dataesr/scientific-paragraphs-categorization huggingface dataset:
This analysis did not affect the top eight ranks (data not shown). We have conducted and report the first ever research prioritisation exercise in GLILD. Importantly, our results give equal weight to the voice of clinicians and those affected by GLILD. The three top ranked questions, addressing the role of corticosteroids and alternative regimens as first-line treatment in GLILD, would best be answered by a randomised trial of watchful waiting versus intervention in newly diagnosed patients with GLILD, with intervention randomised to corticosteroids or an alternative regime, and with weaning according to a pre-defined protocol. The eGLILDnet consortium will now work towards designing and conducting such a study. It is notable that questions directly affecting treatment decisions were prioritised over diagnostic and basic research.
However, since all our questions received votes, all of these areas may still be considered relevant for further research. In addition to prioritising research questions, the process generated seven areas for resource and/or consensus development. These included the need to revisit the definition and diagnostic criteria for ILD in CVID in general (and GLILD in particular), and tools to support research (such as standardised radiology reporting and a database), clinical practice (including diagnostic criteria and protocols for both adults and children), and educational resources for patients and clinicians. The eGLILDnet collaboration will work with partners to address these. The strengths of our approach were the wide international engagement, and a prioritisation process giving equal weight in the final prioritisation list to patients and clinicians.
iv. The mechanistic and LRU simulated COS fluxes were used with the atmospheric transport model LMDz  (Hourdin et al. , 2006) , to provide atmospheric COS concentrations that were evaluated against measurements at sites of the NOAA network. • C, V max,25 , is derived from literature survey and observation databases and possibly later calibrated using FLUXNET observations (e. g. Kuppel et al. , 2012). To compute the maximum photosynthetic capacity at leaf level, V max , the reference value is multiplied at a daily time step by the relative photosynthetic efficiency of leaves based on the mean leaf age following Ishida et al. (1999) (see Eq. A12 and Fig. A12  in  Krinner et al. , 2005). Leaves are very efficient when they are young and stay so till they approach their pre-defined leaf lifespan.
, 2016). Fluxes from empty chambers were regularly measured to be able to correct for gas exchange by the chamber and tubing material  (Kooijmans et al. , 2019). We also used the Hyytiälä COS ecosystem fluxes  (Kohonen et al. , 2020) ; eddy covariance fluxes were measured during the years 2013-2017 at 23 m height, approximately 6 m above the canopy height. Flux data were processed, quality-screened, and gap-filled according to recommendations by  Kohonen et al. (2020). Soil fluxes were also available for the year 2015  (Sun et al. , 2018a). We thus derived the COS vegetation fluxes at the canopy scale for that year from the difference between ecosystem and soil fluxes. Soil fluxes were generally low compared to plant uptake. The Harvard Forest site (42. 54 • N, 72. 17 • W) is a temperate deciduous broadleaf forest with mainly red oak (Quercus rubra), red maple (Acer rubrum), and hemlock (Tsuga canadensis).
The atmospheric transport model LMDz In order to simulate COS and CO 2 concentrations in the atmosphere, we used the version of the atmospheric component LMDz of the Institut Pierre Simon Laplace Coupled Model (IPSL-CM)  (Dufresne et al. , 2013) , which has been contributing to the CMIP6 exercise. To reduce the computation time, we used its off-line mode: precomputed air mass fluxes provided by the full version of LMDz are used to transport the different tracers  (Hourdin et al. , 2006). This version is further called LMDz6 and is described in  Remaud et al. (2018)  and references therein for the transport of CO 2. The horizontal winds are nudged towards ECMWF meteorological analyses (ERA-5, https://www. ecmwf. int/en/forecasts/datasets/ archive-datasets/reanalysis-datasets/era5) to realistically account for large-scale advection.
The measurements are retained only if the difference between the pair flasks is less than 6. 3 ppt for COS. These COS measurements can be downloaded from the ftp site ftp://ftp. cmdl. noaa. gov/hats/ carbonyl_sulfide/ (last access: 19 April 2021). The CO 2 atmospheric measurements come from the NOAA's Glob-alView Plus Observation Package (ObsPack; Cooperative Global Atmospheric Data Integration Project, 2018).
C1 ). For most PFTs, it is in fact obvious that the relationship shows non-linear features, disagreeing with the classical linear LRU model. Based on these findings, we fitted a simple exponential model as F COS = a e bGPP [COS] a [CO 2 ]a -1 , with two parameters a and b. However, given the large spread of the data around the model, the Akaike criterion is always favourable to the LRU linear model, so we will not investigate further with this exponential model. More specific research is needed here in order to bridge this data gap. Still, it is important to note that the larger COS fluxes will in gen-Figure  6. Distributions of the LRU values computed from the mechanistic approach over the 2000-2009 period. Each subplot represents one of the 14 vegetated PFTs used in ORCHIDEE, considering all grid cells where the PFT is present.
