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268 results on '"Aria, S."'

103. MetaPathways v2.5: quantitative functional, taxonomic and usability improvements

Author

Hiu Kan Cheung, Dongjae Kim, Niels W. Hanson, Kishori M. Konwar, Aria S. Hahn, Maya P. Bhatia, Connor Morgan-Lang, Shang-Ju Wu, and Steven J. Hallam

Subjects
Statistics and Probability, Computer science, Information Storage and Retrieval, Genomics, computer.software_genre, Biochemistry, 03 medical and health sciences, Annotation, Software, Databases, Genetic, Humans, Molecular Biology, Phylogeny, 030304 developmental biology, 0303 health sciences, Information retrieval, 030306 microbiology, business.industry, High-Throughput Nucleotide Sequencing, Usability, Molecular Sequence Annotation, Sequence Analysis, DNA, Genome Analysis, Pipeline (software), Applications Notes, Computer Science Applications, Computational Mathematics, Projection (relational algebra), Computational Theory and Mathematics, Data mining, business, computer, Algorithms
Abstract

Summary: Next-generation sequencing is producing vast amounts of sequence information from natural and engineered ecosystems. Although this data deluge has an enormous potential to transform our lives, knowledge creation and translation need software applications that scale with increasing data processing and analysis requirements. Here, we present improvements to MetaPathways, an annotation and analysis pipeline for environmental sequence information that expedites this transformation. We specifically address pathway prediction hazards through integration of a weighted taxonomic distance and enable quantitative comparison of assembled annotations through a normalized read-mapping measure. Additionally, we improve LAST homology searches through BLAST-equivalent E-values and output formats that are natively compatible with prevailing software applications. Finally, an updated graphical user interface allows for keyword annotation query and projection onto user-defined functional gene hierarchies, including the Carbohydrate-Active Enzyme database. Availability and implementation: MetaPathways v2.5 is available on GitHub: http://github.com/hallamlab/metapathways2. Contact: shallam@mail.ubc.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2015

104. Function and functional redundancy in microbial systems

Author

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Martin F. Polz, Polz, Martin F, Louca, Stilianos, Mazel, Florent, Albright, Michaeline B. N., Huber, Julie A., O’Connor, Mary I., Ackermann, Martin, Hahn, Aria S., Srivastava, Diane S., Crowe, Sean A., Doebeli, Michael, Parfrey, Laura Wegener, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Martin F. Polz, Polz, Martin F, Louca, Stilianos, Mazel, Florent, Albright, Michaeline B. N., Huber, Julie A., O’Connor, Mary I., Ackermann, Martin, Hahn, Aria S., Srivastava, Diane S., Crowe, Sean A., Doebeli, Michael, and Parfrey, Laura Wegener

Abstract

Microbial communities often exhibit incredible taxonomic diversity, raising questions regarding the mechanisms enabling species coexistence and the role of this diversity in community functioning. On the one hand, many coexisting but taxonomically distinct microorganisms can encode the same energy-yielding metabolic functions, and this functional redundancy contrasts with the expectation that species should occupy distinct metabolic niches. On the other hand, the identity of taxa encoding each function can vary substantially across space or time with little effect on the function, and this taxonomic variability is frequently thought to result from ecological drift between equivalent organisms. Here, we synthesize the powerful paradigm emerging from these two patterns, connecting the roles of function, functional redundancy and taxonomy in microbial systems. We conclude that both patterns are unlikely to be the result of ecological drift, but are inevitable emergent properties of open microbial systems resulting mainly from biotic interactions and environmental and spatial processes.

Published
2018

105. Shifts in soil microbial community biomass and resource utilization along a Canadian glacier chronosequence

Author

Sylvie A. Quideau and Aria S. Hahn

Subjects
geography, Biomass (ecology), geography.geographical_feature_category, Microbial population biology, Ecology, Chronosequence, Soil Science, Glacier, Biology, complex mixtures, Resource utilization
Abstract

Hahn, A. S. and Quideau, S. A. 2013. Shifts in soil microbial community biomass and resource utilization along a Canadian glacier chronosequence. Can. J. Soil Sci. 93: 305–318. We aimed to describe soil microbial community composition and functional diversity as well as determine the influence of Engelmann spruce (Picea engelmannii Parry) and yellow mountain avens (Dryas drummondii Rich.) on soil microbial community succession along a Canadian glacier chronosequence. Soil microbial composition and functional activity were assessed using phospholipid fatty acid (PLFA) analysis, substrate-induced respiration and enzyme activity analysis. To the best of our knowledge, this is the first study investigating peroxidase and phenol oxidase activities, indicators of fungal activity, along any glacial chronosequence. While no difference in soil microbial community composition along the chronosequence was detected from the PLFA analysis, both total microbial biomass and fungal activity increased with time since deglaciation. Yellow mountain avens, a plant known to support microbial nitrogen fixation in mid- and late successional stages, increased soil microbial biomass, although this effect took 40 yr after deglaciation to emerge. Additionally, significant correlations between microbial respiration of N-acetyl-glucosamine, protocatechuic acid, glucose and percent soil N were found along the chronosequence, indicating that the soil microbial community was influencing changes in the soil environment.

Published
2013
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106. Transfer of

Author

Brian J, Pickles, Roland, Wilhelm, Amanda K, Asay, Aria S, Hahn, Suzanne W, Simard, and William W, Mohn

Subjects
Carbon Isotopes, Soil, Seedlings, Mycorrhizae, Plant Exudates, Meristem, Fungi, Hyphae, Linear Models, Photosynthesis, Carbon, Phospholipids, Pseudotsuga
Abstract

Processes governing the fixation, partitioning, and mineralization of carbon in soils are under increasing scrutiny as we develop a more comprehensive understanding of global carbon cycling. Here we examined fixation by Douglas-fir seedlings and transfer to associated ectomycorrhizal fungi, soil microbes, and full-sibling or nonsibling neighbouring seedlings. Stable isotope probing with 99%

Published
2016

112. FragGeneScan-plus for scalable high-throughput short-read open reading frame prediction

Author

Kishori M. Konwar, Shang-Ju Wu, Niels W. Hanson, Steven J. Hallam, Dongjae Kim, and Aria S. Hahn

Subjects
Multi-core processor, Computer science, Encoding (memory), Gene prediction, Path (graph theory), Scalability, Single-core, Data mining, Hidden Markov model, computer.software_genre, Throughput (business), computer
Abstract

A fundamental step in the analysis of environmental sequence information is the prediction of potential genes or open reading frames (ORFs) encoding the metabolic potential of individual cells and entire microbial communities. FragGeneScan, a software designed to predict intact and incomplete ORFs on short sequencing reads combines codon usage bias, sequencing error models and start/stop codon patterns in a hidden Markov model to find the most likely path of hidden states from a given input sequence, provides a promising route for gene recovery in environmental datasets with incomplete assemblies. However, the current implementation of FragGeneScan does not scale efficiently with increasing input data size. Thus, FragGeneScan cannot be applied to contemporary environmental datasets that can exceed 100s of Gb. Here, we present FragGeneScan-Plus, an improved implementation of the FragGeneScan gene prediction model that leverages algorithmic thread synchronization and efficient in-memory data management to utilize multiple CPU cores without blocking I/O operations. FragGeneScan-Plus can process data approximately 5-times faster than FragGeneScan using a single core and approximately 50-times faster using eight hyper-threaded cores when benchmarked against simulated and real world environmental datasets.

