Showing papers by "Chalmers University of Technology published in 2020"
Donostia International Physics Center1, Rovira i Virgili University2, MacDiarmid Institute for Advanced Materials and Nanotechnology3, Victoria University of Wellington4, University of Cambridge5, University of California, Santa Barbara6, Queen's University Belfast7, Technical University of Denmark8, University of Victoria9, Chung-Ang University10, University of Jena11, Leibniz Institute of Photonic Technology12, Rutgers University13, University of Strathclyde14, University of Liverpool15, University of Iowa16, University of Minnesota17, Heidelberg University18, National Institute of Advanced Industrial Science and Technology19, Chalmers University of Technology20, Humboldt University of Berlin21, University of Michigan22, Jiangnan University23, Stanford University24, Xiamen University25, Ludwig Maximilian University of Munich26, Hokkaido University27, Seoul National University28, University of Illinois at Urbana–Champaign29, Kwansei Gakuin University30, University of Vigo31, Free University of Berlin32, Northwestern University33, University of Duisburg-Essen34, National Research Council35, Indian Institute of Science Education and Research, Thiruvananthapuram36, Duke University37, Northeastern University (China)38, Temple University39, Wuhan University40, Japan Advanced Institute of Science and Technology41, Jilin University42, Ikerbasque43
TL;DR: Prominent authors from all over the world joined efforts to summarize the current state-of-the-art in understanding and using SERS, as well as to propose what can be expected in the near future, in terms of research, applications, and technological development.
Abstract: The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.
1,768 citations
TL;DR: This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
Abstract: Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding.
837 citations
TL;DR: The superconducting qubits are leading candidates in the race to build a quantum computer capable of realizing computations beyond the reach of modern supercomputers as discussed by the authors, but their performance has not yet been evaluated.
Abstract: Superconducting qubits are leading candidates in the race to build a quantum computer capable of realizing computations beyond the reach of modern supercomputers. The superconducting qubit modality...
701 citations
TL;DR: A platform for ultra-high throughput serum and plasma proteomics that builds on ISO13485 standardisation and high-flow liquid chromatography to facilitate implementation in clinical laboratories and identifies 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19.
Abstract: The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
407 citations
TL;DR: In this article, the authors propose a synthesized definition of an innovation ecosystem, which is compatible with related conceptualizations of innovation systems and natural ecosystems, and the validity of it is illustrated with three empirical examples of innovation ecosystems.
389 citations
07 Apr 2020
TL;DR: In this article, the authors identify the environmental impacts at critical points in the textile and fashion value chain, from production to consumption, focusing on water use, chemical pollution, CO2 emissions and textile waste.
Abstract: The fashion industry is facing increasing global scrutiny of its environmentally polluting supply chain operations. Despite the widely publicized environmental impacts, however, the industry continues to grow, in part due to the rise of fast fashion, which relies on cheap manufacturing, frequent consumption and short-lived garment use. In this Review, we identify the environmental impacts at critical points in the textile and fashion value chain, from production to consumption, focusing on water use, chemical pollution, CO2 emissions and textile waste. Impacts from the fashion industry include over 92 million tonnes of waste produced per year and 79 trillion litres of water consumed. On the basis of these environmental impacts, we outline the need for fundamental changes in the fashion business model, including a deceleration of manufacturing and the introduction of sustainable practices throughout the supply chain, as well a shift in consumer behaviour — namely, decreasing clothing purchases and increasing garment lifetimes. These changes stress the need for an urgent transition back to ‘slow’ fashion, minimizing and mitigating the detrimental environmental impacts, so as to improve the long-term sustainability of the fashion supply chain. The increase in clothing consumption, exemplified in fast fashion, has severe environmental consequences. This Review discusses the impacts of fashion on natural resources and the environment, and examines how technology, policy and consumer behaviour can mitigate the negative effects of the fashion industry.
373 citations
TL;DR: Results point to that the current research is aligned with the goals defined by different national industrial programs, but there are research gaps and opportunities for field development.
Abstract: This systematic review intends to identify how sustainable manufacturing research is contributing to the development of the Industry 4.0 agenda and for a broader understanding about the links betwe...
