Article
Computer Science, Artificial Intelligence
Yifan Shi, Zhiwen Yu, Wenming Cao, C. L. Philip Chen, Hau-San Wong, Guoqiang Han
Summary: In this work, an active density peak (ADP) clustering algorithm is proposed to improve the performance of semisupervised clustering by considering both representativeness and informativeness. A fast-update-strategy is designed to efficiently update labels, and an active clustering ensemble framework is introduced to better separate clusters. The experiments demonstrate the effectiveness of the proposed methods compared to the state-of-the-art methods on real-world data sets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Yu-Quan Zhu, Hua Zhou, Juncai Dong, Si-Min Xu, Ming Xu, Lirong Zheng, Qian Xu, Lina Ma, Zhenhua Li, Mingfei Shao, Haohong Duan
Summary: Transition-metal-based oxyhydroxides are efficient catalysts for biomass electrooxidation, but the identification of active sites is still challenging. In this study, cobalt oxyhydroxide (CoOOH) was used as an archetype, and the electrocatalytic glucose oxidation reaction (GOR) was used as a model reaction to track the dynamic transformation of the catalyst's electronic and atomic structure. Two types of reducible Co3+-oxo species were identified, including adsorbed hydroxyl on Co3+ ion (mu(1)-OH-Co3+) and di-Co3+-bridged lattice oxygen (mu(2)-O-Co3+). Theoretical calculations revealed that mu(1)-OH-Co3+ was responsible for oxygenation, while mu(2)-O-Co3+ mainly contributed to dehydrogenation, both playing crucial roles in the glucose-to-formate transformation. This work provides a framework for understanding the complex near-surface chemistry of metal oxyhydroxides in biomass electrorefining.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2023)
Article
Engineering, Marine
Zi-Lu Ouyang, Gang Chen, Zao-Jian Zou
Summary: A fast and accurate nonparametric modeling method, based on local Gaussian process regression (LGPR), is proposed for the identification modeling and prediction of ship maneuvering motion. The training dataset is automatically divided into clusters using the k-means algorithm. Local nonparametric models are identified based on the data in each cluster, reducing the computational cost compared to the classic Gaussian process regression (CGPR). Experimental data from the KVLCC2 tanker and an unmanned surface vehicle (USV) are used to identify the models, and the predictions of maneuvers not included in the training data show that LGPR has higher computational efficiency with acceptable accuracy.
Article
Chemistry, Physical
Lin Hu, Hemanth Somarajan Pillai, Corbin Feit, Kaige Shi, Zhengning Gao, Parag Banerjee, Hongliang Xin, Xiaofeng Feng
Summary: In this study, Ru nanoparticles prepared by atomic layer deposition were found to exhibit size-dependent activity, with the highest specific activity observed on 3.8 nm particles and a 5-fold decrease in activity on 8.4 nm particles. Density functional theory calculations and free energy analysis identified the Ru D-5 step site on 4 nm particles as the active site for NRR on Ru.
ACS ENERGY LETTERS
(2022)
Article
Chemistry, Multidisciplinary
Konstantin Khivantsev, Nicholas R. Jaegers, Ja-Hun Kwak, Janos Szanyi, Libor Kovarik
Summary: The study identified specific sites on the surface of gamma-alumina responsible for its catalytic activity and elucidated their nature and role in the reaction.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2021)
Article
Engineering, Electrical & Electronic
Cunxiang Xie, Limin Zhang, Zhaogen Zhong
Summary: This study proposes a few-shot unsupervised SEI method, which preprocesses RF signals, trains latent vectors with deep learning networks, and clusters and labels training samples using density peak clustering and autoencoder networks. Experimental results show that this method efficiently performs SEI in few-shot scenarios.
IEEE SENSORS JOURNAL
(2022)
Article
Chemistry, Multidisciplinary
Youngho Kang, Sungwoo Kang, Seungwu Han
Summary: Based on density functional theory calculations, it was found that in transition metal- and nitrogen-codoped graphene, single Zn atoms do not serve as active sites for CO production in CO2 reduction reactions, while the nearest neighbor C atom (C-NN) exhibits high activity and the Zn atom enhances the catalytic activity of C-NN. The study also revealed that *COOH formation is favorable at the initial electrochemical step on the C-NN site, and each reaction step becomes downhill in energy at small applied potentials, elucidating the origin of the CO2 reduction activity.
Article
Chemistry, Multidisciplinary
Chen Wang, Panlong Zhai, Mingyue Xia, Wei Liu, Junfeng Gao, Licheng Sun, Jungang Hou
Summary: This study reports the reversible reconstruction behavior accompanied by copper dynamic evolution, leading to the formation of high-valent cobalt species in active sites during water-alkali electrolysis. The crucial roles of electronic structure evolution and oxygen-vacancy-site mechanism in catalyst activity are revealed through experimental and theoretical investigations.
