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1. MojoBench: Language Modeling and Benchmarks for Mojo

Author

Raihan, Nishat, Santos, Joanna C. S., and Zampieri, Marcos

Subjects
Computer Science - Computation and Language
Abstract

The recently introduced Mojo programming language (PL) by Modular, has received significant attention in the scientific community due to its claimed significant speed boost over Python. Despite advancements in code Large Language Models (LLMs) across various PLs, Mojo remains unexplored in this context. To address this gap, we introduce MojoBench, the first framework for Mojo code generation. MojoBench includes HumanEval-Mojo, a benchmark dataset designed for evaluating code LLMs on Mojo, and Mojo-Coder, the first LLM pretrained and finetuned for Mojo code generation, which supports instructions in 5 natural languages (NLs). Our results show that Mojo-Coder achieves a 30-35% performance improvement over leading models like GPT-4o and Claude-3.5-Sonnet. Furthermore, we provide insights into LLM behavior with underrepresented and unseen PLs, offering potential strategies for enhancing model adaptability. MojoBench contributes to our understanding of LLM capabilities and limitations in emerging programming paradigms fostering more robust code generation systems.

Published
2024

2. Large Language Models in Computer Science Education: A Systematic Literature Review

Author

Raihan, Nishat, Siddiq, Mohammed Latif, Santos, Joanna C. S., and Zampieri, Marcos

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction
Abstract

Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computer science and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research., Comment: Accepted at 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2025)

Published
2024

3. A Large-Scale Exploit Instrumentation Study of AI/ML Supply Chain Attacks in Hugging Face Models

Author

Casey, Beatrice, Santos, Joanna C. S., and Mirakhorli, Mehdi

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning, Computer Science - Software Engineering
Abstract

The development of machine learning (ML) techniques has led to ample opportunities for developers to develop and deploy their own models. Hugging Face serves as an open source platform where developers can share and download other models in an effort to make ML development more collaborative. In order for models to be shared, they first need to be serialized. Certain Python serialization methods are considered unsafe, as they are vulnerable to object injection. This paper investigates the pervasiveness of these unsafe serialization methods across Hugging Face, and demonstrates through an exploitation approach, that models using unsafe serialization methods can be exploited and shared, creating an unsafe environment for ML developers. We investigate to what extent Hugging Face is able to flag repositories and files using unsafe serialization methods, and develop a technique to detect malicious models. Our results show that Hugging Face is home to a wide range of potentially vulnerable models.

Published
2024

4. The Fault in our Stars: Quality Assessment of Code Generation Benchmarks

Author

Siddiq, Mohammed Latif, Dristi, Simantika, Saha, Joy, and Santos, Joanna C. S.

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

Large Language Models (LLMs) are gaining popularity among software engineers. A crucial aspect of developing effective code generation LLMs is to evaluate these models using a robust benchmark. Evaluation benchmarks with quality issues can provide a false sense of performance. In this work, we conduct the first-of-its-kind study of the quality of prompts within benchmarks used to compare the performance of different code generation models. To conduct this study, we analyzed 3,566 prompts from 9 code generation benchmarks to identify quality issues in them. We also investigated whether fixing the identified quality issues in the benchmarks' prompts affects a model's performance. We also studied memorization issues of the evaluation dataset, which can put into question a benchmark's trustworthiness. We found that code generation evaluation benchmarks mainly focused on Python and coding exercises and had very limited contextual dependencies to challenge the model. These datasets and the developers' prompts suffer from quality issues like spelling and grammatical errors, unclear sentences to express developers' intent, and not using proper documentation style. Fixing all these issues in the benchmarks can lead to a better performance for Python code generation, but not a significant improvement was observed for Java code generation. We also found evidence that GPT-3.5-Turbo and CodeGen-2.5 models may have data contamination issues., Comment: Accepted at the 24th IEEE International Conference on Source Code Analysis and Manipulation(SCAM 2024) Research Track

Published
2024

5. A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks

Author

Casey, Beatrice, Santos, Joanna C. S., and Perry, George

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security
Abstract

Machine learning techniques for cybersecurity-related software engineering tasks are becoming increasingly popular. The representation of source code is a key portion of the technique that can impact the way the model is able to learn the features of the source code. With an increasing number of these techniques being developed, it is valuable to see the current state of the field to better understand what exists and what's not there yet. This paper presents a study of these existing ML-based approaches and demonstrates what type of representations were used for different cybersecurity tasks and programming languages. Additionally, we study what types of models are used with different representations. We have found that graph-based representations are the most popular category of representation, and Tokenizers and Abstract Syntax Trees (ASTs) are the two most popular representations overall. We also found that the most popular cybersecurity task is vulnerability detection, and the language that is covered by the most techniques is C. Finally, we found that sequence-based models are the most popular category of models, and Support Vector Machines (SVMs) are the most popular model overall.

Published
2024

6. Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms

Author

Loss, Flavio P., da Cunha, Pedro H., Rocha, Matheus B., Zanoni, Madson Poltronieri, de Lima, Leandro M., Nascimento, Isadora Tavares, Rezende, Isabella, Canuto, Tania R. P., Vieira, Luciana de Paula, Rossoni, Renan, Santos, Maria C. S., Frasson, Patricia Lyra, Romão, Wanderson, Filgueiras, Paulo R., and Krohling, Renato A.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Skin lesions are classified in benign or malignant. Among the malignant, melanoma is a very aggressive cancer and the major cause of deaths. So, early diagnosis of skin cancer is very desired. In the last few years, there is a growing interest in computer aided diagnostic (CAD) using most image and clinical data of the lesion. These sources of information present limitations due to their inability to provide information of the molecular structure of the lesion. NIR spectroscopy may provide an alternative source of information to automated CAD of skin lesions. The most commonly used techniques and classification algorithms used in spectroscopy are Principal Component Analysis (PCA), Partial Least Squares - Discriminant Analysis (PLS-DA), and Support Vector Machines (SVM). Nonetheless, there is a growing interest in applying the modern techniques of machine and deep learning (MDL) to spectroscopy. One of the main limitations to apply MDL to spectroscopy is the lack of public datasets. Since there is no public dataset of NIR spectral data to skin lesions, as far as we know, an effort has been made and a new dataset named NIR-SC-UFES, has been collected, annotated and analyzed generating the gold-standard for classification of NIR spectral data to skin cancer. Next, the machine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutional neural network (1D-CNN) were investigated to classify cancer and non-cancer skin lesions. Experimental results indicate the best performance obtained by LightGBM with pre-processing using standard normal variate (SNV), feature extraction providing values of 0.839 for balanced accuracy, 0.851 for recall, 0.852 for precision, and 0.850 for F-score. The obtained results indicate the first steps in CAD of skin lesions aiming the automated triage of patients with skin lesions in vivo using NIR spectral data.

