Showing papers by "Michigan Technological University published in 2021"
Daniel J. Klionsky1, Amal Kamal Abdel-Aziz2, Sara Abdelfatah3, Mahmoud Abdellatif4 +2980 more•Institutions (777)
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
1,129 citations
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Abstract: U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.
425 citations
University of Manchester1, City University of New York2, Istanbul Technical University3, New York University4, Heidelberg University5, Niels Bohr Institute6, University of Edinburgh7, University of Bologna8, Academy of Athens9, Sapienza University of Rome10, University of Naples Federico II11, Stanford University12, Institut d'Astrophysique de Paris13, University of Portsmouth14, Cardiff University15, Universidade Federal do Espírito Santo16, University of Michigan17, Asia Pacific Center for Theoretical Physics18, University of New Mexico19, University of Barcelona20, University of St. Thomas (Minnesota)21, Princeton University22, National Autonomous University of Mexico23, California Institute of Technology24, INAF25, University of Chicago26, Michigan Technological University27, Lawrence Berkeley National Laboratory28, University of Cambridge29, Imperial College London30, Ruhr University Bochum31, University of Waterloo32, University of Oxford33, Johns Hopkins University34, University of Pennsylvania35, University of California, Davis36, Birla Institute of Technology and Science37, RWTH Aachen University38, Université libre de Bruxelles39, University of Padua40, Indian Institute of Technology Kharagpur41, Spanish National Research Council42, University of North Carolina at Chapel Hill43, University of Arizona44, University of Oslo45, Jamia Millia Islamia46, University of Southern Denmark47, National Institute for Space Research48, Fermilab49, Presidency University, Kolkata50, Université Paris-Saclay51, University of Montpellier52, University of Szczecin53, Korea Astronomy and Space Science Institute54, University of California, Los Angeles55, University of Paris56, Leiden University57, Swarthmore College58, University of Sheffield59, University of Amsterdam60, United College, Winnipeg61, Liaoning Normal University62
TL;DR: In this article, the authors focus on the 4.4σ tension between the Planck estimate of the Hubble constant H0 and the SH0ES collaboration measurements and discuss how the next decade's experiments will be crucial.
322 citations
University of Manchester1, City University of New York2, Istanbul Technical University3, New York University4, Heidelberg University5, Niels Bohr Institute6, University of Edinburgh7, University of Bologna8, Academy of Athens9, Sapienza University of Rome10, University of Naples Federico II11, Stanford University12, Institut d'Astrophysique de Paris13, University of Portsmouth14, Cardiff University15, Universidade Federal do Espírito Santo16, University of Michigan17, Asia Pacific Center for Theoretical Physics18, University of New Mexico19, University of Barcelona20, Centre national de la recherche scientifique21, Princeton University22, National Autonomous University of Mexico23, Jet Propulsion Laboratory24, INAF25, University of Chicago26, Michigan Technological University27, University of Cambridge28, Imperial College London29, Ruhr University Bochum30, University of Waterloo31, Johns Hopkins University32, University of Pennsylvania33, University of California, Davis34, Birla Institute of Technology and Science35, RWTH Aachen University36, Université libre de Bruxelles37, University of Padua38, Indian Institute of Technology Kharagpur39, Spanish National Research Council40, University of North Carolina at Chapel Hill41, University of Arizona42, University of Oslo43, Jamia Millia Islamia44, National Institute for Space Research45, Fermilab46, Presidency University, Kolkata47, Université Paris-Saclay48, University of Montpellier49, University of Szczecin50, Korea Astronomy and Space Science Institute51, University of California, Los Angeles52, Leiden University53, University of Southern Denmark54, Swarthmore College55, University of Sheffield56, University of Amsterdam57, United College, Winnipeg58, Liaoning Normal University59
TL;DR: In this article, the authors focus on the tension between Planck data and weak lensing measurements and redshift surveys, and discuss the importance of trying to fit multiple cosmological datasets with complete physical models, rather than fitting individual datasets with a few handpicked theoretical parameters.
