rigor in terms of database completeness. Treating carbonation depth databases as time series is a favorable alternative for quality assurance. Therefore, the aim of this article is to identify the best technique for imputing missing data in time series of carbonation depths of concrete with different compositions. The database used was information collected from concretes subjected to natural carbonation over 20 years of exposure belonging to the GEDur/UFG. Ten imputation techniques were tried, such as foward fill, moving average, interpolation and Kalman filter. All the techniques and analyses were implemented using the Python programming language in an integrated development environment. Based on the performance metrics and visual analysis, it was found that monotonic cubic spline interpolation captured the pattern of the carbonation depth curve as a function of time with greater precision and accuracy, achieving a performance index of 0.998 and RMSE between 0.106 mm and 0.863 mm depending on the concrete sample. fronts networks completeness assurance Therefore compositions 2 GEDurUFG GEDur UFG GEDur/UFG tried fill average filter environment analysis accuracy 0998 0 998 0.99 0106 106 0.10 0863 863 0.86 sample 099 99 0.9 010 10 0.1 086 86 0.8 09 9 0. 01 1 08 8

Resumo O crescente uso de modelos contemporâneos de previsão de frentes de carbonatação, como redes neurais artificiais, exige maior rigor sobre a completude dos bancos de dados. Tratar os bancos de dados de profundidades de carbonatação como séries temporais é uma alternativa favorável à garantia da qualidade. Dessa forma, este artigo tem o objetivo de identificar a melhor técnica de imputação de dados ausentes em séries temporais de profundidades de carbonatação de concretos com diferentes composições. Como banco de dados foram utilizadas as informações coletadas de concretos submetidos à carbonatação natural ao longo de 20 anos de exposição pertencentes ao grupo GEDur/UFG. Foram experimentadas dez técnicas de imputação entre as quais destacam-se, foward fill, média móvel, interpolação e filtro de Kalman. Todas as técnicas e análises foram implementadas utilizando linguagem de programação em python dentro de um ambiente de desenvolvimento integrado. Com base nas métricas de desempenho e análise visual, constatou-se que a interpolação spline monotônica cúbica capturou o padrão da curva de profundidade de carbonatação em função do tempo com maior precisão e acurácia, alcançando índice de desempenho de 0,998 e RMSE entre 0,106mm e 0,863mm dependendo da amostra de concreto. artificiais qualidade forma composições 2 GEDurUFG GEDur UFG GEDur/UFG destacamse, destacamse destacam se, se destacam-se fill móvel Kalman integrado visual constatouse constatou acurácia 0998 0 998 0,99 0106mm mm 106mm 0863mm 863mm concreto 099 99 0,9 09 9 0,

Abstract: The purpose of this article is to achieve optimal coordination for timed overcurrent relays modeled in a test system. The traditional adjustment that is usually used in the field is taken as the base criterion to determine the improvements achieved with the implemented optimization methods. Optimal parameters are determined for two timed overcurrent relays modeled on the IEEE 13-node test system using PowerFactory software. The optimization algorithms are coded in Python. PowerFactory links with Python to generate interoperability between the algorithm and the electrical system. Satisfactory results are achieved from the COA and GSA algorithms with a reduction of more than 50% in relay operating times.

Resumen: El objeto de este artículo es conseguir la coordinación óptima para los relés de sobrecorriente temporizados, modelados en un sistema de prueba. El ajuste tradicional que se emplea usualmente en el campo se toma como criterio base para determinar las mejoras conseguidas con los métodos de optimización implementados. Se determinan parámetros óptimos para dos relés de sobrecorriente temporizados modelados en el sistema de prueba de 13 nodos de la IEEE mediante el software PowerFactory. Los algoritmos de optimización son codificados en Python. Gracias a la interfaz que presenta PowerFactoy con Python, cada algoritmo se ejecuta dentro del sistema eléctrico. Se consiguen resultados satisfactorios de los algoritmos COA y GSA con una reducción de más del 50 % en los tiempos de operación de los relés.