373 citations
Joint Institute for VLBI in Europe1, University of Amsterdam2, ASTRON3, McGill University4, Max Planck Society5, California Institute of Technology6, West Virginia University7, University of Newcastle8, Academia Sinica Institute of Astronomy and Astrophysics9, National Radio Astronomy Observatory10, University of Toronto11, University of Waterloo12, Perimeter Institute for Theoretical Physics13, University of British Columbia14, National Autonomous University of Mexico15, Ioffe Institute16, Chalmers University of Technology17, National Research Council18, Massachusetts Institute of Technology19
TL;DR: Only one repeating fast radio burst has been localized, to an irregular dwarf galaxy; now another is found to come from a star-forming region of a nearby spiral galaxy, suggesting that repeating FRBs may have a wide range of luminosities, and originate from diverse host galaxies and local environments.
Abstract: Fast radio bursts (FRBs) are brief, bright, extragalactic radio flashes1,2. Their physical origin remains unknown, but dozens of possible models have been postulated3. Some FRB sources exhibit repeat bursts4–7. Although over a hundred FRB sources have been discovered8, only four have been localized and associated with a host galaxy9–12, and just one of these four is known to emit repeating FRBs9. The properties of the host galaxies, and the local environments of FRBs, could provide important clues about their physical origins. The first known repeating FRB, however, was localized to a low-metallicity, irregular dwarf galaxy, and the apparently non-repeating sources were localized to higher-metallicity, massive elliptical or star-forming galaxies, suggesting that perhaps the repeating and apparently non-repeating sources could have distinct physical origins. Here we report the precise localization of a second repeating FRB source6, FRB 180916.J0158+65, to a star-forming region in a nearby (redshift 0.0337 ± 0.0002) massive spiral galaxy, whose properties and proximity distinguish it from all known hosts. The lack of both a comparably luminous persistent radio counterpart and a high Faraday rotation measure6 further distinguish the local environment of FRB 180916.J0158+65 from that of the single previously localized repeating FRB source, FRB 121102. This suggests that repeating FRBs may have a wide range of luminosities, and originate from diverse host galaxies and local environments. Only one repeating fast radio burst has been localized, to an irregular dwarf galaxy; now another is found to come from a star-forming region of a nearby spiral galaxy.
347 citations
DOI•
Trinity College, Dublin1, Heidelberg University2, University of Cambridge3, Autonomous University of Madrid4, Université Paris-Saclay5, Spanish National Research Council6, University of Zaragoza7, University of Nottingham8, RWTH Aachen University9, Kessler Foundation10, Istituto Italiano di Tecnologia11, Georgia Institute of Technology12, University of Grenoble13, Dresden University of Technology14, Polytechnic University of Turin15, University of Zurich16, Bielefeld University17, University of Strasbourg18, Technical University of Denmark19, Swiss Federal Laboratories for Materials Science and Technology20, Ikerbasque21, University of Erlangen-Nuremberg22, Technische Universität München23, Bundeswehr University Munich24, University of Bern25, University of Patras26, Foundation for Research & Technology – Hellas27, Center for Theoretical Studies, University of Miami28, École Polytechnique Fédérale de Lausanne29, University of Ulm30, University of Eastern Finland31, Aristotle University of Thessaloniki32, University of Manchester33, Polish Academy of Sciences34, Aalto University35, University of Castilla–La Mancha36, ShanghaiTech University37, University of Exeter38, Chalmers University of Technology39, Warsaw University of Technology40, University of Trieste41, Instituto Politécnico Nacional42, Queen Mary University of London43, University of Trento44, University of the Basque Country45, Chemnitz University of Technology46, Charles University in Prague47, University of Jena48, University of Texas at Dallas49
TL;DR: In this article, the authors present an overview of the main techniques for production and processing of graphene and related materials (GRMs), as well as the key characterization procedures, adopting a 'hands-on' approach, providing practical details and procedures as derived from literature and from the authors' experience, in order to enable the reader to reproduce the results.
