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FUNIN - a fungal glycoside hydrolases 32 enzyme database for developing optimized inulinases

International journal of biological macromolecules 2025年 318卷 145050页

作者： Lucas Sousa Palmeira Gabriel Quintanilha-Peixoto Andréa Miura da Costa Emanuelle Santos de Carvalho Cardoso Ana Paula Trovatti Uetanabaro Jailan da Silva Sousa Danilo Silva Ronison A Guimarães Dener Eduardo Bortolini Joicymara S Xavier Bruno Andrade Aristóteles Góes-Neto Programa Interunidades de Pós-Graduação em Bioinformática Universidade Federal de Minas Gerais Belo Horizonte Brazil Laboratório de Bioinformática e Química Computacional Universidade Estadual do Sudoeste da Bahia Jequié Brazil. Centro de Biociências e Biotecnologia Universidade Estadual do Norte Fluminense Darcy Ribeiro Campos dos Goytacazes Brazil. Departmento de Ciências Biológicas Universidade Estadual de Santa Cruz Ilhéus Brazil. Computer Science Division Department of Mathematical Sciences Faculty of Science Stellenbosch University Stellenbosch South Africa Centre for Epidemic Response and Innovation (CERI) School of Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa. Laboratório de Biologia Molecular e Computacional de Fungos Instituo de Ciências Biológicas Universidade Federal de Minas Gerais Belo Horizonte Brazil. Centre for Epidemic Response and Innovation (CERI) School of Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Divisão de Ciências da Computação Instituto Tecnológico de Aeronáutica São José dos Campos Brazil. Laboratório de Bioinformática e Química Computacional Universidade Estadual do Sudoeste da Bahia Jequié Brazil INRAE Institut Agro STLO Rennes France. Laboratório de Biologia Molecular e Computacional de Fungos Instituo de Ciências Biológicas Universidade Federal de Minas Gerais Belo Horizonte Brazil. Electronic address: arigoesneto@***.

The enzymatic hydrolysis of inulin, a fructose-rich polysaccharide from plants like Agave spp., is crucial for bioethanol production. Fungal glycoside hydrolase family 32 (GH32) enzymes, especially inulinases, are central to this process, yet no dedicated database existed. To fill this gap, we developed FUNIN, a cloud-based, non-relational database cataloging and analyzing fungal GH32 enzymes relevant to inulin hydrolysis. Built with MongoDB and hosted on AWS, FUNIN integrates enzyme sequences, taxonomic data, physicochemical properties, and annotations from UniProt and InterPro via an automated ELT pipeline. Tools like CLEAN and ProtParam were used for EC number prediction and sequence characterization. The database currently includes 3420 GH32 enzymes, with strong representation from Ascomycota (91.2 %) and key genera such as Fusarium, Aspergillus, and Penicillium. Exo-inulinases (43.9 %), endo-inulinases (33.4 %), and invertases (21.6 %) dominate the dataset. These enzymes share conserved domains (PF00251-PF08244), acidic pI values, and moderate hydrophobicity. A network similarity analysis revealed structural conservation among exo-inulinases. FUNIN includes an automated monthly update via InterPro API, ensuring current data. Publicly accessible at http://***, FUNIN enables rapid data retrieval and supports the development of optimized enzyme cocktails for Agave-based bioethanol production.

关键词： Agave Bioethanol Cloud computing Fungal enzymes GH32 Inulinases NoSQL database

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An empirical study on the knowledge management practice in software testing 21

An empirical study on the knowledge management practice in s...

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21st Ibero-American Conference on Software Engineering, CIbSE 2018

作者： Maciel, Claudia P.C. De Souza, Erica Ferreira Vijaykumar, Nandamudi L. De Almeida Falbo, Ricardo Meinerz, Giovani Volnei Felizardo, Katia Romero Department of Computer Federal Technological University of Paraná/UTFPR Cornelio Procópio/PR Brazil Lab. of Comp and Applied Math. National Institute for Space Research INPE Sao José dos Campos/SP Brazil Department of Computer Science Federal University of Espirito Santo UFES Vitória/ES Brazil

