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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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Incorporating Participants’ Welfare into Sequential Multiple Assignment Randomized Trials

arXiv

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arXiv 2022年

作者： Wang, Xinru Deliu, Nina Narita, Yusuke Chakraborty, Bibhas Centre for Quantitative Medicine Duke-NUS Medical School Singapore MRC - Biostatistics Unit University of Cambridge Cambridge United Kingdom Department of Methods and Models for Economics Territory and Finance Sapienza University of Rome Rome Italy Department of Economics and Cowles Foundation Yale University New HavenCT United States Program in Health Services and Systems Research Duke-NUS Medical School Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States

Dynamic treatment regimes (DTRs) are sequences of decision rules that recommend treatments based on patients’ time-varying clinical conditions. The sequential multiple assignment randomized trial (SMART) is an experimental design that can provide high-quality evidence for constructing optimal DTRs. In a conventional SMART, participants are randomized to available treatments at multiple stages with balanced randomization probabilities. Despite its relative simplicity of implementation and desirable performance in comparing embedded DTRs, the conventional SMART faces inevitable ethical issues including assigning many participants to the empirically inferior treatment or the treatment they dislike, which might slow down the recruitment procedure and lead to higher attrition rates, ultimately leading to poor internal and external validities of the trial results. In this context, we propose a SMART under the Experiment-as-Market framework (SMART-EXAM), a novel SMART design that holds the potential to improve participants’ welfare by incorporating their preferences and predicted treatment effects into the randomization procedure. We describe the steps of conducting a SMART-EXAM and evaluate its performance compared to the conventional SMART. The results indicate that the SMART-EXAM can improve the welfare of the participants enrolled in the trial, while also achieving a desirable ability to construct an optimal DTR when the experimental parameters are suitably specified. We finally illustrate the practical potential of the SMART-EXAM design using data from a SMART for children with attention-deficit/hyperactivity disorder (ADHD). © 2022, CC BY.

关键词： Design of experiments

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Single-cell analysis of angiotensin-converting enzyme II expression in human kidneys and bladders reveals a potential route of 2019 novel coronavirus infection

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Chinese Medical Journal 2021年第8期134卷 935-943页

作者： Wei Lin Jue Fan Long-Fei Hu Yan Zhang Joshua DOoi Ting Meng Peng Jin Xiang Ding Long-Kai Peng Lei Song Rong Tang Zhou Xiao Xiang Ao Xiang-Cheng Xiao Qiao-Ling Zhou Ping Xiao Yong Zhong Department of Pathology Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Bioinformatics and Data Science Singleron BiotechnologiesNanjingJiangsu 210032China Centre for Inflammatory Diseases Monash University Department of MedicineMonash Medical CentreClaytonVIC 3168Australia Department of Nephrology Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Organ Transplantation Xiangya HospitalCentral South UniversityChangshaHunan 410008China Department of Kidney Transplantation The Second Xiangya Hospital of Central South UniversityChangshaHunan 410011China

Background::Since 2019,a novel coronavirus named 2019 novel coronavirus(2019-nCoV)has emerged *** from fever and respiratory complications,acute kidney injury has been observed in a few patients with coronavirus disease ***,according to recent findings,the virus has been detected in ***-converting enzyme II(ACE2)has been proposed to serve as the receptor for the entry of 2019-nCoV,which is the same as that for the severe acute respiratory *** study aimed to investigate the possible cause of kidney damage and the potential route of 2019-nCoV infection in the urinary ***::We used both published kidney and bladder cell atlas data and new independent kidney single-cell RNA sequencing data generated in-house to evaluate ACE2 gene expression in all cell types in healthy kidneys and *** Pearson correlation coefficients between ACE2 and all other genes were first ***,genes with r values larger than 0.1 and P values smaller than 0.01 were deemed significant co-expression genes with ***::Our results showed the enriched expression of ACE2 in all subtypes of proximal tubule(PT)cells of the ***2 expression was found in 5.12%,5.80%,and 14.38%of the proximal convoluted tubule cells,PT cells,and proximal straight tubule cells,respectively,in three published kidney cell atlas *** addition,ACE2 expression was also confirmed in 12.05%,6.80%,and 10.20%of cells of the proximal convoluted tubule,PT,and proximal straight tubule,respectively,in our own two healthy kidney *** the analysis of public data from three bladder samples,ACE2 expression was low but detectable in bladder epithelial *** 0.25%and 1.28%of intermediate cells and umbrella cells,respectively,had ACE2 ***::This study has provided bioinformatics evidence of the potential route of 2019-nCoV infection in the urinary system.