This research presents the implementation and evaluation of machine learning models to predict solar irradiance (W/m²). The objective is to provide valuable insights for making informed decisions regarding solar system design and financing. A thorough exploratory data analysis was conducted on the Southern African Universities Radiometric Network (SAURAN) data collected at the University of Pretoria's station to gain insights into the patterns of solar irradiance over the past 10 years. Python's functions and libraries are utilized extensively for conducting exploratory data analysis, model implementation, model testing, forecasting, and data visualization. Random Forest (RF), k-Nearest Neighbors (KNN), Feedforward Neural Network (FFNN), Support Vector Regression (SVR), and extreme Gradient Boosting models (XGBoost) are implemented and evaluated. The KNN model was found to be superior achieving a relative Root Mean Squared Error (RMSE), relative Mean Absolute Error (MAE), and R-Squared (R²) of 5.77%, 4.51% and 0.89 respectively on testing data. The variable importance analysis revealed that temperature (°C) exerted the greatest influence on predicting solar irradiance, accounting for 44% of the predictive power. The KNN model is suitable to inform solar systems design and financing decisions. Directions for future studies are identified and suggestions for areas of exploration are provided to contribute to the advancement of solar irradiance predictions.

This study aims to develop a semantic web application that maps geographic locations in Machado de Assis's works, storing them in a triplestore. By integrating data from machadodeassis.net encyclopedia with geographic coordinates from Geonames.org and GoogleMaps, the project offers an interactive map-based reading experience, supporting the spatial references made by the writer in the 19th century. Using the Python library BeautifulSoup, the application extracts citations, structures them according to schema.org parameters, and submits them to gpt3.5-instruct and gpt4-turbo models to identify current names and classifications of these locations as per Geonames.org ontology. Finally, SPARQL queries are made to dados.literaturabrasileira.ufsc.br to obtain unique identifiers for each book, integrating maps, citations, and full texts in line with Linked Data standards.

Este estudo tem o objetivo de desenvolver uma aplicação web semântica que mapeia localidades geográficas nas obras de Machado de Assis, armazenando-as em uma triplestore. A partir da integração dos dados disponibilizados pela enciclopédia machadodeassis.net com as coordenadas geográficas de Geonames.org e GoogleMaps, o projeto visa oferecer uma experiência de leitura através de mapas interativos , que servirão de suporte para as menções aos espaços realizadas pelo escritor ao longo do século XIX. Para a extração das citações, a aplicação utiliza a biblioteca python BeautifulSoup que realiza consultas, requisições e coleta os dados da enciclopédia estruturando-os de acordo com os parâmetros do schema.org. As citações coletadas serão submetidas aos modelos gpt3.5-instruct e gpt4-turbo com o intuito de obter os nomes atuais das localidades, bem como a devida classificação destes espaços de acordo com a ontologia Geonames.org. Ao final, são realizadas consultas SPARQL ao portal dados.literaturabrasileira.ufsc.br com o objetivo de obter identificadores únicos para cada livro, oferecendo uma integração entre mapas, citações e textos completos, em consonância com os padrões Linked Data

South Sudan is expansive and sparsely populated with over 80% of the population living in rural areas. The country has no national grid connecting its cities and towns, thus making rural areas "good candidates" for stand-alone renewable energy systems. This study was conducted to determine the technical feasibility and economic viability of a stand-alone photovoltaic (PV) system compared to a diesel generator. A techno-economic model was developed to forecast the performance of the PV system. The system was initially designed using the IEEE Recommended Practice for Sizing of Stand-Alone Photovoltaic Systems (IEEE P1562-2021) and the IEEE Recommended Practice for Sizing Lead-Acid Batteries for Stand-Alone Photovoltaic Systems (IEEE 1013-2019). The solar radiation data used for modeling were acquired from the Ineichen clear sky model and then transposed to the plane of array irradiation using pvlib python. The system optimization and sensitivity analysis was performed under various diesel fuel costs using the Hybrid Optimization of Multiple Energy Resources (HOMER) software. Results show that at a fuel price of $ 2 per liter, the levelized cost of electricity (LCOE) of the PV system is 64% lower than that of the diesel generator and that the system can earn 11% return on investment (ROI) and recover the investment in about 5.5 years. With a drop in price of diesel fuel to $1 per liter, the payback period increases to about 7 years. These results show that standalone PV systems are technically feasible and economically viable in rural and peri-urban areas of South Sudan.

