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Computational prediction and validation of specific EmbR binding site on PknH

Synthetic and Systems Biotechnology 2021年第4期6卷 429-436页

作者： Insung Na Huanqin Dai Hantian Li Anvita Gupta David Kreda Powell Zhang Xiangyin Chen Lixin Zhang Gil Alterovitz Computational Health Informatics Program Boston Children’s Hospital/Harvard Medical SchoolBostonMA 02115USA Department of Medicine Brigham and Women’s Hospital/Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Mycology Institute of MicrobiologyBeijing100101 China Department of Computer Sciences Stanford UniversityStanford CA 94305USA Center for Biomedical Informatics Harvard Medical SchoolBostonMA 02115USA State Key Laboratory of Bioreactor Engineering East China University of Science and TechnologyShanghaiChina Department of Biology The Johns Hopkins UniversityBaltimoreMD 21218USA Lexington High School BostonMA 02115USA

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not ***(EMB),one of the well-known first-line drugs in tuberculosis treatment is,unfortunately,not free from drug resistance *** studies have shown that some genetic mutations in Mycobacterium tuberculosis(Mtb)EmbR,and EmbC/A/B genes cause EMB ***-PknH pair controls embC/A/B operon,which encodes EmbC/A/B genes,and EMB interacts with EmbA/B ***,the EmbR binding site on PknH was *** conducted molecular simulation on the EmbR-peptides binding structures and discovered phosphorylated PknH 273-280(N′-HEALS^(P)DPD-C′)makesβstrand with the EmbR FHA domain,asβ-MoRF(MoRF;molecular recognition feature)does at its binding *** bond number analysis also supported the peptides’β-MoRF forming activity at the EmbR FHA ***,we discovered that previously known phosphorylation residues might have their chronological order according to the phosphorylation *** discovery validated that Mtb PknH 273-280(N′-HEALSDPD-C′)has reliable EmbR binding *** approach is revolutionary in the computer-aided drug discovery field,because it is the first trial to discover the protein-protein interaction site,and find binding partner in nature from this site.

关键词： Disorder-to-order transition Protein intrinsic disorder Binding site prediction Drug resistance Molecular simulation

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A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence

A Study on Medicinal Plant Leaf Recognition Using Artificial...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Vina Ayumi Ermatita Ermatita Abdiansah Abdiansah Handrie Noprisson Mariana Purba Marissa Utami Doctoral Program in Engineering Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Faculty of Engineering Muhammadiyah University of Bengkulu Bengkulu Indonesia

ISBN: (纸本)9781665427340

Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019–2020) from IEEE, Springer and Science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction; and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier.

关键词： Support vector machines Systematics Bibliographies Multimedia systems Transfer learning Backpropagation algorithms Feature extraction

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Digital Nomads during the COVID-19 Pandemic: Evidence from Narratives on Reddit discussions

Digital Nomads during the COVID-19 Pandemic: Evidence from N...

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International Conference on Computer Supported Cooperative Work in Design

作者： Marcos Antonio De Almeida Antó nio Correia Jano Moreira De Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Tr&#x00E1 s-os-Montes e Alto Douro Vila Real Portugal Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil NCE/UFRJ T&#x00E9 rcio Pacitti Institute of Computer Applications and Research Rio de Janeiro Brazil

In this paper, we report on new findings about the results of an empirical study which aims to investigate how the COVID-19 pandemic has been shaping nomadic work practices and also challenging the lifestyles of digital nomads (DN). To do this, we collected textual data from posts in a Reddit community. We argue that, in order to understand how to design technical solutions for the so-called ‘new normal’ working conditions, one way to approach this is to understand how digital nomads are being impacted in their work practices and routines, and also how they are seeing the future of their technology-mediated work-life space. Finally, we show how evidence collected from DNs about their experiences and difficulties perceived during the pandemic period can inform CSCW researchers worldwide about future design-oriented strands.

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Rahmad Kurniawan Iwan Iskandar Fitra Lestari Habibi Al Rasyid Harpizon Ilyas Husti Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Riau Pekanbaru Indonesia Department of Informatics Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Industrial Engineering Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia’s top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube’s features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments.

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AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review

arXiv

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arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

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Heterogeneous Ensemble for Multi-Step Solar Irradiance Forecasting in Brazilian's Sites

Heterogeneous Ensemble for Multi-Step Solar Irradiance Forec...

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Workshop on Communication Networks and Power Systems (WCNPS)

作者： Domingos Sávio de O. Santos Douglas A. P. Dantas Paulo S. G. de Mattos Neto Hugo Valadares Siqueira Aranildo R. Lima Francisco Madeiro João. F. L. de Oliveira Alex C. Pereira Manoel H. N. Marinho Escola Politécnica de Pernambuco Universidade de Pernambuco Recife PE Brazil Centro de Informática Universidade Federal de Pernambuco Recife PE Brazil Graduate Program in Electrical Engineering Federal University of Technology-Paraná Ponta Grossa PR Brazil Aquatic Informatics Vancouver Canada São Francisco Hydroelectric Company (Chesf) Recife PE Brazil

Multi-step solar irradiance forecast is an important part of a successful power grid management. However that forecast can be difficult due to nonlinear patterns and random fluctuation. This work applies statistical and machine learning methods to forecast solar irradiance time series from three different locations of Brazil. The applied models are: Autoregressive Integrated Moving Average (ARIMA), Deep Belief Network (DBN), Extreme Learning Machine (ELM), Multi Layer Perceptron neural network (MLP), Random Forest (RF), and Support Vector Regression (SVR). Taking into account these methods, an ensemble of them is proposed, which has the objective of reducing: the uncertainty of selecting a single model, overfitting, and underfitting. Compared with single models and based on two performance metrics, the ensemble found good overall results when applied in the time series with horizons of 6, 12 and 24 hours ahead.