Abstract: © 2020 The Author(s). We present an overview of the main techniques for production and processing of graphene and related materials (GRMs), as well as the key characterization procedures. We adopt a 'hands-on' approach, providing practical details and procedures as derived from literature as well as from the authors' experience, in order to enable the reader to reproduce the results. Section I is devoted to 'bottom up' approaches, whereby individual constituents are pieced together into more complex structures. We consider graphene nanoribbons (GNRs) produced either by solution processing or by on-surface synthesis in ultra high vacuum (UHV), as well carbon nanomembranes (CNM). Production of a variety of GNRs with tailored band gaps and edge shapes is now possible. CNMs can be tuned in terms of porosity, crystallinity and electronic behaviour. Section II covers 'top down' techniques. These rely on breaking down of a layered precursor, in the graphene case usually natural crystals like graphite or artificially synthesized materials, such as highly oriented pyrolythic graphite, monolayers or few layers (FL) flakes. The main focus of this section is on various exfoliation techniques in a liquid media, either intercalation or liquid phase exfoliation (LPE). The choice of precursor, exfoliation method, medium as well as the control of parameters such as time or temperature are crucial. A definite choice of parameters and conditions yields a particular material with specific properties that makes it more suitable for a targeted application. We cover protocols for the graphitic precursors to graphene oxide (GO). This is an important material for a range of applications in biomedicine, energy storage, nanocomposites, etc. Hummers' and modified Hummers' methods are used to make GO that subsequently can be reduced to obtain reduced graphene oxide (RGO) with a variety of strategies. GO flakes are also employed to prepare three-dimensional (3d) low density structures, such as sponges, foams, hydro- or aerogels. The assembly of flakes into 3d structures can provide improved mechanical properties. Aerogels with a highly open structure, with interconnected hierarchical pores, can enhance the accessibility to the whole surface area, as relevant for a number of applications, such as energy storage. The main recipes to yield graphite intercalation compounds (GICs) are also discussed. GICs are suitable precursors for covalent functionalization of graphene, but can also be used for the synthesis of uncharged graphene in solution. Degradation of the molecules intercalated in GICs can be triggered by high temperature treatment or microwave irradiation, creating a gas pressure surge in graphite and exfoliation. Electrochemical exfoliation by applying a voltage in an electrolyte to a graphite electrode can be tuned by varying precursors, electrolytes and potential. Graphite electrodes can be either negatively or positively intercalated to obtain GICs that are subsequently exfoliated. We also discuss the materials that can be amenable to exfoliation, by employing a theoretical data-mining approach. The exfoliation of LMs usually results in a heterogeneous dispersion of flakes with different lateral size and thickness. This is a critical bottleneck for applications, and hinders the full exploitation of GRMs produced by solution processing. The establishment of procedures to control the morphological properties of exfoliated GRMs, which also need to be industrially scalable, is one of the key needs. Section III deals with the processing of flakes. (Ultra)centrifugation techniques have thus far been the most investigated to sort GRMs following ultrasonication, shear mixing, ball milling, microfluidization, and wet-jet milling. It allows sorting by size and thickness. Inks formulated from GRM dispersions can be printed using a number of processes, from inkjet to screen printing. Each technique has specific rheological requirements, as well as geometrical constraints. The solvent choice is critical, not only for the GRM stability, but also in terms of optimizing printing on different substrates, such as glass, Si, plastic, paper, etc, all with different surface energies. Chemical modifications of such substrates is also a key step. Sections IV-VII are devoted to the growth of GRMs on various substrates and their processing after growth to place them on the surface of choice for specific applications. The substrate for graphene growth is a key determinant of the nature and quality of the resultant film. The lattice mismatch between graphene and substrate influences the resulting crystallinity. Growth on insulators, such as SiO2, typically results in films with small crystallites, whereas growth on the close-packed surfaces of metals yields highly crystalline films. Section IV outlines the growth of graphene on SiC substrates. This satisfies the requirements for electronic applications, with well-defined graphene-substrate interface, low trapped impurities and no need for transfer. It also allows graphene structures and devices to be measured directly on the growth substrate. The flatness of the substrate results in graphene with minimal strain and ripples on large areas, allowing spectroscopies and surface science to be performed. We also discuss the surface engineering by intercalation of the resulting graphene, its integration with Si-wafers and the production of nanostructures with the desired shape, with no need for patterning. Section V deals with chemical vapour deposition (CVD) onto various transition metals and on insulators. Growth on Ni results in graphitized polycrystalline films. While the thickness of these films can be optimized by controlling the deposition parameters, such as the type of hydrocarbon