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Data analysis guidelines for single‑cell RNA‑seq in biomedical studies and clinical applications

Military Medical Research 2023年第4期10卷 529-553页

作者： Min Su Tao Pan Qiu‑Zhen Chen Wei‑Wei Zhou Yi Gong Gang Xu Huan‑Yu Yan Si Li Qiao‑Zhen Shi Ya Zhang Xiao He Chun‑Jie Jiang Shi‑Cai Fan Xia Li Murray J.Cairns Xi Wang Yong‑Sheng Li State Key Laboratory of Reproductive Medicine Nanjing Medical UniversityNanjing 211166China College of Biomedical Information and Engineering the First Affiliated Hospital of Hainan Medical UniversityHainan Medical UniversityHaikou 571199HainanChina College of Bioinformatics Science and Technology Harbin Medical UniversityHarbin 150081HeilongjiangChina Department of Immunology Nanjing Medical UniversityNanjing 211166China Department of Laboratory Medicine Women and Children’s Hospital of Chongqing Medical UniversityChongqing 401174China Baylor College of Medicine HoustonTX 77030USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110GuangdongChina School of Biomedical Sciences and Pharmacy Faculty of Health and Medicinethe University of NewcastleUniversity DriveCallaghanNSW 2308Australia Precision Medicine Research Program Hunter Medical Research InstituteNew Lambton HeightsNSW 2305Australia

The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic *** the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this ***,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific *** summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the *** and caveats are pointed out for some specific analysis tasks and *** hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.

关键词： Single-cell RNA-sequencing(scRNA-seq) Data analysis Biomedical research Clinical applications

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Brain Tumor Detection and Segmentation Using Deep Learning Models

Brain Tumor Detection and Segmentation Using Deep Learning M...

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Artificial Intelligence (WSAI), World Symposium on

作者： Moses Kazeem Abiodun Abidemi Emmanuel Adeniyi Joseph Bamidele Awotunde Halleluyah Aworinde George Tinuoye Agbotiname Lucky Imoize Chun-Ta Li Cheng-Chi Lee Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science Bowen University Iwo Osun State Nigeria Department of Computer Science University of Ilorin Ilorin Kwara State Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Nigeria Bachelor's Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City Taiwan Department of Library and Information Science Fu Jen Catholic University New Taipei City Taiwan

ISBN: (数字)9798350374162

ISBN: (纸本)9798350374179

This study focuses on brain tumor detection and segmentation using Convolutional Neural Networks (CNN) with architectures of Fully Convolutional Net-work (FCN) and VGG16. The dataset imported for this study consists of brain MRI images, which were preprocessed to improve the overall image resolution. Data augmentation techniques were applied to heighten the variability and volume of the training data. For feature extraction, CNN models were utilized, specifically the VGG16 architectures. This model was trained on a preprocessed dataset to learn the important features associated with the presence of brain tumor. Image segmentation was performed to precisely outline and identify the tumor region within the brain images. FCN architecture was employed for this task, which allowed pixel-level classification of tumor regions. Finally, image classification was carried out to group brain images based on the absence or presence of a tumor. The trained models were evaluated using accuracy, precision, recall and F1score as the performance metrics. The results of the study demonstrated promising outcomes, achieving an accuracy score of 93% and a precision, recall and f1score ranging from 75% to 100% based on the specific type of tumor enlisted. This indicates that the developed CNN models, with the FCN and VGG16 architectures, effectively detect and segment brain tumors from MRI images. The findings of this study contribute to the field of medical image analysis and gives a foundation for further research and development in brain tumor diagnosis and treatment. The study recommends the use of more deep learning algorithms for the process of detecting brain tumors.

关键词： Deep learning Image segmentation Accuracy Magnetic resonance imaging Computational modeling Brain tumors Brain modeling Distance measurement Classification algorithms Convolutional neural networks

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Reporting of Hospital Facility on Smartphone

