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Molecular characterization of transesterification activity of novel lipase family I.1

Bioscience Reports 2022年第10期42卷 BSR20220654页

作者： Haryati, Titin Widhiastuty, Made Puspasari Warganegara, Fida Madayanti Akhmaloka, Akhmaloka Doctoral Program of Chemistry Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jl. Ganesha 10 Jawa Barat Bandung 40132 Indonesia National Research and Innovation Agency Gedung B.J. Habibie Jalan M.H. Thamrin Nomor 8 Jakarta Pusat 10340 Indonesia Biochemistry Research Group Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jl. Ganesha 10 Jawa Barat Bandung 40132 Indonesia Department of Chemistry Faculty of Science and Computer Universitas Pertamina Jl. Teuku Nyak Arief Jakarta Jakarta Selatan 12220 Indonesia

Lipase's thermostability and organic solvent tolerance are two crucial properties that enable it to function as a biocatalyst. The present study examined the characteristics of two recombinant thermostable lipases (Lk2, Lk3) based on transesterification activity. Conversion of C12-C18 methyl ester with paranitrophenol was investigated in various organic solvent. Both lipases exhibited activity on difference carbon chain length (C12 - C18, C18:1, C18:2) of substrates. The activity of Lk2 was higher in each of substrate compared with that of Lk3. Experimental findings showed that the best substrates for Lk2 and Lk3 are C18:1 and C18:2 respectively, in agreement with the computational analysis. The activity of both enzymes prefers on nonpolar solvent. On nonpolar solvent the enzymes are able to keep its native folding shown by the value of radius gyration, solvent-enzyme interaction and orientation of triad catalytic residues. Lk3 appeared to be more thermostable, with maximum activity at 55°C. The presence of Fe3+ increased the activity of Lk2 and Lk3. However, the activity of both enzymes were dramatically decreased by the present of Ca2+ despite of the enzymes belong to family I.1 lipase known as calcium dependent enzyme. Molecular analysis on His loop of Lk2 and Lk3 on the present of Ca2+ showed that there were shifting on the orientation of catalytic triad residues. All the data suggest that Lk2 and Lk3 are novel lipase on the family I.1 and both lipase available as a biocatalyst candidate. © 2022 The Author(s).

关键词： Lipase recombinant molecular docking molecular interactions Solvent tolerance Thermostability Transesterification

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On Solving a class of stochastic multiobjective integer linear programming problems with Interactive Based Approach 1

On Solving a class of stochastic multiobjective integer line...

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1st International Conference on computer science and Applied Mathematic, ICCSAM 2018

作者： Suparni Mawengkang, Herman Sitompul, Opim Salim Suwilo, Saib Mathematics Doctorate Program University of Sumatera Utara Medan Indonesia Department of Mathematics University of Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia

Decision problems of stochastic or probabilistic optimization arise when certain coefficient of an optimization model are not fixed or known but are instead, to some extent, stochastic(or random or probabilistic) quantities. This paper focused on multiobjective stochastic optimization. We propose a method for solving a multiobjective chance constraints integer programming problem based on interactive approach. We assume that there is randomness in the right-hand sides of the constraints only and that the random variables are normally distributed. Some examples are presented. © Published under licence by IOP Publishing Ltd.

关键词： Integer programming

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Temporal motifs in patent opposition and collaboration networks

arXiv

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arXiv 2021年

作者： Liu, Penghang Masuda, Naoki Kito, Tomomi Sarıyüce, Ahmet Erdem Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY United States Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Faculty of Science and Engineering Waseda University Tokyo Japan

Patents are intellectual properties that reflect innovative activities of companies and organizations. The literature is rich with the studies that analyze the citations among the patents and the collaboration relations among companies that own the patents. However, the adversarial relations between the patent owners are not as well investigated. One proxy to model such relations is the patent opposition, which is a legal activity in which a company challenges the validity of a patent. Characterizing the patent oppositions, collaborations, and the interplay between them can help better understand the companies’ business strategies. Temporality matters in this context as the order and frequency of oppositions and collaborations characterize their interplay. In this study, we construct a two-layer temporal network to model the patent oppositions and collaborations among the companies. We utilize temporal motifs to analyze the oppositions and collaborations from structural and temporal perspectives. We first characterize the frequent motifs in patent oppositions and investigate how often the companies of different sizes attack other companies. We show that large companies tend to engage in opposition with multiple companies. Then we analyze the temporal interplay between collaborations and oppositions. We find that two adversarial companies are more likely to collaborate in the future than two collaborating companies oppose each other in the future. © 2021, CC BY.