Published
2024

7. SALLM: Security Assessment of Generated Code

Author

Siddiq, Mohammed Latif, Santos, Joanna C. S., Devareddy, Sajith, and Muller, Anna

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract

With the growing popularity of Large Language Models (LLMs) in software engineers' daily practices, it is important to ensure that the code generated by these tools is not only functionally correct but also free of vulnerabilities. Although LLMs can help developers to be more productive, prior empirical studies have shown that LLMs can generate insecure code. There are two contributing factors to the insecure code generation. First, existing datasets used to evaluate LLMs do not adequately represent genuine software engineering tasks sensitive to security. Instead, they are often based on competitive programming challenges or classroom-type coding tasks. In real-world applications, the code produced is integrated into larger codebases, introducing potential security risks. Second, existing evaluation metrics primarily focus on the functional correctness of the generated code while ignoring security considerations. Therefore, in this paper, we described SALLM, a framework to benchmark LLMs' abilities to generate secure code systematically. This framework has three major components: a novel dataset of security-centric Python prompts, configurable assessment techniques to evaluate the generated code, and novel metrics to evaluate the models' performance from the perspective of secure code generation., Comment: Accepted at the 6th International Workshop on Automated and verifiable Software sYstem DEvelopment (ASYDE) with ASE Conference 2024

Published
2023
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8. Seneca: Taint-Based Call Graph Construction for Java Object Deserialization

Author

Santos, Joanna C. S., Mirakhorli, Mehdi, and Shokri, Ali

Subjects
Computer Science - Software Engineering
Abstract

Object serialization and deserialization are widely used for storing and preserving objects in files, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. This mechanism relies on reflection, a dynamic language that introduces serious challenges for static analyses. Current state-of-the-art call graph construction algorithms do not fully support object serialization/deserialization, i.e., they are unable to uncover the callback methods that are invoked when objects are serialized and deserialized. Since call graphs are a core data structure for multiple types of analysis (e.g., vulnerability detection), an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. In this paper, we present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. Our approach relies on taint analysis and API modeling to construct sound call graphs. We evaluated our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities. Our results show that Seneca can create sound call graphs with respect to serialization features. The resulting call graphs do not incur significant runtime overhead and were shown to be useful for performing identification of vulnerable paths caused by untrusted object deserialization., Comment: Accepted at OOPSLA 2024

Published
2023
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9. FRANC: A Lightweight Framework for High-Quality Code Generation

Author

Siddiq, Mohammed Latif, Casey, Beatrice, and Santos, Joanna C. S.

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

In recent years, the use of automated source code generation utilizing transformer-based generative models has expanded, and these models can generate functional code according to the requirements of the developers. However, recent research revealed that these automatically generated source codes can contain vulnerabilities and other quality issues. Despite researchers' and practitioners' attempts to enhance code generation models, retraining and fine-tuning large language models is time-consuming and resource-intensive. Thus, we describe FRANC, a lightweight framework for recommending more secure and high-quality source code derived from transformer-based code generation models. FRANC includes a static filter to make the generated code compilable with heuristics and a quality-aware ranker to sort the code snippets based on a quality score. Moreover, the framework uses prompt engineering to fix persistent quality issues. We evaluated the framework with five Python and Java code generation models and six prompt datasets, including a newly created one in this work (SOEval). The static filter improves 9% to 46% Java suggestions and 10% to 43% Python suggestions regarding compilability. The average improvement over the NDCG@10 score for the ranking system is 0.0763, and the repairing techniques repair the highest 80% of prompts. FRANC takes, on average, 1.98 seconds for Java; for Python, it takes 0.08 seconds., Comment: Accepted at the 24th IEEE International Conference on Source Code Analysis and Manipulation (SCAM 2024)

Published
2023

11. Using Large Language Models to Generate JUnit Tests: An Empirical Study

Author

Siddiq, Mohammed Latif, Santos, Joanna C. S., Tanvir, Ridwanul Hasan, Ulfat, Noshin, Rifat, Fahmid Al, and Lopes, Vinicius Carvalho

Subjects
Computer Science - Software Engineering, Computer Science - Machine Learning
Abstract

A code generation model generates code by taking a prompt from a code comment, existing code, or a combination of both. Although code generation models (e.g., GitHub Copilot) are increasingly being adopted in practice, it is unclear whether they can successfully be used for unit test generation without fine-tuning for a strongly typed language like Java. To fill this gap, we investigated how well three models (Codex, GPT-3.5-Turbo, and StarCoder) can generate unit tests. We used two benchmarks (HumanEval and Evosuite SF110) to investigate the effect of context generation on the unit test generation process. We evaluated the models based on compilation rates, test correctness, test coverage, and test smells. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the EvoSuite SF110 benchmark. The generated tests also suffered from test smells, such as Duplicated Asserts and Empty Tests., Comment: Accepted in Research Track of The 28th International Conference on Evaluation and Assessment in Software Engineering (EASE 2024)

Published
2023
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12. Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering

Author

Paul, Rishov, Hossain, Md. Mohib, Siddiq, Mohammed Latif, Hasan, Masum, Iqbal, Anindya, and Santos, Joanna C. S.

Subjects
Computer Science - Machine Learning, Computer Science - Software Engineering
Abstract

Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review could improve the program repair further. Large language models, trained with Natural Language (NL) and Programming Language (PL), can contain inherent knowledge of both. In this study, we investigate if this inherent knowledge of PL and NL can be utilized to improve automated program repair. We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both PL and NL, on two such natural language-based program repair datasets and found that the pre-trained language models fine-tuned with datasets containing both code review and subsequent code changes notably outperformed each of the previous models. With the advent of code generative models like Codex and GPT-3.5-Turbo, we also performed zero-shot and few-shots learning-based prompt engineering to assess their performance on these datasets. However, the practical application of using LLMs in the context of automated program repair is still a long way off based on our manual analysis of the generated repaired codes by the learning models., Comment: 12 pages, 2 figures, 4 tables

Published
2023

14. ArCode: Facilitating the Use of Application Frameworks to Implement Tactics and Patterns

Author

Shokri, Ali, Santos, Joanna C. S., and Mirakhorli, Mehdi

Subjects
Computer Science - Software Engineering
Abstract

Software designers and developers are increasingly relying on application frameworks as first-class design concepts. They instantiate the services that frameworks provide to implement various architectural tactics and patterns. One of the challenges in using frameworks for such tasks is the difficulty of learning and correctly using frameworks' APIs. This paper introduces a learning-based approach called ArCode to help novice programmers correctly use frameworks' APIs to implement architectural tactics and patterns. ArCode has several novel components: a graph-based approach for learning specification of a framework from a limited number of training software, a program analysis algorithm to eliminate erroneous training data, and a recommender module to help programmers use APIs correctly and identify API misuses in their programs. We evaluated our technique across two popular frameworks: JAAS security framework used for authentication and authorization tactic and Java RMI framework used to enable remote method invocation between client and server and other object-oriented patterns. Our evaluation results show (i) the feasibility of using ArCode to learn the specification of a framework; (ii) ArCode generates accurate recommendations for finding the next API call to implement an architectural tactic/pattern based on the context of the programmer's code; (iii) it accurately detects API misuses in the code that implements a tactic/pattern and provides fix recommendations. Comparison of ArCode with two prior techniques (MAPO and GrouMiner) on API recommendation and misuse detection shows that ArCode outperforms these approaches., Comment: This paper has been accepted in the main track of 2021 IEEE International Conference on Software Architecture (ICSA 2021) and is going to be published. Please feel free to cite it