Published
2015
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113. Assembly independent functional annotation of short-read data using SOFA: Short-ORF functional annotation

Author

Dongjae Kim, Steven J. Hallam, Niels W. Hanson, Kishori M. Konwar, and Aria S. Hahn

Subjects
FASTQ format, Open source, Functional annotation, Database, Computer science, Data deduplication, Data mining, ORFS, MIT License, Short read, computer.software_genre, computer, Merge (version control)
Abstract

Accurate description of the microbial communities driving matter and energy transformations in complex ecosystems such as soils cannot yet be effectively accomplished using assembly-based approaches despite the rise of next generation sequencing technologies. Here we present SOFA, an open source pipeline enabling comparative functional annotation of unassembled short-read data. The pipeline attempts to merge mate pairs in fastq files, predicts open reading frames (ORFs) on merged and unmerged reads as small as 70 bps, and completes an additional step, we term ‘deduplication’. Deduplication prevents the double counting of ORFs predicted from unmerged paired-end reads by checking for homologous annotations that span the same ORF, allowing for quantitatively accurate predictions. The effectiveness of SOFA is validated with both simulated and bone fide soil metagenomes, and empirical results are compared to existing strategies for obtaining accurate ORF counts, and an analytical model of read duplication. SOFA enables downstream processing stages within the existing MetaPathways pipeline, and is available for download as a stand alone application at https://github.com under the MIT license.

Published
2015
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114. A geographically-diverse collection of 418 human gut microbiome pathway genome databases

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Konwar, Kishori Mohan, Hallam, Steven, Hahn, Aria S., Altman, Tomer, Hanson, Niels W., Kim, Dongjae, Relman, David A., Dill, David L., Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Konwar, Kishori Mohan, Hallam, Steven, Hahn, Aria S., Altman, Tomer, Hanson, Niels W., Kim, Dongjae, Relman, David A., and Dill, David L.

Abstract

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GutCyc, a compendium of environmental pathway genome databases (ePGDBs) constructed from 418 assembled human microbiome datasets using MetaPathways, enabling reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the Pathway Tools software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GutCyc provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GutCyc data products are searchable online, or may be downloaded and explored locally using MetaPathways and Pathway Tools., Alexander Graham Bell Canada (Graduate Scholarships-Doctoral Program (CGS D)), Tula Foundation, University of British Columbia. Faculty of Graduate and Postdoctoral Studies, Stanford University. School of Medicine (Dean's Funds), National Institutes of Health (U.S.) (Biotechnology Training Grant, grant number 5T32 GM008412), King Abdullah University of Science and Technology (research grant under the KAUST Stanford Academic Excellence Alliance program), National Institutes of Health (U.S.) (NIH/NIGMS 5R01GM099534), Thomas C. and Joan M. Merigan Endowment

Published
2017

115. Rare taxa have potential to make metabolic contributions in enhanced biological phosphorus removal ecosystems

Author

Steven J. Hallam, Christopher E. Lawson, Eric R. Hall, Barry Rabinowitz, William D. Ramey, Blake J. Strachan, Aria S. Hahn, Donald S. Mavinic, and Niels W. Hanson

Subjects
Community, Bacteria, Ecology, Phosphorus, Microorganism, chemistry.chemical_element, Ribosomal RNA, Biology, Wastewater, Microbiology, DNA, Ribosomal, Enhanced biological phosphorus removal, Biodegradation, Environmental, Bioreactors, chemistry, Microbial population biology, Abundance (ecology), RNA, Ribosomal, Ecosystem, Ecology, Evolution, Behavior and Systematics, Phylogeny, Water Pollutants, Chemical
Abstract

Enhanced biological phosphorus removal (EBPR) relies on diverse but specialized microbial communities to mediate the cycling and ultimate removal of phosphorus from municipal wastewaters. However, little is known about microbial activity and dynamics in relation to process fluctuations in EBPR ecosystems. Here, we monitored temporal changes in microbial community structure and potential activity across each bioreactor zone in a pilot-scale EBPR treatment plant by examining the ratio of small subunit ribosomal RNA (SSU rRNA) to SSU rRNA gene (rDNA) over a 120 day study period. Although the majority of operational taxonomic units (OTUs) in the EBPR ecosystem were rare, many maintained high potential activities based on SSU rRNA : rDNA ratios, suggesting that rare OTUs contribute substantially to protein synthesis potential in EBPR ecosystems. Few significant differences in OTU abundance and activity were observed between bioreactor redox zones, although differences in temporal activity were observed among phylogenetically cohesive OTUs. Moreover, observed temporal activity patterns could not be explained by measured process parameters, suggesting that other ecological drivers, such as grazing or viral lysis, modulated community interactions. Taken together, these results point towards complex interactions selected for within the EBPR ecosystem and highlight a previously unrecognized functional potential among low abundance microorganisms in engineered ecosystems.

Published
2014

117. GutCyc

Author

Aria S Hahn, Tomer Altman, Kishori M Konwar, Niels W Hanson, Dongjae Kim, David A Relman, David L Dill, Steven J Hallam, Aria S Hahn, Tomer Altman, Kishori M Konwar, Niels W Hanson, Dongjae Kim, David A Relman, David L Dill, and Steven J Hallam

Abstract

Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body with implications for prevention and therapeutic intervention. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed GutCyc, a compendium of environmental pathway genome databases constructed from 431 assembled human microbiome datasets using the open-source MetaPathways that enable reproducible functional metagenomic annotation. We also generated metabolic network reconstructions for each metagenome using the ptools software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GutCyc provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn's disease, and type 2 diabetes. GutCyc data products are searchable online, or may be downloaded and explored locally using the MetaPathways graphical user interface and ptools.

Published
2016
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121. Miley, What's Good? Nicki Minaj's Anaconda, Instagram Reproductions, and Viral Memetic Violence.

Author

Halliday, Aria S.