Abstract This paper investigates trends in antiretroviral medication prices and their impact on public health in Mexico during 2019. Using three machine learning models developed in Python (logistic regression, random forest, and K-Nearest Neighbors or KNN), this study discerns increasing or decreasing patterns in antiretroviral (ARV) drug price fluctuations using a dataset comprising 15,220 observations of ARV drugs acquired between 2016 and 2019. Results indicate that random forests exhibited the highest precision in predicting price changes, followed by KNN and logistic regression. Significant factors affecting acquisition prices, such as drug type and duration of procurement strategy, were identified. In addition to analyzing price trends, the paper explores the budgetary considerations associated with these fluctuations, providing insights into the financial implications for healthcare systems and stakeholders. It is important to note that this paper focuses on a specific ARV pharmaceutical purchasing scheme. Moreover, the study emphasizes the creation of a unified and detailed medication price database, highlighting the significant effort invested in compiling complete and comprehensive information from various sources. This study’s findings underscore the effectiveness of initiatives such as consolidated purchasing approaches and the integration of newer, cost-effective medications into treatment protocols. These initiatives have led to significant cost savings in antiretroviral medication procurement, contributing to improved access for individuals living with HIV/AIDS. Overall, the research highlights the importance of data-driven approaches and strategic planning in optimizing pharmaceutical purchasing processes and ensuring sustainable access to essential medications for public health interventions.

Resumen Este trabajo examina las tendencias en los precios de medicamentos antirretrovirales y su impac to en la salud pública, centrándose en el contexto mexicano en 2019. Por medio de técnicas de aprendizaje automático, el estudio analiza las fluctuaciones en los precios de los medicamentos antirretrovirales, con base en un conjunto de datos que comprende 15,220 observaciones de medicamentos antirretrovirales (ARV) adquiridos entre 2016 y 2019, con énfasis particular en el año 2019. Los resultados indican que los “bosques aleatorios” demostraron la mayor precisión en la predicción de cambios de precios, seguidos por K-Nearest Neighbors o KNN (algoritmo de k vecinos más próximos) y la regresión logística. Se identificaron factores significativos que afectan los precios de adquisición, como el tipo de medicamento y la duración de la estrategia de adqui sición. Además, el estudio profundiza en las consideraciones presupuestarias, evaluando las im plicaciones financieras de estas fluctuaciones de precios. Estos hallazgos destacan la efectividad de iniciativas como los enfoques de adquisición consolidada y la integración de medicamentos más nuevos y rentables en los protocolos de tratamiento, lo que conduce a ahorros significativos y mejor acceso para las personas que viven con VIH/sida. Es importante destacar que este artículo se centra en analizar un esquema específico de adquisición de medicamentos antirretrovirales. Los estudios futuros ampliarán este análisis para abarcar un espectro más amplio de esquemas de adquisición de medicamentos, proporcionando así una comprensión más completa de la di námica de fijación de precios y sus implicaciones para la salud pública. Además, la investigación en curso perfeccionará la metodología e integrará fuentes de datos adicionales para mejorar la precisión y profundidad del análisis.