precursor and temperature, it is difficult to attain single layer graphene (SLG) across large areas, owing to the simultaneous nucleation/growth and solution/precipitation mechanisms. The differing characteristics of polycrystalline Ni films facilitate the growth of graphitic layers at different rates, resulting in regions with differing numbers of graphitic layers. High-quality films can be grown on Cu. Cu is available in a variety of shapes and forms, such as foils, bulks, foams, thin films on other materials and powders, making it attractive for industrial production of large area graphene films. The push to use CVD graphene in applications has also triggered a research line for the direct growth on insulators. The quality of the resulting films is lower than possible to date on metals, but enough, in terms of transmittance and resistivity, for many applications as described in section V. Transfer technologies are the focus of section VI. CVD synthesis of graphene on metals and bottom up molecular approaches require SLG to be transferred to the final target substrates. To have technological impact, the advances in production of high-quality large-area CVD graphene must be commensurate with those on transfer and placement on the final substrates. This is a prerequisite for most applications, such as touch panels, anticorrosion coatings, transparent electrodes and gas sensors etc. New strategies have improved the transferred graphene quality, making CVD graphene a feasible option for CMOS foundries. Methods based on complete etching of the metal substrate in suitable etchants, typically iron chloride, ammonium persulfate, or hydrogen chloride although reliable, are time- and resourceconsuming, with damage to graphene and production of metal and etchant residues. Electrochemical delamination in a low-concentration aqueous solution is an alternative. In this case metallic substrates can be reused. Dry transfer is less detrimental for the SLG quality, enabling a deterministic transfer. There is a large range of layered materials (LMs) beyond graphite. Only few of them have been already exfoliated and fully characterized. Section VII deals with the growth of some of these materials. Amongst them, h-BN, transition metal tri- and di-chalcogenides are of paramount importance. The growth of h-BN is at present considered essential for the development of graphene in (opto) electronic applications, as h-BN is ideal as capping layer or substrate. The interesting optical and electronic properties of TMDs also require the development of scalable methods for their production. Large scale growth using chemical/physical vapour deposition or thermal assisted conversion has been thus far limited to a small set, such as h-BN or some TMDs. Heterostructures could also be directly grown.
330 citations
TL;DR: In this article, a comprehensive review of important findings and developments in this field that have enabled their tremendous success with an overview of very recent trends concerning the active materials for the negative and positive electrode as well as the electrolyte is presented.
314 citations
King's College London1, Chalmers University of Technology2, Macquarie University3, Karolinska Institutet4, Stockholm University5, University of Newcastle6, University of Gothenburg7, Dalian Institute of Chemical Physics8, KAIST9, Institut national de la recherche agronomique10, Science for Life Laboratory11, Royal Institute of Technology12
TL;DR: This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities’ compositions of terminal ileum and large intestine in 5 healthy individuals, and details which species are involved with the tryptophan/indole pathway and the antimicrobial resistance biogeography along the intestine.
Abstract: Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.
TL;DR: Neurochemical evidence of neuronal injury and glial activation in patients with moderate and severe COVID-19 is shown, perhaps reflecting a sequence of early astrocytic response and more delayed axonal injury.
Abstract: OBJECTIVE: To test the hypothesis that coronavirus disease 2019 (COVID-19) has an impact on the CNS by measuring plasma biomarkers of CNS injury. METHODS: We recruited 47 patients with mild (n = 20), moderate (n = 9), or severe (n = 18) COVID-19 and measured 2 plasma biomarkers of CNS injury by single molecule array, neurofilament light chain protein (NfL; a marker of intra-axonal neuronal injury) and glial fibrillary acidic protein (GFAp; a marker of astrocytic activation/injury), in samples collected at presentation and again in a subset after a mean of 11.4 days. Cross-sectional results were compared with results from 33 age-matched controls derived from an independent cohort. RESULTS: The patients with severe COVID-19 had higher plasma concentrations of GFAp (p = 0.001) and NfL (p < 0.001) than controls, while GFAp was also increased in patients with moderate disease (p = 0.03). In patients with severe disease, an early peak in plasma GFAp decreased on follow-up (p < 0.01), while NfL showed a sustained increase from first to last follow-up (p < 0.01), perhaps reflecting a sequence of early astrocytic response and more delayed axonal injury. CONCLUSION: We show neurochemical evidence of neuronal injury and glial activation in patients with moderate and severe COVID-19. Further studies are needed to clarify the frequency and nature of COVID-19-related CNS damage and its relation to both clinically defined CNS events such as hypoxic and ischemic events and mechanisms more closely linked to systemic severe acute respiratory syndrome coronavirus 2 infection and consequent immune activation, as well as to evaluate the clinical utility of monitoring plasma NfL and GFAp in the management of this group of patients.