181 citations
University of Maryland, College Park1, Ford Motor Company2, University of Colorado Boulder3, Lawrence Berkeley National Laboratory4, Oak Ridge National Laboratory5, Argonne National Laboratory6, University of California, Los Angeles7, Applied Materials8, Michigan Technological University9, Pacific Northwest National Laboratory10, Iowa State University11, Georgia Institute of Technology12, University of California, San Diego13, University of Michigan14, General Motors15
TL;DR: In this paper, Albertus, P; Anandan, V; Ban, C; Balsara, N; Belharouak, I; Buettner-Garrett, J; Chen, Z; Daniel, C, Doeff, M; Dudney, NJ; Dunn, B; Harris, SJ; Herle, S; Herbert, E; Kalnaus, S, Libera, JA; Lu, D; Martin, S., McCloskey, BD; McDowell, MT; Meng, YS; Nanda, J, Sak
Abstract: Author(s): Albertus, P; Anandan, V; Ban, C; Balsara, N; Belharouak, I; Buettner-Garrett, J; Chen, Z; Daniel, C; Doeff, M; Dudney, NJ; Dunn, B; Harris, SJ; Herle, S; Herbert, E; Kalnaus, S; Libera, JA; Lu, D; Martin, S; McCloskey, BD; McDowell, MT; Meng, YS; Nanda, J; Sakamoto, J; Self, EC; Tepavcevic, S; Wachsman, E; Wang, C; Westover, AS; Xiao, J; Yersak, T
170 citations
TL;DR: In this paper, black anatase-TiO2 was demonstrated to be an effective catalyst for tetracycline (TC) visible light photodegradation, achieving 66.2% removal efficiency over black TiO2 under visible light illumination.
169 citations
TL;DR: In this article, a comprehensive review on the progress in EC and PEC for degradation of antibiotics, with emphasis on the development of catalysts and their performances, is provided, and the effect of experimental parameters (current density, applied potential, pH, electrolyte concentration, coexisting ions, and light source and intensity) on the degradation of antibiotic and the toxicity of intermediate, final products, and treated water are also assessed.
108 citations
TL;DR: In this article, in-depth interviews with solar industry professionals were conducted and findings suggest that the potential for an agrivoltaic project to retain agricultural interests and consequently increase local support for development is the most significant opportunity of dual use solar.
Abstract: Large-scale development of solar-generated electricity is hindered in some regions of the U.S. by land use competition and localized social resistance. One approach to alleviate these coupled challenges is agrivoltaics: the strategic co-location of solar photovoltaics and agriculture. To explore the opportunities and barriers for agrivoltaics, in-depth interviews with solar industry professionals were conducted and findings suggest that the potential for an agrivoltaic project to retain agricultural interests and consequently increase local support for development is the most significant opportunity of dual use solar. Capable of increasing community acceptance, participants expect agrivoltaics to play an important role in future solar endeavors, especially in places where development may be perceived as a threat to agricultural interests. The results further reveal the interconnections among the various dimensions of social acceptance and suggest that the growth of agrivoltaics is contingent on market adoption of the technology through community acceptance and supportive local regulatory environments. As solar photovoltaic systems transcend niche applications to become larger and more prevalent, the dimensions of social acceptance, including the opportunities and barriers associated with each dimension, can help inform decision making to enhance the growth of agrivoltaics and thus photovoltaic development. The findings can help land use planners, solar developers, and municipal governments make informed decisions that strategically and meaningfully integrate agriculture and solar, and in turn provide multiple benefits including the retention of agricultural land, local economic development, and broad adoption of solar energy technologies.
84 citations
TL;DR: In this paper, a comprehensive review on the recent advances in SRM is presented to provide a constructive insight into the development of SRM technology, however, a critical perspective is provided to enlighten future work on this significant area.
