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Integration of Droplet Microfluidic Tools for Single-cell Functional Metagenomics:An engineering Head Start

Genomics, Proteomics & Bioinformatics 2021年第3期19卷 504-518页

作者： David Conchouso Amani Al-Ma’abadi Hayedeh Behzad Mohammed Alarawi Masahito Hosokawa Yohei Nishikawa Haruko Takeyama Katsuhiko Mineta Takashi Gojobori Department of Industrial and Mechanical Engineering Universidad de las Américas PueblaPuebla 72810Mexico Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Research Organization for Nano&Life Innovation Waseda UniversityTokyo 162-0041Japan Department of Life Science and Medical Bioscience Waseda UniversityTokyo 162-8480Japan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and EngineeringWaseda UniversityTokyo 169-8555Japan Computational Bio Big-Data Open Innovation Laboratory AIST-Waseda UniversityTokyo 169–0072Japan Computer Electricaland Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia

Droplet microfluidic techniques have shown promising outcome to study single cells at high ***,their adoption in laboratories studying“-omics”sciences is still irrelevant due to the complex and multidisciplinary nature of the *** facilitate their use,here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high ***,a device encapsulating single cells in droplets at a rate of~250 Hz is described considering droplet size and cell ***,we expand on previously reported fluorescence-activated droplet sorting systems to integrate the use of 4 independent fluorescence-exciting lasers(i.e.,405,488,561,and 637 nm)in a single platform to make it compatible with different fluorescence-emitting *** this sorter,both hardware and software are provided and optimized for effortlessly sorting droplets at 60 ***,a passive droplet merger is also integrated into our pipeline to enable adding new reagents to already-made droplets at a rate of 200 ***,we provide an optimized recipe for manufacturing these chips using silicon dry-etching *** of the overall integration and the technical details presented here,our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput capability(>50,000 cells/day)for mining and bioprospecting metagenomic data.

关键词： Droplet microfluidics Single cell Metagenomics Droplet sorter Biotechnology

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Enhancing Chronic Kidney Disease Prediction Through data Preprocessing Optimization and Machine Learning Techniques

Enhancing Chronic Kidney Disease Prediction Through Data Pre...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Stephen Obono Ekwe Sunday Oladayo Oladejo Department of Compt. Science & Electrical Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria Department of Elect. Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa

Health care is the improvement of health through the avoidance and cure of diseases. Chronic kidney disease (CKD) poses a substantial health issue, ranking as the eighth leading cause of death and the tenth leading cause of disability-adjusted life years for both genders. This research aims to utilize machine learning (ML) algorithms to accurately predict the presence of CKD, which affects people worldwide. Timely identification of CKD is crucial to prevent it from progressing to its final and most critical phase. Machine learning algorithms, such as Decision Tree (DT), Naïve Bayes (NB), and others, are used in this study for the prediction of CKD. Various health parameters such as age, albumin level, and more are used as input into each ML algorithm to predict the presence of CKD. data preprocessing is carried out to improve the quality of the data for more accurate prediction. The data is analyzed to uncover the connections between CKD health parameters and their impact on the occurrence of CKD. This research further examines the influence of different train and test data ratios on the accuracy of the employed algorithms. Evaluation metrics such as accuracy, sensitivity, and more are employed to gauge the effectiveness of each algorithm in making predictions. Random Forest stood out as the most accurate algorithm, with a score of 96.25%. Naive Bayes and XGBoost demonstrated the highest sensitivity, achieving a perfect score of 100%.

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A Network-Based, data-Driven Methodology for Identifying and Ranking Freight Bottlenecks

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data science for Transportation 2024年第3期6卷 1-29页

作者： Ma, Yunfei Liu, Chien An Hassini, Elkafi Razavi, Saiedeh Computational Science and Engineering McMaster University 1280 Main Street West Hamilton L8S 4L8 ON Canada Data and Analytics Geotab Inc. 2440 Winston Park Drive Oakville L6H 7V2 ON Canada DeGroote School of Business McMaster University 1280 Main Street West Hamilton L8S 4L8 ON Canada Department of Civil Engineering McMaster University 1280 Main Street West Hamilton L8S 4L8 ON Canada