Published
2021
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15. A Methodological Approach to Verify Architecture Resiliency

Author

Santos, Joanna C. S., Suloglu, Selma, Cataño, Néstor, Mirakhorli, Mehdi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Batista, Thais, editor, Bureš, Tomáš, editor, Raibulet, Claudia, editor, and Muccini, Henry, editor

Published
2023
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16. PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones

Author

Pacheco, Andre G. C., Lima, Gustavo R., Salomão, Amanda S., Krohling, Breno A., Biral, Igor P., de Angelo, Gabriel G., Alves Jr, Fábio C. R., Esgario, José G. M., Simora, Alana C., Castro, Pedro B. C., Rodrigues, Felipe B., Frasson, Patricia H. L., Krohling, Renato A., Knidel, Helder, Santos, Maria C. S., Santo, Rachel B. do Espírito, Macedo, Telma L. S. G., Canuto, Tania R. P., and de Barros, Luíz F. S.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

Over the past few years, different computer-aided diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to design them. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 22 features. The dataset consists of 1,373 patients, 1,641 skin lesions, and 2,298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to aid future research and the development of new tools to assist clinicians to detect skin cancer.

Published
2020

19. A Large-Scale Study on the Usage of Testing Patterns that Address Maintainability Attributes (Patterns for Ease of Modification, Diagnoses, and Comprehension)

Author

Gonzalez, Danielle, Santos, Joanna C. S., Popovich, Andrew, Mirakhorli, Mehdi, and Nagappan, Mei

Subjects
Computer Science - Software Engineering
Abstract

Test case maintainability is an important concern, especially in open source and distributed development environments where projects typically have high contributor turnover with varying backgrounds and experience, and where code ownership changes often. Similar to design patterns, patterns for unit testing promote maintainability quality attributes such as ease of diagnoses, modifiability, and comprehension. In this paper, we report the results of a large-scale study on the usage of four xUnit testing patterns which can be used to satisfy these maintainability attributes. This is a first-of-its-kind study which developed automated techniques to investigate these issues across 82,447 open source projects, and the findings provide more insight into testing practices in open source projects. Our results indicate that only 17% of projects had test cases, and from the 251 testing frameworks we studied, 93 of them were being used. We found 24% of projects with test files implemented patterns that could help with maintainability, while the remaining did not use these patterns. Multiple qualitative analyses indicate that usage of patterns was an ad-hoc decision by individual developers, rather than motivated by the characteristics of the project, and that developers sometimes used alternative techniques to address maintainability concerns., Comment: Mining Software Repositories (MSR) 2017 Research Track

Published
2017
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21. Techniques and Tools for Selection and Strategic Alignment of Projects and Projects Portfolio Balancing: A Systematic Mapping

Author

dos Santos, Djenane C. S., de Souza, Adler D., Mota, Flávio B. S., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, and Latifi, Shahram, editor

Published
2021
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26. Increasing the vitamin C transporter SVCT2 in microglia improves synaptic plasticity and restrains memory impairments in Alzheimer’s disease models

Author

Portugal, Camila C., primary, Santos, Evelyn C. S., additional, Pacheco, Ana M., additional, Costa-Pinto, Sara, additional, Almeida, Tiago O., additional, Tedim-Moreira, Joana, additional, Gavin, Dora, additional, Canedo, Teresa, additional, Oliveira, Fabiana, additional, Summavielle, Teresa, additional, Vaz, Sandra H., additional, Socodato, Renato, additional, and Relvas, João B., additional

Published
2024
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27. An Updated Review of Recent Applications and Perspectives of Hydrogen Production from Biomass by Fermentation: A Comprehensive Analysis

Author

Dari, Dayana Nascimento, primary, Freitas, Isabelly Silveira, additional, Aires, Francisco Izaias da Silva, additional, Melo, Rafael Leandro Fernandes, additional, dos Santos, Kaiany Moreira, additional, da Silva Sousa, Patrick, additional, Gonçalves de Sousa Junior, Paulo, additional, Luthierre Gama Cavalcante, Antônio, additional, Neto, Francisco Simão, additional, da Silva, Jessica Lopes, additional, Carlos de Castro, Érico, additional, Santos Alexandre, Valdilane, additional, da S. Lima, Ana M., additional, Serpa, Juliana de França, additional, Souza, Maria C. M. de, additional, and Santos, José C. S. dos, additional

Published
2024
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31. Seneca: Taint-Based Call Graph Construction for Java Object Deserialization

Author

Santos, Joanna C. S., Mirakhorli, Mehdi, Shokri, Ali, Santos, Joanna C. S., Mirakhorli, Mehdi, and Shokri, Ali

Abstract

Object serialization and deserialization are widely used for storing and preserving objects in files, memory, or database as well as for transporting them across machines, enabling remote interaction among processes and many more. This mechanism relies on reflection, a dynamic language that introduces serious challenges for static analyses. Current state-of-the-art call graph construction algorithms do not fully support object serialization/deserialization, i.e., they are unable to uncover the callback methods that are invoked when objects are serialized and deserialized. Since call graphs are a core data structure for multiple types of analysis (e.g., vulnerability detection), an appropriate analysis cannot be performed since the call graph does not capture hidden (vulnerable) paths that occur via callback methods. In this paper, we present Seneca, an approach for handling serialization with improved soundness in the context of call graph construction. Our approach relies on taint analysis and API modeling to construct sound call graphs. We evaluated our approach with respect to soundness, precision, performance, and usefulness in detecting untrusted object deserialization vulnerabilities. Our results show that Seneca can create sound call graphs with respect to serialization features. The resulting call graphs do not incur significant runtime overhead and were shown to be useful for performing identification of vulnerable paths caused by untrusted object deserialization.

Published
2024

32. Quality Assessment of Prompts Used in Code Generation

Author

Siddiq, Mohammed Latif, Dristi, Simantika, Saha, Joy, Santos, Joanna C. S., Siddiq, Mohammed Latif, Dristi, Simantika, Saha, Joy, and Santos, Joanna C. S.