Abstract: Methane (CH4) is the major component of currently abundant natural gas and a prominent green-house gas. Steam reforming of methane (SRM) is an important technology for the conversion of CH4 into H2 and syngas. To improve the catalytic activity and coking resistance of SRM catalysts, great efforts (including the addition of promoters, development of advanced supports, and structural modification, etc.) have been made with considerable progress in the past decade. Meanwhile, a series of novel processes have been explored for more efficient and energy-saving SRM. In this scenario, a comprehensive review on the recent advances in SRM is necessary to provide a constructive insight into the development of SRM technology, however, is still lacking. Herein, the improvements in catalyst construction for conventional SRM and the newly developed SRM processes in the past decade are presented and analyzed. First, the critical issues of SRM catalysts are briefly introduced. Then, the recent research advances of the most popular Ni based catalysts and the catalysts based on the other non-noble metals (Co, Cu, Mo etc.) and the efficient but costly noble metals (Au, Pt, Pd, Rh, Ru etc.) are discussed. Furthermore, the development of the representative modified SRM processes, including thermo-photo hybrid SRM, sorbent enhanced SRM, oxidative SRM, chemical looping SRM, plasma and electrical-field enhanced SRM, is demonstrated, and their advantages and limits are compared. Finally, a critical perspective is provided to enlighten future work on this significant area.
80 citations
TL;DR: In this paper, the structural evolution of catalysts caused by the interplay with electric fields, electrolytes or reactants/intermediates brings about the formation of real active sites.
Abstract: The structure-activity correlation study of electrocatalysts is essential for improving conversion from electrical to chemical energy. Recently, increasing evidences obtained by operando characterization techniques reveal that the structural evolution of catalysts caused by the interplay with electric fields, electrolytes or reactants/intermediates brings about the formation of real active sites. Hence, it is time to summarize the structural evolution-related research advances and envisage their future developments. In this minireview, we first introduce the fundamental concepts associated with structural evolution ( e.g., catalyst, active site/center and stability/lifetime) and their relevance. Then, the multiple inducements of structural evolution and advanced operando characterizations are discussed. Lastly, a brief overview of structural evolution and its reversibility in heterogeneous electrocatalysis, especially for representative electrocatalytic oxygen evolution reaction (OER) and CO 2 reduction reaction (CO 2 RR), along with key challenges and opportunities, is highlighted.
76 citations
TL;DR: In this paper, the authors review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understand microbial communities.
Abstract: Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.
University of Manchester1, City University of New York2, Istanbul Technical University3, New York University4, Heidelberg University5, University of Copenhagen6, University of Edinburgh7, University of Bologna8, Academy of Athens9, Sapienza University of Rome10, University of Naples Federico II11, Stanford University12, Institut d'Astrophysique de Paris13, University of Portsmouth14, Cardiff University15, Universidade Federal do Espírito Santo16, University of Michigan17, Asia Pacific Center for Theoretical Physics18, University of New Mexico19, University of Barcelona20, Princeton University21, National Autonomous University of Mexico22, California Institute of Technology23, INAF24, University of Chicago25, Michigan Technological University26, University of Cambridge27, Imperial College London28, Ruhr University Bochum29, University of Waterloo30, Johns Hopkins University31, University of Pennsylvania32, University of California, Davis33, Birla Institute of Technology and Science34, RWTH Aachen University35, Université libre de Bruxelles36, University of Padua37, Indian Institute of Technology Kharagpur38, Spanish National Research Council39, University of North Carolina at Chapel Hill40, University of Arizona41, University of Oslo42, Jamia Millia Islamia43, National Institute for Space Research44, Durban University of Technology45, Fermilab46, Presidency University, Kolkata47, Université Paris-Saclay48, University of Montpellier49, University of Szczecin50, Korea Astronomy and Space Science Institute51, University of California, Los Angeles52, Centre national de la recherche scientifique53, Leiden University54, Swarthmore College55, University of Sheffield56, University of Amsterdam57, United College, Winnipeg58, Liaoning Normal University59
TL;DR: In this paper, the authors present a list of important goals that need to be addressed in the next decade, also taking into account the current discordances present between the different cosmological probes, as the Hubble constant H0 value, the σ8S8 tension, and the anomalies present in the Planck results.
