dataesr/scientific-paragraphs-categorization huggingface dataset:
This new system was implemented in the Community Inversion Framework (CIF), supported by the European Union H2020 project VERIFY (http://community-inversion. eu, last access: 3 June 2022) and required to implement new forward, tangent linear and adjoint operations. The forward operations were previously used to estimate the impact of the Cl sink on the modeling of CH 4 and δ 13 C(CH 4 ) in LMDz-SACS  (Thanwerdas et al. , 2019). The purpose of this study is to present the technical implementation of the isotopic constraint in a variational inversion system and to investigate the sensitivity of this new configuration to different parameters. Our aim is not to estimate trends in sectoral emissions over the last 2 decades: future studies will address the estimation of CH 4 emissions over longer periods of time using this new system.
Control vector x and B matrix We adopt the CH 4 emissions compiled for inversions performed as part of the global methane budget  (Saunois et al. , 2020). Anthropogenic (including biofuels) and biomass burning emissions are based on the EDGARv4. 3. 2 database  (Janssens-Maenhout et al. , 2017)  and the GFED4s databases (van der Werf et al. , 2017), respectively. Statistics from British Petroleum (BP) and the Food and Agriculture Organization of the United Nations (FAO) have been used to extend the EDGARv4. 3. 2 database, which ended 2012, until 2017. The natural source emissions are based on averaged literature values; see  Poulter et al. (2017)  for wetlands,  Kirschke et al. (2013)  for termites,  Lambert and Schmidt (1993)  for oceanic sources, and  Etiope (2015)  for geological (onshore) sources.
The prior input must be adapted to the new model (spatial and time resolution), but the format of the observational data and of the prescribed errors can be preserved. Also, due to the variational method benefits, the efforts dedicated to the preparation of inputs do not scale with either the size of the ob-servational datasets or the length of the simulation time window. Therefore, this system is very powerful and is particularly relevant to study, in a consistent way, the influence of multiple physical parameters on atmospheric isotopic ratios such as the transport, the isotopic signatures, the emission scenarios, the KIE values, etc. We did not try to assess the sensitivity of the system to these parameters here as only the technical aspects of the system were tested. This will be part of future analyses.
Acknowledgements. This work has been supported by the  CEA  ( Commissariat à l'Energie Atomique et aux Energies Alternatives ). The study relies extensively on the meteorological data provided by the ECMWF. Calculations were performed using the computing resources of LSCE, maintained by  François Marabelle  and the LSCE IT team. The authors thank the reviewers ( Peter Rayner  and two anonymous referees) for their fruitful comments and suggestions on our work. We are also grateful to many station maintainers that provided the CH 4 data. For the AGAGE network, we note that AGAGE is supported principally by  NASA (USA)  grants to MIT and  SIO  and also by  BEIS (UK)  and  NOAA (USA)  grants to  Bristol University, CSIRO and BoM (Australia) ,  FOEN grants  to  Empa (Switzerland) ,  NILU (Norway) ,  SNU  ( South Korea ),  CMA (China) ,  NIES (Japan) , and  Urbino University (Italy).
At the South African Weather Service Cape Point GAW station, we thank  Casper Labuschagne ,  Thumeka Mkololo , and  Warren Joubert. For the EC network, we thank  Doug Worthy. For the MGO network, we thank  Nina Paramonova  and  Victor Ivakhov. For the AMY network, we thank  Haeyoung Lee  and  Sepyo Lee. The AMY data were funded by the  Korea Meteorological Administration Research and Development Program  (grant no. KMA2018-00522 ). For the ENEA network, we thank  Alcide Disarra ,  Salvatore Piacentino , and  Damiano Sferlazzo. For the EMPA network, we thank  Martin Steinbacher  and  Brigitte Buchmann. The CH 4 measurements at JFJ are run by  EMPA  in collaboration with FOEN and are also supported by  ICOS-CH. For the  BMKG-EMPA network , we thank  Martin Steinbacher ,  Alberth Christian , and  Sugeng Nugroho.
Data availability. The code files of the CIF version used in the present paper are registered under the following link: https://doi. org/10. 5281/zenodo. 6304912 (Berchet et al. , 2022). Prior anthropogenic fluxes (EDGARv4. 3. 2) can be downloaded from the EDGAR website (https://edgar. jrc. ec. europa. eu/dataset_ ghg432, last access: 23 February 2022). Biomass burning fluxes can be downloaded from the GFED website (https://globalfiredata. org/ pages/data/, last access: 23 February 2022). Prior natural fluxes and other data are available upon request (joel. thanwerdas@lsce. ipsl. fr). The CH 4 data used in the present paper were provided by many stations and measurement networks around the world (a comprehensive list can be found in the Supplement and in the acknowledgements). Their data are freely available upon request to the station maintainers or via dedicated websites.