BACKGROUND. The COVID-19 pandemic highlighted the weakness of relying on in-person tuition in higher education. Massive open online courses (MOOCs) have been a successful addition to higher education. In this study, educators had to replace a planned elective in the medical curriculum with an online option during the pandemic. The roles of the competency framework of the Health Professions Council of South Africa (HPCSA) (Leader and Manager, Health Advocate, Professional, Communicator, Collaborator, Scholar, and Healthcare Practitioner) were used to guide its development. This elective emphasised the non-clinical roles of medical practitioners and was offered in 2020 and 2021. OBJECTIVES. To describe the choices of third-year medical students and time spent participating in a modified online elective in 2020 and 2021. METHODS. A descriptive cross-sectional study design was used, involving the participation of 629 medical students. Data were collected and analysed from three primary sources: registration data from LinkedIn Learning, data from the Foundation for Professional Development, and self-reported estimates by students of the average time spent on selected courses. Data included identification of the associated competency acquired. Data analysis was conducted using Python, version 3.10.11. RESULTS. The course choices of 629 students were analysed. In 2020 there were 300 participants and in 2021 there were 329. All the students had one compulsory inclusion in the elective (Management and Leadership Short Course for Undergraduate Healthcare Students). Students in both years reported spending the most average time on courses related to clinical knowledge (Healthcare Practitioner), followed by financial literacy and management (Professional), diversity management (Collaborator), and priority actions to identify and/or respond to (Health Advocate). The most popular courses related to the Leader and Manager role were around decision-making in human resources, problem-solving, and managing healthcare teams. Based on the top 10 LinkedIn Learning course selections of both cohorts, there appeared to be a preference for courses that were consistent with the role of medical professionals in practice. The most popular LinkedIn Learning course was The Six Morning Habits of High Performers. CONCLUSION. Students gravitated toward courses aligned with their role as professional doctors within the HPCSA competency framework. More studies are needed to understand how medical students develop the six non-clinical roles in the HPCSA framework and the effectiveness of MOOCs in a medical curriculum.

Resumo A inteligência artificial (IA) emergiu como uma tecnologia revolucionária que transformou muitos aspectos da nossa vida quotidiana, incluindo a educação. Portanto, o objetivo desta pesquisa é propor uma sintaxe de aplicação detalhada do prompt e dos comandos que devem ser fornecidos ao ChatGPT para a realização de diferentes atividades acadêmicas. Para tanto, o ChatGPT foi concebido como uma ferramenta de assistência educacional, pois está intimamente ligado a diversas linguagens de programação, como C e Python. Além disso, são apresentados exemplos específicos que mostram a aplicação destes comandos, os quais foram explicados detalhadamente para reduzir a margem de erro nos resultados obtidos. Em relação aos programas que o ChatGPT disponibiliza juntamente com os exemplos, foi realizada uma verificação do seu correto funcionamento para garantir a representação adequada dos conceitos exigidos em cada atividade acadêmica.

Abstract Artificial Intelligence (AI) has emerged as a revolutionary technology that has transformed many aspects of our daily lives, and education is no exception. This research aims to propose a syntax along with a detailed description of the command prompt that users must provide to ChatGPT to carry out different academic activities, using numerous examples. With this purpose in mind, ChatGPT has been designated as a teaching assistance tool due to its close association with different programming languages like C and Python. Furthermore, concrete examples are presented to demonstrate the practical application of these commands, which are elaborated upon in detail to minimize the margin of error in the obtained results. Regarding the programs provided by ChatGPT alongside the examples, a verification of their proper functionality has been conducted to ensure the accurate representation of the concepts required in each academic activity.

Resumen La inteligencia artificial (IA) ha emergido como una tecnología revolucionaria que ha transformado muchos aspectos de nuestra vida diaria, incluso de la educación. Por ello, la presente investigación tiene como objetivo proponer una sintaxis de aplicación detallada del prompt y de los comandos que se deben proporcionar a ChatGPT para la realización de diferentes actividades académicas. Para este fin, se ha concebido a ChatGPT como una herramienta de asistencia en la educación, ya que se encuentra estrechamente vinculada con distintos lenguajes de programación, como lo son C y Python. Además, se presentan ejemplos concretos que muestran la aplicación de estos comandos, los cuales han sido explicados de manera minuciosa para disminuir el margen de error en los resultados obtenidos. En relación con los programas que ChatGPT ha proporcionado junto con los ejemplos, se ha realizado una verificación de su correcto funcionamiento para asegurar la adecuada representación de los conceptos requeridos en cada actividad académica.