TL;DR: A tremendous amount of research has been done on refining the flotation process for iron ore and designing the reagents which go into it as discussed by the authors and this paper reviews the industrial practices and fundamental principles.
Abstract: A tremendous amount of research has been done on refining the flotation process for iron ore and designing the reagents which go into it. This paper reviews the industrial practices and fundamental...
Wilfrid Laurier University1, Northern Arizona University2, Humboldt State University3, Auburn University4, Université du Québec à Rimouski5, National Park Service6, Université du Québec en Abitibi-Témiscamingue7, Université du Québec à Chicoutimi8, Michigan Technological University9, St. John's University10, Natural Resources Canada11, Institute of Arctic and Alpine Research12, VU University Amsterdam13, University of Kentucky14, Yukon College15
TL;DR: In this paper, the authors synthesize data from 1,538 field sites across boreal North America to evaluate compositional changes in tree species following 58 recent fires (1989 to 2014).
Abstract: Intensifying wildfire activity and climate change can drive rapid forest compositional shifts. In boreal North America, black spruce shapes forest flammability and depends on fire for regeneration. This relationship has helped black spruce maintain its dominance through much of the Holocene. However, with climate change and more frequent and severe fires, shifts away from black spruce dominance to broadleaf or pine species are emerging, with implications for ecosystem functions including carbon sequestration, water and energy fluxes, and wildlife habitat. Here, we predict that such reductions in black spruce after fire may already be widespread given current trends in climate and fire. To test this, we synthesize data from 1,538 field sites across boreal North America to evaluate compositional changes in tree species following 58 recent fires (1989 to 2014). While black spruce was resilient following most fires (62%), loss of resilience was common, and spruce regeneration failed completely in 18% of 1,140 black spruce sites. In contrast, postfire regeneration never failed in forests dominated by jack pine, which also possesses an aerial seed bank, or broad-leaved trees. More complete combustion of the soil organic layer, which often occurs in better-drained landscape positions and in dryer duff, promoted compositional changes throughout boreal North America. Forests in western North America, however, were more vulnerable to change due to greater long-term climate moisture deficits. While we find considerable remaining resilience in black spruce forests, predicted increases in climate moisture deficits and fire activity will erode this resilience, pushing the system toward a tipping point that has not been crossed in several thousand years.
TL;DR: In this article, the recent progress in the development of efficient and robust DRM catalysts is highlighted, after a brief introduction of the thermodynamics and general reaction mechanisms for DRM, and with a firm belief in the great promise of DRM technology, the remaining challenges for DRM catalyst development are discussed along with their perspectives on the future research directions.
Abstract: The increasing amount of greenhouse gases, especially CO2, in the atmosphere during the past decades has been a matter of great concern. Meanwhile, with the extensive exploration of natural gas resources, there is abundant CH4 waiting for valorization. CO2 or dry reforming of methane (CRM/DRM) is a promising approach to simultaneously utilize the two gases for the production of syngas. High-quality (free of sintering and carbon deposition during the reaction) and cost-effective catalysts are the key to the practical application of DRM. In this review article, the recent progress in the development of efficient and robust DRM catalysts is highlighted, after a brief introduction of the thermodynamics and general reaction mechanisms for DRM. The key factors in constructing highly efficient catalysts are addressed and the two major types of DRM catalysts, i.e., conventional supported catalysts and reduced solid solution catalysts, are clearly classified. Furthermore, with a firm belief in the great promise of DRM technology, the remaining challenges for DRM catalyst development are discussed along with our perspectives on the future research directions.