关键词： Patents and inventions

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Digital Audio Watermarking by Quantization Embedding System 15th

Digital Audio Watermarking by Quantization Embedding System

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2019

作者： Chen, Ching-Ju Zhao, Ming Chen, Shuo-Tsung Lin, Meng-Ju College of Future Bachelor Program in Interdisciplinary Studies National Yunlin University of Science and Technology DouliuYunlin Taiwan64002 Taiwan School of Computer Science Yangtze University Jingzhou CityHubei Province China Department of Applied Mathematics Tunghai University No. 1727 Sec. 4 Taiwan Boulevard TaichungTaiwan40704 Taiwan National Yunlin University of Science and Technology DouliuYunlin Taiwan64002 Taiwan

ISBN: (纸本)9789811397134

This paper designs a digital audio watermarking scheme using a quantization embedding system in the wavelet domain. In this paper, the relationship between the watermarked coefficients and the original coefficients is first presented and then the quantization-based embedding rules are combined into a quantization-based embedding system that incorporates a system state. For the embedding in this system, the information includes synchronization codes and the watermarks are embedded into the DWT lowest frequency sub-band coefficients, which can be described by a matrix form. Moreover, the SNR is rewritten as a performance index which is a quadratic form. Then, a nonlinear function connecting the performance index and the embedding system is derived. Accordingly, an optimization-based embedding system is proposed. Finally, the Lagrange Principle is used to obtain the optimal solution which is an important formula for watermarking. In addition, the hidden information can be extracted without knowledge of the original audio. In the experiments, the performance of the proposed system is tested and the results verify the high SNR and strong robustness against multimedia signal processing or malicious attacks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Audio watermarking

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Fractal geometry predicts dynamic differences in structural and functional connectomes

arXiv

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arXiv 2025年

作者： Rădulescu, Anca Kaslik, Eva Fikl, Alexandru Nakuci, Johan Muldoon, Sarah Anderson, Michael Department of Mathematics SUNY New Paltz United States Department of Computer Science West University of Timișoara Romania Institute for Advanced Environmental Research West University of Timișoara Romania U.S. ARMY DEVCOM Army Research Laboratory United States Department of Mathematics Institute for Artificial Intelligence and Data Science Neuroscience Program University of Buffalo SUNY United States Department of Data Analytics Georgia Tech United States

Understanding the intricate architecture of brain networks and its connection to brain function is essential for deciphering the underlying principles of cognition and disease. While, traditional graph-theoretical measures have been widely used to characterize these networks, they often fail to fully capture the emergent properties of large-scale neural dynamics. Here, we introduce an alternative approach to quantify brain networks that is rooted in complex dynamics, fractal geometry, and asymptotic analysis. We apply these concepts to brain connectomes and demonstrate how quadratic iterations and geometric properties of Mandelbrot-like sets can provide novel insights into structural and functional network dynamics. Our findings reveal fundamental distinctions between structural (positive) and functional (signed) connectomes, such as the shift of cusp orientation and the variability in equi-M set geometry. Notably, structural connectomes exhibit more robust, predictable features, while functional connectomes show increased variability for non-trivial tasks. We further demonstrate that traditional graph-theoretical measures, when applied separately to the positive and negative sub-networks of functional connectomes, fail to fully capture their dynamic complexity. Instead, size and shape-based invariants of the equi-M set effectively differentiate between rest and emotional task states, which highlights their potential as superior markers of emergent network dynamics. These results suggest that incorporating fractal-based methods into network neuroscience provides a powerful tool for understanding how information flows in natural systems beyond static connectivity measures, while maintaining their simplicity. © 2025, CC BY.