TL;DR: In this article, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper.
Abstract: Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. Hence, the security of IoT should start with foremost securing WSNs ahead of the other components. However, owing to the absence of a physical line-of-defense, i.e., there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. More specifically, for the application areas in which CIA (confidentiality, integrity, availability) has prime importance, WSNs and emerging IoT technology might constitute an open avenue for the attackers. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: “Passive Attacks” and “Active Attacks”. Understanding these attacks and their associated defense mechanisms will help paving a secure path towards the proliferation and public acceptance of IoT technology.
Technical University of Denmark1, VU University Amsterdam2, Heidelberg University3, École Polytechnique Fédérale de Lausanne4, RWTH Aachen University5, University of California, San Diego6, University of Toronto7, Institute for Systems Biology8, National Autonomous University of Mexico9, University of Tübingen10, University of Queensland11, Argonne National Laboratory12, Leiden University13, Spanish National Research Council14, Technical University of Madrid15, Norwegian University of Life Sciences16, Hanze University of Applied Sciences17, Wellcome Trust18, KAIST19, Max Planck Society20, Humboldt University of Berlin21, Wageningen University and Research Centre22, Agency for Science, Technology and Research23, Sungkyunkwan University24, King's College London25, Royal Institute of Technology26, Chinese Academy of Sciences27, University of Virginia28, Chalmers University of Technology29, University of Arkansas for Medical Sciences30, Oxford Brookes University31, Nova Southeastern University32, University of Minho33, University of Düsseldorf34
TL;DR: A community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality, and advocate adoption of the latest version of the Systems Biology Markup Language level 3 flux balance constraints (SBML3FBC) package as the primary description and exchange format.
Abstract: We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjansdottir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).
TL;DR: Eutectic high-entropy alloys (EHEAs) are becoming a new research hotspot in the metallic materials community because of their excellent castability, fine and uniform microstructures even in the as-cast state, high strength, and good ductility as discussed by the authors.
TL;DR: In this paper, three narrow band-gap polymer acceptors with different bridging atoms (i.e., C, Si, and Ge) were developed for all-polymer solar cells (all-PSCs).
TL;DR: In this paper, a stochastic-robust optimization method was developed to consider both low impact variations and extreme events, and applied to 30 cities in Sweden, by considering 13 climate change scenarios, reveal that uncertainties in renewable energy potential and demand can lead to a significant performance gap brought by future climate variations and a drop in power supply reliability due to extreme weather events.
Abstract: Climate induced extreme weather events and weather variations will affect both the demand of energy and the resilience of energy supply systems. The specific potential impact of extreme events on energy systems has been difficult to quantify due to the unpredictability of future weather events. Here we develop a stochastic-robust optimization method to consider both low impact variations and extreme events. Applications of the method to 30 cities in Sweden, by considering 13 climate change scenarios, reveal that uncertainties in renewable energy potential and demand can lead to a significant performance gap (up to 34% for grid integration) brought by future climate variations and a drop in power supply reliability (up to 16%) due to extreme weather events. Appropriate quantification of the climate change impacts will ensure robust operation of the energy systems and enable renewable energy penetration above 30% for a majority of the cities.
TL;DR: It is found that a 28-day IF regimen for diabetic mice improves behavioral impairment via a microbiota-metabolites-brain axis: IF enhances mitochondrial biogenesis and energy metabolism gene expression in hippocampus, re-structures the gut microbiota, and improves microbial metabolites that are related to cognitive function.
Abstract: Cognitive decline is one of the complications of type 2 diabetes (T2D). Intermittent fasting (IF) is a promising dietary intervention for alleviating T2D symptoms, but its protective effect on diabetes-driven cognitive dysfunction remains elusive. Here, we find that a 28-day IF regimen for diabetic mice improves behavioral impairment via a microbiota-metabolites-brain axis: IF enhances mitochondrial biogenesis and energy metabolism gene expression in hippocampus, re-structures the gut microbiota, and improves microbial metabolites that are related to cognitive function. Moreover, strong connections are observed between IF affected genes, microbiota and metabolites, as assessed by integrative modelling. Removing gut microbiota with antibiotics partly abolishes the neuroprotective effects of IF. Administration of 3-indolepropionic acid, serotonin, short chain fatty acids or tauroursodeoxycholic acid shows a similar effect to IF in terms of improving cognitive function. Together, our study purports the microbiota-metabolites-brain axis as a mechanism that can enable therapeutic strategies against metabolism-implicated cognitive pathophysiologies.