Abstract Among the skills to be developed in the study of reactive power compensation control devices in electrical power systems is the analysis of the control models of static reactive compensators. Python is free software that allows data analysis, control and simulation of electrical power systems. The objective of the article is to propose an application that allows the study of reactive power compensation that helps improve the teaching-learning process of the subject of Power Electronics. The study was based on a qualitative - descriptive methodology in which analytical - synthetic and inductive - deductive methods were used and the survey was used as empirical methods. For the processing and analysis of the information collected, the calculation of absolute and relative frequencies was used as a statistical method. The results corroborate the importance of using the proposed method to improve the teaching-learning process.

Resumen Dentro de las habilidades a desarrollar en el estudio de los dispositivos de control de compensación de la potencia reactiva en los sistemas eléctrico de potencia se encuentra el análisis de los modelos de control de los compensadores estáticos de reactivo. Python es un software libre que permite realizar análisis de datos, control y simulación de sistemas eléctricos de potencia. El objetivo del artículo es proponer una aplicación que permita el estudio de la compensación de la potencia reactiva que ayude a mejorar el proceso de enseñanza - aprendizaje de la asignatura de Electrónica de Potencia. El estudio se basó en una metodología cualitativa - descriptiva en la que se utilizaron los métodos analíticos - sintético e inductivo-deductivo y como métodos empíricos se empleó la encuesta. Para el procesamiento y análisis de la información recopilada se utilizó como método estadístico el cálculo de las frecuencias absolutas y relativas. Los resultados corroboran la importancia de utilizar el método propuesto para mejorar el proceso de enseñanza - aprendizaje.

Resumen Los recursos energéticos distribuidos (RED) son actores líderes en la transición energética en todo el mundo. A pesar de los beneficios ambientales, los RED son intermitentes (la energía de consumo/generación cambia según las variables de entrada), y el uso de dispositivos de electrónica de potencia afecta la calidad de la energía en las redes de distribución. Por lo general, se proponen modelos y simulaciones (deterministas o probabilísticas) para estimar dichos impactos. Sin embargo, estudios previos han analizado los principales efectos causados por uno o dos RED durante un día, con un nivel de detalle reducido en la metodología aplicada en el modelado y evaluación de impacto, o sin considerar que el nivel de distorsión armónica depende de las variables de entrada. El objetivo del presente artículo fue desarrollar un enfoque de simulación armónica probabilística para caracterizar el efecto horario de los sistemas fotovoltaicos (SFV), los sistemas de almacenamiento de energía de baterías y las estaciones de carga de vehículos eléctricos en un alimentador de prueba IEEE de 34 nodos. La estimación del efecto individual y colectivo del RED consistió en la cuantificación horaria del valor efectivo de tensión, desequilibrio de tensión, factor de potencia y distorsión armónica, con datos obtenidos mediante co-simulación Python y PowerFactory para 23 escenarios de operación. Estos impactos fueron evaluados a través de indicadores propuestos en la literatura y pruebas de hipótesis (estadística inferencial). Cada RED generalmente opera en un momento específico, lo que facilita la identificación de los efectos individuales de los parámetros de la red. El SFV aumentó el valor de voltaje efectivo y la distorsión armónica total. Sin embargo, los vehículos eléctricos y las baterías aumentan el desequilibrio de tensión y el factor de potencia debido a la conexión bifásica en algunos nodos de la red.