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Modular boundaries in recurrent neural networks

arXiv

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arXiv 2023年

作者： Tanner, Jacob C. Mansour, Sina L. Coletta, Ludovico Gozzi, Alessandro Betzel, Richard F. Cognitive Science Program 2 School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States National University of Singapore Singapore Fondazione Bruno Kessler Trento Italy Functional Neuroimaging Lab Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems Rovereto Italy Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States

Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds –subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this "neural manifold hypothesis" are continuous and not cleanly divided into separate neural populations. This perspective clashes with the "modular hypothesis" of brain organization, wherein neural elements maintain an "all- or-nothing" affiliation with modules. In line with this modular hypothesis, recent research on recurrent neural networks suggests that multi-task networks become modular across training, such that different modules specialize for task-general dynamical motifs [1, 2]. If the modular hypothesis is true, then it would be important to use a dimensionality reduction technique that captures modular structure. Here, we investigate the features of such a method. We leverage RNNs as a model system to study the character of modular neural populations, using a community detection method from network science known as modularity maximization to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system? We find evidence that they do. First, we find that these boundaries neatly divide the representational content and role of neurons. Next, we find that these boundaries can be directly inferred from features of the weight matrix in feed-forward neural networks, and are related to clustering of the Jacobian matrix in recurrent neural networks. We also find that the weights of input neurons to recurrent neurons partially inform their modular structure, an observation that we corroborated using structural and functional imaging data from mice and humans. Finally, we find that the dynamics of these RNNs reflected the boundaries between modules. Collectively, our results suggest that neural populations in RNNs sometimes form modu

关键词： Recurrent neural networks

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Content design: E-learning module for study on cultural tourism information systems based on local wisdom 2

Content design: E-learning module for study on cultural tour...

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2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Pageh, I.M. Permana, A.A.J. Historical Education Faculty of Law and Social Sciences Ganesha University of Education Singaraja Indonesia Information Technology Department Informatics Management Study Program Faculty of Engineering and Vocational Ganesha University of Education Singaraja Indonesia

Facing the 4.0 industrial revolution, it is necessary to carry out reforms and innovations related to national education. Reforms and innovations in the implementation of components of the education system involve changes in curriculum, graduate competencies and assessments, teacher qualifications, funding, facilities and infrastructure, decentralization and educational autonomy. Bali as a world tourist destination must be maintained and passed on to the younger generation. The younger generation can use information technology to help promote Balinese culture. Bali has a lot of local wisdom that can be implemented in the tourism sector. Therefore to promote those local wisdoms, a module that can be accessed through e-learning is developed to help students develop a cultural tourism package based on local wisdom to be promoted online. The modules developed have been tested by content and media experts, which can be used independently by students. Modules can be accessed online at e-learning and they are ready to use instructions for students so they can work on assignments and projects independently. Modules containing this theory and practice are for making brochures, tour packages, videos, and websites, all of which are connected to promote tourism based on local Balinese wisdom. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Students

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Exergy evaluation while drying fish waste in the forced convection rotary dryer

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AIP Conference Proceedings 2024年第1期3145卷

作者： I. G. N. B. Catrawedarma Akhmad Afandi Anis Usfah Prastujati Endi Sailul Haq Department of Mechanical Engineering Politeknik Negeri Banyuwangi Banyuwangi Indonesia Livestock Product Processing Technology Study Program Politeknik Negeri Banyuwangi Banyuwangi Indonesia Department of Informatics Engineering Politeknik Negeri Banyuwangi Banyuwangi Indonesia

This study focuses on the exergy analysis of the fish waste drying system using a rotary dryer. The effect of the initial mass of fish waste on the exergy parameters was carried out. The exergy balance was applied in the drying chamber. The exergy parameters analyzed include exergy inflow, exergy outflow, exergy loss, and exergy efficiency. In terms of exergy sustainability, the sustainability index, environmental impact factor, waste energy ratio, and improvement potential were also analyzed. As a result, it was found that the exergy inflow for all initial masses was not significantly different. Meanwhile, if the fish waste’s initial mass is decreasing, the exergy outflow and exergy efficiency increase, and exergy loss decrease. The sustainability index decreases, and the environmental impact factor increases with increasing initial mass. Waste energy ratio and improvement potential increase with increasing initial mass.

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Stiff matrix induces exosome secretion to promote tumour growth (vol 25, pg 415, 2023)

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NATURE CELL BIOLOGY 2024年第3期26卷 490-491页

作者： Wu, Bin Liu, Di-Ao Guan, Lei Myint, Phyoe Kyawe Chin, Likang Dang, Hien Xu, Ye Ren, Jinqi Li, Ting Yu, Ziyan Jabban, Sophie Mills, Gordon B. Nukpezah, Jonathan Chen, Youhai H. Furth, Emma E. Gimotty, Phyllis A. Wells, Rebecca G. Weaver, Valerie M. Radhakrishnan, Ravi Wang, Xin Wei Guo, Wei Department of Biology School of Arts & Sciences University of Pennsylvania Philadelphia PA USA Department of Medicine University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Bioengineering School of Engineering and Applied Science University of Pennsylvania Philadelphia PA USA Department of Surgery Sidney Kimmel Cancer Center Thomas Jefferson University Philadelphia PA USA Abramson Cancer Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Knight Cancer Institute Oregon Health and Science University Portland OR USA Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia PA USA Department of Surgery Center for Bioengineering and Tissue Regeneration University of California San Francisco San Francisco CA USA Laboratory of Human Carcinogenesis and Liver Cancer Program Center for Cancer Research National Cancer Institute Bethesda MD USA

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