In complex road networks, the persistent challenge of inefficiency prominently arises from traffic bottlenecks. Freight bottleneck is a unique type of road traffic bottleneck that exclusively involves the analysis of truck freight mobility. Precise detection and comprehensive analysis of freight bottlenecks are pivotal in shaping optimal goods movement strategies, facilitating transportation policy-making, and improving supply chain efficiency. With the rapid growth of connected vehicles and telematics devices worldwide, there is a need for large-scale data processing and computing methods to harness the potential of big data to provide efficient and robust large-scale detection and analysis of freight bottlenecks. This research presents a methodology based on telematics big data to detect and analyze freight bottlenecks. The presented study includes a novel network-level definition of freight bottlenecks, a parallel implementation of a connected components algorithm for detecting such freight bottlenecks, and case studies on the effectiveness and performance of the proposed methodology. For the implementation of the freight bottleneck detection algorithm, the authors use 2-layer labeling and a graph contraction method, resulting in a reduction of 30% in the volume of processed bytes. This study represents a data-driven, large-scale, and network-level perspective on freight bottlenecks. The parallel implementation proves scalable and efficient database application, resulting in a large-scale and data-driven bottleneck analysis. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Big data Freight bottleneck Parallel processing Telematics

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization

arXiv

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arXiv 2023年

作者： Cao, Jian Kang, Myeongjong Jimenez, Felix Sang, Huiyan Schäfer, Florian Katzfuss, Matthias Department of Statistics Institute of Data Science Texas A&M University College StationTX United States Department of Statistics Texas A&M University College StationTX United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States

To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matrices have sparse inverse Cholesky (SIC) factors. We combine this variational approximation of the posterior with a similar and efficient SIC-restricted Kullback-Leibler-optimal approximation of the prior. We then focus on a particular SIC ordering and nearest-neighbor-based sparsity pattern resulting in highly accurate prior and posterior approximations. For this setting, our variational approximation can be computed via stochastic gradient descent in polylogarithmic time per iteration. We provide numerical comparisons showing that the proposed double-Kullback-Leibler-optimal Gaussian-process approximation (DKLGP) can sometimes be vastly more accurate for stationary kernels than alternative approaches such as inducing-point and mean-field approximations at similar computational complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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Development of Phylum Chordata Sound Recognition System using Machine Learning and Deep Learning Techniques: A Case Study of Catfish

Development of Phylum Chordata Sound Recognition System usin...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Lateef Adesola Akinyemi Olamide Peter Oshinuga Oluwafikunmi Adegbenga Eshilokun Sunday Oladayo Oladejo Stephen Obono Ekwe Departments of Computer Science and Electrical Engineering CSET University of South Africa Johannesburg South Africa Department of Electronic and Computer Engineering Lagos State University Lagos Nigeria School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa Department of Electrical Electronic and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa

Sound recognition refers to the technology or process of identifying and classifying different sounds or audio signals. This study aims to develop a sound recognition system using machine learning (ML) and deep learning (DL) techniques to classify and identify the gender of catfish based on their speech patterns. The study explores a range of supervised ML and DL algorithms, such as the Artificial Neural Network (ANN), Naïve Bayes (NB), and others, to predict the gender of catfish. The extracted features from the audio signals of the catfish serve as inputs to these algorithms, which produce binary predictions classifying the catfish samples as either male or female. This study further explores data analysis of the audio signals from the male and female catfish. The automated sound recognition system proves to be a more efficient and accurate approach compared to traditional methods, which rely on time-consuming visual observations. Evaluation metrics such as Accuracy, Sensitivity, and more, were employed to gauge the effectiveness of the algorithms in predicting the gender of catfish based on speech patterns. Among the employed algorithms, ANN and NB demonstrate the highest accuracy, achieving a score of 89.47%.

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Optimizing AI Algorithms for Accurate Remote Diagnosis in Cardiovascular Monitoring Systems

Optimizing AI Algorithms for Accurate Remote Diagnosis in Ca...