关键词： Fractal dimension

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SCALE RELIANT INFERENCE

arXiv

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arXiv 2022年

作者： Nixon, Michelle Pistner McGovern, Kyle C. Letourneau, Jeffrey David, Lawrence A. Lazar, Nicole A. Mukherjee, Sayan Silverman, Justin D. College of Information Sciences and Technology The Pennsylvania State University United States Program in Bioinformatics and Genomics The Pennsylvania State University United States Department of Molecular Genetics and Microbiology Duke University United States Department of Statistics The Pennsylvania State University United States Huck Institutes for the Life Sciences The Pennsylvania State University United States Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University United States Center for Scalable Data Analytics and Artificial Intelligence University of Leipzig Germany Max Planck Institute for Mathematics in the Natural Sciences Germany Department of Medicine The Pennsylvania State University United States

Many scientific fields, including human gut microbiome science, collect multivariate count data where the sum of the counts is unrelated to the scale of the underlying system being measured (e.g., total microbial load in a subject’s colon). This disconnect complicates downstream analyses such as differential analysis in case-control studies. This article is motivated by a novel study of in vitro human gut microbiome models. Popular tools for analyzing these data led to dramatically elevated rates of both false positives and false negatives. To understand those failures, we provide a formal problem statement that frames these challenges of scale in terms of the classical theory of identifiability. We call this the problem of Scale Reliant Inference (SRI). We use this formulation to prove fundamental limits on SRI in terms of criteria such as consistency and type-I error control. We show that the failures of existing methods stem from a fundamental failure to properly quantify uncertainty in the system scale. We demonstrate that a particular type of Bayesian model called a Bayesian Partially Identified Model (PIMs) can correctly quantify uncertainty in SRI. We introduce Scale Simulation Random Variables (SSRVs) as a flexible and efficient approach to specifying and inferring Bayesian PIMs. In the context of both real and simulated data, we find SSRVs drastically decrease type-I and type-II error rates. © 2022, CC BY.

关键词： Bayesian networks

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Spectral Top-Down Recovery of Latent Tree Models

arXiv

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arXiv 2021年

作者： Aizenbud, Yariv Jaffe, Ariel Wang, Meng Hu, Amber Amsel, Noah Nadler, Boaz Chang, Joseph T. Kluger, Yuval Program in Applied Mathematics Yale University New HavenCT06511 United States Department of Computer Science Weizmann Institute of Science Rehovot76100 Israel Department of Statistics Yale University New HavenCT06520 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Department of Pathology Yale University New HavenCT06511 United States

Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size. For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps. First, recover the structure separately of multiple, possibly random subsets of the terminal nodes. Second, merge the resulting subtrees to form a full tree. Here, we develop Spectral Top-Down Recovery (STDR), a deterministic divide-and-conquer approach to infer large latent tree models. Unlike previous methods, STDR partitions the terminal nodes in a non random way, based on the Fiedler vector of a suitable Laplacian matrix related to the observed nodes. We prove that under certain conditions, this partitioning is consistent with the tree structure. This, in turn, leads to a significantly simpler merging procedure of the small subtrees. We prove that STDR is statistically consistent and bound the number of samples required to accurately recover the tree with high probability. Using simulated data from several common tree models in phylogenetics, we demonstrate that STDR has a significant advantage in terms of runtime, with improved or similar accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Recovery

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A Neural Network Based Modeling of Closed Room Thermal Comfort Environmental Prediction for Sensor Hub

A Neural Network Based Modeling of Closed Room Thermal Comfo...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Pornpawee Thongkhome Natasha Dejdumrong Science-Mathematics Program Triamudomsuksa Pattanakarn School Bangkok Thailand Dept. of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

ISBN: (数字)9781728164861

ISBN: (纸本)9781728164878

Currently, environmental protection and intelligence system are topics of interest in smart homes and intelligent buildings. This work represents an environmental prediction study of the closed room which installed a split-type air-conditioning system and a newly designed sensor hub using a feed-forward back-propagation artificial neural network-based prediction for human thermal comfort and indirectly concerned to the growth or existence of diseases, contagious diseases and allergies. The RPROP algorithm was utilized to train the closed room environmental parameters that get from sensor hub especial relative humidity in the ANN model and validate the model by using cohen's kappa and ROC curve to represents the degree of accuracy and reliability. The result obtained from the ANN trained data agreed closely with those obtained from a sensor hub with the high perfect agreement. The benefit of the ANN-based strategy in this study could be utilized for the design of a sensor hub that can realize the adaptive adjustment of environmental parameters through the air conditioning system.