关键词： 2019-nCoV Acute kidney injury Angiotensin-converting enzyme 2 Bladder COVID-19 Kidney RNA sequence analysis Single-cell analysis

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Deep learning-based transcription factor activity for stratification of breast cancer patients

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Biochimica et Biophysica Acta - Gene Regulatory Mechanisms 2022年第6期1865卷 194838页

作者： Xiong, Yuqiang Wang, Shiyuan Wei, Haodong Li, Hanshuang Lv, Yingli Chi, Meng Su, Dongqing Lu, Qianzi Yu, Yao Zuo, Yongchun Yang, Lei College of Bioinformatics Science and Technology Harbin Medical University Harbin 150081 China The State key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock College of Life Sciences Inner Mongolia University Hohhot 010070 China Department of Anesthesiology Harbin Medical University Cancer Hospital Harbin 150081 China Digital College Inner Mongolia Intelligent Union Big Data Academy Inner Mingolia Wesure Date Technology Co. Ltd. Hohhot 010010 China

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients. © 2022 Elsevier B.V.

关键词： Breast cancer Deep learning Prognosis Subtype Transcription factor

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Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review

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JMIR medical informatics 2025年 13卷 e67859页

作者： Suhila Sawesi Arya Jadhav Bushra Rashrash Health Informatics and Bioinformatics Program College Of Computing Grand Valley State University 333 Michigan St. NE Grand Rapids MI 49503 United States 1 616-331-7827 ext 17827. Data Science College Of Computing Grand Valley State University Allendale MI United States. Department of Biomedical Science College of Liberal Arts and Sciences Grand Valley State University Allendale MI United States.

Background:Leptospirosis, a zoonotic disease caused by Leptospira bacteria, continues to pose significant public health risks, particularly in tropical and subtropical regions. Objective:This systematic review aimed to evaluate the application of machine learning (ML) and deep learning (DL) techniques in predicting and diagnosing leptospirosis, focusing on the most used algorithms, validation methods, data types, and performance metrics. Methods:Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Checklist for Critical Appraisal and data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction model Risk of Bias Assessment Tool (PROBAST) tools, we conducted a comprehensive review of studies applying ML and DL models for leptospirosis detection and prediction, examining algorithm performance, data sources, and validation approaches. Results:Out of a total of 374 articles screened, 17 studies were included in the qualitative synthesis, representing approximately 4.5% of the initial pool. The review identified frequent use of algorithms such as support vector machines, artificial neural networks, decision trees, and convolutional neural networks (CNNs). Among the included studies, 88% (15/17) used traditional ML methods, and 24% (4/17) used DL techniques. Several models demonstrated high predictive performance, with reported accuracy rates ranging from 80% to 98%, notably with the U-Net CNN achieving 98.02% accuracy. However, public datasets were underused, with only 35% (6/17) of studies incorporating publicly available data sources; the majority (65%, 11/17) relied primarily on private datasets from hospitals, clinical records, or regional surveillance systems. Conclusions:ML and DL techniques demonstrate potential for improving leptospirosis prediction and diagnosis, but future research should focus on using larger, more diverse datasets, adopting transfer learning strategies, and integratin

关键词： artificial intelligence convolutional neural networks deep learning diagnosis leptospirosis machine learning prediction models support vector machines transfer learning zoonotic diseases

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Centralized Microgrid: Agricultural Load in Rural Areas of Bangladesh

Centralized Microgrid: Agricultural Load in Rural Areas of B...