ADVANCED MATERIALS
(2023)
Review
Chemistry, Physical
Dongge Wang, Juanxia Wu, Liying Jiao, Liming Xie
Summary: The rational design of efficient, low-cost, and durable catalysts is crucial for industrial applications of electrocatalytic hydrogen production. Identifying the catalytic active sites in real-time for the hydrogen evolution reaction (HER) is a key step towards designing high-performance catalysts, but it poses great challenges. This review summarizes recent advances in the in situ investigation of active sites on low-dimensional catalysts for HER and highlights various characterization techniques used for this purpose. Future opportunities in this emerging field are also discussed, along with the current technical limitations.
Article
Construction & Building Technology
Alireza Mostafavi, Young-Jin Cha
Summary: Although construction noise pollution is a serious issue for governments in metropolitan cities, there is currently no effective and practical solution. This paper proposes a novel deep-learning-based feedforward ANC controller that takes into account the delay and nonlinear behavior of acoustic devices to attenuate construction-related noise. The developed network, with approximately 128,500 parameters, can be expanded to a multi-channel ANC method without increasing computational costs and is suitable for open space environments like construction sites. It achieved broadband noise attenuation of around 8.3 dB for a wide variety of construction noises, with minor degradation at very high-frequency ranges (7.5-8 kHz). The presented network outperformed traditional and state-of-the-art ANC algorithms.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Chemistry, Physical
Rongpeng Ma, Xian Wang, Xiaolong Yang, Yang Li, Changpeng Liu, Junjie Ge, Wei Xing
Summary: In this study, a Ru-based catalyst comprising of both Ru nanoclusters and Ru single sites was reported. It demonstrated higher activity and mass activity than Pt catalyst under acidic conditions. By precisely controlling the dispersion status of the catalysts, it was revealed that Ru nanoclusters actively catalyze the hydrogen evolution reaction (HER) via a Volmer-Tafel mechanism, while Ru single atom sites barely catalyze HER.
Article
Chemistry, Multidisciplinary
Cameron L. Bentley, Lachlan F. Gaudin, Minkyung Kang
Summary: Local voltammetric analysis with a scanning electrochemical droplet cell technique, in combination with a new data processing protocol, is used to directly identify previously unseen regions of elevated electrocatalytic activity on the basal plane of molybdenum disulfide. Understanding the nature of these microscopic catalytic active sites is crucial for the rational design of renewable fuel production materials.
CHEMICAL COMMUNICATIONS
(2023)
Article
Materials Science, Multidisciplinary
Guilherme M. Pereira, Thelma S. P. Cellet, Manuel E. G. Winkler, Adley F. Rubira, Rafael Silva
Summary: One of the major challenges in clean energy research is to develop simple, cost-effective, and feasible strategies for catalyst preparation. In this study, N-doped carbonaceous catalysts were synthesized using a cost-effective method, which can be used for both the anode and cathode of fuel cells. The presence of metal during the pyrolysis process is crucial for generating active sites on carbon structures, although the activities for oxygen reduction reaction (ORR) and hydrazine oxidation reaction (HzOR) mainly come from N-doped carbon moieties.
MATERIALS CHEMISTRY AND PHYSICS
(2023)
Article
Forestry
Yini Zhang, Xianyin Ding, Qifu Luan, Jingmin Jiang, Shu Diao
Summary: In this study, CYP720B candidate genes were identified and classified in slash pine and loblolly pine based on genome and transcriptome data. Most of the genes showed higher expression levels in roots and stems, which corresponded with the detection of resin components, indicating the importance of roots and stems in resin biosynthesis.
Article
Chemistry, Physical
Kunal Lodaya, Nathan D. Ricke, Kelly Chen, Troy Van Voorhis
Summary: Graphite-conjugated catalysts (GCCs) combine advantages of heterogeneous and homogeneous catalysts and aryl-pyridinium active sites are effective non-metal catalysts for the oxygen reduction reaction (ORR). Structural and electronic analysis of nitrogen-containing aromatic molecules identified carbon atoms ortho or para to nitrogen and at the edge of aromatic systems as active sites for binding O-2 in ORR initiation. Machine learning models trained on both structural and electronic features successfully identified catalyst active sites, with electronic features having the greatest impact on model performance.