Subjects
ANACONDA, MEMETICS, POPULAR culture
Abstract

Images on popular social media platforms like Instagram and Twitter that are the most entertaining are loaded with memetic power because their value is based on cultural attitudes that already constitute our lives in the everyday. Focusing on memes appropriating the artwork from Nicki Minaj's single, Anaconda, I explore how popular memetic culture is fueled by Black women's creativity yet positions Black women's bodies as the fodder for potent viral images on social media platforms and in everyday experiences; Black girlhoods, at this level of representation and in lived experiences, are rarely awarded the distinction from womanhood that many other girlhoods enjoy. Thus, Black feminist discourses of desire which speak to both girlhoods and womanhoods inform my argument that social media has become a site of reproduction and consumption--a technological auction block where Black women's bodies, aesthetics, and experiences are vilified for viral enjoyment. [ABSTRACT FROM AUTHOR]

Published
2018
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128. Transfer of 13C between paired Douglas-fir seedlings reveals plant kinship effects and uptake of exudates by ectomycorrhizas.

Author

Pickles, Brian J., Wilhelm, Roland, Asay, Amanda K., Hahn, Aria S., Simard, Suzanne W., and Mohn, William W.

Subjects
ECTOMYCORRHIZAS, MYCORRHIZAS, HOST plants, PHOSPHOLIPIDS, SOIL microbiology
Abstract

Processes governing the fixation, partitioning, and mineralization of carbon in soils are under increasing scrutiny as we develop a more comprehensive understanding of global carbon cycling. Here we examined fixation by Douglas-fir seedlings and transfer to associated ectomycorrhizal fungi, soil microbes, and full-sibling or nonsibling neighbouring seedlings., Stable isotope probing with 99% 13C- CO2 was applied to trace 13C-labelled photosynthate throughout plants, fungi, and soil microbes in an experiment designed to assess the effect of relatedness on 13C transfer between plant pairs. The fixation and transfer of the 13C label to plant, fungal, and soil microbial tissue was examined in biomass and phospholipid fatty acids., After a 6 d chase period, c. 26.8% of the 13C remaining in the system was translocated below ground. Enrichment was proportionally greatest in ectomycorrhizal biomass. The presence of mesh barriers (0.5 or 35 μm) between seedlings did not restrict 13C transfer., Fungi were the primary recipients of 13C-labelled photosynthate throughout the system, representing 60-70% of total 13C-enriched phospholipids. Full-sibling pairs exhibited significantly greater 13C transfer to recipient roots in two of four Douglas-fir families, representing three- and fourfold increases (+ c. 4 μg excess 13C) compared with nonsibling pairs. The existence of a root/mycorrhizal exudation-hyphal uptake pathway was supported. [ABSTRACT FROM AUTHOR]

Published
2017
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138. Hive Panel Explorer: an interactive network visualization tool.

Author

Perez, Sarah E I, Hahn, Aria S, Krzywinski, Martin, and Hallam, Steven J

Subjects
VISUALIZATION, FOREST soils, SYSTEMS biology, INTERNET servers, APPLICATION software, SYSTEM dynamics
Abstract

Motivation Networks are used to relate topological structure to system dynamics and function, particularly in ecology systems biology. Network analysis is often guided or complemented by data-driven visualization. Hive one of many network visualizations, distinguish themselves as providing a general, consistent and coherent rule-based representation to motivate hypothesis development and testing. Results Here, we present HyPE, H ive P anel E xplorer, a software application that creates a panel of interactive hive plots. HyPE enables network exploration based on user-driven layout rules and parameter combinations for simultaneous of multiple network views. We demonstrate HyPE's features by exploring a microbial co-occurrence network constructed from forest soil microbiomes. Availability and implementation HyPE is available under the GNU license: https://github.com/hallamlab/HivePanelExplorer. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Published
2021
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139. Entrepreneurial and innovation ecosystem for space: A handbook on how to start your own space company

Author

Patten, N., Jacopo Panerati, Gaset, M. L., Antonio, E., Mckellar, M., Milza, F., Coelho, P., Ramirez, B. P., Raurell, D. S., Cheng, T., Wang, H., Aria, S., Calnan, G., Chao, C., Clanton, M., Elkins, A., Grzesiak, K., Higgins, N., Kotwal, C. P., Liu, K., Parks, A., Reddy, S., Ruehl, T., Wexler, H., Zhongming, W., Hu, Z., Wang, X., Ni, Z., Belingheri, P., and Zhang, M.

Subjects
Business, Entrepreneurship, Handbook, Network, New space, Startup, New space, Startup, Entrepreneurship, Handbook, Aerospace Engineering, Business, Network, Space

140. A Computational Intelligence Approach to Detect Future Trends of COVID-19 in France by Analyzing Chinese Data

Author

Sazvar, Z., Tanhaeean, M., Aria, S. S., Akbari, A., Ghaderi, S. F., and Seyed Hossein Iranmanesh

Subjects
pandemic, Science, coronavirus, genetic algorithm, Medicine, artificial neural network
Abstract

Aims: Due to the terrible effects of 2019 novel coronavirus (COVID-19) on health systems and the global economy, the necessity to study future trends of the virus outbreaks around the world is seriously felt. Since geographical mobility is a risk factor of the disease, it has spread to most of the countries recently. It, therefore, necessitates to design a decision support model to 1) identify the spread pattern of coronavirus and, 2) provide reliable information for the detection of future trends of the virus outbreaks. Materials & Methods: The present study adopts a computational intelligence approach to detect the possible trends in the spread of 2019-nCoV in China for a one-month period. Then, a validated model for detecting future trends in the spread of the virus in France is proposed. It uses ANN (Artificial Neural Network) and a combination of ANN and GA (Genetic Algorithm), PSO (Particle Swarm Optimization), and ICA (Imperialist Competitive Algorithm) as predictive models. Findings: The models work on the basis of data released from the past and the present days from WHO (World Health Organization). By comparing four proposed models, ANN and GA-ANN achieve a high degree of accuracy in terms of performance indicators. Conclusion: The models proposed in the present study can be used as decision support tools for managing and controlling of 2019-nCoV outbreaks.

144. Author Correction: Survival strategies of an anoxic microbial ecosystem in Lake Untersee, a potential analog for Enceladus.