University of Manchester1, City University of New York2, Istanbul Technical University3, New York University4, Heidelberg University5, Niels Bohr Institute6, University of Edinburgh7, University of Bologna8, Academy of Athens9, Sapienza University of Rome10, University of Naples Federico II11, Stanford University12, Pierre-and-Marie-Curie University13, Istituto Nazionale di Fisica Nucleare14, Cardiff University15, Universidade Federal do Espírito Santo16, University of Michigan17, Asia Pacific Center for Theoretical Physics18, University of New Mexico19, University of Barcelona20, National Autonomous University of Mexico21, Jet Propulsion Laboratory22, University of Chicago23, Michigan Technological University24, University of Cambridge25, Ruhr University Bochum26, Johns Hopkins University27, University of Pennsylvania28, University of California, Davis29, Birla Institute of Technology and Science30, RWTH Aachen University31, Université libre de Bruxelles32, University of Padua33, Indian Institute of Technology Kharagpur34, Spanish National Research Council35, University of North Carolina at Chapel Hill36, University of Arizona37, University of Oslo38, Jamia Millia Islamia39, National Institute for Space Research40, Fermilab41, University of Paris42, University of Montpellier43, University of Szczecin44, Korea Astronomy and Space Science Institute45, University of Southern California46, Centre national de la recherche scientifique47, Leiden University48, Swarthmore College49, University of Sheffield50, University of Amsterdam51, Chinese Academy of Sciences52, United College, Winnipeg53, Liaoning Normal University54
TL;DR: In particular, the Planck Cosmic Microwave Background power spectra, assuming the nominal likelihood, prefer a closed universe at more than 99% confidence level as mentioned in this paper. But this anomaly may be the result of an unresolved systematic error or just a statistical fluctuation.
TL;DR: This is the first review covering methodological approaches and considerations, aiming to provide a holistic and fundamental basis to choose an appropriate method for determining the k values for bimolecular reactions between target compounds and radicals in the aqueous phase.
TL;DR: In this article, the authors provide a comprehensive review on the strategies of tuning catalysts for efficient photodegradation of antibiotics, including the doping of metals and nonmetals, coupling semiconductors, hydrogenation, ligand-to-metal charge transfer effect, and perovskite structure construction.
Abstract: The photocatalytic degradation of antibiotics is a very promising technique to solve the pollution issues of antibiotics in water. Furthermore, catalysts play a critical role in the photocatalytic process. This article provides the first comprehensive review on the strategies of tuning catalysts for efficient photodegradation of antibiotics. It is shown that the doping of metals and nonmetals, coupling semiconductors, hydrogenation, ligand-to-metal charge transfer effect, and perovskite structure construction are widely exploited to improve visible light activity. Supporting catalysts on mesoporous materials, morphology (size and shape) modification of catalysts, and deposition of metals on the catalysts are demonstrated as efficient approaches for the enhancement of photodegradation efficiency. The generation pathways for reactive oxygen species overi the catalysts, the influencing factors in the photodegradation, and the assessment methods for catalyst performance are evaluated. Finally, the challenges and future research directions are discussed.
TL;DR: Red mud is generated at a rate of up to 1755 million tons per year The global stockpile of red mud is near 4 billion tons This material is hazardous with pH values from 11 to 13 Reduction of th
Abstract: Red mud is generated at a rate of up to 1755 million tons per year The global stockpile of red mud is near 4 billion tons This material is hazardous with pH values from 11 to 13 Reduction of th
TL;DR: In this paper, two kinds of bio-rejuvenators (BR-1 and BR-5) were chosen to represent the straight-chain and aromatic structures, respectively, to accelerate the diffusion process between the virgin and aged asphalt binder.
TL;DR: In this paper, a comprehensive climate-atmosphere-land-ocean-ecosystem and exposure-risk model framework for mercury and its application to project the health effects of future atmospheric emissions is presented.
Abstract: Mercury is a potent neurotoxin that poses health risks to the global population. Anthropogenic mercury emissions to the atmosphere are projected to decrease in the future due to enhanced policy efforts such as the Minamata Convention, a legally-binding international treaty entered into force in 2017. Here, we report the development of a comprehensive climate-atmosphere-land-ocean-ecosystem and exposure-risk model framework for mercury and its application to project the health effects of future atmospheric emissions. Our results show that the accumulated health effects associated with mercury exposure during 2010-2050 are $19 (95% confidence interval: 4.7-54) trillion (2020 USD) realized to 2050 (3% discount rate) for the current policy scenario. Our results suggest a substantial increase in global human health cost if emission reduction actions are delayed. This comprehensive modeling approach provides a much-needed tool to help parties to evaluate the effectiveness of Hg emission controls as required by the Minamata Convention.