TL;DR: A model predictive control framework is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation.
Abstract: Energy management is an enabling technology for increasing the economy of fuel cell/battery hybrid electric vehicles. Existing efforts mostly focus on optimization of a certain control objective (e.g., hydrogen consumption), without sufficiently considering the implications for on-board power sources degradation. To address this deficiency, this article proposes a cost-optimal, predictive energy management strategy, with an explicit consciousness of degradation of both fuel cell and battery systems. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, a model predictive control framework, for the first time, is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation. The efficacy of this framework is evaluated, accounting for various sizes of prediction horizon and prediction uncertainties. Second, the effects of driving and pricing scenarios on the optimized vehicular economy are explored.
Katholieke Universiteit Leuven1, University of Paris2, University of Copenhagen3, Leipzig University4, University of Gothenburg5, Université Paris-Saclay6, Fudan University7, Imperial College London8, National Institutes of Health9, Chalmers University of Technology10, Molecular Medicine Partnership Unit11, University of Würzburg12, French Institute of Health and Medical Research13
TL;DR: A cross-sectional analysis of participants in the MetaCardis Body Mass Index Spectrum cohort finds that the higher prevalence of gut microbiota dysbiosis in individuals with obesity is not observed in those who take statin drugs, and statin therapy is identified as a key covariate of microbiome diversification.
Abstract: Microbiome community typing analyses have recently identified the Bacteroides2 (Bact2) enterotype, an intestinal microbiota configuration that is associated with systemic inflammation and has a high prevalence in loose stools in humans1,2. Bact2 is characterized by a high proportion of Bacteroides, a low proportion of Faecalibacterium and low microbial cell densities1,2, and its prevalence varies from 13% in a general population cohort to as high as 78% in patients with inflammatory bowel disease2. Reported changes in stool consistency3 and inflammation status4 during the progression towards obesity and metabolic comorbidities led us to propose that these developments might similarly correlate with an increased prevalence of the potentially dysbiotic Bact2 enterotype. Here, by exploring obesity-associated microbiota alterations in the quantitative faecal metagenomes of the cross-sectional MetaCardis Body Mass Index Spectrum cohort (n = 888), we identify statin therapy as a key covariate of microbiome diversification. By focusing on a subcohort of participants that are not medicated with statins, we find that the prevalence of Bact2 correlates with body mass index, increasing from 3.90% in lean or overweight participants to 17.73% in obese participants. Systemic inflammation levels in Bact2-enterotyped individuals are higher than predicted on the basis of their obesity status, indicative of Bact2 as a dysbiotic microbiome constellation. We also observe that obesity-associated microbiota dysbiosis is negatively associated with statin treatment, resulting in a lower Bact2 prevalence of 5.88% in statin-medicated obese participants. This finding is validated in both the accompanying MetaCardis cardiovascular disease dataset (n = 282) and the independent Flemish Gut Flora Project population cohort (n = 2,345). The potential benefits of statins in this context will require further evaluation in a prospective clinical trial to ascertain whether the effect is reproducible in a randomized population and before considering their application as microbiota-modulating therapeutics. A cross-sectional analysis of participants in the MetaCardis Body Mass Index Spectrum cohort finds that the higher prevalence of gut microbiota dysbiosis in individuals with obesity is not observed in those who take statin drugs.
TL;DR: This paper focuses on NMPC based on the real-time iteration (RTI) scheme, as this technique has been successfully tested and, in some applications, requires computational times that are only marginally larger than linear MPC.
Abstract: Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to recent progress in algorithms for solving online the underlying structured quadratic programs. In contrast, nonlinear MPC (NMPC) requires the deployment of more elaborate algorithms, which require longer computation times than linear MPC. Nonetheless, computational speeds for NMPC comparable to those of MPC are now regularly reported, provided that the adequate algorithms are used. In this paper, we aim at clarifying the similarities and differences between linear MPC and NMPC. In particular, we focus our analysis on NMPC based on the real-time iteration (RTI) scheme, as this technique has been successfully tested and, in some applications, requires computational times that are only marginally larger than linear MPC. The goal of the paper is to promote the understanding of RTI-based NMPC within the linear MPC community.
TL;DR: It is proposed that exposure to microbial amyloids in the gastrointestinal tract can accelerate αSyn aggregation and disease in the gut and the brain.
