Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

Inflammatory factors and risk of meningiomas: a bidirectional mendelian-randomization study

BackgroundMeningiomas are one of the most common intracranial tumors, and the current understanding of meningioma pathology is still incomplete. Inflammatory factors play an important role in the pathophysiology of meningioma, but the causal relationship between inflammatory factors and meningioma is still unclear.MethodMendelian randomization (MR) is an effective statistical method for reducing bias based on whole genome sequencing data. It’s a simple but powerful framework, that uses genetics to study aspects of human biology. Modern methods of MR make the process more robust by exploiting the many genetic variants that may exist for a given hypothesis. In this paper, MR is applied to understand the causal relationship between exposure and disease outcome.ResultsThis research presents a comprehensive MR study to study the association of genetic inflammatory cytokines with meningioma. Based on the results of our MR analysis, which examines 41 cytokines in the largest GWAS datasets available, we were able to draw the relatively more reliable conclusion that elevated levels of circulating TNF-β, CXCL1, and lower levels of IL-9 were suggestive associated with a higher risk of meningioma. Moreover, Meningiomas could cause lower levels of interleukin-16 and higher levels of CXCL10 in the blood.ConclusionThese findings suggest that TNF-β, CXCL1, and IL-9 play an important role in the development of meningiomas. Meningiomas also affect the expression of cytokines such as IL-16 and CXCL10. Further studies are needed to determine whether these biomarkers can be used to prevent or treat meningiomas

Roadmap on energy harvesting materials

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere

Impact of Crisis on Sustainable Business Model Innovation—The Role of Technology Innovation

The transformation of old and new technologies, the normalized crisis situation, and global economic integration blur industrial boundaries and cause the business pattern to fluctuate and become unsustainable, especially when considering the impact of the COVID-19 pandemic. This study focuses on crisis situations and combines the types of technology innovation (introduction, socialization, and differentiation) and sustainable business model innovation (efficiency, novelty, and co-benefit innovations) to theoretically analyze the dynamic impact of technology innovation on different types of sustainable business model innovations. Using a multi-case comparative analysis method, typical enterprises are selected as the sample cases. This study discusses the influences of different technology innovation schemes on sustainable business model innovation in different crisis situations. Enterprises should consider introducing technology for rapid value updates to maintain an efficient business model in an urgent production factor crisis, search for valuable and scarce technical components or introduce other entities to facilitate technical cooperation and form a novel business model in a market environment crisis, and use big data, artificial intelligence, and other technologies to create co-benefit business model innovation in a business ethics crisis. The conclusion guides enterprises and provides a framework for the optimal technical scheme under the corresponding crisis

Impact of Crisis on Sustainable Business Model Innovation—The Role of Technology Innovation

The transformation of old and new technologies, the normalized crisis situation, and global economic integration blur industrial boundaries and cause the business pattern to fluctuate and become unsustainable, especially when considering the impact of the COVID-19 pandemic. This study focuses on crisis situations and combines the types of technology innovation (introduction, socialization, and differentiation) and sustainable business model innovation (efficiency, novelty, and co-benefit innovations) to theoretically analyze the dynamic impact of technology innovation on different types of sustainable business model innovations. Using a multi-case comparative analysis method, typical enterprises are selected as the sample cases. This study discusses the influences of different technology innovation schemes on sustainable business model innovation in different crisis situations. Enterprises should consider introducing technology for rapid value updates to maintain an efficient business model in an urgent production factor crisis, search for valuable and scarce technical components or introduce other entities to facilitate technical cooperation and form a novel business model in a market environment crisis, and use big data, artificial intelligence, and other technologies to create co-benefit business model innovation in a business ethics crisis. The conclusion guides enterprises and provides a framework for the optimal technical scheme under the corresponding crisis

Roadmap on energy harvesting materials

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere

Roadmap on energy harvesting materials

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.M C thanks the Centre Québécois sur les Matériaux Fonctionnels (CQMF, a Fonds de recherche du Québec – Nature et Technologies strategic network) and A L thanks the Canada Research Chairs program for financial support. G C W thanks the University of Calgary. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308 with writing support for BWL by ARPA-E DIFFERENTIATE program under Grant No. DE-AR0001215. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government.Peer reviewe