Abstract

Large Language Models (LLMs) are gaining popularity among software engineers. A crucial aspect of developing effective code-generation LLMs is to evaluate these models using a robust benchmark. Evaluation benchmarks with quality issues can provide a false sense of performance. In this work, we conduct the first-of-its-kind study of the quality of prompts within benchmarks used to compare the performance of different code generation models. To conduct this study, we analyzed 3,566 prompts from 9 code generation benchmarks to identify quality issues in them. We also investigated whether fixing the identified quality issues in the benchmarks' prompts affects a model's performance. We also studied memorization issues of the evaluation dataset, which can put into question a benchmark's trustworthiness. We found that code generation evaluation benchmarks mainly focused on Python and coding exercises and had very limited contextual dependencies to challenge the model. These datasets and the developers' prompts suffer from quality issues like spelling and grammatical errors, unclear sentences to express developers' intent, and not using proper documentation style. Fixing all these issues in the benchmarks can lead to a better performance for Python code generation, but not a significant improvement was observed for Java code generation. We also found evidence that GPT-3.5-Turbo and CodeGen-2.5 models possibly have data contamination issues., Comment: Under review

Published
2024

35. Novel Directed Enzyme Prodrug Therapy for Cancer Treatment Based on 2′-Deoxyribosyltransferase-Conjugated Magnetic Nanoparticles.

Author

Pérez, Elena, Acosta, Javier, Pisabarro, Victor, Cordani, Marco, dos Santos, José C. S., Sanz-Landaluze, Jon, Gallo, Juan, Bañobre-López, Manuel, and Fernández-Lucas, Jesús

Subjects
CHEMOTHERAPY complications, IMMOBILIZED enzymes, IRON oxide nanoparticles, HELA cells, LEISHMANIA mexicana
Abstract

Directed enzyme prodrug therapy (DEPT) strategies show promise in mitigating chemotherapy side effects during cancer treatment. Among these, the use of immobilized enzymes on solid matrices as prodrug activating agents (IDEPT) presents a compelling delivery strategy, offering enhanced tumor targeting and reduced toxicity. Herein, we report a novel IDEPT strategy by employing a His-tagged Leishmania mexicana type I 2′-deoxyribosyltransferase (His-LmPDT) covalently attached to glutaraldehyde-activated magnetic iron oxide nanoparticles (MIONPs). Among the resulting derivatives, PDT-MIONP3 displayed the most favorable catalyst load/retained activity ratio, prompting its selection for further investigation. Substrate specificity studies demonstrated that PDT-MIONP3 effectively hydrolyzed a diverse array of 6-oxo and/or 6-amino purine 2′-deoxynucleosides, including 2-fluoro-2′-deoxyadenosine (dFAdo) and 6-methylpurine-2′-deoxyribose (d6MetPRib), both well-known prodrugs commonly used in DEPT. The biophysical characterization of both MIONPs and PDT-MIONPs was conducted by TEM, DLS, and single particle ICPMS techniques, showing an ideal nanosized range and a zeta potential value of −47.9 mV and −78.2 mV for MIONPs and PDT-MIONPs, respectively. The intracellular uptake of MIONPs and PDT-MIONPs was also determined by TEM and single particle ICPMS on HeLa cancer cell lines and NIH3T3 normal cell lines, showing a higher intracellular uptake in tumor cells. Finally, the selectivity of the PDT-MIONP/dFAdo IDEPT system was tested on HeLa cells (24 h, 10 µM dFAdo), resulting in a significant reduction in tumoral cell survival (11% of viability). Based on the experimental results, PDT-MIONP/dFAdo presents a novel and alternative IDEPT strategy, providing a promising avenue for cancer treatment. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Quality of life of women from a quilombola community in northeastern Brazil

Author

Santos, E. N. A., primary, Magalhães, P. K. A., additional, Santos, A. M., additional, Correia, M. S., additional, Santos, J. C. S., additional, Carvalho Neto, A. P. M., additional, Souza, M. A., additional, Lima, R. F., additional, Fonseca, S. A., additional, Ferreira-Júnior, G. C., additional, Cavalcanti, M. G. S., additional, Costa, J. G., additional, and Matos-Rocha, T. J., additional

Published
2024
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40. FUNDAMENTOS E PRÁTICAS PEDIÁTRICAS E NEONATAIS