Abstract: Amyloids are a class of protein with unique self-aggregation properties, and their aberrant accumulation can lead to cellular dysfunctions associated with neurodegenerative diseases. While genetic and environmental factors can influence amyloid formation, molecular triggers and/or facilitators are not well defined. Growing evidence suggests that non-identical amyloid proteins may accelerate reciprocal amyloid aggregation in a prion-like fashion. While humans encode ~30 amyloidogenic proteins, the gut microbiome also produces functional amyloids. For example, curli are cell surface amyloid proteins abundantly expressed by certain gut bacteria. In mice overexpressing the human amyloid α-synuclein (αSyn), we reveal that colonization with curli-producing Escherichia coli promotes αSyn pathology in the gut and the brain. Curli expression is required for E. coli to exacerbate αSyn-induced behavioral deficits, including intestinal and motor impairments. Purified curli subunits accelerate αSyn aggregation in biochemical assays, while oral treatment of mice with a gut-restricted amyloid inhibitor prevents curli-mediated acceleration of pathology and behavioral abnormalities. We propose that exposure to microbial amyloids in the gastrointestinal tract can accelerate αSyn aggregation and disease in the gut and the brain.
TL;DR: In this article, an interdisciplinary team of social science researchers consider the implications of Covid-19 for the politics of sustainable energy transitions and highlight continuities and discontinuities with pre-pandemic trends.
Abstract: In this perspectives piece, an interdisciplinary team of social science researchers considers the implications of Covid-19 for the politics of sustainable energy transitions. The emergency measures adopted by states, firms, and individuals in response to this global health crisis have driven a series of political, economic and social changes with potential to influence sustainable energy transitions. We identify some of the initial impacts of the ‘great lockdown’ on sustainable and fossil sources of energy, and consider how economic stimulus packages and social practices in the wake of the pandemic are likely to shape energy demand, the carbon-intensity of the energy system, and the speed of transitions. Adopting a broad multi-scalar and multi-actor approach to the analysis of energy system change, we highlight continuities and discontinuities with pre-pandemic trends. Discussion focuses on four key themes that shape the politics of sustainable energy transitions: (i) the short, medium and long-term temporalities of energy system change; (ii) practices of investment around clean-tech and divestment from fossil fuels; (iii) structures and scales of energy governance; and (iv) social practices around mobility, work and public health. While the effects of the pandemic continue to unfold, some of its sectoral and geographically differentiated impacts are already emerging. We conclude that the politics of sustainable energy transitions are now at a critical juncture, in which the form and direction of state support for post-pandemic economic recovery will be key.
01 Mar 2020
TL;DR: How microorganisms can be engineered for CO2 fixation and industrial valorization of this key molecule is described, and a shift from sugar-based feedstocks and biomass to the use of atmospheric CO2 for the bioproduction of fuels and chemicals is desirable.
Abstract: Concerns regarding petroleum depletion and global climate change caused by greenhouse gas emissions have spurred interest in renewable alternatives to fossil fuels. Third-generation (3G) biorefineries aim to utilize microbial cell factories to convert renewable energies and atmospheric CO2 into fuels and chemicals, and hence represent a route for assessing fuels and chemicals in a carbon-neutral manner. However, to establish processes competitive with the petroleum industry, it is important to clarify/evaluate/identify the most promising CO2 fixation pathways, the most appropriate CO2 utilization models and the necessary productivity levels. Here, we discuss the latest advances in 3G biorefineries. Following an overview of applications of CO2 feedstocks, mainly from flue gas and waste gasification, we review prominent opportunities and barriers in CO2 fixation and energy capture. We then summarize reported CO2-based products and industries, and describe trends and key challenges for future advancement of 3G biorefineries. A shift from sugar-based feedstocks and biomass to the use of atmospheric CO2 for the bioproduction of fuels and chemicals is desirable. This Review describes how microorganisms can be engineered for CO2 fixation and industrial valorization of this key molecule.
TL;DR: This Review flows from past attempts to develop a (rechargeable) battery technology based on Ca via crucial breakthroughs to arrive at a comprehensive discussion of the current challenges at hand, and concludes with recommendations for future strategies to propel the Ca battery technology to reality and ultimately reach its full potential for energy storage.