ISBN: (纸本)9789584837547

Context: Software testing is a knowledge intensive process, and therefore can benefit from the use of experience gained from past projects. In this context, principles of Knowledge Management (KM) can be applied to promote knowledge capture and sharing as well as the emergence of new knowledge. In spite of this, there are only few studies that present practitioners' perspective about KM initiatives in software testing. Objective: The main goal of this study is to identify the perception of professionals in software testing on the use of KM initiatives in software engineering companies, such as KM adoption, potential benefits or hindrances. Method: A survey was conducted in software development companies. In addition, the survey results were compared with another survey conducted previously in the same context, however, with different objectives and target audience. 39 software companies participated in this research. Results: Testing planning activity and the test case reuse are the aspects that have received more attention by companies. A strategic planning and reuse of existing test cases have a potential to significantly reduce software development costs and time. Conclusion: Applying KM in software companies can bring several benefits in terms of quality of results, reduction costs, time and effort. However, it is still a challenge to be faced and tackled. © 2018 Conferencia Iberoamericana de Software Engineering - CIbSE. All Rights Reserved.

关键词： Software testing

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Knowledge management diagnostics in software development organizations: A systematic literature review 18

Knowledge management diagnostics in software development org...

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17th Brazilian Symposium on Software Quality, SBQS 2018

作者： Maciel, Claudia P.C. de Souza, Érica Ferreira de Almeia Falbo, Ricardo Felizardo, Katia Romero Vijaykumar, Nandamudi L. Department of Computer Federal Technological University of Paraná – UTFPR Cornélio Procópio Brazil Depart. of Computer Science Federal University of Espírito Santo – UFES Vitória ES Brazil Lab. of Computing and Applied Mathematics National Institute for Space Research – INPE São José dos Campos SP Brazil

ISBN: (纸本)9781450365659

Context: Managing knowledge is one of the main challenges for software development organizations. Thus, the principles of Knowledge Management (KM) are presented as determinant and effective factors for the software product quality. There are several approaches to applying KM in an organization. However, for a KM approaches to succeed, it is important to conduct a KM diagnostic in order to analyze the KM current state that already exists in the organization. Objective: The objective of this paper is to present the results of a Systematic Literature Review (SLR) conducted to summarize existing research on KM diagnostic in software development organizations. Method: SLR was performed by searching four electronic databases. We also performed backward snowballing from reference lists of selected studies. Results: From the SLR, we identified 24 studies addressing investigated differents approaches related to KM diagnostics in software development organizations. Conclusion: Based on our results we conclude that in the software engineering context the KM diagnosis practice still does not seem consolidated. © 2018 Association for Computing Machinery.

关键词： Knowledge management

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Analysis of the Brazilian Research Agencies using a Multicriteria Decision Aid known as TODIM

Analysis of the Brazilian Research Agencies using a Multicri...

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Annual IEEE Systems Conference

作者： Rachel F. Magalhães Luís A. D. Rangel Elton F. Sbruzzi Cairo L. Nascimento Michel C. R. Leles School of Industrial Metallurgical Engineering Universidade Federal Fluminense Volta Redonda - RJ Brazil Division of Computer Science Instituto Tecnológico de Aeronáutica São José dos Campos - SP Brazil Division of Electronic Engineering Instituto Tecnológico de Aeronáutica São José dos Campos - SP Brazil Department of Technology Universidade Federal de São Josédel-Rei Ouro Branco - MG Brazil

The objective of this work is to analyze the productivity of different Brazilian research promotion agencies by using the Method of Interactive Decision Making and Multicriteria (Acronym TODIM in Portuguese), which is based on the Prospect Theory. The proposed model classify of 22 Brazilian research agencies (two federal and twenty state agencies) under six different criteria. The result shows that some small state agencies are more efficient than bigger ones in terms of the funding/scholarship ratio. This suggests that the winner model could be further investigated to be promoted among the other agencies of this country.

关键词： Mathematical model Technological innovation Investment Decision making Terminology Productivity Government

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A Modified Symbiotic Organisms Search Algorithm Applied to Flow Shop Scheduling Problems

A Modified Symbiotic Organisms Search Algorithm Applied to F...