, 2017; Hilton et al. , 2017). We nevertheless note in this study that we found an uncertainty on the global vegetation COS uptake of 40 % when considering three different LSMs  (Launois et al. , 2015b) , to be compared to an uncertainty of 70 % when considering three LRU datasets. Setting aside the uncertainty for the moment, how could we use atmospheric COS concentrations to constrain GPP? A first optimisation was performed with the ORCHIDEE model in  Launois et al. (2015b) , who optimised a single scaling parameter applied on the vegetation COS fluxes simulated with the LRU approach, thus equivalent to a scaling factor applied on the GPP or the LRU. They assimilated the atmospheric COS concentrations measured at the NOAA air sampling stations, using the LMDz transport model  (Hourdin et al.
Acknowledgements. The authors thank the reviewers for their constructive and useful comments which helped to further improve this study. The LSCE group thanks the administrative and IT teams for managing the recruitment of  Camille Abadie  and providing the necessary facilities and tools to run the ORCHIDEE model and analyse the outputs. Operation of the  US-Ha1  site is supported by the  AmeriFlux Management Project  with funding by the  U. S. Department of Energy's Office of Science  under contract no. DE-AC02-05CH11231  and additionally is a part of the  Harvard Forest  LTER site supported by the  National Science Foundation  ( DEB-1237491 ). The authors are very grateful to the  ObsPack people  who collected and archived the CO 2 surface air samples used in this study. Financial support.
Camille Abadie ,  Fabienne Maignan , and  Philippe Peylin  have been mainly supported by the  European Commission ,  Horizon 2020 Framework Programme ,  4C  (grant no. 821003 ) and to a smaller extent VERIFY (grant no. 776810 ). Marine Remaud  was funded by the  CO 2 Human Emissions  ( CHE ) project which received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 776186. Kukka-Maaria Kohonen  thanks the  Vilho, Yrjö , and  Kalle Väisälä Fund and ICOS-FINLAND  ( 319871 ) for their financial support. Linda M. J. Kooijmans  received funding from the  ERC  project  COSOCS  under grant no. 742798. Review statement. This paper was edited by  Akihiko Ito  and reviewed by  Georg Wohlfahrt  and one anonymous referee.
The best-known projects concern sensor networks located on sidewalks [14], fixed or mobile cameras  [15, 16]  capturing parking spaces or ultra-sonic sensors on parking lots  [10]. The information captured is sent to a central server which has got an overall information on free parking spaces. With the development of the IoT (Internet of Things), some approaches have proposed IoT-based architecture to address the parking space problem. For instance, Lanza et al. [17]  propose an IoT platform able to provide real-time data about available parking places both indoor and outdoor. The available parking spots can then be visualised on a mobile app. More recent works have addressed this issue as a complex system  [18]  in which agents interact to facilitate the management of parking spaces. The very strong potential of collaborative approaches to address parking space problem had already been highlighted in  [9] , where the authors showed how "collaborative sensing can provide key benefits in urban transportation, contributing to higher travel efficiency, safety, and reduced pollution, through innovative applications that benefit from the data sensed collaboratively".
Simulations and Results This section, devoted to the simulation of the NFS model, aims to present and discuss the experiments with their results. Experimental simulations are performed with the NetLogo multi-agent programmable environment  [33] [34] [35]. As all the algorithms are provided and the source code is available on request from the author, computational results can be easily replicates. The results can be interpreted via qualitative data and/or plots obtain from significant runs or averaged over 50 independent runs. The world is a 99 × 99 grid of NetLogo patches. A building block consists of 10 × 10 patches surrounded by streets. A crossroad corresponds to a patch at the intersection of 4 streets. To ensure a fix numbers of cars in the city, the world wrap horizontally and vertically.
Beyond the numerous previous works in the field of urban parking modelling requiring infrastructures based on sensors or cameras  [9, 10, [13] [14] [15] [16] , many works deal with specific issues that need to take into account particular characteristics such as giver/taker agreement  [23] , knowledge of places availability  [21] , giver/taker asymmetry  [20] , smart parking availability  [13] , outdoor/indoor parking  [17] , private/public parking, free/paid parking, etc. All these approaches therefore differ from the present work which, on the contrary, proposes a purely software solution supported by a parsimonious modelling approach dedicated to city community members searching and leaving public on-street parking spots with giver/taker symmetrical role. However, most of these approaches, including ReadyPark, operate in the same general context of urban parking assistance by offering intelligent management and promoting collaborative dimension.
New genomes are released every week  (Mukherjee et al. 2019 ) and inferring orthology relationships at least linearly and typically quadratically with the number of available genomes. Thus, a continuous effort of data integration and standardisation is essential to keep up with the speed with which novel data arrives. Genomes are shaped by speciation and duplication events, but also gene loss, domain architecture rearrangements and horizontal gene transfers, which complexifies orthology predictions and constantly pushes development of new computational methods  (Forslund et al. 2018). The Quest of Orthologs Consortium was founded In 2009 to address the challenges and opportunities of orthology prediction in the genomic era. This community effort united dozens of researchers interested in orthology database construction, orthology software development, and applications of orthologs  (Gabaldón et al.
