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Statistical analysis and data visualization of Indonesia and Malaysia SARS Cov-2 metadata

AIP Conference Proceedings 2023年第1期2594卷

作者： D. Sudigyo A. Budiarto B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

arXiv

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arXiv 2024年

作者： Masri, Sari Ashqar, Huthaifa I. Elhenawy, Mohammed Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States CARRS-Q Centre for Data Science Queensland University of Technology 130 Victoria Park Rd Kelvin GroveQLD4059 Australia

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also delivere tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs' benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs transformative potential for traffic control, enhancing efficiency and safety a

关键词： Street traffic control

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Improved Prediction Of Ligand-Protein Binding Affinities By Meta-Modeling

arXiv

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arXiv 2023年

作者： Lee, Ho-Joon Emani, Prashant S. Gerstein, Mark B. Dept. of Genetics and Yale University New HavenCT06510 United States Yale Center for Genome Analysis Yale University New HavenCT06510 United States Dept. of Molecular Biophysics & Biochemistry Yale University New HavenCT06520 United States Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Dept. of Computer Science Yale University New HavenCT06520 United States Dept. of Statistics & Data Science Yale University New HavenCT06520 United States Dept. of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over base models. Our best meta-models achieve comparable performance to state-of-the-art deep learning tools exclusively based on 3D structures, while allowing for improved database scalability and flexibility through the explicit inclusion of features such as physicochemical properties or molecular descriptors. We further demonstrate improved generalization capability by our models using a large-scale benchmark of affinity prediction as well as a virtual screening application benchmark. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain meaningful improvement in binding affinity prediction. © 2023, CC BY-NC-SA.

关键词： Prediction models

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Model-Based Assessment of Photoplethysmogram Signal Quality in Real-Life Environments

Model-Based Assessment of Photoplethysmogram Signal Quality ...

引用

European Signal Processing Conference (EUSIPCO)

作者： Yan-Wei Su Chia-Cheng Hao Gi-Ren Liu Yuan-Chung Sheu Hau-Tieng Wu Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu Taiwan Data Science Degree Program National Taiwan University & Academia Sinica Taipei Taiwan Department of Mathematics National Cheng-Kung University Tainan Taiwan National Center for Theoretical Sciences Taipei Taiwan Courant Institute of Mathematical Sciences New York University New York NY USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Assessing signal quality is crucial for photoplethys-mogram analysis, yet a precise mathematical model for defining signal quality is often lacking, posing challenges in the quantitative analysis. To tackle this problem, we propose a Signal Quality Index (SQI) based on the adaptive non-harmonic model (ANHM) and a Signal Quality Assessment (SQA) model, which is trained using the boosting learning algorithm. The effectiveness of the proposed SQA model is tested on publicly available databases with experts' annotations. Result: The DaLiA database [20] is used to train the SQA model, which achieves favorable accuracy and macro- F1 scores in other public databases (accuracy 0.83, 0.76 and 0.87 and macro-F1 0.81, 0.75 and 0.87 for DaLiA-testing dataset, TROIKA dataset [32], and WESAD dataset [23], respectively). This preliminary result shows that the ANHM model and the model-based SQI have potential for establishing an interpretable SOA system.

关键词： Adaptation models Accuracy databases Statistical analysis Signal processing algorithms Europe Signal processing Mathematical models Quality assessment Indexes

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Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

arXiv

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arXiv 2024年

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management. © 2024, CC BY.

关键词： Traffic congestion

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MAATrica: a measure for assessing consistency and methods in medicinal and nutraceutical chemistry papers

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EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY 2024年 273卷 116522页

作者： Panzarella, Giulia Gallo, Alessandro Coecke, Sandra Querci, Maddalena Ortuso, Francesco Hofmann-Apitius, Martin Veltri, Pierangelo Bajorath, Juergen Alcaro, Stefano Magna Graecia Univ Catanzaro Dipartimento Sci Salute Campus Univ S VenutaViale Europa I-88100 Catanzaro Italy European Commiss Joint Res Ctr Ispra VA Italy Fraunhofer Inst Algorithms & Sci Comp Dept Bioinformat D-53757 St Augustin Germany Univ Calabria Dipartimento Ingn Informat Modellist Elettron & Si Arcavacata Di Rende CS Italy Rhein Friedrich Wilhelms Univ Dept Life Sci Informat & Data Sci LIMES Program Unit Chem Biol & Med Chem B IT Friedrich Hirzebruch Allee 5-6 D-53115 Bonn Germany Magna Graecia Univ Catanzaro Net4Science Srl Campus Univ S VenutaViale Europa I-88100 Catanzaro Italy Assoc CRISEA Ctr Ric & Serv Avanzati Innovaz Rurale I-88055 Belcastro CZ Italy

The growing number of scientific papers and document sources underscores the need for methods capable of evaluating the quality of publications. Researchers who are looking for relevant papers for their studies need ways to assess the scientific value of these documents. One approach involves using semantic search engines that can automatically extract important knowledge from the growing body of text. In this study, we introduce a new metric called "MAATrica," which serves as the foundation for an innovative method designed to evaluate research papers. MAATrica offers a new way to analyze and categorize text, focusing on the consistency of research documents in the life sciences, particularly in the fields of medicinal and nutraceutical chemistry. This method utilizes semantic descriptions to cover in silico experiments, as well as in vitro and in vivo essays. Created to aid in evaluation processes like peer review, MAATrica uses toolkits and semantic applications to build the proposed measure, identify scientific entities, and gather information. We have applied MAATrica to roughly 90,000 papers and present our findings here.