Reporting of Hospital Facility on Smartphone

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2019 International Conference of science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Rusdi, Jack Febrian Salam, Sazilah Abu, Nur Azman Sunaryo, Budi Agustina, Nova Gusdevi, Harya Hermansah Hadhiwibowo, Ari Heryati, Tuty Hardi, Richki Taufiq, Rohmat Jufri, Muhammad Taher Sudarsono, Ignatius Utomo, Eko Wahyu Kodong, Frans Richard Fannya, Puteri Informatics Engineering Sekolah Tinggi Teknologi Bandung Bandung Indonesia Information and Communication Technology Faculty Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Electrical Engineering Andalas University Padang Indonesia Rumah Sakit Umum Daerah Majalaya Kabupaten Bandung Majalaya Indonesia Informatics Engineering Computer Science Faculty Universitas Mulya Balikpapan Indonesia Department of Informatics Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang15117 Indonesia Program Studi Sistem Informasi Fakultas Teknik Dan Sistem Informasi Universitas Yapis Papua Indonesia Civil Engineering Department Engineering Faculty Universitas Langlangbuana Bandung Bandung Indonesia Department of Informatics Engineering UPN Veteran Yogyakarta Yogyakarta Indonesia Department of Medical Record and Health Information Faculty of Health Universitas Esa Unggul Jakarta Indonesia

In the context of achieving Good Corporate Governance (GCG) in hospitals, among others, it is carried out with control and supervision, including in the case of hospital facility maintenance installations. Besides that, the hospital needs to improve its services. The asset reporting system is one of the high needs in this field, so it requires a solution that can deal with existing problems. Unfortunately, the current reporting system so far is more likely to be conventional, such as by telephone. This study proposes a new solution to the hospital facility maintenance reporting process, namely by utilising smartphone-based applications that directly connected to the server. So, the information distributed to the competent parties. This solution can speed up the reporting process and actions. Also, save the historical equipment that is available from each service room - the results of this study used as a hospital facility maintenance reporting system. © Published under licence by IOP Publishing Ltd.

关键词： Hospitals

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Effects of Fir-Wood Biochar on Ch4 Oxidation Rates and Methanotrophs in Landfill Cover Soils Packed at Three Different Proctor Compaction Levels

SSRN

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SSRN 2023年

作者： Yi, Susan C. Heijbroek, Anne Cutz, Luis Pillay, Stephanie de Jong, Wiebren Abeel, Thomas Gebert, Julia Delft University of Technology Faculty of Mechanical Maritime and Materials Engineering Leeghwaterstraat 39 Delft2628 CB Netherlands Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Van Mourik Broekmanweg 6 Delft2628 XE Netherlands Broad Institute of MIT and Harvard Infectious Disease and Microbiome Program 415 Main St. CambridgeMA02142 United States

Application of biochar to landfill cover soils can purportedly improve methane (CH4) oxidation rates. However, understanding of the combined effects of soil texture, compaction, and biochar on activity and composition of the methanotrophs is limited. The amendment of wood biochar on two differently textured landfill cover soils at three compaction levels of the Proctor density were explored by analyzing changes in soil physical properties relevant to methane oxidation, the effects on CH4 oxidation rates and on the composition of the methanotrophic community. Loose soils with and without biochar were pre-incubated to equally elevate the CH4 oxidation rates. Hereafter, soils were compacted and re-incubated. Methane oxidation rates, gas diffusivity, water retention characteristics and pore size distribution were analyzed on the compacted soils. Abundance of methanotrophic bacteria (MOB) was determined at the end of both the pre-incubation tests and the incubation tests of the packed samples. Biochar significantly increased porosity at all compaction levels, resulting in enhanced diffusion coefficients. Also, a re-distribution in pore sizes was observed. Increased gas diffusivity from low compaction and amendment of biochar, however, did not reflect higher methane oxidation rates due to high diffusive oxygen fluxes over the limited height of the compacted soil specimens. All soils, with and without biochar, were strongly dominated by type II methanotrophs. In the sandy soil, biochar amendment strongly increased MOB abundance, which could be attributed to a corresponding increase in the abundance of Methylocystis species, while in the clayey soil no such response was observed. Compaction did not change the community composition in either soil. Biochar addition to landfill cover soils may not always enhance methanotrophic activity and hence reduce fugitive methane emissions with the effect being soil-specific. However, especially in finer and more compacted soils, biochar

关键词： Compaction

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Undersampled Magnetic Resonance Image Reconstructions Based on a Combination of U-Nets and L1, L2, and TV Optimizations

Undersampled Magnetic Resonance Image Reconstructions Based ...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Aline A.S. Thomaz Jonathan A.S. Lima Cristiano J. Miosso Mylé ne C.Q. Farias Andrey S. Krylov Yong Ding Department of Electrical Engineering University of Brasilia (UnB) Brasilia Brazil Biomedical Engineering Graduate Program University of Brasilia (UnB) Brasilia Brazil Faculty of Computational Mathematics and Cybernetics Lomonosov Moscow State University Moscow Russia College of Information Science and Electronic Engineering Zhejiang University Zhejiang China