Abstract Distributed energy resources (DERs) are leading actors in energy transition worldwide. Despite the environmental benefits, DERs are intermittent (consumption/generation power changes according to the input variables), and the use of power electronic devices affects the power quality in distribution networks. Typically, models and simulations (deterministic or probabilistic) are proposed to estimate such impacts. However, previous studies have analyzed the main effects caused by one or two DERs during a day. These studies had a reduced level of detail in the applied methodology in modeling and impact evaluation, or they did not consider that the harmonic distortion level depends on input variables. The aim of this article was to develop a probabilistic harmonic simulation approach to characterize the hourly effect of photovoltaic systems (PVS), battery energy storage systems, and electric vehicle charging stations in a 34-node IEEE test feeder. The individual and collective effect estimation of the DERs consists of the hourly quantification of the effective voltage value, voltage unbalance, power factor, and harmonic distortion, with data obtained using Python and PowerFactory co-simulation for 23 operation scenarios. These impacts were assessed through indicators proposed in the literature and hypothesis tests (inferential statistics). Each DER generally operates at a specific time, making it easy to identify the individual effects of network parameters. The PVS increased the effective voltage value and total harmonic distortion. However, electric vehicles and batteries increase the voltage unbalance and power factor due to the two-phase connection in some network nodes.

Abstract Biological sequence analysis is essential in bioinformatics, but the diversity of available tools presents significant challenges. This article proposes a web application that unifies useful functionalities in biological sequence analysis. Developed in Python and Django, it integrates features from BioPython, BioPandas, 3Dmol.js, and BLAST, among others, making it usable for users without programming knowledge. The tool covers the analysis of DNA and protein sequences, transcription, alignment, sequence search, and more. Its most notable features are efficiency, precision in obtaining quantitative data from the running process, and ease of use, supported by validation and results from the components used. Its modular architecture ensures scalability and adaptability. It is concluded that the tool represents a significant advance in simplifying and improving the processes of biological sequence analysis, promising to enhance efficiency and effectiveness in bioinformatics.

Resumen El análisis de secuencias biológicas es esencial en bioinformática, pero la diversidad de herramientas disponibles presenta desafíos significativos. Este artículo propone una aplicación web que unifica diferentes funcionalidades útiles en el análisis de secuencias biológicas. Desarrollada en Python y Django, integra funcionalidades de BioPython, BioPandas, 3Dmol.js y BLAST, entre otras, que además posibilita su uso a usuarios sin conocimientos de programación. La herramienta abarca el análisis de secuencias de ADN y proteínas, transcripción, alineación, búsqueda de secuencias y más. Dentro de las características más destacables está la eficiencia, precisión al obtener datos cuantitativos del proceso que se está ejecutando y la facilidad de uso.

Part of the scientific community has spent considerable time and resources to somehow validate Collatz's conjecture, countless efforts have achieved considerable progress in this direction, however this conjecture lacked definitive confirmation that choosing any odd number xi ∈ N∗, we will obtain x(i+1) = 3xi + 1, this being an even number x(i+1), divide it by the number two (successively) until another odd number ∈ N∗ is obtained, the process is repeated xn = 3x(n−1) + 1 and divisions by two until a number equal to 1 finally results.This work presents deductions, algorithms and equations that corroborate this proposition, supporting this perception and conclusion that Collatz's conjecture points to the final cycle 4 → 2 → 1.

Parte de la comunidad científica ha dedicado un tiempo y recursos considerables a validar de alguna manera la conjetura de Collatz, innumerables esfuerzos han logrado avances considerables en esta dirección, sin embargo a esta conjetura le faltaba una confirmación definitiva de que eligiendo cualquier número impar xi ∈ N∗, obtendremos x(i+ 1) = 3xi + 1, siendo este un número par x(i+1), se divide por el número dos (sucesivamente) hasta obtener otro número impar ∈ N∗, se repite el proceso xn = 3x(n−1) + 1 y divisiones por dos hasta que finalmente resulte un número igual a 1.Este trabajo presenta deducciones, algoritmos y ecuaciones que corroboran esta proposición, apoyando esta percepción y conclusión de que la conjetura de Collatz apunta al ciclo final 4 → 2 → 1.