TL;DR: In this article, an efficient photocatalytic H2O2 production in pure water was realized on graphitic carbon nitride (GCN) decorated by oxidative red phosphorus (ORP).
Abstract: Photocatalytic H2O2 production in pure water is prerequisite for diverse on-site applications, but challengeable to be realized owing to sluggish water oxidation and significant recombination of photogenerated charges. Herein, efficient photocatalytic H2O2 production in pure water was realized on graphitic carbon nitride (GCN) decorated by oxidative red phosphorus (ORP). The composite produces 250 μM of H2O2 within 120 min under visible light irradiation via synchronous water oxidation and oxygen reduction reactions, which is more than 25-fold enhancement of pristine GCN. It is revealed that red phosphorus (RP) can significantly promote charge separation but induce reductive decomposition of H2O2. Successfully, H2O2 decomposition over RP is dramatically suppressed by preventing the interaction between H2O2 and P atoms through oxidizing RP with the formation of P O bonds by an oxidation post-treatment. This strategy opens a new door for the rational design of highly active metal-free photocatalysts toward solar-to-H2O2 conversion in pure water.
TL;DR: The potential impact of automation to decision making is demonstrated by deploying a novel change detection method implementing change feature extraction using convolutional neural networks under an OBIA framework to map a large geographic area affected by a recent natural disaster.
University of Utah1, Los Alamos National Laboratory2, National Autonomous University of Mexico3, Universidad Michoacana de San Nicolás de Hidalgo4, Pennsylvania State University5, Polish Academy of Sciences6, University of Rochester7, Stanford University8, University of Maryland, College Park9, National Institute of Astrophysics, Optics and Electronics10, University of Tokyo11, University of Guadalajara12, University of Wisconsin-Madison13, Michigan Technological University14, Max Planck Society15, National Astronomical Research Institute of Thailand16, Chulalongkorn University17, University of Erlangen-Nuremberg18, Michigan State University19, Polytechnic University of Puerto Rico20, Benemérita Universidad Autónoma de Puebla21, Instituto Politécnico Nacional22, University of New Mexico23, Universidad Autónoma del Estado de Hidalgo24, Seoul National University25, Spanish National Research Council26, California University of Pennsylvania27, Shanghai Jiao Tong University28
TL;DR: In this paper, the Cygnus Cocoon is considered to be a source of very high-energy (TeV to PeV) Galactic cosmic rays, and the measured flux likely originates from hadronic interactions.
Abstract: Cosmic rays with energies up to a few PeV are known to be accelerated within the Milky Way1,2. Traditionally, it has been presumed that supernova remnants were the main source of these very-high-energy cosmic rays3,4, but theoretically it is difficult to accelerate protons to PeV energies5,6 and observationally there simply is no evidence of the remnants being sources of hadrons with energies above a few tens of TeV7,8. One possible source of protons with those energies is the Galactic Centre region9. Here, we report observations of 1–100 TeV γ rays coming from the ‘Cygnus Cocoon’10, which is a superbubble that surrounds a region of massive star formation. These γ rays are likely produced by 10–1,000 TeV freshly accelerated cosmic rays that originate from the enclosed star-forming region Cyg OB2. Until now it was not known that such regions could accelerate particles to these energies. The measured flux likely originates from hadronic interactions. The spectral shape and the emission profile of the Cocoon changes from GeV to TeV energies, which reveals the transport of cosmic particles and historical activity in the superbubble. Following HAWC observations of the Cygnus Cocoon, massive star-forming regions can now be considered to be sources of very-high-energy (TeV to PeV) Galactic cosmic rays.