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AI-Driven Smart Healthcare for Society 5.0

作者： Geetha Manoharan Lalit Mohan Trivedi S. Suma Christal Mary Divya Nimma Malik Bader Alazzam B Kiran Bala School of Business SR University Warangal Telangana India Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Information Technology Panimalar Engineering College Chennai PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

In recent years a new trend of architectures named the hybrid deep learning models have been derived as using several architectures jointly provides better performance for the problem. Out of these models, using CNN and GRU is therefore promising for upcoming tasks that need feature extraction and predicting sequential data. The primary contribution of the present work is an attempt to combine CNN for spatial feature extraction and GRU for temporal features, making the model efficient in handling sequential input data with lesser errors. The CNN-GRU structure based on this work combines CNN layer for feature extraction from raw input data and GRU layer to process features sequentially. This hybrid architecture is then fine-tuned by applying state of the art deep learning methods achieving higher classification accuracy. Accuracy of the proposed model was 98.9%, precision was 98.5%, recall was 99.1% and F1 score was 98.8%. An analysis of the results obtained shows that these values are markedly higher than when using only CNN or GRU, Random Forest, and SVM for standalone models. The proving work also confirms that the CNN-GRU hybrid model provides better solutions than the traditional model for various complicated classification problems in views of the better accuracy, precision, and recall. Nonetheless, it has the major drawbacks related to computational efficiency because it requires the input of large labeled data. Possible improvements for future work include further improvements to the efficiency of the algorithm as well as to investigate its effectiveness in practical domains such as healthcare, finance, and autonomous vehicles.

关键词： Deep learning Support vector machines Accuracy computational modeling Computer architecture Medical services Feature extraction data models Classification algorithms Convolutional neural networks

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Collaborative learning of discrete distributions under heterogeneity and communication constraints 22

Collaborative learning of discrete distributions under heter...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Xinmeng Huang Donghwan Lee Edgar Dobriban Hamed Hassani Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania Philadelphia PA Department of Statistics and Data Science University of Pennsylvania Philadelphia PA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA

ISBN: (纸本)9781713871088

In modern machine learning, users often have to collaborate to learn distributions that generate the data. Communication can be a significant bottleneck. Prior work has studied homogeneous users—i.e., whose data follow the same discrete distribution—and has provided optimal communication-efficient methods. However, these methods rely heavily on homogeneity, and are less applicable in the common case when users' discrete distributions are heterogeneous. Here we consider a natural and tractable model of heterogeneity, where users' discrete distributions only vary sparsely, on a small number of entries. We propose a novel two-stage method named SHIFT: First, the users collaborate by communicating with the server to learn a central distribution; relying on methods from robust statistics. Then, the learned central distribution is fine-tuned to estimate the individual distributions of users. We show that our method is minimax optimal in our model of heterogeneity and under communication constraints. Further, we provide experimental results using both synthetic data and n-gram frequency estimation in the text domain, which corroborate its efficiency.

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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The Prediction of Micro Plasma Impacts of Farm Fresh Vegetables Using Machine Learning

The Prediction of Micro Plasma Impacts of Farm Fresh Vegetab...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Maragatham G K. Sundaramoorthy K. Maheswari T. Sajana Mary Shiba C Ashok Kumar Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Information Technology Jerusalem College of Engineering (Autonomous) Chennai Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Computer Science and Business Systems R. M K Engineering College Chennai Tamil Nadu India Department of Computer Science BanasthaliVidyapith Rajasthan India

In general, the innovative foods produced on fruit and vegetable based farms are always high quality and healthy. Indicators of fruit and vegetable consumption include plasma vitamin C and arytenoids, which are plant pigments discovered in blood samples. The researchers decided to utilize blood samples rather than the more common food frequency questionnaire in their investigation. to assess the amount of food consumed in order to forestall measuring errors and to establish dependencies. Because vitamin C and arytenoids may be found in a wide variety of fruits and vegetables, we can use them as objective measures of our consumption of these food groups. The fact that individuals who do not consume a diet that is abundant in fruits and vegetables do not consume significant quantities of vitamin C and arytenoids is reflected in the plasma levels of these individuals. In this paper a smart machine learning algorithm was proposed to predict the micro plasma impacts. This monitors the regular shape and harvesting of different farm fresh products and predicts the impacts of it. This will helpful for farmers to enhance the harvesting.

关键词： Machine learning algorithms Shape Measurement uncertainty Machine learning Pigments Knowledge discovery Plasmas

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