Informatica (South Korea)

El reto Bebras es un desafío a nivel internacional que promueve el pensamiento computacional y la informática entre los estudiantes y maestros. En este trabajo presentamos las experiencias y resultados de la aplicación del primer Reto Bebras en México, destacando su potencial para promover una participación activa, inclusiva y accesible en el desarrollo del pensamiento computacional en nuestro país. Se logró la participación de 11526 jóvenes entre 8 y 16 años, de 26 de los 32 estados de la República Mexicana, en cuatro de los seis niveles que opera internacionalmente el reto. Los resultados sugieren que hay mucho por hacer en nuestro país para dotar a los niños y jóvenes de las competencias necesarias para hacer un uso adecuado y eficiente de los conceptos y herramientas de las ciencias computacionales y sus fundamentos lógicos y matemáticos, para la resolución de problemas en un entorno de vida digitalizado.

Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host-pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low-cost and high-speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites. DeepPhoPred incorporates a two-headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep-learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available at https://github.com/faisalahm3d/DeepPhoPred.

Using embedding methods, compounds with similar properties will be closely located in latent space, and these embedding vectors can be used to find other compounds with similar properties based on the distance between compounds. However, they often require computational resources and programming skills. Here we develop Dr.Emb Appyter, a user-friendly web-based chemical compound search platform for drug discovery without any technical barriers. It uses embedding vectors to identify compounds similar to a given query in the embedding space. Dr.Emb Appyter provides various types of embedding methods, such as fingerprinting, SMILES, and transcriptional response-based methods, and embeds numerous compounds using them. The Faiss-based search system efficiently finds the closest compounds of query in the library. Additionally, Dr.Emb Appyter offers information on the top compounds; visualizes the results with 3D scatter plots, heatmaps, and UpSet plots; and analyses the results using a drug-set enrichment analysis. Dr.Emb Appyter is freely available at https://dremb.korea.ac.kr.

e16553 Background: The clinical outcome of urothelial carcinoma presents significant variability, reflecting its intricate molecular composition, epidemiologic attributes, and primary site of origin. Despite histological similarities, upper tract urothelial carcinoma (UTUC) and bladder urothelial carcinoma (BLCA) represent two distinct disease entities with disparate treatment responses and mutational patterns. Methods: To assess the impact of molecular heterogeneity on treatment outcomes, we conducted a comprehensive genomic analysis of 231 Korean BLCA and UTUC patients from the K-MASTER cohort. Results: Our investigation unveiled dynamic molecular properties and mutational patterns in key molecules, including TP53, FGFR3, CREBBP, and SPEN. BLCA patients demonstrated enrichment of APOBEC mutational signature activities, whereas UTUC patients were characterized by activation of the cell cycle pathway. Transcriptome analysis uncovered unique biological signatures, highlighting increased cell migration and WNT signaling in BLCA, while FGFR3 and stem-cell-like pathways predominated in UTUC. Moreover, our study identified increased treatment resistance to both platinum-based chemotherapy and immunotherapy in UTUC patients. Notably, tumor mutational burden (TMB) and microsatellite instability score independently demonstrated robust predictive capabilities for immunotherapy response in BLCA and UTUC, respectively. Lastly, a machine learning-based multivariable model identified FGFR3 mutation as a molecular determinant associated with favorable clinical outcomes, contrasting with mutations in FGFR2 linked to increased resistance to chemotherapy. Conclusions: Collectively, our findings provide important insights into the significance of molecular and clinical disparities in urothelial carcinoma, facilitating disease-specific treatment interventions.