Magnetic Resonance Imaging (MRI) plays a major role in the diagnosis of several diseases. However, the acquisition of measurements in the k-space domain, which is the basis for image reconstruction, takes a long time compared to other imaging modalities and is comparatively costly. In this context, undersampled MRI reconstruction is an approach for decreasing the acquisition duration and the exam's final cost. With this objective, both Compressed Sensing (CS) and Deep Learning (DL) provide techniques for generating good quality MRI images from undersampled measurements. In this paper, we combine CS and DL methods in order to investigate the potential increase in image quality over each isolated approach. We use reconstructions from from highly undersampled MRI signals using two CS approaches, the L1 and total minimizations, as inputs to a U-Net. We also use, for comparison, the reconstructions from the same undersampled signals using L2 minimization, and also test them as inputs to a U-net. The goal is to evaluate whether the the U-Net can improve the results of the CS reconstructions after learning from degraded and original image pairs. Our experimental results suggest that the combination of L1 or TV minimization with U-Nets can improve reconstruction, in terms of objective image quality, over each technique used in isolation.

关键词： Image quality TV Magnetic resonance imaging Magnetic resonance Minimization Time measurement Sensors

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Guava Trees Disease Monitoring Using the Integration of Machine Learning and Predictive Analytics 3

Guava Trees Disease Monitoring Using the Integration of Mach...

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3rd Novel Intelligent and Leading Emerging sciences Conference, NILES 2021

作者： Elsayed, Mahmoud Hassan, Nourhan Maher, Marina Waleed, Nouran Reda, Rehab Eldin, Haitham Sharaf Mostafa, Hassan The American University of Cairo Mechatronics Department Egypt Nile University Giza12677 Egypt Communication Engineering Alexandria University Egypt Biomedical Engineering Cairo University Egypt Tanta University Egypt Cairo University Faculty of Science Biophysics Department Egypt Cairo University Electronics and Electrical Communications Engineering Department Giza Egypt University of Science and technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology October Gardens 6th of October Giza12578 Egypt

ISBN: (纸本)9781665421577

The increase in population, food demand, and the pollution levels of the environment are considered major problems of this era. For these reasons, the traditional ways of farming are no longer suitable for early and accurate detection of biotic stress. Recently, precision agriculture has been extensively used as a potential solution for the aforementioned problems using high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. In this paper, several methods of machine learning have been utilized in order to study pests, their types, population, and agricultural conditions in terms of soil and climate for some crops such as potatoes, guava, and cotton, which are among the main Egyptian crops. In the process of obtaining a suitable estimate of insects population affecting each of the aforementioned crops, a hardware model control, based on the results provided by the predictive analysis, an estimate of the electromagnetic force is applied to the cultivated areas to get rid of the pests as well as giving a background to farmers about the possibility of infecting a crop such as Potato with Late Blight, according to climatic conditions. © 2021 IEEE.

关键词： Model predictive control

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A comparison on the use of Perlin-noise and Gaussian noise based augmentation on X-ray classification of lung cancer patient 1

A comparison on the use of Perlin-noise and Gaussian noise b...

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1st International Symposium on Physics and Applications, ISPA 2020

作者： Haekal, M. Septiawan, R.R. Haryanto, F. Arif, I. Department of Physics Faculty of Sciences and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo-Surabaya Jawa Timur60111 Indonesia Computer Engineering Study Program School of Electrical Engineering Telkom University Jl. Telekomunikasi No.1 Jawa Barat Bandung40257 Indonesia Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl. Ganeca 10 Jawa Barat Bandung40132 Indonesia

The use of deep learning in medical image classification has become an important study in the past few years. The proper use of this method, however, is still hindered by many problems, one of it being the imbalance of dataset available for training which resulted in small-set database. In this study, the effect of noise-based augmentation on the performance of deep learning based classification will be studied. The noises which were used for the augmentation method were Perlin-noise and Gaussian noise. The modality of medical image used in this study is X-ray. 174 X-ray images (87 cancer, 87 normal) were used in this study and will be classified by using transfer learning from previously trained deep learning architecture. The deep learning architecture used was vgg-19. The images were divided into two groups, 138 (69 cancer, 69 normal) images were used for training phase and 36 (18 cancer, 18 normal) were images used for testing phase. Three deep learning models were used for the classification tasks, the first one was retrained to classify the original images, the second one was retrained by using mix of original images and images with Perlin-noise, and the third one was retrained by using mix of original images and images with Gaussian noise. The results showed that the three models returned similar accuracy of 0.8 which indicate that the use of noise-based augmentation can increase the performance of deep learning in classifying medical images with small set training database. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Periodic Wave Trains in Nonlinear Media: Talbot Revivals, Akhmediev Breathers, and Asymmetry Breaking