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Computational Performance Evaluation (ComPE), International Conference on

作者： Sayeed Hasan Dewan Mahnaaz Mahmud Sanjid Islam Eftakhar Hossain S.M. Masum Ahmed Mohammad Zeyad Md. Sadik Tasrif Anubhove Computational Biology and Data Science Laboratory Bangladesh Energy and Technology Research Division Advanced Bioinformatics Chattogram Bangladesh Erasmus+ Joint MSC in Smart Cities and Communities Faculty of Engineering University of Mons (UMONS) Mons Belgium Erasmus+ Joint MSC in Smart Cities and Communities School of Engineering and Physical Sciences (EPS) Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9781665436571

Energy is regarded as one of the most significant challenges confronting humanity; in fact, based on current growth trends, the world will undoubtedly be resource and carbon-constrained; as such, various studies have been conducted in recent years to identify the benefits of using renewable sources to provide the electricity requirements with clean, secure, reasonably priced, and scalable energy. Hybrid renewable energy systems (HRES) can offer reliable, environment-friendly, and cost-effective means for meeting energy demand. The design of a grid-connected microgrid employing renewables, using photovoltaic (PV) and biogas (BG) generator to power agricultural loads for urban areas is presented in this paper. The goals of the optimal design of renewable energy systems include lowering CO2 emissions while also reducing grid energy usage by incorporating more renewable resources for agricultural uses. Hybrid Optimization Multiple Energy Resources (HOMER) pro was used for modeling and simulation for the optimal design of the HRES. The results demonstrate that the grid-connected microgrid with PV and battery outperforms the grid and the standalone renewable power system. In terms of energy cost (0.0236$/kWh), renewable fraction (88.3%), and carbon reduction, the proposed design in this manuscript is a reliable alternative for the agriculture farm.

关键词： Renewable energy sources Costs Sensitivity analysis Urban areas Rural areas Microgrids Reliability engineering

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Solar Energy Home Systems for Rural Areas Communities in Bangladesh

Solar Energy Home Systems for Rural Areas Communities in Ban...

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Computational Performance Evaluation (ComPE), International Conference on

作者： Md. Sadik Tasrif Anubhove Sayeed Hasan Dewan Mahnaaz Mahmud Eftakhar Hossain Mohammad Zeyad S.M. Masum Ahmed Sanjid Islam Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Chattogram Bangladesh Erasmus+ Joint MSC in Smart Cities and Communities School of Engineering and Physical Sciences (EPS) Heriot-Watt University Edinburgh United Kingdom Erasmus+ Joint MSC in Smart Cities and Communities Faculty of Engineering University of Mons (UMONS) Mons Belgium

ISBN: (数字)9781665436564

ISBN: (纸本)9781665436571

Bangladesh's power generation is wholly reliant on non-renewable resources. Modern life is reliant on technology, and in order to keep up with the world's population and increase living standards, electricity production is crucial. Electricity outages disrupt the regular flow of life. Due to a higher demand than generation, impoverished people in countries like Bangladesh are subjected to load shedding. Utilization of renewable energy and effective power conservation are critical in meeting people's needs. Low income people, in particular, suffer greatly during daily load shedding of more than five hours. This manuscript offers an environment friendly, independent, cost-effective design of a system that provides power not only during daylight hours but also during load-shedding in the Bangladeshi sub-district of Parbatipur. With the right use of renewable energy, the suggested system sends energy directly to low-powered DC loads and charges the battery when sunlight is available, minimizing the inverter's cost and the unit cost of the electricity bill. To charge a 12 V DC battery, a photovoltaic (PV) panel is employed, and an Arduino is used to monitor the battery and manage the charging process. Through analysis, the suggested system is shown to be effective in reducing the trend of emissions toward greenhouse gases.