Parte da comunidade cientifica tem dispendido considerável tempo e recursos para de alguma forma validar a conjectura de Collatz, inúmeros esforços tem logrado considerável avanço nesta direção, porém tal conjectura carecia de uma confirmação definitiva de que escolhido um número impar qualquer xi ∈ N∗, obteremos x(i+1) = 3xi + 1, sendo este um número x(i+1) par, divide-se o mesmo pelo número dois (sucessivamente) até obter-se outro número impar ∈ N∗ , repete-se o processo xn = 3x(n−1) + 1 e divisões por dois até que resulte finalmente um número igual 1.Neste trabalho apresentam-se deduções, algoritmos e equações que corroboram com tal proposição, embasam tal percepção e conclusão de que a conjectura de Collatz aponta para o ciclo final 4 → 2 → 1.

Resumo Nos últimos anos, o desenvolvimento de tecnologias de alto rendimento para obtenção de dados sequenciais potencializou a possibilidade de análise de dados proteicos in silico. No entanto, quando se trata de estudos de interação de poliproteínas virais, existe uma lacuna na representação dessas proteínas, devido ao seu tamanho e comprimento. Para estudos utilizando técnicas de ponta como o Aprendizado de Máquina, uma boa representação dessas proteínas é imprescindível. Apresentamos uma alternativa para este problema, implementando um protocolo de fragmentação e modelagem para preparar essas poliproteínas na forma de fragmentos de peptídeos. Tal procedimento é feito por diversos scripts, implementados em conjunto no workflow que chamamos de PolyPRep, uma ferramenta escrita em script Python e disponível no GitHub. Este software está disponível gratuitamente apenas para usuários não comerciais.

Abstract In recent years, the development of high-throughput technologies for obtaining sequence data leveraged the possibility of analysis of protein data in silico. However, when it comes to viral polyprotein interaction studies, there is a gap in the representation of those proteins, given their size and length. The prepare for studies using state-of-the-art techniques such as Machine Learning, a good representation of such proteins is a must. We present an alternative to this problem, implementing a fragmentation and modeling protocol to prepare those polyproteins in the form of peptide fragments. Such procedure is made by several scripts, implemented together on the workflow we call PolyPRep, a tool written in Python script and available in GitHub. This software is freely available only for noncommercial users.

Resumo Python regius ou pítons-bola são os famosos animais de estimação exóticos por causa de sua bela cor e padrão. A píton-bola albina é um tipo de píton-bola, mas é muito difícil determinar a diferença de fenótipo entre o genótipo selvagem e heterozigoto de albino (albino het). Neste estudo, PCR e qPCR podem distinguir entre albinos selvagens e het. O tamanho do produto de PCR de albinos selvagens e het foi de 415 pb, mas a intensidade do produto de PCR de selvagens foi mais intensa do que a de albinos het. Nenhum amplicon de PCR foi encontrado em albinos e o valor de Ct de selvagens foi menor do que Ct de albinos het. A técnica de detecção molecular, especialmente PCR e qPCR, pode determinar a diferença entre albinos selvagens e het de pítons-bola. pítonsbola pítons bola padrão pítonbola píton pítonbola, bola, . het) estudo 41 pb molecular pítonsbola. bola. 4

Abstract Python regius or ball pythons are the famous exotic pets because of their beautiful color and pattern. The albino ball python is one type of ball python, but it is very difficult to determine the difference of phenotype between wildtype and heterozygous genotype of albino (het albino). In this study, PCR and qPCR can distinguish between wildtype and het albino. The PCR product size of wildtype and het albino was 415 bp, but the intensity of PCR product of wildtype was more intense than that of het albinos. No PCR amplicon was found in albinos and the Ct value of wildtype was lower than Ct of het albinos. The molecular detection technique, especially PCR and qPCR, can determine the difference between wildtype and het albinos of ball pythons. pattern . albino) study 41 bp technique 4