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Congress on Evolutionary Computation

作者： Leonardo R. Rodrigues João Paulo P. Gomes Ajalmar R. Rocha Neto Amauri H. Souza Electronics Division Institute of Aeronautics and Space São José dos Campos Brazil Department of Computer Science Federal University of Ceará Fortaleza Brazil

The Symbiotic Organism Search (SOS) algorithm is an optimization metaheuristic inspired by the symbiotic relationships that occur among organisms in nature. In the last few years, the SOS algorithm attracted increasing attention due to its good performance on various real-world problems, despite the fact that no specific parameter adjustment is required. In this paper, we propose an improved version of SOS by modifying the organisms selection strategy. In the proposed version of the algorithm, three organisms are selected from the population without having a predefined symbiotic relationship. Once the organisms are selected, an assignment step is conducted to assign each organism to a symbiotic relationship. We tested the performance of the proposed algorithm using twenty benchmark instances of the flow shop scheduling problem. We compared the results with the results obtained using the original SOS algorithm. The proposed modification improved the performance of the SOS algorithm in the search for the global optimum value in most of the instances.

关键词： Organisms Symbiosis Job shop scheduling Optimization Sociology Statistics

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Convolutional Neural Networks for Automatic Meter Reading

arXiv

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arXiv 2019年

作者： Laroca, Rayson Barroso, Victor Diniz, Matheus A. Gonçalves, Gabriel R. Schwartz, William Robson Menotti, David Federal University of Paraná Laboratory of Vision Robotics and Imaging Department of Informatics Av. Coronel Francisco Heráclito dos Santos 100 Curitiba81530-000 Brazil Federal University of Minas Gerais Smart Surveillance Interest Group Department of Computer Science Av. Antônio Carlos 6627 Belo Horizonte31270-010 Brazil

In this paper, we tackle Automatic Meter Reading (AMR) by leveraging the high capability of Convolutional Neural Networks (CNNs). We design a two-stage approach that employs the Fast-YOLO object detector for counter detection and evaluates three different CNN-based approaches for counter recognition. In the AMR literature, most datasets are not available to the research community since the images belong to a service company. In this sense, we introduce a new public dataset, called UFPR-AMR dataset, with 2,000 fully and manually annotated images. This dataset is, to the best of our knowledge, three times larger than the largest public dataset found in the literature and contains a well-defined evaluation protocol to assist the development and evaluation of AMR methods. Furthermore, we propose the use of a data augmentation technique to generate a balanced training set with many more examples to train the CNN models for counter recognition. In the proposed dataset, impressive results were obtained and a detailed speed/accuracy trade-off evaluation of each model was performed. In a public dataset, state-of-the-art results were achieved using less than 200 images for training. Copyright © 2019, The Authors. All rights reserved.

关键词： Public utilities

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Recursive Singular Spectrum Analysis Applied to the Design of a Trading System

Recursive Singular Spectrum Analysis Applied to the Design o...

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Annual IEEE Systems Conference

作者： Michel C. R. Leles Adriano S. Vale-Cardoso Mariana G. Moreira Elton F. Sbruzzi Cairo L. Nascimento Leonardo A. Mozelli Homero N. Guimarães Department of Technology Federal University of São João del-Rei Ouro Branco - MG Brazil Division of Computer Science Instituto Tecnológico de Aeronáutica São José dos Campos - SP Brazil Division of Electronic Engineering Instituto Tecnológico de Aeronáutica São José dos Campos - SP Brazil Department of Electronic Engineering Federal University of Minas Gerais Belo Horizonte - MG Brazil Department of Electrical Engineering Federal University of Minas Gerais Belo Horizonte - MG Brazil

Singular Spectrum Analysis (SSA) is a nonparametric approach that can be used to decompose a time series as trends, oscillations and noise. In online applications, the SSA algorithm must be recalculated for each new sample available. The so called Causal SSA have been used in this context. In this manuscript, an alternative version of online SSA, the Recursive SSA, is proposed as a technical indicator. Based on a “forgetting factor” parameter, it is possible to control the amount of previous samples that are used in the SSA algorithm. This functionality may confer adaptive and hybrid features, thereby providing a crucial characteristic to technical indicators. The Recursive SSA technical trading rules (SSA-TTR) are applied to the DJIA time-series and compared against popular technical indicators. The results show the advantages of SSA-TTR over the popular ones.