Author

Wagner, Nicole Yasmin, Andersen, Dale T., Hahn, Aria S., and Johnson, Sarah Stewart

Subjects
*LAKES, *ECOSYSTEMS, *INTERNET publishing, *ANOXIC zones
Abstract

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-10876-8, published online 05 May 2022 The Data Availability section in the original version of this Article was omitted. The original article can be found online at https://doi.org/10.1038/s41598-022-10876-8. [Extracted from the article]

Published
2022
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145. Helper-dependent adenoviral vectors for liver-directed gene therapy of primary hyperoxaluria type 1

Author

Raffaele Castello, Nicola Brunetti-Pierri, Patrizia Annunziata, Stefania D’Aria, Roberta Borzone, Pasquale Piccolo, Castello, R, Borzone, Roberta, D'Aria, S, Annunziata, P, Piccolo, P, and BRUNETTI PIERRI, Nicola

Subjects
0301 basic medicine, Genetic enhancement, medicine.medical_treatment, Genetic Vectors, Glyoxylate cycle, Gene delivery, Liver transplantation, Bioinformatics, medicine.disease_cause, Article, Adenoviridae, Helper-dependent adenoviral vectors, Primary hyperoxaluria, Mice, 03 medical and health sciences, primary hyperoxaluria type 1, Genetics, medicine, Animals, Humans, Vector (molecular biology), Molecular Biology, Transaminases, Kidney transplantation, business.industry, Genetic Therapy, medicine.disease, gene therapy, 030104 developmental biology, Liver, Hyperoxaluria, Primary, Cancer research, Molecular Medicine, business
Abstract

Primary hyperoxaluria type 1 (PH1) is an inborn error of liver metabolism due to deficiency of the peroxisomal enzyme alanine:glyoxylate aminotransferase (AGT) which catalyzes conversion of glyoxylate into glycine. AGT deficiency results in overproduction of oxalate which ultimately leads to end-stage renal disease and death. Organ transplantation as either preemptive liver transplantation or combined liver/kidney transplantation is the only available therapy to prevent disease progression. Gene therapy is an attractive option to provide an alternative treatment for PH1. Towards this goal, we investigated helper-dependent adenoviral (HDAd) vectors for liver-directed gene therapy of PH1. Compared to saline controls, AGT-deficient mice injected with an HDAd encoding the AGT under the control of a liver-specific promoter showed a significant reduction of hyperoxaluria and less increase of urinary oxalate following challenge with Ethylene Glycol (EG), a precursor of glyoxylate. These studies may thus pave the way to clinical application of HDAd for PH1 gene therapy.Gene Therapy accepted article preview online, 26 November 2015. doi:10.1038/gt.2015.107.

Published
2015

146. Overview of JET results for optimising ITER operation

Author

J. Mailloux, N. Abid, K. Abraham, P. Abreu, O. Adabonyan, P. Adrich, V. Afanasev, M. Afzal, T. Ahlgren, L. Aho-Mantila, N. Aiba, M. Airila, M. Akhtar, R. Albanese, M. Alderson-Martin, D. Alegre, S. Aleiferis, A. Aleksa, A.G. Alekseev, E. Alessi, P. Aleynikov, J. Algualcil, M. Ali, M. Allinson, B. Alper, E. Alves, G. Ambrosino, R. Ambrosino, V. Amosov, E.Andersson Sundén, P. Andrew, B.M. Angelini, C. Angioni, I. Antoniou, L.C. Appel, C. Appelbee, S. Aria, M. Ariola, G. Artaserse, W. Arter, V. Artigues, N. Asakura, A. Ash, N. Ashikawa, V. Aslanyan, M. Astrain, O. Asztalos, D. Auld, F. Auriemma, Y. Austin, L. Avotina, E. Aymerich, A. Baciero, F. Bairaktaris, J. Balbin, L. Balbinot, I. Balboa, M. Balden, C. Balshaw, N. Balshaw, V.K. Bandaru, J. Banks, Yu.F. Baranov, C. Barcellona, A. Barnard, M. Barnard, R. Barnsley, A. Barth, M. Baruzzo, S. Barwell, M. Bassan, A. Batista, P. Batistoni, L. Baumane, B. Bauvir, L. Baylor, P.S. Beaumont, D. Beckett, A. Begolli, M. Beidler, N. Bekris, M. Beldishevski, E. Belli, F. Belli, É. Belonohy, M. Ben Yaala, J. Benayas, J. Bentley, H. Bergsåker, J. Bernardo, M. Bernert, M. Berry, L. Bertalot, H. Betar, M. Beurskens, S. Bickerton, B. Bieg, J. Bielecki, A. Bierwage, T. Biewer, R. Bilato, P. Bílková, G. Birkenmeier, H. Bishop, J.P.S. Bizarro, J. Blackburn, P. Blanchard, P. Blatchford, V. Bobkov, A. Boboc, P. Bohm, T. Bohm, I. Bolshakova, T. Bolzonella, N. Bonanomi, D. Bonfiglio, X. Bonnin, P. Bonofiglo, S. Boocock, A. Booth, J. Booth, D. Borba, D. Borodin, I. Borodkina, C. Boulbe, C. Bourdelle, M. Bowden, K. Boyd, I.Božičević Mihalić, S.C. Bradnam, V. Braic, L. Brandt, R. Bravanec, B. Breizman, A. Brett, S. Brezinsek, M. Brix, K. Bromley, B. Brown, D. Brunetti, R. Buckingham, M. Buckley, R. Budny, J. Buermans, H. Bufferand, P. Buratti, A. Burgess, A. Buscarino, A. Busse, D. Butcher, E.de