JOURNAL OF PHYSICAL CHEMISTRY C
(2023)
Article
Biochemistry & Molecular Biology
Diego Mariano, Pedro Martins, Lucianna Helene Santos, Raquel Cardoso de Melo-Minardi
BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
(2019)
Article
Biochemistry & Molecular Biology
Leon Sulfierry Correa Costa, Diego Cesar Batista Mariano, Rafael Eduardo Oliveira Rocha, Johannes Kraml, Carlos Henrique da Silveira, Klaus Roman Liedl, Raquel Cardoso de Melo-Minardi, Leonardo Henrique Franca de Lima
Article
Biochemical Research Methods
Vagner S. Ribeiro, Charles A. Santana, Alexandre V. Fassio, Fabio R. Cerqueira, Carlos H. da Silveira, Joao P. R. Romanelli, Adriana Patarroyo-Vargas, Maria G. A. Oliveira, Valdete Goncalves-Almeida, Sandro C. Izidoro, Raquel C. de Melo-Minardi, Sabrina de A. Silveira
BMC BIOINFORMATICS
(2020)
Article
Biochemical Research Methods
Jose Renato M. S. Barroso, Diego Mariano, Sandro R. Dias, Rafael E. O. Rocha, Lucianna H. Santos, Ronaldo A. P. Nagem, Raquel C. de Melo-Minardi
BMC BIOINFORMATICS
(2020)
Article
Cell Biology
Diego Mariano, Naiara Pantuza, Lucianna H. Santos, Rafael E. O. Rocha, Leonardo H. F. de Lima, Lucas Bleicher, Raquel Cardoso de Melo-Minardi
BMC MOLECULAR AND CELL BIOLOGY
(2020)
Review
Biochemistry & Molecular Biology
Ryung Rae Kim, Zheng Chen, Timothy J. Mann, Karine Bastard, Kieran F. Scott, W. Bret Church
Article
Biochemical Research Methods
Charles A. Santana, Sabrina de A. Silveira, Joao P. A. Moraes, Sandro C. Izidoro, Raquel C. de Melo-Minardi, Antonio J. M. Ribeiro, Jonathan D. Tyzack, Neera Borkakoti, Janet M. Thornton
Article
Biochemical Research Methods
Pedro M. Martins, Lucianna H. Santos, Diego Mariano, Felippe C. Queiroz, Luana L. Bastos, Isabela de S. Gomes, Pedro H. C. Fischer, Rafael E. O. Rocha, Sabrina A. Silveira, Leonardo H. F. de Lima, Mariana T. Q. de Magalhaes, Maria G. A. Oliveira, Raquel C. de Melo-Minardi
Summary: Propedia is a comprehensive database that contains over 19,000 high-resolution protein-peptide complex structures, allowing for multi-faceted analysis and comparison of peptides through a hybrid clustering algorithm, supporting rational peptide design and research.
BMC BIOINFORMATICS
(2021)
Article
Oncology
Trisha Dwight, Edward Kim, Karine Bastard, Diana E. Benn, Graeme Eisenhofer, Susan Richter, Massimo Mannelli, Elena Rapizzi, Aleksander Prejbisz, Mariola Peczkowska, Karel Pacak, Roderick Clifton-Bligh
Summary: Somatic mutations in EPAS1 are associated with various phenotypes, while the pathogenic potential of germline variants remains unclear. Functional studies showed that three germline variants of EPAS1 have similarities to somatic activating mutations in PPGL, suggesting they may act as modifiers in the expression of PPGL.
ENDOCRINE-RELATED CANCER
(2021)
Article
Biology
Vinicius de Almeida Paiva, Isabela de Souza Gomes, Cleiton Rodrigues Monteiro, Murillo Ventura Mendonca, Pedro Magalhaes Martins, Charles Abreu Santana, Valdete Goncalves-Almeida, Sandro Carvalho Izidoro, Raquel Cardoso de Melo-Minardi, Sabrina de Azevedo Silveira
Summary: Proteins play crucial roles in organisms, performing various functions in cells. Resources for protein structural bioinformatics, such as modeling, docking, dynamics, interaction, and mutation analysis, are usually scattered across online repositories, making it challenging for students and professionals to know which resources to use and where to find them. This paper introduces the main subareas of protein structural bioinformatics and discusses several online resources, aiming to provide an overview of this research field and serve as a starting point for those interested in building competencies in this area.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Chemistry, Medicinal
Alexandre Fassio, Laura Shub, Luca Ponzoni, Jessica McKinley, Matthew J. O'Meara, Rafaela S. Ferreira, Michael J. Keiser, Raquel C. de Melo Minardi
Summary: This paper introduces a machine learning-based drug discovery method that utilizes the LUNA toolkit to calculate and encode protein-ligand interactions into new fingerprints. The method also provides visual strategies for interpretable fingerprints. Experimental results show that this method outperforms traditional fingerprints in reproducing scores and identifying similarities. Therefore, LUNA and its interface fingerprints are promising approaches for machine learning-based drug discovery.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Vivian Morais Paixao, Raquel C. de Melo Minardi
ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, BSB 2022
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Lucas Moraes dos Santos, Raquel C. de Melo Minardi
Summary: This work presents the development of a convolutional neural network model to identify large scale conformational changes in proteins, using images that illustrate the interatomic distance matrices. The model achieves satisfactory results in cross validation, successfully distinguishing open and closed states of the S protein from SARS-CoV-2. The model shows potential for identifying even more subtle conformational changes in the future.
ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, BSB 2022
(2022)
Article
Biochemical Research Methods
Alexandre V. Fassio, Lucianna H. Santos, Sabrina A. Silveira, Rafaela S. Ferreira, Raquel C. de Melo-Minardi
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2020)
Article
Computer Science, Cybernetics
Marcos F. M. Silva, Pedro M. Martins, Diego C. B. Mariano, Lucianna Helene Santos, Isabela Pastorini, Naiara Pantuza, Cristiane N. Nobre, Raquel C. de Melo-Minardi, Proteingo Players
ENTERTAINMENT COMPUTING
(2019)