Author

VILAÇA, JHEMILY LOPES LIMA, primary, Ferreira, Vitória de Araújo, additional, Andrade, Fabianne Gomes de, additional, Santos, Vinicius de Oliveira, additional, Diniz, Isabella Hayashi, additional, Cunha, Lara Pontes, additional, Araújo, Hannah Áurea Girão dos Santos, additional, Nascimento, Lourdes Maria Pereira Marques, additional, Mota, Renata Cardoso Pelegrine, additional, Almeida, Isadora Alfenas, additional, Dantas, Veridiana Célia Bezerra, additional, Lima, Letícia Bastos, additional, SILVA, GABRIEL SOUZA SANTOS DA, additional, Moraes, Brenda Cariolita Japiassu, additional, Pacheco, Letícia Mesquita, additional, Severiano, Marina Oliveira, additional, Locarno, Camila Salles, additional, Santana, Rhayssa Gomes de, additional, Parente, Ariana Ximenes, additional, Magalhães, Camila Holanda, additional, Araújo, Italo Magalhães de, additional, Escóssia, Lara Nogueira da, additional, Freitas, Bianca Batista Diniz, additional, Sousa, Cristiane Rodrigues de, additional, Lima, Isabelle Girão De Oliveira, additional, Mendes, Karen Soares, additional, Guimarães, Maria de Fátima de Menezes, additional, Pacheco, Beatriz Carvalho Costa Saunders, additional, Paiva, Vitor Sauwen, additional, Vasconcelos, Rafael Barroso, additional, Pinheiro, Murilo Magalhães Couto, additional, Gomes, Maria Eduarda Felicio Philomeno, additional, Vargas, Larissa Jardim, additional, Lucena, Talytta Marinho de, additional, Damasceno, Rachel Pinto, additional, Pinheiro, Maria Jéssica Alves, additional, Pacheco, Fernanda Vidal, additional, Pires, Maria Eugênia de Paula, additional, Rôlo, Brunno, additional, Leite, Fernanda Moema Mendes, additional, Coelho, Ricardo Soares, additional, Quintairos, Midia Quaresma, additional, Maia, Juliane Tayse Ribeiro, additional, Passos, Ana Letícia Leite Sena, additional, Alves, Dennis Ramos, additional, Silva, Juliana da Silva e, additional, Dias, Sheila Mara, additional, Teles, José Ricardo Silvestre, additional, Calisto, Sarah de Pádua, additional, Gomes, Amanda Maria Silva, additional, Tenório, Jordão Lima, additional, Santos, Maria Eugênia Cavalcante Ferreira, additional, Almeida, Maria Júlia Souza De, additional, Praxedes, Marianne Aguiar Vitório, additional, Lessa, Millena Medeiros Maux, additional, Oliveira, José João Felipe Costa De, additional, Monteiro, Beatriz Torres, additional, Monteiro, Pablo Felipe dos Santos, additional, Leite, Sarah Edwarda Silva, additional, Herrmann, Délia Maria de Moura Lima, additional, Nogueira, Janaína da Silva, additional, Souza, Natasha Kelly de, additional, Oliveira, Thayná Barbosa de, additional, Rezende, Sophia Oliveira, additional, Lacerda, Alessandra Silvestrini, additional, Pereira, Fabianny de Lima, additional, Fernandes, Rafaelle de Lima, additional, Montenegro, Raylson Araújo, additional, Fin, Lucas Grangeiro, additional, Pereira, Kallen Rayane, additional, Souza, Dhara Martins de, additional, Gomes, Gabriela Moraes, additional, Resende, Joana de Sousa, additional, Carneiro, Lucas, additional, Ferreira, Ana Iara Costa, additional, Nakashima, Fabiana, additional, Lins, Cynthia Dantas, additional, Martins, Antônio Carlos Sansevero, additional, Costa, Bianca Jorge Sequeira, additional, Briglia, Charlote Aguiar Buffi, additional, Ribeiro, Leila Braga, additional, Prudente, Ana Luiza Naves, additional, Luiz, Victória Carolinne Alves, additional, Garrote, Celso Henrique Denófrio, additional, Andrade, Gabriela Couto, additional, Reis, Isabella França dos, additional, Alves, Jéssica Caroline de Deus, additional, Nogueira, Lyandra Yuri Katsuyama, additional, Sampaio, Luiza Caroline Felicianno, additional, Rios, Mateus Felipe Batista, additional, Silvério, Natália Ribeiro, additional, Gomes, Sayonara Caetano de Almeida, additional, Perillo, Valentina Machado, additional, Musmanno, Luiz Henrique Fernandes, additional, Nogueira, Aline Araújo, additional, Leal, Maria Carolina Bezerra Di Medeiros, additional, Silva, Gabriella Damares Sousa, additional, Paula, Ana Cecília Nunes de, additional, Cassilhas, Amanda Bonzi, additional, Oliveira, Giovanna Viana Pereira de, additional, Lobato, Júlia Lacerda, additional, Silva, Matheus Felipe de Abreu, additional, Souza, Nathália Carvalho de, additional, Dias, Isabela Karina Silva, additional, Ribeiro, Sarah Carvalho, additional, Silva, Ana Carla Ferreira Lana e, additional, Dias, Anna Beatriz Soares, additional, Maciel, Ana Luísa dos Santos, additional, Vaz, Lucas Fahel, additional, Castelani, Laura Mendes Ferreira, additional, Pedrosa, Yago Ricardo, additional, Leite, Larissa Cruvinel, additional, Sperancini, Larissa Fontes Cal, additional, Santos, Bruno Cassiano dos, additional, Lopes, Lais de Almeida Fraga, additional, Campos, Lucas Bernardes Cerqueira, additional, Moreira, Silvia de Andrade Toscano Mendes, additional, OLIVEIRA, MARCELA MARINHO DE, additional, Pinheiro, Elisama Pereira, additional, Santos, Jehovahnna Anttonioni, additional, Campos, Orotides Silvestre, additional, Borges, Polliane, additional, Moral, Lorena Ramos, additional, Rego, Maria Eduarda Carvalho, additional, Oliveira, Beatriz Dias, additional, SPÓSITO, LAVÍNIA ARRUDA, additional, GOMES, CLARA DE SOUSA, additional, Belke, Giovana, additional, Goetten, Áquila Jasmile de Moraes, additional, lisboa, Débora DAgostini Jorge, additional, Rodrigues, Marina Machado, additional, Rottenfusser, Robson, additional, Belke, Larissa, additional, Pinheiro, Ana Carolina Sardo de Oliveira, additional, Lopes, Andrea Marcela dos Santos, additional, Leitão, Camilla Cristina Pereira, additional, Machado, Carla Leonor Melo Vinagre, additional, Barletta, César Augusto Barata, additional, Albuquerque, Fernanda Cristina de Oliveira, additional, Pereira, Giovana Duarte, additional, Carneiro, João Vítor Tavares, additional, Moura, Larissa Victória Barra De, additional, Gonçalves, Lorena Oliveira, additional, Pantoja, Marla Cavalca, additional, Ferreira, Thayane Thais Pantoja, additional, Souza, Vilma Francisca Hutim Gondim de, additional, Santos, Yasmin A, additional, Defeo, Sarah Furtado, additional, Mendes, Rafaella Yasmin Ribeiro, additional, Moreira, Saulo Guimarães, additional, Amaral, Bernardo Merrighi de Figueiredo, additional, Santos, Lucas Siqueira dos, additional, Diniz, Ana Carollina Simão, additional, Moraes, Luzia Reis Rabelo de, additional, Morais, Vanessa Maria Oliveira, additional, Vitral, Gabriela Luiza Nogueira, additional, Drumond, Ana Beatriz Carvalho, additional, Nasser, Arthur Kalil Santana, additional, Estácio, Rayssa Carolinne Costa Mota, additional, Albuquerque, Aglaé Travassos, additional, Teixeira, Amanda Monteiro, additional, Oliveira, Amanda Távora, additional, Souza, Anelise Marques Feitosa de, additional, Morais, Bruna Almeida de Souza, additional, Andrade, Gabrielle Barbosa Lima de, additional, Cunha, Isadora Valentina dos Santos, additional, Matias, Laís Costa, additional, Costa, Lara Letycia Araujo, additional, Farias, Mariana Guimarães Nolasco, additional, Graças, Thallita Vasconcelos das, additional, Lopes, Izailza Matos Dantas, additional, Fonseca, Fabiana Soares, additional, Baptista, Mauricio Da Costa, additional, Lima, Messilane Cavalcante, additional, Cardoso, Paula Thaís Sousa de Oliveira, additional, Silva, Victor Menezes, additional, Cardoso, Clarisse Angelim Soares, additional, Santos, Camila de Oliveira, additional, Silva, Gabriel Felipe SantAna, additional, Folgado, Gustavo Reis, additional, Gomes, Izabella Caroline Prado, additional, Santos, Lorena Ferreira, additional, Porto, Sofia Sturzeneker, additional, Nogueira, Janaina da Silva, additional, Moraes, Victória Eduarda Cavalcanti de, additional, Galdino, Ana Carolina Brito, additional, Monte, Elaine Cristina Barros Rocha Do, additional, Costa, Larissa Barbosa Caldas, additional, Cerqueira, Maria Layane De Oliveira, additional, Barros, Matheus Ramos De, additional, Silva, Paulo Henrique Alves Da, additional, Duarte, Rafaela Maria Bezerra, additional, Sirqueira, Sarah Valões Tenorio, additional, Varjão, Stephany Abdias, additional, Silva, Vitória Viana, additional, Lima, Zuíla Caroline Olegário, additional, MOREIRA, EMANUELLE ALVES, additional, Silva, Arthur Nunes, additional, Almeida, Anne