ABSTRACT Objective: To present the retrieval and analysis of clinical data from anamneses in the. Electronic Health Record (EHR), referred to in this research as Interface Terminology. Methods: The clinical data collection process in this research was carried out on electronic patient records from a private hospital. The data sample consisted of 18,256 anamneses from the field of gynecology in 2018. The clinical data was retrieved through Natural Language Processing using the Python language. The most frequent terms related to clinical data were analysed, such as abbreviations and acronyms, stop words, procedures, and n-grams. Results: Clinical data has the potential to be reused for scientific production, epidemiological profiling and in the creation of dictionaries and enrichment of controlled vocabularies for PEP and other health information systems. They are also important in defining algorithms for information retrieval. As a result, a repository was created in the OSF (https://osf.io/de43a/) containing spreadsheets and tables with clinical data for reuse in the delimitation of algorithms, as well as the creation of a word cloud to identify the most frequent terms in electronic patient records in the field of Gynecology. The algorithms used to retrieve the information were made available on the GitHub digital repository. Conclusions: Clinical data is information about the patient, used for care purposes, hospital administrative issues, allowing research related to the patient's health and illness. The Interface Terminology, exemplified in the research hospital's EHR, presented a diversity of clinical data in the anamneses. Objective EHR , (EHR) Terminology Methods 18256 18 256 18,25 2018 language analysed acronyms words procedures ngrams. ngrams n grams. grams n-grams Results production systems result https//osf.io/de43a/ httpsosfiode43a httpsosfiodea https //osf.io/de43a/ osf io de43a de (https://osf.io/de43a/ Gynecology Conclusions purposes issues patients s illness hospitals (EHR 1825 1 25 18,2 201 https//osf.io/de43a httpsosfiode osfiode43a osfiodea //osf.io/de43a dea (https://osf.io/de43a 182 2 18, 20 osfiode

RESUMO Objetivo: Apresentar a recuperação e análise de dados clínicos provenientes de anamnese de Prontuário Eletrônico de Pacientes (PEP), denominada nesta pesquisa como Terminologia de Interface Método: O processo de coleta de dados clínicos desta pesquisa foi realizado em prontuários eletrônicos de pacientes de hospital privado. A amostra dos dados foi composta por 18.256 anamneses do domínio da ginecologia do ano de 2018. Os dados clínicos foram recuperados por meio de Processamento de Linguagem Natural utilizando a linguagem Python. Foram analisados os termos mais frequentes relacionados aos dados clínicos do tipo: abreviaturas e siglas, stop words, procedimento e n-gramas. Resultados: Os dados clínicos têm potencial de reutilização para produção científica, traçar perfil epidemiológico e na criação de dicionários e enriquecimento de vocabulários controlados para o PEP e outros sistemas de informação em saúde. Além disso são importantes na delimitação de algoritmos para fins de recuperação da informação. Como resultados um repositório foi criado no OSF (https://osf.io/de43a/) contendo planilhas e tabelas com os dados clínicos para seu reuso na delimitação de algoritmos além da criação de nuvem de palavras para identificar os termos mais frequentes em prontuários eletrônicos do paciente no domínio da Ginecologia. Os algoritmos utilizados na recuperação da informação foram disponibilizados no repositório digital GitHub. Conclusões: Os dados clínicos são informações sobre o paciente, utilizados para fins de assistência, questões administrativas hospitalares, permitindo pesquisas relacionadas à saúde e à doença do paciente. A Terminologia de Interface, exemplificada no PEP dos Hospital da pesquisa, apresentou diversidade de dados clínicos nas anamneses. Objetivo PEP, , (PEP) Método privado 18256 18 256 18.25 2018 Python tipo siglas words ngramas. ngramas n gramas. gramas n-gramas Resultados científica https//osf.io/de43a/ httpsosfiode43a httpsosfiodea https //osf.io/de43a/ osf io de43a (https://osf.io/de43a/ Ginecologia GitHub Conclusões assistência hospitalares (PEP 1825 1 25 18.2 201 https//osf.io/de43a httpsosfiode osfiode43a osfiodea //osf.io/de43a dea (https://osf.io/de43a 182 2 18. 20 osfiode