TL;DR: This work uses bird captures spanning > 35 years from 55 sites within a vast area of intact Amazonian rainforest to reveal reduced abundance of terrestrial and near-ground insectivores in the absence of deforestation, edge effects or other direct anthropogenic landscape change.
Abstract: How are rainforest birds faring in the Anthropocene? We use bird captures spanning > 35 years from 55 sites within a vast area of intact Amazonian rainforest to reveal reduced abundance of terrestrial and near-ground insectivores in the absence of deforestation, edge effects or other direct anthropogenic landscape change. Because undisturbed forest includes far fewer terrestrial and near-ground insectivores than it did historically, today's fragments and second growth are more impoverished than shown by comparisons with modern 'control' sites. Any goals for bird community recovery in Amazonian second growth should recognise that a modern bird community will inevitably differ from a baseline from > 35 years ago. Abundance patterns driven by landscape change may be the most conspicuous manifestation of human activity, but biodiversity declines in undisturbed forest represent hidden losses, possibly driven by climate change, that may be pervasive in intact Amazonian forests and other systems considered to be undisturbed.
TL;DR: Fluorite is a scarce nonrenewable strategic non-metallic mineral resource and is the primary raw material for fluorine products used in diverse fields such as metallurgy, national defense, chemical and optical industries as discussed by the authors.
TL;DR: In this paper, the authors describe strategies that would ensure a more equitable academy for working mothers now and in the future, while the data are clear that mothers are disproportionately impacted by COVID-19, many groups could benefit from these strategies.
Abstract: The issues facing academic mothers have been discussed for decades. Coronavirus Disease 2019 (COVID-19) is further exposing these inequalities as womxn scientists who are parenting while also engaging in a combination of academic related duties are falling behind. These inequities can be solved by investing strategically in solutions. Here we describe strategies that would ensure a more equitable academy for working mothers now and in the future. While the data are clear that mothers are being disproportionately impacted by COVID-19, many groups could benefit from these strategies. Rather than rebuilding what we once knew, let us be the architects of a new world.
TL;DR: In this paper, the authors developed a convenient and reliable approach to generate micromolar level superoxide radical (O 2 ∙ - ) in aqueous solution by photolysis of formate and H2O2.
TL;DR: Deep learning was able to predict yield directly using raw satellite imagery to the extent that was comparable to feature-fed deep learning approaches, and both 2D and 3D CNN models were able to explain nearly 90% variance in field-scale yield.
Abstract: Agricultural management at field-scale is critical for improving yield to address global food security, as providing enough food for the world’s growing population has become a wicked problem for both scientists and policymakers. County- or regional-scale data do not provide meaningful information to farmers who are interested in field-scale yield forecasting for effective and timely field management. No studies directly utilized raw satellite imagery for field-scale yield prediction using deep learning. The objectives of this paper were twofold: (1) to develop a raw imagery-based deep learning approach for field-scale yield prediction, (2) investigate the contribution of in-season multitemporal imagery for grain yield prediction with hand-crafted features and WorldView-3 (WV) and PlanetScope (PS) imagery as the direct input, respectively. Four WV-3 and 25 PS imagery collected during the growing season of soybean were utilized. Both 2-dimensional (2D) and 3-dimensional (3D) convolution neural network (CNN) architectures were developed that integrated spectral, spatial, temporal information contained in the satellite data. For comparison, hundreds of carefully selected spectral, spatial, textural, and temporal features that are optimal for crop growth monitoring were extracted and fed into the same deep learning model. Our results demonstrated that (1) deep learning was able to predict yield directly using raw satellite imagery to the extent that was comparable to feature-fed deep learning approaches; (2) both 2D and 3D CNN models were able to explain nearly 90% variance in field-scale yield; (3) limited number of WV-3 outperformed multi-temporal PS data collected during entire growing season mainly attributed to RedEdge and SWIR bands available with WV-3; and (4) 3D CNN increased the prediction power of PS data compared to 2D CNN due to its ability to digest temporal features extracted from PS data.