la Cal, G. Calabrò, L. Calacci, R. Calado, Y. Camenen, G. Canal, B. Cannas, M. Cappelli, S. Carcangiu, P. Card, A. Cardinali, P. Carman, D. Carnevale, M. Carr, D. Carralero, L. Carraro, I.S. Carvalho, P. Carvalho, I. Casiraghi, F.J. Casson, C. Castaldo, J.P. Catalan, N. Catarino, F. Causa, M. Cavedon, M. Cecconello, C.D. Challis, B. Chamberlain, C.S. Chang, A. Chankin, B. Chapman, M. Chernyshova, A. Chiariello, P. Chmielewski, A. Chomiczewska, L. Chone, G. Ciraolo, D. Ciric, J. Citrin, Ł. Ciupinski, M. Clark, R. Clarkson, C. Clements, M. Cleverly, J.P. Coad, P. Coates, A. Cobalt, V. Coccorese, R. Coelho, J.W. Coenen, I.H. Coffey, A. Colangeli, L. Colas, C. Collins, J. Collins, S. Collins, D. Conka, S. Conroy, B. Conway, N.J. Conway, D. Coombs, P. Cooper, S. Cooper, C. Corradino, G. Corrigan, D. Coster, P. Cox, T. Craciunescu, S. Cramp, C. Crapper, D. Craven, R. Craven, M.Crialesi Esposito, G. Croci, D. Croft, A. Croitoru, K. Crombé, T. Cronin, N. Cruz, C. Crystal, G. Cseh, A. Cufar, A. Cullen, M. Curuia, T. Czarski, H. Dabirikhah, A.Dal Molin, E. Dale, P. Dalgliesh, S. Dalley, J. Dankowski, P. David, A. Davies, S. Davies, G. Davis, K. Dawson, S. Dawson, I.E. Day, M. De Bock, G. De Temmerman, G. De Tommasi, K. Deakin, J. Deane, R. Dejarnac, D. Del Sarto, E. Delabie, D. Del-Castillo-Negrete, A. Dempsey, R.O. Dendy, P. Devynck, A. Di Siena, C. Di Troia, T. Dickson, P. Dinca, T. Dittmar, J. Dobrashian, R.P. Doerner, A.J.H. Donné, S. Dorling, S. Dormido-Canto, D. Douai, S. Dowson, R. Doyle, M. Dreval, P. Drewelow, P. Drews, G. Drummond, Ph. Duckworth, H. Dudding, R. Dumont, P. Dumortier, D. Dunai, T. Dunatov, M. Dunne, I. Ďuran, F. Durodié, R. Dux, A. Dvornova, R. Eastham, J. Edwards, Th. Eich, A. Eichorn, N. Eidietis, A. Eksaeva, H. El Haroun, G. Ellwood, C. Elsmore, O. Embreus, S. Emery, G. Ericsson, B. Eriksson, F. Eriksson, J. Eriksson, L.G. Eriksson, S. Ertmer, S. Esquembri, A.L. Esquisabel, T. Estrada, G. Evans, S. Evans, E. Fable, D. Fagan, M. Faitsch, M. Falessi, A. Fanni, A. Farahani, I. Farquhar, A. Fasoli, B. Faugeras, S. Fazinić, F. Felici, R. Felton, A. Fernandes, H. Fernandes, J. 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Zocco, A. Zohar, V. Zoita, S. Zoletnik, V.K. Zotta, I. Zoulias, W. Zwingmann, I. Zychor, VTT Technical Research Centre of Finland, Culham Science Centre, Princeton Plasma Physics Laboratory, Department of Applied Physics, Uppsala University, European Commission, Forschungszentrum Jülich, Universidade Lisboa, Fusion and Plasma Physics, University of Milan - Bicocca, General Atomics, ITER, University of Toyama, CEA, Oak Ridge National Laboratory, Technical University of Madrid, Swiss Federal Institute of Technology Lausanne, Dutch Institute for Fundamental Energy Research, Royal Military Academy, Seoul National University, Chalmers University of Technology, Max-Planck-Institut für Plasmaphysik, KTH Royal Institute of Technology, Aalto-yliopisto, Aalto University, JET Contributor, Mailloux, Joelle, Chiariello, Andrea, Martone, Raffaele, Formisano, Alessandro, Mattei, Massimiliano, Faculdade de Engenharia, Universitat Politècnica de Catalunya. 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Subjects
Technology, TOKAMAKS, PLASMAS, overview, CONFINEMENT, plasma facing components (PFC), ILW, tritium operations, d-t preparation, deuterium, D–T preparation, nuclear technology, JET with ITER-like wall, isotope, Física::Física de fluids::Física de plasmes [Àrees temàtiques de la UPC], Settore FIS/01, GAS HANDLING-SYSTEM, JET, Overview, ITER-like wall (ILW), D-T preparation, energetic particles, scenario development, Shattered Pellet Injection (SPI), plasma-wall interactions (PWI), tritium, Physics, Settore ING-IND/18 - Fisica dei Reattori Nucleari, shutdown dose-rate, gas handling-system, Condensed Matter Physics, simulation, Fusion, Plasma and Space Physics, ilw, confinement, Physical Sciences, SIMULATION, JET with ITER-like wallisotope, ddc:620, performance, plasma facing components (pfc), Nuclear and High Energy Physics, DEUTERIUM, jet with iter-like wall, Fusion, plasma och rymdfysik, Physics, Fluids & Plasmas, BERYLLIUM, divertor, TRITIUM, Science & Technology, Reactors de fusió, tritium operation, Nuclear energy, PERFORMANCE, beryllium, SHUTDOWN DOSE-RATE, Fusion reactors, Physics and Astronomy, JET programme, Energia nuclear, DIVERTOR, ddc:600
Abstract