Caroline Araújo, additional, Santos, Lislayle Silva, additional, Santos, Fernanda Marques da Silva, additional, Araújo, Anastácia Santiago, additional, REIS, FERNANDA FONTES PRADO, additional, Campos, Silvia Sayonara Silveira, additional, Faro, Caroliny Biasuz, additional, Teixeira, Giovanna de Jesus, additional, MOREIRA, GABRIELLE DOS SANTOS, additional, Oliveira, Isadora Ribeiro, additional, Oliveira, Halley Ferraro, additional, melo, Amélia soares de, additional, Coutinho, Camila Hostalacio Duarte, additional, Tolentino, Carolina Carvalho, additional, Alvarenga, Carolina Diniz, additional, Araújo, Carolina Muzzi Lamounier, additional, Santos, Clara Chagas Barbosa, additional, Bernardes, Clara Manoel, additional, Pimenta, Clara Rubião, additional, Lage, Fernanda Duarte Oliveira, additional, Murad, Gabriela Abreu, additional, Campos, Julia Pinheiro, additional, Sousa, Lara Oliveira, additional, Ferreira, João Vítor Gonçalves, additional, Meneses, Gustavo Henrique Rodrigues, additional, Ferreira, Thaize Prates, additional, Alves, Lucas Ferreira, additional, Gonçalves, Gleisy Kelly Neves, additional, Araújo, Allana Moura de, additional, Azevedo, Ana Carla Costa, additional, Silva, Arilson Lima da, additional, Andrade, Bianca Evelyn Piedade, additional, Reis, Giovana Silva Correa, additional, Kato, Jéssika Sayuri Campelo, additional, Castro, Lucas Matheus da Silva, additional, Santos, Mainã C S, additional, Fernandes, Maria Suely Bezerra, additional, Gonçalves, Paula Gabriela Nascimento, additional, Loureiro, Amanda Guimarães, additional, Moura, Ana L G, additional, Junqueira, Beatriz Guimarães, additional, Farias, Brenda P, additional, Kadosaki, Danilo Jun, additional, Gonçalves, Isabella Rocha, additional, Sousa, Karina Rodrigues de, additional, Rojas, Marcia de Fátima Maciel de, additional, Lourenço, Maria Fernanda, additional, Lira, Renata Barros, additional, Lobo, Giulli Catarini Cei Ribeiro, additional, Cardoso, Keise Bastos, additional, Silva, Ianka A, additional, Góes, Nicole Garcia dos Santos, additional, Andrade, Carla Hineida da Silva de, additional, Souza, Marilia Vitoria Santos de, additional, Santos, Mainã Cristina Santos dos, additional, Neves, Marcela Daun E Lorena Saraty, additional, Marques, Bruna Cristina Ribeiro, additional, Monteiro, Gabriel Felipe Perdigão Barros, additional, Carneiro, João Vitor Tavares, additional, Oliveira, Cláudia Maria Maciel de, additional, Corradi, Analaura, additional, Louzada, Cibelle Ferreira, additional, Oliveira, Déborah Luiza Vilela de, additional, Santos, Estevan Rodrigues dos, additional, Fernandes, Igor Cardoso, additional, Cruz, Olívia Maria Carvalho Lopes da, additional, Fernandes, Jairo Vinícius Cota, additional, Alves, Rayssa Stephanie Batista, additional, Oliveira, Pauline Pereira de, additional, Cruz, Karenn Santos Souza, additional, Santos, Áleff Edrei Gomes, additional, Silva, Francisca Roberta Oliveira, additional, Silva, Loranny Santana, additional, Trindade, Yasmin Tourinho Delmondes, additional, Silveira, Danilo José de Andrade Santos, additional, Bomfim, Raul, additional, Vieira, Arthur Sobral, additional, Araujo, Ângelo Marcel Santana Duarte, additional, Ferreira, Isabella Lisboa, additional, Santos, Eduarda Lavínia Carneiro, additional, Moreira, João Victor Aguiar, additional, Lima, Caio Augusto de, additional, Martins, Marcos Vinicius Teixeira, additional, Brito, Veronica Perius de, additional, Alves, Caroline Coutinho Horácio, additional, Coelho, Larissa Mariano, additional, Satokata, Alessandra Akemi Cury, additional, Ferro, Mateus da Silva, additional, Tavares, Eduardo Fellipe Capini de Almeida, additional, Henriques, Gustavo de Souza, additional, Canella, Douglas Alves Da Costa, additional, Lima, Monnyka Castro, additional, Calegari, Tatiany, additional, Campos, Carolina Calafiori de, additional, Yun, Chen Qi, additional, Ferreira, Gabrielly Gomes, additional, Ticianeli, Giovanna França, additional, Freitas, Isabella Gomes de, additional, Fonseca, Júlia Lana, additional, Cavalcanti, Isabela Lins, additional, Cavalcanti, Isabela de Farias, additional, Melo, Izabel rocha de, additional, Lemos, Letícia, additional, Soledade, Sarah Luanna Ferreira, additional, Souza, Renata Da Silva, additional, Gondim, Alice Batista, additional, Filetti, Benjamin Martinuzo, additional, Sousa, Débora Ferreira de, additional, Borges, Luan Machado, additional, Montenegro, Mariana Fonseca dos Santos, additional, Carvalho, Mateus de Souza Oliveira, additional, Serrano, Matheus Sampaio, additional, Oliveira, Pedro Henrique Duarte, additional, Ferreira, Phablo Fernandes José Oliveira, additional, Aragão, Rômulo Nunes, additional, Macedo, Tifany Cerqueira, additional, Santos, Vanessa Oliveira, additional, Raskin, Ana Pudles, additional, Fridman, Ana Flávia Wolff, additional, Nascimento, Eduarda Faria do, additional, BINKOWSKI, LUDMILA LIMPIAS TERRAZAS, additional, FULBER, JADE GUIMARÃES, additional, MANSUR, ISABELA MARTINS FERREIRA, additional, Fulber, Jade Guimarães, additional, Mansur, Isabela Martins Ferreira, additional, Binkowski, Ludmila Limpias Terrazas, additional, Leon, Cristiano do Amaral de, additional, Faccioli, Manoela Sauer, additional, Iglesias, Carolina da Mota, additional, Krug, Bruna Reis, additional, Dallapicola, Giovanni Reveilleau, additional, Severo, Bernardo Riveira Fernandes, additional, Tannhauser, Pedro Henrique Andreolio, additional, Bonamigo, Eduarda Rodrigues, additional, Goldmann, Ignacio Salonia, additional, Busin, Isabella Montemaggiore, additional, Marchese, Isabella Salzano, additional, Zanella, Alana, additional, Thofehrn, Stefano, additional, Zampieri, Giulia Parmeggiani, additional, Zanatta, Stefanie Flach, additional, Leon, Cristiano do A de, additional, Santos, Mariana Silva dos, additional, Moraes, Emanuela Caroline, additional, Seroiska, Gabriel, additional, Bischoff, Helena Marcon, additional, Schemkel, João Marcelo Libardoni, additional, Rosa, Rafael Fabiano Machado, additional, Rezende, Raíssa Queiroz, additional, Martins, Juliana Bezerra, additional, Rafael, Ana Luísa Bonfim, additional, Pontes, Nayanne Hardy Lima, additional, Fechine, José Ueides, additional, Melo, Amélia Soares de, additional, Coutinho, Camila Hostalácio Duarte, additional, Silveira, Nícolas Semaan, additional, Dayrell, Sophia Perrupato, additional, Ferreira, Rachel Myrrha, additional, Vasconcelos, Júlia Cunha, additional, Pereira, Juliana Monção Nippes, additional, Farnetti, Laura Normando, additional, Diniz, Letícia Tanure, additional, Ribeiro, Pedro Heleno Valente, additional, Faria, Regina Bicalho Gomes de, additional, Cruz, Victor Fonseca Alves da, additional, Fagundes, Eleonora Druve Tavares, additional, Resende, Paula Valladares Guerra, additional, Gontijo, Mariana Scapellato, additional, Pacheco, Gustavo G, additional, Almeida, Mileny C, additional, Melo, Luíza Alves, additional, Silva, Graciele Aparecida Bachião da, additional, Bernabé, Heloisa Cervantes, additional, Teixeira, Maria Laura Verissimo, additional, Silva, Grazielle lima, additional, Oliveira, Phablo Fernandes José, additional, Brito, Inácio Gomes de, additional, Albuquerque, Leonardo Siqueira, additional, Matos, Estela Mares Santos Salmito, additional, Andrade, Maria Denise Fernandes Carvalho de, additional, Bachur, Tatiana Paschoalette Rodrigues, additional, Leite, Alice Cristovão Delatorri, additional, Silva, Lucas Bagundes da, additional, Melo, Waldomauro Ferreira de, additional, Dias, Cláudio Gellis de Mattos, additional, Fecury, Amanda Alves, additional, Gonçalves, Ana Carolina Miranda, additional, Oliveira, Lara Carvalho de, additional, Gomes, Laura Vitória Melo, additional, Vital, Mabio Vigilato, additional, Silva, Marina Blanco, additional, Vasconcelos, Rafael Barroso de, additional, Silva, Fernanda Lacerda Santos, additional, Silva, Iara, additional, Messias, Beatriz Chiari, additional, Clemente, Alice França, additional, Rodrigues, Alícia Zeferino, additional, Lima, Messilene Cavalcanti, additional, Gusmão, Maria Brennda Ferreira de, additional, Leite, Débora Cristina Fontes, additional, Rodrigues, Letycia Santos, additional, and Ferreira, Nathálya Menezes de Menezes, additional