0). All patients were free of nasal symptoms at the time of the study, none actually reported previous upper airways disorders before infection by SARS-CoV-2, and none were treated with nasal corticosteroids either during COVID-19 infection or at the time of the study. Nasal NO measurements were systematically performed twice or thrice in all patients. The coefficients of variations of these measurements were 5. 1 6 3. 5% and 5. 8 6 4. 1% (mean 6 SD) in patients with COVID-19 with and without anosmia, respectively. This is consistent with data from the literature and coefficients of variation ranging from 7% to 13%  (8). Comparisons between patients with COVID-19 with and without anosmia showed that the former had significantly higher levels of nasal NO than the latter (363 6 127 vs. 263 6 107 nl/min, P , 0.
I. INTRODUCTION The past decade has witnessed a rapid boom of cloud computing services. Large cloud service providers, such as Amazon AWS  [1] , Microsoft Azure  [2]  and Google Cloud Platform  [3] , host hundreds of thousands of tenants, each of which possibly running hundreds of applications. Many of these applications  [4] -  [6]  are rife with one-to-many communication patterns, making multicast the choice for supporting this type of communication, as it greatly conserves network bandwidth and reduces server overhead. IP multicast The associate editor coordinating the review of this manuscript and approving it for publication was Zhenzhou  Tang. can meet these requirements; however, it gives rise to two major challenges: scalability and load balancing. Scalability limitations arise in both the control and the data planes of the network.
B. MULTI-TENANT DCs Multi-tenancy is one of the key features of cloud DCs. In Multi-tenant DCs (like Microsoft Azure  [2] , Amazon Web Services (AWS)  [1] , and Google Cloud Platform  [3] ), a fraction of computing resources (e. g. , CPUs, memory, storage, and network) are rented to customers/tenants (e. g. , commercial, government, individual) by means of virtualization technology. On the other hand, in multi-tenant cloud computing, infrastructures, applications, and databases are shared among all tenants. By moving towards multi-tenant DCs, tenants can lower their operational costs of maintaining private infrastructure, meet scalability demands with changing workload, and withstand disasters. For example, Netflix, the world's leading online video streaming service provider, uses AWS for nearly all its computing and storage needs  [59].
E. SOURCE-ROUTED MULTICAST In multicast source-routing approaches, a multicast tree is encoded into the header of each packet, and switches can read this information to make forwarding decisions. Recent source-routed schemes  [15] ,  [16]  address both control and data planes scalability limitations by exploiting DC topology symmetry as well as hardware switch reconfigurability. Topology symmetry of DC networks (e. g. Fat-tree) is utilized to efficiently compact forwarding header size whereas emerging programmable switches are exploited to parse and process packets header efficiently. By doing so, the need for network switches to store routing information is minimized, and the burden on the controller is alleviated. Now we present two recently proposed source-routed multicast routing schemes: 1) ELMO Elmo  [15]  primarily focuses on how to efficiently encode and compact a multicast forwarding information in the packet header.
Data analysis The database was filled with 217 variables corresponding to the number of detected masses for the three different conditions UV, O 3 , and UV_O 3 (as mentioned previously). The statistical analysis of the entire dataset was performed using the R software (Version 1. 2. 5019 -© 2009-2019 RStudio). At first, we selected the variables that were normally distributed by the Shapiro-Wilk test (W >0. 9). Secondly, we tested the homogeneity of the variance by the Levene-test to perform the analysis of variance (ANOVA) test followed by the Tukey post hoc test. Furthermore, we tested the differences between the conditions using the principal component analysis (PCA -package FactormineR). The summed VOC emissions from the different conditions showed a statistical difference for every day of measurement.
Code availability. The code is available under request at letizia. abis@tu-berlin. de. Data availability. Data are available for download at https://doi. org/10. 25326/0JXT-M252  (George, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-12613-2021-supplement. Author contributions. LA took charge of the conceptualization, data curation, investigation, formal analysis, methodology, visualization, and writing. CK handled the methodology, investigation, writing, review and editing. BL handled the methodology and investigation. TW, LZ, and JC took care of the data curation and investigation. SP took care of the investigation, methodology and resources. BL performed writing, review, editing, and investigation. RC performed the conceptualization, data curation, methodology, writing, review and editing, funding acquisition, and project administration, supervision, and validation.