The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (a) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under Grant Agreement No. 633053. Peer Reviewed Article signat per 1223 autors/autores: J. Mailloux1, N. Abid1, K. Abraham1, P. Abreu2, O. Adabonyan1, P. Adrich3, V. Afanasev4, M. Afzal1, T. Ahlgren5, L. Aho-Mantila6, N. Aiba7, M. Airila6, M. Akhtar1, R. Albanese8, M. Alderson-Martin1, D. Alegre9, S. Aleiferis10, A. Aleksa1, A.G. Alekseev11, E. Alessi12, P. Aleynikov13, J. Algualcil14, M. Ali1, M. Allinson1, B. Alper1, E. Alves2, G. Ambrosino8, R. Ambrosino8, V. Amosov15, E.Andersson Sunden16, P. Andrew13, B.M. Angelini17, C. Angioni18, I. Antoniou1, L.C. Appel1, C. Appelbee1, S. Aria1, M. Ariola8, G. Artaserse17, W. Arter1, V. Artigues18, N. Asakura7, A. Ash1, N. Ashikawa19, V. Aslanyan20, M. Astrain21, O. Asztalos22, D. Auld1, F. Auriemma23, Y. Austin1, L. Avotina24, E. Aymerich25, A. Baciero9, F. Bairaktaris26, J. Balbin27, L. Balbinot23, I. Balboa1, M. Balden18, C. Balshaw1, N. Balshaw1, V.K. Bandaru18, J. Banks1, Yu.F. Baranov1, C. Barcellona28, A. Barnard1, M. Barnard1, R. Barnsley13, A. Barth1, M. Baruzzo17, S. Barwell1, M. Bassan13, A. Batista2, P. Batistoni17, L. Baumane24, B. Bauvir13, L. Baylor29, P.S. Beaumont1, D. Beckett1, A. Begolli1, M. Beidler29, N. Bekris30,31, M. Beldishevski1, E. Belli32, F. Belli17, É. Belonohy1, M. Ben Yaala33, J. Benayas1, J. Bentley1, H. Bergsåker34, J. Bernardo2, M. Bernert18, M. Berry1, L. Bertalot13, H. Betar35, M. Beurskens36, S. Bickerton1, B. Bieg37, J. Bielecki38, A. Bierwage7, T. Biewer29, R. Bilato18, P. Bílková39, G. Birkenmeier18, H. Bishop1, J.P.S. Bizarro2, J. Blackburn1, P. Blanchard40, P. Blatchford1, V. Bobkov18, A. Boboc1, P. Bohm39, T. Bohm41, I. Bolshakova42, T. Bolzonella23, N. Bonanomi18, D. Bonfiglio23, X. Bonnin13, P. Bonofiglo43, S. Boocock1, A. Booth1, J. Booth1, D. Borba2,30, D. Borodin44, I. Borodkina39,44, C. Boulbe45, C. Bourdelle27, M. Bowden1, K. Boyd1, I.Bozicevic Mihalic46, S.C. Bradnam1, V. Braic47, L. Brandt48, R. Bravanec49, B. Breizman50, A. Brett1, S. Brezinsek44, M. Brix1, K. Bromley1, B. Brown1, D. Brunetti1,12, R. Buckingham1, M. Buckley1, R. Budny, J. Buermans51, H. Bufferand27, P. Buratti17, A. Burgess1, A. Buscarino28, A. Busse1, D. Butcher1, E.de la Cal9, G. Calabrò52, L. Calacci53, R. Calado2, Y. Camenen54, G. 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Walker1, R. Walker1, M. Walsh13, E. Wang44, N. Wang1, S. Warder1, R. Warren1, J. Waterhouse1, C. Watts13, T. Wauters51, A. Weckmann34, H.Wedderburn Maxwell1, M. Weiland18, H. Weisen40, M. Weiszflog16, P. Welch1, N. Wendler58, A. West1, M. Wheatley1, S. Wheeler1, A. Whitehead1, D. Whittaker1, A. Widdowson1, S. Wiesen44, J. Wilkinson1, J.C. Williams1, D. Willoughby1, I. Wilson1, J. Wilson1, T. Wilson1, M. Wischmeier18, P. Wise1, G. Withenshaw1, A. Withycombe1, D. Witts1, A. Wojcik-Gargula38, E. Wolfrum18, R. Wood1, C. Woodley1, R. Woodley1, B. Woods1, J. Wright1, J.C. Wright20, T. Xu1, D. Yadikin74, M. Yajima19, Y. Yakovenko82, Y. Yang13, W. Yanling44, V. Yanovskiy39, I. Young1, R. Young1, R.J. Zabolockis24, J. Zacks1, R. Zagorski3, F.S. Zaitsev88, L. Zakharov5, A. Zarins24, D. Zarzoso Fernandez54, K.-D. Zastrow1, Y. Zayachuk1, M. Zerbini17, W. Zhang18, Y. Zhou34, M. Zlobinski44, A. Zocco36, A. Zohar65, V. Zoita63, S. Zoletnik22, V.K. Zotta81, I. Zoulias1, W. Zwingmann2 and I. Zychor3 // 1 United Kingdom Atomic Energy Authority, Culham Science Centre, Abingdon, Oxon, OX14 3DB, United Kingdom of Great Britain and Northern Ireland 2 Instituto de Plasmas e Fusao Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal 3 National Centre for Nuclear Research (NCBJ), 05-400 Otwock-Swierk, Poland 4 Ioffe Physico-Technical Institute, 26 Politekhnicheskaya, St Petersburg 194021, Russia 5 University of Helsinki, PO Box 43, FI-00014 University of Helsinki, Finland 6 VTT Technical Research Centre of Finland, PO Box 1000, FIN-02044 VTT, Finland 7 National Institutes for Quantum and Radiological Science and Technology, Naka, Ibaraki 311-0193, Japan 8 Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy 9 Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain 10 NCSR ‘Demokritos’ 153 10, Agia Paraskevi Attikis, Greece 11 NRC Kurchatov Institute, 1 Kurchatov Square, Moscow 123182, Russia 12 Institute for Plasma Science and Technology, CNR, via R. Cozzi 53, 20125 Milano, Italy 13 ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 13067 Saint Paul Lez Durance Cedex, France 14 Universidad Nacional de Educacion a Distancia, Dept Ingn Energet, Calle Juan del Rosal 12, E-28040 Madrid, Spain 15 Troitsk Insitute of Innovating and Thermonuclear Research (TRINITI), Troitsk 142190, Moscow Region, Russia 16 Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala, Sweden 17 Dip.to Fusione e Tecnologie per la Sicurezza Nucleare, ENEA C. R. Frascati, via E. Fermi 45, 00044 Frascati (Roma), Italy 18 Max-Planck-Institut für Plasmaphysik, D-85748 Garching, Germany 19 National Institute for Fusion Science, Oroshi, Toki, Gifu 509-5292, Japan 20 MIT Plasma Science and Fusion Center, Cambridge, MA 02139, United States of America 21 Universidad Politécnica de Madrid, Grupo I2A2, Madrid, Spain 22 Centre for Energy Research, POB 49, H-1525 Budapest, Hungary 23 Consorzio RFX, Corso Stati Uniti 4, 35127 Padova, Italy 24 University of Latvia, 19 Raina Blvd., Riga, LV 1586, Latvia 25 Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi 09123 Cagliari, Italy 26 National Technical University of Athens, Iroon Politechniou 9, 157 73 Zografou, Athens, Greece 27 CEA, IRFM, F-13108 Saint Paul Lez Durance, France 28 Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, 95125 Catania, Italy 29 Oak Ridge National Laboratory, Oak Ridge, TN 37831, TN, United States of America 30 EUROfusion Programme Management Unit, Culham Science Centre, Culham, OX14 3DB, United Kingdom of Great Britain and Northern Ireland 31 Karlsruhe Institute of Technology, PO Box 3640, D-76021 Karlsruhe, Germany 32 General Atomics, PO Box 85608, San Diego, CA 92186-5608, United States of America 33 Department of Physics, University of Basel, Switzerland 34 Fusion Plasma Physics, EECS, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden 35 Institut Jean Lamour, UMR 7198, CNRS-Université de Lorraine, 54500 Vandoeuvre-lès-Nancy, France 36 Max-Planck-Institut für Plasmaphysik, Teilinsitut Greifswald, D-17491 Greifswald, Germany 37 Maritime University of Szczecin Faculty of Marine Engineering, Waly Chrobrego 1-2, 70-500 Szczecin, Poland 38 Institute of Nuclear Physics, Radzikowskiego 152, 31-342 Kraków, Poland 39 Institute of Plasma Physics of the CAS, Za Slovankou 1782/3, 182 00 