Published
2021
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41. INSTITUIÇÕES ESCOLARES: memórias e narrativas Coleção Instituições Escolares - Volume 1

Author

NASCIMENTO, NASCIMENTO, J. C., primary, FARIA, L. C. M., additional, SILVA JUNIOR, A. F., additional, CASINI, ADRIENA, additional, BALASSIANO, Ana Luiza Grillo, additional, CONCEIÇÃO, A. G., additional, MACHADO, Á. M. B., additional, REGIS, C. V., additional, PONTES, C. M., additional, DAVID, H. C. S., additional, SANTOS, H. H. M., additional, PINHEIRO JR., Jefte, additional, PISTILLI, J. C. T., additional, NASCIMENTO, J. C., additional, SANTOS, K. C. S., additional, BAROLDI, Karyne Alves, additional, SANTOS, K. C. S. M., additional, FONTANA, L. R., additional, and FERNANDES, MAURO, additional

Published
2020
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43. An in-depth exploration of recent advances and promising outlooks in biogas production.

Author

Rodrigues, Antonio Felipe da Silva, Sales, Misael B., Braz, Ana K. de S., Bizerra, Viviane de C., Sousa, Patrick da S., Lima, Rita K. C. de, Guimarães, Artemis P., Souza, Maria C. M. de, Lopes, Ada A. S., Junior, Paulo G. de S., Neto, Francisco S., Melo, Rafael L. F., Valério, Roberta B. R., Cavalcante, Antônio L. G., Lima, Ana M. da S., Serpa, Juliana de França, Rios, Maria A. de S., Vale, Maria do Socorro, and Santos, José C. S. dos

Subjects
BIOGAS production, SUSTAINABILITY, BIOGAS, MANUFACTURING processes, BIBLIOMETRICS, BIODEGRADATION
Abstract

Biogas is a product composed of a mixture of gases resulting from the biological decomposition of organic material, consisting primarily of methane gas and carbon dioxide, besides smaller amounts of other gases. The current study aims to comprehensively analyze waste-based biogas production to ensure sustainability in the biofuel production process. An advanced systematic bibliometric analysis using keywords, co-citations, and bibliographic coupling analysis was performed on 641 peer-reviewed articles from Web of Science to conclude this goal further. This analysis covers the period from 2000 to 2022, a little more than 20 years. The methodology used reveals several themes that have been identified and addressed in the articles: (1) the importance of the topic in academia by country in which they were analyzed; (2) sectors contributing to biofuel production; (3) equipment used in biofuel production; (4) the most cited waste sources in the database; (5) application purpose of biogas; (6) relevance of other energy sources; (7) areas of interest where biofuels are used; and (8) a comparison between the energy production capacity and the number of publications on the topic by country. Furthermore, the potentials, limitations, perspectives, and future trends highlighted to improve the production process are also considered. Therefore, the conclusion is that organic waste can be used in the sustainable production of goods with added value for society. [ABSTRACT FROM AUTHOR]

Published
2024
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44. A novel hybrid biocatalyst from immobilized Eversa® Transform 2.0 lipase and its application in biolubricant synthesis.

Author

Germano de Sousa, Isamayra, Valério Chaves, Anderson, de Oliveira, André Luiz Barros, da Silva Moreira, Katerine, Gonçalves de Sousa Junior, Paulo, Simão Neto, Francisco, Cristina Freitas de Carvalho, Simone, Bussons Rodrigues Valério, Roberta, Vieira Lima, Gledson, Sanders Lopes, Ada Amélia, Martins de Souza, Maria Cristiane, da Fonseca, Aluísio Marques, Fechine, Pierre Basílio Almeida, de Mattos, Marcos Carlos, and dos Santos, José C. S.