VOC E VOC log (E VOC ), where the sum is over all VOCs recorded in the mass table. The PCA al-lowed a graphical representation of the whole dataset differ-entiating the VOCs emission profiles for the different tested conditions without bias. A table with the 30 most emitted compounds and their relative abundance at the three differ-ent conditions is presented in Table 1. Finally, the calculation of the Shannon index was performed. The Shannon index is a quantitative measure reflecting how many different VOCs were emitted from each sample. It was calculated with the diversity function of the vegan package (version 2. 4-3) in R software (version 3. 2. 3). The diversity index was calculated L. Abis et al. : Biogenic VOC emission profiles as H = 12618 3 Results 3. 1 Ozone and UV light irradiation effect on the average VOC concentrations VOC emissions were measured for 6 d for each condition.
The genomes of the S. symbiotica strains 24. 1 (hereafter SsAf24. 1) isolated from A. fabae and Apa8A1 (hereafter SsApa8A1) isolated from the sage aphid Aphis passeriniana  (Grigorescu et al. , 2018)  had been previously sequenced by whole-genome paired-end sequencing using Illumina on a HiSeq 4000 platform with 2 × 100 paired-end reads (Illumina, San Diego, CA, USA) and a sequencing depth >100x  (Renoz et al. , 2020). These data were complemented by long reads obtained using a ONT MinION sequencer. Prior to the MinION nanopore sequencing, the integrity of genomic DNA was evaluated using an Agilent 2200 TapeStation with the Genomic DNA ScreenTape (Agilent). Both samples displayed a DNA Integrity Number value ≥9. 5. Libraries were then prepared from 400 ng of genomic DNA using the Rapid Barcoding Sequencing kit (SQK-RBK004) protocol (Oxford Nanopore Technologies).
Briefly, the DNA molecules were cleaved by a transposase and barcoded tags were attached to the cleaved ends. The barcoded samples were then pooled and Rapid Sequencing Adapters were added to the tagged ends. The pooled libraries were sequenced into a FLO-MIN106 (R9. 4. 1) flow cell for a 48h run according to manufacturer's instruction. After the run, fast5 files were basecalled on the MinIT using default settings in MinKNOWv18. 12 and Guppy v3. 0. 3 and a High Accuracy (HAC) Flip-Flop methodology. Finally, a hybrid assembly strategy combining the data generated by the Illumina and ONT MinION sequencing approaches was performed using Unicycler v0. 4. 8  (Wick et al. , 2017). Raw sequence reads have been deposited in the European Nucleotide Archive (studies with accession numbers: PRJEB44297 for SsAf2.
Genome Annotation and Comparative Analyses The three genomes were annotated with the Prokaryotic Genome Annotation Pipeline (PGAP)  (Tatusova et al. , 2016). To perform genomic comparative analyses that include the other available sequenced genomes of S. symbiotica, the datasets corresponding to their annotation were downloaded from NCBI (GenBank annotations were preferred when available) (Figure  1 ). The panand core-genomes were computed using the EDGAR software platform  (Blom et al. , 2016)  and the protein sequences were functionally annotated using eggNOG mapper v2  (Huerta-Cepas et al. , 2017). The fifteen available genomes of S. symbiotica were deposited on the MicroScope platform  (Medigue et al. , 2019; Vallenet et al. , 2020)  and their metabolic capabilities compared using MicroCyc, a collection of microbial Pathway/Genome Databases (PGDBs) based on the MetaCyc database and specifically dedicated to the analysis of microbial pathways  (Caspi et al.
, 2020). Metabolic reconstructions generated by MicroCyc were manually verified and CDSs were considered as pseudogenes when their length was less than 80% of the one of their orthologues in the other genomes  (Lerat and Ochman, 2005; Burke and Moran, 2011). AntiSMASH v5. 0. 0 and the MIBiG database were used to predict regions coding for complete secondary metabolites in the different genomes  (Blin et al. , 2019). Virulence genes were identified using the virulence factor database (VFDB, http://www. mgc. ac. cn/VFs/)  (Chen et al. , 2005; Liu et al. , 2019) : datasets of virulence genes were downloaded and BLASTp were performed (minimum 50% aa identity, 80% align. coverage). Complete secretion systems were identified with MacSyFinder  (Abby et al. , 2014). Insertion sequences (hereafter IS) predictions were generated with ISsaga  (Varani et al.