Abstract: This Review flows from past attempts to develop a (rechargeable) battery technology based on Ca via crucial breakthroughs to arrive at a comprehensive discussion of the current challenges at hand. The realization of a rechargeable Ca battery technology primarily requires identification and development of suitable electrodes and electrolytes, which is why we here cover the progress starting from the fundamental electrode/electrolyte requirements, concepts, materials, and compositions employed and finally a critical analysis of the state-of-the-art, allowing us to conclude with the particular roadblocks still existing. As for crucial breakthroughs, reversible plating and stripping of calcium at the metal-anode interface was achieved only recently and for very specific electrolyte formulations. Therefore, while much of the current research aims at finding suitable cathodes to achieve proof-of-concept for a full Ca battery, the spectrum of electrolytes researched is also expanded. Compatibility of cell components is essential, and to ensure this, proper characterization is needed, which requires design of a multitude of reliable experimental setups and sometimes methodology development beyond that of other next generation battery technologies. Finally, we conclude with recommendations for future strategies to make best use of the current advances in materials science combined with computational design, electrochemistry, and battery engineering, all to propel the Ca battery technology to reality and ultimately reach its full potential for energy storage.
TL;DR: It is shown analytically that spacetimes that deviate from the Kerr metric but satisfy weak-field tests can lead to large deviations in the predicted black-hole shadows that are inconsistent with even the current EHT measurements.
Abstract: The 2017 Event Horizon Telescope (EHT) observations of the central source in M87 have led to the first measurement of the size of a black-hole shadow. This observation offers a new and clean gravitational test of the black-hole metric in the strong-field regime. We show analytically that spacetimes that deviate from the Kerr metric but satisfy weak-field tests can lead to large deviations in the predicted black-hole shadows that are inconsistent with even the current EHT measurements. We use numerical calculations of regular, parametric, non-Kerr metrics to identify the common characteristic among these different parametrizations that control the predicted shadow size. We show that the shadow-size measurements place significant constraints on deviation parameters that control the second post-Newtonian and higher orders of each metric and are, therefore, inaccessible to weak-field tests. The new constraints are complementary to those imposed by observations of gravitational waves from stellar-mass sources.
TL;DR: In this article, the authors discuss the digitalization efforts of 26 leading manufacturing firms, the difficulties encountered, and how they can be handled, and show that many firms are far from ready to benefit from digitalization.
Abstract: This article discusses the digitalization efforts of 26 leading manufacturing firms, the difficulties encountered, and how they can be handled. It shows that many firms are far from ready to benefi...
01 Jun 2020
TL;DR: A general framework for tactical decision making is introduced, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning, based on the AlphaGo Zero algorithm, extended to a domain with a continuous state space where self-play cannot be used.
Abstract: Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This article introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately.
TL;DR: In this article, the authors show that incorporating a third component with lower miscibility and higher lowest unoccupied molecular orbital (LUMO) level into the state-of-the-art PM6:Y6 system can significantly enhance the performance of devices.
Abstract: It is widely known that the miscibility between donor and acceptor is a crucial factor that affects the morphology and thus device performance of nonfullerene organic solar cells (OSCs). In this Letter, we show that incorporating a third component with lower miscibility and higher lowest unoccupied molecular orbital (LUMO) level into the state-of-the-art PM6:Y6 system can significantly enhance the performance of devices. The best results of the ternary devices are achieved by adding a small molecular acceptor named ITCPTC (similar to 5% w/w), which significantly improves the power conversion efficiency (PCE) of the host system from 16.44% to 17.42%. The higher LUMO of the third component increases the open-circuit voltage (V-oc), while the low miscibility enlarges the domains and leads to improved short-circuit current density (J(sc)) and fill factor (FF). The efficacy of this strategy is supported by using other nonfullerene third components including an asymmetric small molecule (N7IT) and a polymer acceptor (PF2-DTC), which play the same role as ITCPTC and boost the PCEs to 16.96% and 17.04%, respectively. Our approach can be potentially applied to a wide range of OSC material systems and should facilitate the development of the OSC field.
TL;DR: Human1 is generated, an extensively curated, genome-scale model of human metabolism that unified two parallel model lineages using an open source repository to enable rapid, trackable updates and demonstrated the utility of Human1 by highlighting potential metabolic vulnerabilities in acute myeloid leukemia, predicting genes that are essential for specific metabolic tasks, and estimating metabolic fluxes and growth rates.
Abstract: Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.