Subjects
LIPASES, GLUTARALDEHYDE, ENZYMES, FOURIER transform infrared spectroscopy, ORGANIC solvents, IONIC strength
Abstract

In this study, a Taguchi experimental design was used for optimzing the immobilization of the lipase Eversa® Transform 2.0 (EVS) onto a hybrid support consisting of chitosan (CHI) and agarose (AGA), with glutaraldehyde (GLU) used as the support activator. The biocatalyst obtained was characterized by X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetry (TGA), Energy Dispersive Spectroscopy (EDS), and Scanning Electron Microscopy (SEM). The optimized reaction conditions (60 min, 5 mM ionic strength, 1% GLU concentration, and 5 mg protein load per g of support) resulted in a highly active biocatalyst (74.39 ± 0.48 U/g) and delivered an immobilization yield of 74.20 ± 0.28%. The biocatalyst produced was observed to lose only 15.3% of its activity after 61 days of storage. The activity was also observed to increase by 96.70% ± 0.76, 27.34% ± 2.34, and 84.35% ± 1.68 in the presence of the organic solvents hexane, cyclohexane, and methanol, respectively. Additionally, the byocatalist showed more pronounced activity at temperatures above 50 °C and was still able to retain approximately 30% of it at 70 °C. These values were found to be higher at alkaline pHs, as the activity of Eversa® 2.0 Transform saw an increase of up to 140% at pH 9. The desorption tests performed did not reveal any enzymatic detachment from the support. The novel biocatalyst also showed promising ester-lubricating properties. Furthermore, the in silico study revealed a binding affinity of −5.1 kcal/mol between oleic acid and the enzyme, suggesting that the combination of the substrate and the lipase was more stable and therefore, suitable for esterification. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Validation and potential usefulness of the updated PROMISE minimal risk tool in patients with suspected coronary artery disease undergoing coronary CT angiography

Author

Lima, M R L, primary, Lopes, P L, additional, Albuquerque, F A, additional, Presume, J P, additional, Freitas, P F, additional, Silva, C S, additional, Guerreiro, S G, additional, Abecasis, J A, additional, Saraiva, C S, additional, Coutinho Santos, A C S, additional, Goncalves, P A G, additional, Mendes, M M, additional, Ferreira, A F, additional, and Marques, H M, additional

Published
2023
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47. Pediatria - Experiências Profissionais e Casos Clínicos - Volume 1

Author

Rodrigues, Daniela O W, primary, Teixeira, Maria T B, additional, Ribeiro, Luiz C, additional, Santos, Olivia F, additional, Sudário, Lysla C, additional, Sana, Dandara E M, additional, Campos, Estela M S, additional, Leitao, Maria H A, additional, Torres, Larissa M C, additional, Faria, Shirley C R B De, additional, Souza, Adriano R De, additional, Silva, Lúcio Petterson Tôrres da, additional, S, João A Da, additional, Carvalho, Joabson Araujo de, additional, Santos, Willian Da S, additional, Lima, Renan Rodrigues Ferreira, additional, Silva, João P Da, additional, Chaves, André F De C P, additional, Sousa, Ellen C Da C L, additional, Brandão, Tatiane Carvalho da Silva, additional, Almeida, Bárbara M P De, additional, Nascimento, Catarina N, additional, Guedes, Danielli De S, additional, Santos, Ellen A, additional, Filippetti, Eloah P, additional, Rodrigues, Flaviane, additional, Santos, Larissa R, additional, Lima, Rafael G F de, additional, Ratti, Ana F S, additional, Vieira, Élvio L R, additional, Katz, Cíntia R T, additional, Cardoso, Juliana A, additional, Barbosa, Amanda M F, additional, Oliveira, Maria C A, additional, Campos, Gheisa B, additional, Damasceno, Kairo S M, additional, Oliveira, Kayo E S, additional, Meggiolaro, Emilly D A, additional, Scheffer, Alice M, additional, Oliveira, Daniel G, additional, Romualdo, Dayeni M, additional, Nascimento, Karina V, additional, Bueno, Leonardo B, additional, Souza, Luiza S B, additional, Oliveira, Maísa F S, additional, Almeida, Valério L, additional, Oliveira, Kennedy M, additional, Alves, Waneska A, additional, Pitol, Guilherme, additional, Paulino, Rafaela, additional, Bielavski, Jaqueline G, additional, Barcellos, Bibiana B, additional, Maciel, Gabriela T, additional, Portz, Ana C, additional, Venzke, Bruna C, additional, Santi, Daniela F, additional, Osto, Bruna E T Dal, additional, Cunha, Acauã F da, additional, Quadros, Ana L T de, additional, Andrade, Vinicius, additional, Batista, Daniela W, additional, Angeloni, Mariana B, additional, Marchese, Giuliana M, additional, Araújo, Jaqueline B, additional, Sousa, Amanda T de, additional, A, Amaro J, additional, Santos, Bruno L dos, additional, Lopes, Isabella C de O, additional, Pires, Aline M, additional, Santos, Ana F F dos, additional, Lima, Beatriz M de, additional, Santos, Camila A G, additional, Cruvinel, Fernanda C D, additional, Santos, Gabriele R dos, additional, Soares, Jéssica Y S, additional, Gonçalves, João P R, additional, F, Joaquim E, additional, Rezende, Shamara C, additional, Muzetti, Wanessa F, additional, Rezende, Érica R M A, additional, Kock, Tatyana B C, additional, Carvalho, Anne C V, additional, Rezende, Nicole O, additional, Binato, Henrique A, additional, Cupertino, Marli C, additional, Reis, Monique P, additional, Bentes, Onézimo M, additional, Furtado, Pedro F G, additional, Macêdo, Ana E G, additional, Macêdo, Caroline G, additional, Nascimento, Dayane D, additional, Pagno, Andressa R, additional, Kratz, Cristiane de P, additional, Alves, Izabel A, additional, Silveira, Eugênia M de Q F B da, additional, Rios, Giovana B de M, additional, Prata, Ana B M, additional, Aragão, Ana C A, additional, Ramalho, Ana C M, additional, Coelho, Márcia G M, additional, Oliveira, Fernanda Pimentel de, additional, Oliveira, Neusa Camilla Cavalcante Andrade, additional, Amorim, Jussilene Alves, additional, Santos, Juliane M dos, additional, Barbosa, Suelen Rezende, additional, Oliveira, Mireli Sousa de, additional, Gentil, Laiane M F, additional, Brito, Viviane da Paz, additional, Silva, Edilene P da, additional, Sampaio, Elane de A, additional, Leite, Lígia V, additional, Santos, Mirella Raíra Ferreira da Silva, additional, Melo, Juliana Sampaio, additional, Ferreira, Karine R, additional, Silva, Danyele Holanda Da, additional, Fontenelle, Larissa C, additional, Morais, Rafael X B de, additional, Santos, André L B N dos, additional, Morais, Beatriz X B de, additional, Tavares, João F C J, 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2020
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48. EDUCAÇÃO E DEBATES CONTEMPORÂNEOS: uma (re)visita a teorias e práticas pedagógicas

Author

ANDRADE, Wendel Melo, primary, COSTA, G. P., additional, COLARES, G. S., additional, MOTA, A. P. A., additional, EVANGELISTA, A. D. G., additional, BARROS, A. M. C., additional, MONTEIRO, B. de S., additional, ALBUQUERQUE, Bruno Marques, additional, SANTANA, C. R. O., additional, SILVA, C.A., additional, Barbosa, Elisângela dos Santos Carlos, additional, ALMEIDA, E. R., additional, BARBOSA, F. G., additional, SANTOS, G. C. S., additional, OLIVEIRA, Joyce Carneiro de, additional, FERRO, K. E. F., additional, PASSOS, M. V. L., additional, FERREIRA, M. V. S., additional, SANTOS, R. L. C., additional, and OLIVEIRA, T. B., additional

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2020
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