关键词： Renewable energy sources Production Load shedding Batteries Power system reliability Fuels Statistics

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Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

arXiv

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arXiv 2023年

作者： Despotovic, Vladimir Kim, Sang-Yoon Hau, Ann-Christin Kakoichankava, Aliaksandra Klamminger, Gilbert Georg Borgmann, Felix Bruno Kleine Frauenknecht, Katrin B.M. Mittelbronn, Michel Nazarov, Petr V. Bioinformatics Platform Data Integration and Analysis Unit Luxembourg Institute of Health Strassen Luxembourg Dr. Senckenberg Institute of Neurooncology University Hospital Frankfurt Frankfurt am Main Germany Edinger Institute Institute of Neurology Goethe University Frankfurt am Main Germany Frankfurt Cancer Institute Goethe University Frankfurt am Main Germany University Cancer Center Frankfurt Frankfurt am Main Germany University Hospital Goethe University Frankfurt am Main Germany Laboratoire national de santé National Center of Pathology Dudelange Luxembourg Multi-Omics Data Science group Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre of Neuropathology Dudelange Luxembourg Klinik für Frauenheilkunde Geburtshilfe und Reproduktionsmedizin Saarland University Homburg Germany Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre for Systems Biomedicine University of Luxembourg Belval Luxembourg Department of Life Sciences and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist’s efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas;thus, providing insights to pathologists concerning the most informative parts of the WSI. © 2023, CC BY-NC-ND.

关键词： Tumors

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Potential Reduction in CO2 Emission by Heat Pump System in Bangladesh

Potential Reduction in CO2 Emission by Heat Pump System in B...

引用

Computational Performance Evaluation (ComPE), International Conference on

作者： Sanjid Islam S. M. Masum Ahmed Mohammad Zeyad Md. Sadik Tasrif Anubhove Sayeed Hasan Dewan Mahnaaz Mahmud Eftakhar Hossain Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh Chattogram Bangladesh Erasmus+ Joint MSC in Smart Cities and Communities Faculty of Engineering University of Mons (UMONS) Mons Belgium Erasmus+ Joint MSC in Smart Cities and Communities School of Engineering and Physical Sciences (EPS) Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9781665436571

Renewable energy plays an important role to reduce energy costs and meet clean energy production demands. Ground source heat pumps, commonly referred to as geothermal heat pumps, are extremely efficient systems that pull heat from the earth’s surface. Traditional heating and cooling systems can be replaced with ground source heat pump (GSHP) technology. The purpose of this research is to develop and analyze the feasibility of GSHP in Bangladesh. The research study discovered that by utilizing GSHP, the emission factor can be reduced by 3.1 tCO2/Mwh per year. The utilization of green energy contributes to a clean environment. Besides this study employs a horizontal axis closed-loop heat exchanger, which reduces the system’s total cost. The proposed system has a lower initial and operating cost than a conventional power system. The study concluded that the system will generate revenue for the authority within five years of its implementation. Thus, it is financially viable and low-cost to operate. Additionally, global warming is accelerating, and summer temperatures are increasing dramatically. As a result of this research, a green energy-based system will be developed that will be more efficient for Bangladesh. Furthermore, as a developing country Bangladesh already has a lot of natural geothermal resources. To emphasize the possibilities of effective use of these resources and provide a financial viability analysis via accurate simulation approach is also one of the essential outcomes for this research work. Finally, like other countries, Bangladesh will achieve SDG 7 by ensuring the proper utilization of green energy by the year 2031.

关键词： Heating systems Surface cleaning Renewable energy sources Costs Heat pumps Green products Geothermal energy

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Application of expert system for oil palm smallholder-owned plantations

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Umami H. G. Mawandha D. D. Puruhito Purwadi T. Suparyanto D. Sudigyo B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Most of the oil palm smallholders operate independently. As a result of this lack of support and assistance, oil palm owned by independent smallholders has the lowest productivity compared to large private (corporation) plantations and large state plantations. In addition, smallholder-owned plantations often operate without paying attention to sustainability aspects. There is a need for an effective, efficient, and user-friendly mentoring tool for oil palm farmers that are accessed independently. The Android-based platform was developed by applying an expert system to support increased production of oil palm cultivation for smallholders. Several stages of the expert system implementation, including the identification of planters and land profiles, were carried out using the direct interview method. The expert system includes land preparation management, planting material selection, seeding, weeds control, pests and diseases, fertilization, harvesting and transport, plantation administration, chat platforms, and data scrapping from data providers. The type of expert system is a data-driven Decision Support System (DSS).

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