关键词： Time series analysis Oscillators Market research Spectral analysis Matrix decomposition Trajectory Economics

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Magnetic Control of Soft Chiral Phonons in PbTe

arXiv

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arXiv 2021年

作者： Baydin, Andrey Hernandez, Felix G.G. Rodriguez-Vega, Martin Okazaki, Anderson K. Tay, Fuyang Timothy Noe, G. Katayama, Ikufumi Takeda, Jun Nojiri, Hiroyuki Rappl, Paulo H.O. Abramof, Eduardo Fiete, Gregory A. Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Instituto de Física Universidade de São Paulo SP São Paulo05508-090 Brazil Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Laboratório Associado de Sensores e Materiais Instituto Nacional de Pesquisas Espaciais SP São José dos Campos12201-970 Brazil Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama240-8501 Japan Institute for Materials Research Tohoku University Sendai980-8577 Japan Department of Physics Northeastern University BostonMA02115 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

PbTe crystals have a soft transverse optical phonon mode in the terahertz frequency range, which is known to efficiently decay into heat-carrying acoustic phonons, resulting in anomalously low thermal conductivity. Here, we studied this phonon via polarization-dependent terahertz spectroscopy. We observed softening of this mode with decreasing temperature, indicative of incipient ferroelectricity, which we explain through a model including strong anharmonicity with a quartic displacement term. In magnetic fields up to 25 T, the phonon mode splits into two modes with opposite handedness, exhibiting circular dichroism. Their frequencies display Zeeman splitting together with an overall diamagnetic shift with increasing magnetic field. Using a group-theoretical approach, we demonstrate that these observations are the result of magnetic field-induced morphic changes in the crystal symmetries through the Lorentz force exerted on the lattice ions. Thus, our study reveals a novel process of controlling phonon properties in a soft ionic lattice by a strong magnetic field. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic fields

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Study of the Nonlinear Phenomena in Ceramic Dielectric Composite Used in Compact PFLs

Study of the Nonlinear Phenomena in Ceramic Dielectric Compo...

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IEEE Symposium Power Modulator

作者： J.O. Rossi E.G.L. Rangel L.P. Silva Neto E. Schamiloglu Plasma Laboratory National Institute for Space Research Sao Jose dos Campos Brazil Institute of Science and Technology Sao Paulo Federal University São Jose dos Campos Brazil Electrical and Computer Eng. Department University of New Mexico Albuquerque USA

This paper presents a study for developing a new composite for compact pulse forming lines (PFLs) in linear applications using PZT ceramic dispersed in the epoxy as the polymer matrix. This work is based on previous results obtained with polymer composites with barium titanate, which is not ideal for the PFL application due to the stronger nonlinearity characteristic presented by this type of ceramic.

关键词： Ceramics Polymers Capacitors Titanium compounds Barium Electric breakdown

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Multi-label Feature Selection Techniques for Hierarchical Multi-label Protein Function Prediction

Multi-label Feature Selection Techniques for Hierarchical Mu...

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International Joint Conference on Neural Networks

作者： Ricardo Cerri Rafael G. Mantovani Marcio P. Basgalupp Andre C. P. L. F. de Carvalho Department of Computer Science Federal University of São Carlos São Carlos SP Brazil Department of Computer Science University of São Paulo São Carlos SP Brazil Institute of Science and Technology - Federal University of São Paulo São José dos Campos SP Brazil

Protein Function Prediction is a complex Hierarchical Multi-label Classification task where the functional classes involved are organized in a hierarchy. While many Machine Learning methods have been proposed for this task, very few studies were performed for feature selection in such hierarchical scenarios. In this paper, we investigate feature selection techniques for hierarchical multi-label classification of protein functions. As decision trees are natural feature selectors, we rely on a hierarchical multi-label decision tree induction algorithm to extract features represented by the internal nodes of the tree. We also investigated the performance of a ReliefF-based non-hierarchical multi-label feature selection technique on the hierarchical scenario. We tested the different techniques on two classifiers, based on neural networks and genetic algorithms. The experimental results show that, in very few cases, the existing feature selection techniques were able to improve the classifiers performances, showing the need for developing feature selectors specifically to consider hierarchical class relationships.

关键词： Feature extraction Decision trees Proteins Electronic mail Machine learning Neural networks Euclidean distance

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