关键词： Humidity Artificial neural networks Predictive models Buildings Temperature sensors Computational modeling Prediction algorithms

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Two-dimensional Bose-Hubbard model for helium on graphene

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Physical Review B 2021年第23期103卷 235414-235414页

作者： Jiangyong Yu Ethan Lauricella Mohamed Elsayed Kenneth Shepherd, Jr. Nathan S. Nichols Todd Lombardi Sang Wook Kim Carlos Wexler Juan M. Vanegas Taras Lakoba Valeri N. Kotov Adrian Del Maestro Department of Physics University of Vermont Burlington Vermont 05405 USA Materials Science Program University of Vermont Burlington Vermont 05405 USA Department of Physics and Astronomy University of Missouri Columbia Missouri 65211 USA Department of Mathematics & Statistics University of Vermont Burlington Vermont 05405 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

An exciting development in the field of correlated systems is the possibility of realizing two-dimensional (2D) phases of quantum matter. For a system of bosons, an example of strong correlations manifesting themselves in a 2D environment is provided by helium adsorbed on graphene. We construct the effective Bose-Hubbard model for this system which involves hard-core bosons (U≈∞), repulsive nearest-neighbor (V>0) and small attractive (V′<0) next-nearest-neighbor interactions. The mapping onto the Bose-Hubbard model is accomplished by a variety of many-body techniques which take into account the strong He-He correlations on the scale of the graphene lattice spacing. Unlike the case of dilute ultracold atoms where interactions are effectively pointlike, the detailed microscopic form of the short-range electrostatic and long-range dispersion interactions in the helium-graphene system is crucial for the emergent Bose-Hubbard description. The result places the ground state of the first layer of He4 adsorbed on graphene deep in the commensurate solid phase with 1/3 of the sites on the dual triangular lattice occupied. Because the parameters of the effective Bose-Hubbard model are very sensitive to the exact lattice structure, this opens up an avenue to tune quantum phase transitions in this solid-state system.

关键词： Adsorption Quantum phase transitions Graphene Helium-4 superfluids Bose-Hubbard model Density functional theory Extended Hubbard model Quantum Monte Carlo Wannier function methods

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Charting the territory of AI-Mediated informal digital learning in teaching design thinking for addressing design problems

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AIP Conference Proceedings 2024年第1期3207卷

作者： Ahmad Al Yakin Ahmad J. Obaid Muthmainnah Muthmainnah Abdul Razzaq Jabr Al-Majidi Teacher Training and Education Faculty Pancasila and Civic education study program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq Teacher Training and Education Faculty Indonesian Language study program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Al-Esraa University Baghdad Iraq

In the era of digital-based education, the learning process is officially in the AI era. Given recent advances in generative AI, this research seeks to investigate how users of language modelling platforms (Monica Bot) use and utilise this tool to develop design problems (DP) for pre-service teachers and how design thinking can work together. This implies that, today, design thinking can be more practical with the help of AI. Using an interview guide and an adapted Technology Acceptance Model (TAM) questionnaire, this study used an explanatory sequential mixed-methods approach to collect data. The research population was 207 teachers of professional education (PPG), and 32 pre-service teachers were the sample in this study who were selected using purposive techniques and participated in AI and an online survey, with seven pre-service teachers attending follow-up interviews. Quantitative analysis provides a statistical explanation for the implementation of AI bot technology by teacher professional education (PPG) as perceived ease of use, perceived usefulness, intention to use, actual use, and design problem. The interview data revealed three main themes: (1) how students’ ideas about how easy Monica Bot is to use changed as they used it; (2) how students’ desire to use grows as they learn more about AI-Bot’s strengths and weaknesses; and (3) how students actually use AI-Bots for design thinking, which means using them as design partners. How pre-service teachers engage in innovative DT activities while navigating the opportunities and limitations of advanced AI systems can be better understood when quantitative and qualitative data are correlated. This study offers implications for future classroom practice and research by understanding these activities, thereby paying attention to attitudes and adding to the existing body of knowledge about the theoretical foundations of design thinking and education using design thinking and computational thinking and the practice

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