, 2011). CRISPR-Cas systems were detected using CRISPRCasFinder and MacSyFinder  (Abby et al. , 2014; Couvin et al. , 2018). Intact prophage regions were predicted with PHASTER  (Arndt et al. , 2016). Assemblies and annotations are available on Genbank under the following accession numbers: CP050857-CP050857 for SsAf2. 3, CP072650-CP072652 for SsAf24. 1 and WSPO00000000. 3 for SsApa8A1.
Only the responses of participants concerned with MSD-related responses were collected and analyzed. Participants giving incoherent information, e. g. , incorrect zip code regarding the country, were excluded. A contradiction between a physiotherapist's answers to the number of patients managed with TR and the previous use of TR also led to exclusion. Then, the two main parts of the questionnaire followed. Their structure is illustrated in Figure  1. The first part of the questionnaire was designed to record the sociodemographic data of the participants: age, the highest diploma obtained (from secondary school to doctorate), place of residence or occupation, zip code, the existence of access to the Internet, rating of their Internet access (accessibility and quality), the location of the MSD for the patients or the number of different MSDs patients treated per week during the last 6 months, face-to-face and with TR for the physiotherapists.
Data Analysis Data from physiotherapists and patients concerned with MSDs were collected and the percentages of answers to the different questions were computed. Cronbach's α values for patients and physiotherapists have been computed for the answers to the items listed in Table  A1  (1 = disagree, 2 = mostly disagree, 3 = agree, 4 = mostly agree, 5 = no opinion) by using R free software (version 4. 1. 0). This assesses the reliability (internal consistency) of our questionnaire. Various figures were then produced in the form of bar and pie charts to graphically explore the results. X 2 tests were then performed to compare patients' and physiotherapists' answers, and a t-test was used to compare patients' and physiotherapists' ages. SigmaPlot software (version 11. 1, Systat Software, San Jose, CA, USA) was used with a significance threshold of 0.
Participants' Sociodemographic Data Descriptive data concerning patients and physiotherapists are shown in Table  1. Patients are 15 years older than physiotherapists in our study (p ≤ 0. 001). It appears that almost all the participants declared having an adequate connection to the internet and an adequate ease to use it. A great majority of patients and physiotherapists had not used TR at the time that they answered the questionnaire. Extra data have been recorded: we now summarize them. Physiotherapists have resorted to TR because of lockdown (91%) or temporary inability to travel (9%). They used various technologies: phone (11%), smartphone (30%), PC (41%), tablet (18%). The main advantages of TR according to physiotherapists are sanitary safety (25%) and increased autonomy in management (25%).
CO + 4H → CH + 2H O (1) for the higher δ  13  C of CO 2 values at Olla; therefore, the integration of complementary datasets is required to evaluate additional in situ processes. Owing to their inert nature, noble gases provide a powerful tool for tracing and quantifying physiochemical processes associated with CO 2 injection  4, 8, [12] [13] [14] [15] [16] [17] [18] [19]. The average air-corrected helium isotope ( 3 He/  4  He) ratio relative to the atmospheric ratio (R/R A , where R =  3  He/  4  He sample and R A =  3  He/  4  He air ) was determined for the Olla and Nebo-Hemphill oil fields to be 1. 76 ± 0. 31 R A and 0. 46 ± 0. 12 R A , respectively (Extended Data Fig. 1 , Extended Data Table  2 ). The elevated He isotope values in the Olla Oil Field require an enhanced mantle-derived noble gases contribution, which is supported by additional noble gas data (Extended Data Fig.
However, even from the most pristine sample (O5), it is unlikely that any of these processes could generate a fluid with the elevated concentrations of the isotopically enriched CO 2 observed at Olla. Further constraints on the origins and processes affecting these gases are provided by the molecular-average and clumped isotope compositions of the associated CH 4. We observe a relatively high δ  13  C of CO 2 (13. 6 ± 3. 2‰) and CH 4 (-47. 7 ± 1. 7‰) at Olla compared with those observed at Nebo-Hemphill (3. 5 ± 3. 6‰ and -56. 4 ± 3. 6‰ for CO 2 and CH 4 , respectively) (Fig. 2b , Extended Data Fig. 3a , Extended Data Table  2 ). The higher δ  13  C of CH 4 at Olla could be interpreted as evidence that the Olla Oil Field contains a higher proportion of thermogenic gas, or thermogenic gas of higher thermal maturity, whereas the Nebo-Hemphill Oil Field contains more microbial gas or thermogenic gas of lower thermal maturity.