Praha 8, Czech Republic 40 Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Plasma Center (SPC), CH-1015 Lausanne, Switzerland 41 University of Wisconsin-Madison, Madison, WI 53706, United States of America 42 Magnetic Sensor Laboratory, Lviv Polytechnic National University, Lviv, Ukraine 43 Princeton Plasma Physics Laboratory, James Forrestal Campus, Princeton, NJ 08543, NJ, United States of America 44 Forschungszentrum Jülich GmbH, Institut für Energie- und Klimaforschung, Plasmaphysik, 52425 Jülich, Germany 45 Université Cote d’Azur, CNRS, Inria, LJAD, Parc Valrose, 06108 Nice Cedex 02, France 46 Ruder Boskovic Institute, Bijenicka 54, 10000 Zagreb, Croatia 47 The National Institute for Optoelectronics, Magurele-Bucharest, Romania 48 Mechanics, SCI, KTH SE-100 44 Stockholm, Sweden 49 Fourth State Research, 503 Lockhart Dr, Austin, TX, United States of America 50 University of Texas at Austin, Institute for Fusion Studies, Austin, TX 78712, United States of America 51 Laboratory for Plasma Physics LPP-ERM/KMS, B-1000 Brussels, Belgium 52 University of Tuscia, DEIM, Via del Paradiso 47, 01100 Viterbo, Italy 53 Università di Roma Tor Vergata, Via del Politecnico 1, Roma, Italy 54 Aix-Marseille University, CNRS, PIIM, UMR 7345, 13013 Marseille, France 55 Instituto de Física, Universidade de Sao Paulo, Rua do Mat˜ao Travessa R Nr.187, CEP 05508-090 Cidade Universitária, Sao Paulo, Brasil 56 University of Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy 57 Centre for Fusion, Space and Astrophysics, University of Warwick, Coventry, CV4 7AL, United Kingdom of Great Britain and Northern Ireland 58 Institute of Plasma Physics and Laser Microfusion, Hery 23, 01-497 Warsaw, Poland 59 Aalto University, PO Box 14100, FIN-00076 Aalto, Finland 60 FOM Institute DIFFER, Eindhoven, The Netherlands 61 Warsaw University of Technology, 02-507 Warsaw, Poland 62 Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University, Belfast, BT7 1NN, United Kingdom of Great Britain and Northern Ireland 63 The National Institute for Laser, Plasma and Radiation Physics, Magurele-Bucharest, Romania 64 Department of Applied Physics, Ghent University, 9000 Ghent, Belgium 65 Slovenian Fusion Association (SFA), Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia 66 The National Institute for Cryogenics and Isotopic Technology, Ramnicu Valcea, Romania 67 Dublin City University (DCU), Dublin, Ireland 68 University of California at San Diego, La Jolla, CA 92093, United States of America 69 EUROfusion Programme Management Unit, Boltzmannstr. 2, 85748 Garching, Germany 70 UNED, Dpto. Informática y Automática, Madrid, Spain 71 National Science Center ‘Kharkov Institute of Physics and Technology’, Akademichna 1, Kharkiv 61108, Ukraine 72 York Plasma Institute, Department of Physics, University of York, York, YO10 5DD, United Kingdom of Great Britain and Northern Ireland 73 Department of Physics, Chalmers University of Technology, SE-41296 Gothenburg, Sweden 74 Department of Space, Earth and Environment, Chalmers University of Technology, SE-41296 Gothenburg, Sweden 75 European Commission, B-1049 Brussels, Belgium 76 University of Tennessee, Knoxville, TN 37996, TN, United States of America 77 Universitat Politècnica de Catalunya, Barcelona, Spain 78 Barcelona Supercomputing Center, Barcelona, Spain 79 Universidad de Sevilla, Sevilla, Spain 80 Aix-Marseille University, CNRS, IUSTI, UMR 7343, 13013 Marseille, France 81 Dipartimento di Ingegneria Astronautica, Elettrica ed Energetica, SAPIENZA Università di Roma, Via Eudossiana 18, 00184 Roma, Italy 82 Institute for Nuclear Research, Prospekt Nauky 47, Kyiv 03680, Ukraine 83 Studiecentrum voor Kernenergie—Centre d’Etude de l’Energie Nucléaire, Boeretang 200, 2400 Mol, Belgium 84 University of Toyama, Toyama, 930-8555, Japan 85 University of California, Irvine, Irvine, California 92697, United States of America 86 Department of Physics, Technical University of Denmark, Bldg 309, DK-2800 Kgs Lyngby, Denmark 87 Institution ‘Project Center ITER’, Moscow, 123182, Russia 88 Faculty of Mathematics, Department of Experimental Physics, Physics and Informatics Comenius University Mlynska dolina F2, 84248 Bratislava, Slovakia 89 University College Cork (UCC), Cork, Ireland 90 Institute of Physics, Opole University, Oleska 48, 45-052 Opole, Poland 91 Daegu University, Jillyang, Gyeongsan, Gyeongbuk 712-174, Republic of Korea 92 Department of Nuclear Engineering, Seoul National University, Seoul, Republic of Korea 93 Fusion for Energy Joint Undertaking, Josep Pl. 2, Torres Diagonal Litoral B3, 08019, Barcelona, Spain 94 PELIN LLC, 27a, Gzhatskaya Ulitsa, Saint Petersburg, 195220, Russia 95 Arizona State University, Tempe, AZ, United States of America 96 Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy 97 ICREA and Barcelona Supercomputing Center, Barcelona, Spain 98 Universidad Complutense de Madrid, Madrid, Spain 99 Istituto dei Sistemi Complessi—CNR and Dipartimento di Energia—Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129 Torino, Italy 100 Eindhoven University of Technology, The Netherlands 101 Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, United States of America 102 Department of Material Science, Shimane University, 1060 Nishikawatsu, Matsue, 690-8504, Japan 103 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Brehová 78/7, 115 19 Praha 1, Czech Republic 104 College of William and Mary, Williamsburg, VA 23185, United States of America 105 University of California, 1111 Franklin St., Oakland, CA 94607, United States of America 106 University of Strathclyde, Glasgow, G4 0NG, United Kingdom of Great Britain and Northern Ireland 107 Kindai University, Higashi-Osaka, 577-8502, Japan 108 Shizuoka University, Shizuoka, 422-8529, Japan 109 Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, United Kingdom of Great Britain and Northern Ireland 110 Columbia University, New York, NY 10027, United States of America 111 Dipartimento di Fisica ‘G. Galilei’, Universita’ degli Studi di Padova, Padova, Italy 112 Space and Plasma Physics, EECS, KTH SE-100 44 Stockholm, Sweden 113 University of Ioannina, Panepistimioupoli Ioanninon, PO Box 1186, 45110 Ioannina, Greece 114 Universidade do Porto, Faculdade de Engenharia, 4200-465 Porto, Portugal 115 The University of Tokyo, Kashiwa, Chiba, 277-0882, Japan 116 Lithuanian Energy Institute, Breslaujos g. 3, LT-44403, Kaunas, Lithuania 117 HRS Fusion, West Orange, NJ, United States of America 118 Ibaraki University Graduate School of Science and Engineering, Mito, Ibaraki 310-8512, Japan 119 Technische Universität Wien, Fusion@ÖAW Österreichische Akademie der Wissenschaften (ÖAW), Austria