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Physical Review Letters 2022年第21期128卷 214101-214101页

作者： Georgi Gary Rozenman Wolfgang P. Schleich Lev Shemer Ady Arie Raymond and Beverly Sackler School of Physics and Astronomy Faculty of Exact Sciences Tel Aviv University Tel Aviv 69978 Israel School of Electrical Engineering Iby and Aladar Fleischman Faculty of Engineering Tel Aviv University Tel Aviv 69978 Israel Institut für Quantenphysik and Center for Integrated Quantum Science and Technology (IQST) Universität Ulm 89081 Ulm Germany Hagler Institute for Advanced Study at Texas A&M University Texas A&M AgriLife Research Institute for Quantum Science and Engineering (IQSE) and Department of Physics and Astronomy Texas A&M University College Station Texas 77843-4242 USA School of Mechanical Engineering Faculty of Engineering Tel Aviv University Tel Aviv 69978 Israel

We study theoretically and observe experimentally the evolution of periodic wave trains by utilizing surface gravity water wave packets. Our experimental system enables us to observe both the amplitude and the phase of these wave packets. For low steepness waves, the propagation dynamics is in the linear regime, and these waves unfold a Talbot carpet. By increasing the steepness of the waves and the corresponding nonlinear response, the waves follow the Akhmediev breather solution, where the higher frequency periodic patterns at the fractional Talbot distance disappear. Further increase in the wave steepness leads to deviations from the Akhmediev breather solution and to asymmetric breaking of the wave function. Unlike the periodic revival that occurs in the linear regime, here the wave crests exhibit self acceleration, followed by self deceleration at half the Talbot distance, thus completing a smooth transition of the periodic pulse train by half a period. Such phenomena can be theoretically modeled by using the Dysthe equation.

关键词： Nonlinear optics Surface gravity waves Nonlinear waves Schroedinger equation

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Preparation of activated carbon derived from water hyacinth as electrode active material for li-ion supercapacitor 16th

Preparation of activated carbon derived from water hyacinth ...

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16th International Conference on Quality in Research, QiR 2019

作者： Paduraksa, Jagad Luthfi, Muhammad Verdianto, Ariono Subhan, Achmad Widayatno, Wahyu Bambang Prihandoko, Bambang Kartini, Evvy Hudaya, Chairul Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok16424 Indonesia Energy System Engineering Study Program Faculty of Engineering Universitas Indonesia Depok16424 Indonesia Research Center for Physics Indonesian Institute of Sciences PUSPIPTEK SerpongTangerang Selatan15314 Indonesia Center for Science and Technology of Advanced Materials BATAN Kawasan Puspiptek Tangerang15314 Indonesia

ISBN: (纸本)9783035715996

Lithium-Ion Capacitor (LIC) has shown promising performance to meet the needs of high energy and power-density-energy storage system in the era of electric vehicles nowadays. The development of electrode materials and electrolytes in recent years has improvised LIC performance significantly. One of the active materials of LIC electrodes, activated carbon (AC), can be synthesized from various biomass, one of which is the water hyacinth. Its abundant availability and low utilization make the water hyacinth as a promising activated carbon source. To observe the most optimal physical properties of AC, this study also compares various activation temperatures. In this study, full cell LIC was fabricated using LTO based anode, and water hyacinth derived AC as the cathode. The LIC full cell was further characterized to see the material properties and electrochemical performance. Water hyacinth derived LIC can achieve a specific capacitance of 32.11 F/g, the specific energy of 17.83 Wh/kg, and a specific power of 160.53 W/kg. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Activated carbon

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Diabetes Prediction Model for Better Clarification by using Machine Learning

Diabetes Prediction Model for Better Clarification by using ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： J. Lysa Eben R. Jayasudha S. Ramya S. Kaliappan Shobha Aswal Khalid Ali Salem Al-Salehi Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India Division of Research and Development Lovely Professional University Phagwara Punjab India School of Computing Graphic Era Hill University Dehradun India Biomedical engineering study program faculty of science and technology Airlangga University Surabaya Indonesia

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy.

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