O presente trabalho propõe um modelo que visa a análise de performance dos recursos computacionais da computação em nuvem, especificamente, carga de CPU, uso de memória e escalabilidade de recursos, abordando a utilização de algoritmos baseados em linguagem de programação Python, bem como fundamentação em método fuzzy e algoritmo desenvolvido para auxílio à tomada de decisão na detecção da melhor performance em escalabilidade. O interesse pelas áreas da engenharia da produção, engenharia da computação, ciência da computação, lógicas, gestão, proatividade em estudar a aplicabilidade de Lógica Fuzzy com apoio da Ciência de Dados, são fatores que favorecem a elaboração da presente dissertação de mestrado, bem como o importantíssimo acompanhamento da rede de pesquisas dos estudos anteriores desenvolvidos no Programa de Engenharia de Produção da Universidade Federal do Rio de Janeiro. O presente trabalho poderá servir como apoio a outros estudantes e pesquisadores que se interessam pelo assunto, auxiliando na elaboração de projetos e trabalhos na área da engenharia da produção, engenharia da computação, ciência da computação, administração e áreas correlatas a presente dissertação.

O contexto tecnológico atual – com dispositivos eletrônicos inteligentes e interligados à Internet, serviços em nuvem, robótica, carros autônomos, Indústria 4.0, dentre outras inovações – só foi possível graças ao surgimento do Computador (sistemas computacionais). No processo de ensino e aprendizagem de projeto de sistemas computacionais, são necessários conhecimentos relacionados: ao projeto físico da plataforma computacional, sua programação e operação eficiente. Esses conteúdos são extensos, complexos e demandam metodologias desafiadoras e ferramentas computacionais que contemplem o processo de ensino e aprendizagem tanto do arcabouço teórico, quanto da prática de projetos. Assim, o presente trabalho tem como objetivo apresentar uma abordagem para simplificar o aprendizado por meio da prática de projetos de sistemas computacionais em ambiente de simulação. Para tanto, utilizou-se o simulador computacional CompSim que inclui um modelo de simulação de processador RISC-V. Desta forma, é possível: projetar sistemas computacionais de alta complexidade no ambiente de simulação e simulá-los para validar requisitos de projeto, tais como comportamento funcional e desempenho. Estabelece-se assim uma abordagem de apoio ao aprendizado e projeto de novos sistemas computacionais. Para validar a abordagem, foi realizada uma avaliação por meio de questionários aplicados a usuários do sistema, docentes e discentes, em cenários de estudos de casos. As respostas dos questionários, com base na análise de rubricas, permitiram analisar a qualidade da experiência de uso do simulador e do apoio pedagógico proporcionado pela abordagem.

El proyecto consiste en investigar elementos gráficos semióticos como símbolos, signos, dibujos, adornos, de las manifestaciones artísticas y culturales brasileñas

SEM RESUMO

We live in the gilded age of data-driven computing. With public clouds offering virtually unlimited amounts of compute and storage, enterprises collecting data about every aspect of their businesses, and advances in analytics and machine learning technologies, data driven decision making is now timely, cost-effective, and therefore, pervasive. Alas, only a handful of power users can wield today's powerful data engineering tools. For one thing, most solutions require knowledge of specific programming interfaces or libraries. Furthermore, running them requires complex configurations and knowledge of the underlying cloud for cost-effectiveness. We decided that a fundamental redesign is in order to democratize data engineering for the masses at cloud scale. The result is Informatica Cloud Data Integration - Elastic (CDI-E). Since the early 1990s, Informatica has been a pioneer and industry leader in building no-code data engineering tools. Non-experts can express complex data engineering tasks using a graphical user interface (GUI). Informatica CDI-E is built to incorporate the simplicity of GUI in the design layer with an elastic and highly scalable run time to handle data in any format without little to no user input using automated optimizations. Users upload their data to the cloud in any format and can immediately use them in conjunction with their data management and analytic tools of choice using CDI-E GUI. Implementation began in the Spring of 2017, and Informatica CDI-E has been generally available since the Summer of 2019. Today, CDI-E is used in production by a growing number of small and large enterprises to make sense of data in arbitrary formats. In this paper, we describe the architecture of Informatica CDI-E and its novel no-code data engineering interface. The paper highlights some of the key features of CDI-E: simplicity without loss in productivity and extreme elasticity. It concludes with lessons we learned and an outlook of the future.