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147. Effect of scanning speed, scanning pattern, and tip size on the accuracy of intraoral digital scans.

Author

An H, Langas EE, and Gill AS

Subjects
Humans, Computer-Aided Design, In Vitro Techniques, Imaging, Three-Dimensional methods, Dental Impression Technique, Software, Image Processing, Computer-Assisted methods
Abstract

Statement of Problem: Currently available intraoral scanning technology makes intraoral scanning quicker and allows the use of smaller scanner tips. However, studies on the influence of scanning speed, tip size, and scanning patterns on scanning accuracy are lacking., Purpose: The purpose of this in vitro study was to evaluate the effect of scanning speed, scanning pattern, and scanner tip size on scanning trueness and precision., Material and Methods: A total of 120 complete arch intraoral scans were made with an intraoral scanner (Emerald). The 3 variables were tip size (small and regular), scanning pattern (occlusal first and S-shaped), and scanning speed (slow, regular, and fast). Ten scans for each variable combination were made and exported as standard tessellation language (STL) files. A laboratory scanner (E4) was used for the reference scan. The exported images were analyzed with an image analysis software program (Geomagic Control X). Root-mean-square deviation (RMSD) values between the intraoral scans and the reference scan were calculated to assess trueness. RMSD values between each intraoral scan were calculated to assess precision. Three-way analysis of variance (ANOVA) was used to evaluate the influence of each variable, and Tukey HSD tests were used for multiple comparisons (α=.05)., Results: For trueness evaluation, tip size was the only significant factor (P<.001). The scans made with a smaller tip showed lower trueness than the scans made with a regular tip. For precision evaluation, all 3 variables, tip size, scanning speed, and scanning pattern, had significant influence (P≤.001). The use of a small tip, fast scanning speed, and S-shaped scanning pattern made intraoral scanning less precise., Conclusions: The use of a small scanner tip negatively affected both trueness and precision. Fast scanning speed and S-shaped scanning pattern produced scans with lower precision than regular or slow scanning speed and the occlusal-first scanning pattern., (Copyright © 2022 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.)

Published
2024
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148. Nucleic acid liquids.

Author

Abraham GR, Chaderjian AS, N Nguyen AB, Wilken S, and Saleh OA

Subjects
Static Electricity, Nucleic Acids chemistry
Abstract

The confluence of recent discoveries of the roles of biomolecular liquids in living systems and modern abilities to precisely synthesize and modify nucleic acids (NAs) has led to a surge of interest in liquid phases of NAs. These phases can be formed primarily from NAs, as driven by base-pairing interactions, or from the electrostatic combination (coacervation) of negatively charged NAs and positively charged molecules. Generally, the use of sequence-engineered NAs provides the means to tune microsopic particle properties, and thus imbue specific, customizable behaviors into the resulting liquids. In this way, researchers have used NA liquids to tackle fundamental problems in the physics of finite valence soft materials, and to create liquids with novel structured and/or multi-functional properties. Here, we review this growing field, discussing the theoretical background of NA liquid phase separation, quantitative understanding of liquid material properties, and the broad and growing array of functional demonstrations in these materials. We close with a few comments discussing remaining open questions and challenges in the field., (© 2024 IOP Publishing Ltd.)

Published
2024
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149. The relationship between environmentally induced emotion and memory for a naturalistic virtual experience.

Author

Petrucci AS, McCall C, Schofield G, Wardell V, Safi OK, and Palombo DJ

Abstract

Emotional stimuli (e.g. words, images) are often remembered better than neutral stimuli. However, little is known about how memory is affected by an environmentally induced emotional state (without any overtly emotional occurrences) - the focus of this study. Participants were randomly assigned to discovery ( n  = 305) and replication ( n  = 306) subsamples and viewed a desktop virtual environment before rating their emotions and completing objective (i.e. item, temporal-order, duration) and subjective (e.g. vividness, sensory detail, coherence) memory measures. In both samples, a Partial Least Squares Correlation analysis showed that an emotional state characterised by high negative emotion (i.e. threat, fear, anxiety) and arousal was reliably associated with better memory in both objective (i.e. item) and subjective (i.e. vividness and sensory detail) domains. No reliable associations were observed for any temporal memory measures (objective or subjective). Thus, an environmentally induced state of negative emotion corresponds with enhanced memory for indices of episodic memory pertaining to "what" happened, but not necessarily "when" it happened.

Published
2024
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150. Expression of the monocarboxylate transporter MCT1 is required for virus-specific mouse CD8 + T cell memory development.

Author

D'Aria S, Maquet C, Li S, Dhup S, Lepez A, Kohler A, Van Hée VF, Dadhich RK, Frenière M, Andris F, Nemazanyy I, Sonveaux P, Machiels B, Gillet L, and Braun MY

Subjects
Animals, Mice, Biological Transport, Lactic Acid metabolism, CD8-Positive T-Lymphocytes metabolism, Symporters genetics, Symporters metabolism, Monocarboxylic Acid Transporters genetics, Monocarboxylic Acid Transporters metabolism
Abstract

Lactate-proton symporter monocarboxylate transporter 1 (MCT1) facilitates lactic acid export from T cells. Here, we report that MCT1 is mandatory for the development of virus-specific CD8 + T cell memory. MCT1-deficient T cells were exposed to acute pneumovirus (pneumonia virus of mice, PVM) or persistent γ-herpesvirus (Murid herpesvirus 4, MuHV-4) infection. MCT1 was required for the expansion of virus-specific CD8 + T cells and the control of virus replication in the acute phase of infection. This situation prevented the subsequent development of virus-specific T cell memory, a necessary step in containing virus reactivation during γ-herpesvirus latency. Instead, persistent active infection drove virus-specific CD8 + T cells toward functional exhaustion, a phenotype typically seen in chronic viral infections. Mechanistically, MCT1 deficiency sequentially impaired lactic acid efflux from activated CD8 + T cells, caused an intracellular acidification inhibiting glycolysis, disrupted nucleotide synthesis in the upstream pentose phosphate pathway, and halted cell proliferation which, ultimately, promoted functional CD8 + T cell exhaustion instead of memory development. Taken together, our data demonstrate that MCT1 expression is mandatory for inducing T cell memory and controlling viral infection by CD8 + T cells., Competing Interests: Competing interests statement:The authors declare no competing interest.

Published
2024
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