However, equilibration is probably rate limited by AOM, as the abundance of methanogens was 100 times higher than ANME, and AOM probably proceeds at a lower rate per cell. Combined independent geochemical and microbiological data support our assertion that oil field microbes convert injected CO 2 to CH 4 at Olla. This study identifies the microbial processes associated with the injection of CO 2 into the deep subsurface operating on decadal timescales and the scale of the processing. To quantify the impact of these microbial processes on the Olla Oil Field, we have constructed a model that considers both dissolution and microbial methanogenesis (Fig. 2a , Methods). From our most pristine sample (O5, highest CO 2 / 3 He), we find that to match the isotopic composition of CO 2 with its corresponding CO 2 / 3 He, dissolution must account for 3.
A second set of amplifications was performed using specific primers targeting Archaea: (Arch2A519F) CAGCMGCCGCGGTAA and (Arch1017R) GGCC ATGCACCWCCTCTC. Illumina MiSeq 2x300bp paired-end sequencing was performed by Mr. DNA. Microbiome bioinformatics were performed with QIIME 2 2017. 4  47. Raw sequence data were demultiplexed and quality filtered using the q2-demux plugin followed by denoising with DADA2  48. Amplicon sequence variants were aligned with mafft and used to construct a phylogeny with fasttree2  49, 50. Taxonomy was assigned to amplicon sequence variants using the q2-feature-classifier  51  classify-sklearn Naïve Bayes taxonomy classifier against the Greengenes 13_8 99% operational taxonomic unit (OTU) reference sequences  52.
If such high concentrations of CO 2 were present in this field before injection, CO 2 would not have been extracted and transported from the Black Lake Oil Field and would have instead have been extracted from the producing fluids of CO 2 rich fluids at Olla. In addition, we see a clear mantle-derived noble gas signature (see below), which is most evident in the He and neon (Ne) isotopes (Extended Data Fig. 1 ), in the Olla Oil Field, which shows a clear incursion of injection CO 2 into the proximal lithologies. A total of 2. 2 × 10 8 m 3 of CO 2 was injected into the Olla Oil Field and was sourced from the nearby Black Lake Oil Field (produced from the Lower Cretaceous carbonates of the Petit Formation) in Natchitoches Parish, Louisiana (Fig. 1 ). Approximately one-third of the injected CO 2 remained within the reservoir after the cessation of the EOR project  7.
Thus, the difference is probably a result of the injected CO 2 , which probably had mantle-derived  40  Ar/  36  Ar excesses, concordant with the observed mantle-derived He and Ne isotopes. Finally, whereas xenon (Xe) isotope ratios at Nebo-Hemphill are indistinguishable from air, the Olla Oil Field showed excesses in some Xe isotope ratios relative to air. For example, the 129 Xe/ 130 Xe ratio is 6. 59 ± 0. 01 compared with an atmospheric value of 6. 49 (Extended Data Table  3 ); this excess in  129  Xe relative to  130  Xe is consistent with a contribution from mantle fluids with an end member 129 Xe/ 130 Xe value of 8. 2 (ref. 71 ). The Olla Oil Field therefore shows a clear mantle component that is not present within the Nebo-Hemphill control field. The detection of injected fluids within the Olla Oil Field using the conservative noble gas tracers differs from the findings of the previous studies that used the δ 13 C of CO 2 to conclude that injected CO 2 is no longer or never was present in the majority of the sampled wells (for example, ref.
10  ). The subsurface behaviour of the injected CO 2 within the system can therefore be re-examined using a combination of noble gases and stable isotopes. Dissolution within the Olla Oil Field CO 2 is highly soluble in water under reservoir conditions. The most likely trapping mechanisms for free-phase subsurface CO 2 are dissolution or precipitation as carbonate minerals mediated by dissolution  4, 21. A decrease in the CO 2 / 3 He correlates with increasing  4  He and 20 Ne concentrations within the Olla Oil Field (Extended Data Fig. 2 ). 20 Ne (and often 4 He)  4  are derived from the formation water, suggesting that CO 2 trapping in the Olla Oil Field is also proportional to the amount of formation water that has been contacted and degassed, consistent with dissolution within the field  4.