关键词： Medicinal chemistry Nutraceuticals Information extraction Text mining Research metrics

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data Processing of Functional Optical Microscopy for Neuroscience

arXiv

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arXiv 2022年

作者： Benisty, Hadas Song, Alexander Mishne, Gal Charles, Adam S. Yale Neuroscience New HavenCT06510 United States Max Planck Institute for Intelligent Systems Stuttgart Germany Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges. © 2022, CC BY-NC-ND.

关键词： Fluorescence microscopy

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High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series

High-dimensional Multivariate Time Series Forecasting in IoT...

引用

Latin America Congress on Computational Intelligence (LA-CCI)

作者： Hugo Vinicius Bitencourt Frederico Gadelha Guimarães Machine Intelligence and Data Science Lab (MINDS) Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Machine Intelligence and Data Science Lab (MINDS) Universidade Federal de Minas Gerais Belo Horizonte MG Brazil

ISBN: (纸本)9781728188652

In Internet of things (IoT), data is continuously recorded from different data sources and devices can suffer faults in their embedded electronics, thus leading to a high-dimensional data sets and concept drift events. Therefore, methods that are capable of high-dimensional non-stationary time series are of great value in IoT applications. Fuzzy Time Series (FTS) models stand out as data-driven non-parametric models of easy implementation and high accuracy. Unfortunately, FTS encounters difficulties when dealing with data sets of many variables and scenarios with concept drift. We present a new approach to handle high-dimensional non-stationary time series, by projecting the original high-dimensional data into a low dimensional embedding space and using FTS approach. Combining these techniques enables a better representation of the complex content of non-stationary multivariate time series and accurate forecasts. Our model is able to explain 98% of the variance and reach 11.52 of RMSE, 2.68 of MAE and 2.91% of MAPE.

关键词： Home appliances Energy consumption Soft sensors Time series analysis Smart homes Predictive models Feature extraction

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Exploiting CotA laccase from Antarctic Bacillus sp. PAMC28748 for efficient mediator-assisted dye decolorization and ABTS regeneration

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Chemosphere 2025年 372卷 144137页

作者： Karmacharya, Jayram Shrestha, Prasansah Han, So-Ra Lee, Jun Hyuck Oh, Tae-Jin Department of Life Science and Biochemical Engineering SunMoon University Asan31460 Korea Republic of Genome-based Bio-IT Convergence Institute Asan31460 Korea Republic of Bio Big Data-based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan31460 Korea Republic of Division of Life Sciences Korea Polar Research Institute Incheon21990 Korea Republic of Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan31460 Korea Republic of

Laccases are of particular interest in addressing environmental challenges, such as the degradation of triphenylmethane (TPM) dyes, including crystal violet (CV) and Coomassie Brilliant Blue (CBB), which are commonly used in SDS-PAGE for protein visualization. However, these dyes present significant environmental concerns due to their resistance to degradation, which makes their removal from industrial wastewater a major challenge. To address this, the current study investigates the potential of a novel CotA laccase derived from Bacillus sp. PAMC28748, an Antarctic bacterial isolate, for decolorizing these stubborn dyes. The CotA gene was successfully cloned and expressed, and the enzyme demonstrated optimal activity at pH 3 and 50 °C, which favors its maximum catalytic performance. The recombinant Bacillus sp. PAMC28748 rBCLac effectively decolorized CBB without additional mediators, whereas the degradation of CV required the use of the redox mediator ABTS. With ABTS, over 90 % decolorization was achieved at a 0.35 % concentration of CV after 240 min of incubation. Further investigation through molecular docking studies revealed that hydrogen bonding and hydrophobic interactions between the enzyme and the dye molecules are critical for effective degradation, highlighting the enzyme's specific interaction mechanisms. In addition to its catalytic effectiveness, the study also demonstrated the practical potential of the rBCLac system by recovering and reusing both ABTS and rBCLac through ultracentrifugation and acetone precipitation. The process maintained over 75 % efficiency across three cycles, despite a slight decline in enzyme activity, thus showcasing the system's sustainability and reusability. These findings collectively suggest that rBCLac, isolated from an extreme Antarctic environment, holds considerable promise as a candidate for the removal of industrial wastewater containing persistent dyes, with the added potential for cost-effective and sustainable wat

关键词： Hydrogen bonds

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Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

Deep reinforcement learning of event-triggered communication...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Kazuki Shibata Tomohiko Jimbo Takamitsu Matsubara Autonomous Distributed Cooperative Control Program Data Analytics Research-Domain Toyota Central R&D Labs. Inc. Japan Graduate School of Science and Technology Division of Information Science Nara Institute of Science and Technology Nara Japan

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.

关键词： Deep learning Automation Conferences Reinforcement learning Computer architecture Numerical simulation Timing

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