Finally, meta-methods are combining the output of several methods to refine orthology predictions and increase their robustness. They rely on a variety of combinatorial approaches (see  Glover et al. 2019  for a non-exhaustive list) or machine-learning methods  (Sutphin et al. 2016). During the 6th QFO conference, several methods were presented. They mostly belonged to the hybrid and graph-based categories and showed a notable focus on speed and scalability improvement. Wataru Iwasaki presented the software SonicParanoid  (Cosentino and Iwasaki 2019)  , a graph-based approach similar to InParanoid in which sequence similarity scores are not computed by a classical Blast-based approach but via MMSeqs2 (Steinegger and Söding 2017) to accelerate the distance computation necessary to the graph construction.
Orthology inference is then based on a decision tree exploiting these two criteria. While this approach is fast (no alignments), its usefulness appears restricted to closely related genomes. The conference was also noteworthy for many major updates that concerned most of the established databases of pre-computed orthology relationships. Many talks were related to the latest updates in databases built using graph-based and hybrid methods. Most showed vast expansions in their taxonomic coverages, and confirmed their capacity to deal with the current rates of full genome sequencing. Yannis Nevers presented the 3rd version of the OrthoInspector database. This graph-based approach based on pairwise genome comparisons expanded to 4763 species and its website was redesigned to allow browsing orthology via three sub-databases ( Bacteria , Archaea , Eukaryotes ) and a cross-domain database dedicated to model species  (Nevers et al.
2019). Christophe Dessimoz presented the latest state of OMA (Orthologous MAtrix)  (Altenhoff et al. 2018 ) another graph-based approach which has expanded to 2200 complete genomes (June 2019) and recently introduced novel tools for data visualisation tools and semantic data sharing. Ikuo Uchiyama described the last updates of MBGD (MicroBial Genome Database) , an orthology database which focuses on Bacteria , Archaea and unicellular Eukaryotes. MBGD predictions are based on an updated two-step inference pipeline where a faster but less sensitive large-scale UBLAST search is followed by BLASTP and Smith-Waterman alignments at the genus level  (Xiang et al. 2020 ). Among the richest databases for bacterial data, MBGD has expanded to 6218 species in 3566 genera. Similarly, OrthoDB 10, another graph-based method, has expanded to 13772 species, of which 6488 are viruses and 5609 are bacterial genomes (october 2019)  (Kriventseva et al.
2019). OrthoDB predictions are based on an updated pipeline where MMseqs2 is used for homology searches and novel heuristics are used for better selection of seeds for the graph construction and filtering of mispredicted gene fusions. The category of meta-methods was represented by Paul D Thomas that described the latest developments of PANTHER (Protein ANalysis THrough Evolutionary Relationships) version 14. The corresponding database expanded to 142 complete genomes  (Mi, Muruganujan, Huang, et al. 2019). This last iteration particularly focused on collaboration with biocurators  (Gaudet et al. 2011 ) and the Ensembl Compara resource  (Herrero et al. 2016) , leading to refined gene family boundaries for inferring phylogenetic trees and updated annotations  (Mi, Muruganujan, Huang, et al.
2019). Beyond the meeting, it is noteworthy to mention the recent updates of OrthoMaM 10 (Orthologous Mammalian Markers) , an expert database focusing on high-quality tree reconciliation in mammals that expanded to 47 complete genomes with a focus on resolving exon positions in CDS alignments to infer fine-grained exon orthology  (Scornavacca et al. 2019). Finally, EggNOG 5 (Evolutionary genealogy of genes: Non-supervised Orthologous Groups), another hybrid method, expanded to 5090 species including a large expansion in viral genomes (352 to 2502, october 2019).
Scalability, standardisation and benchmarking The long list of orthology database expansions demonstrates the capacity of current orthology inference tools to encompass a large number of genomes, which is particularly true for graph-based and hybrid approaches. Still, the conference highlighted that database updates aiming for exhaustive inclusion of all sequenced genomes may become an unsustainable option in the near future. As shown by the most recent database updates, many authors are now initiating their predictions on very similar, if not identical, gene models and sequence sets. Many tools rely on the Uniprot reference proteomes dataset and in many cases, the same pairwise aligners (Blast, MMseq2…) are used to compute similar sequence distances. The last iteration of EggNOG was one of the first attempts to introduce new strategies to limit such computation duplicates.
Similarly to other hybrid methods, it uses graph-based predictions refined by gene-tree reconciliation and species delineation  (Huerta-Cepas et al. 2019). But the distance matrix used to build the graphs was derived from an all-against-all Smith-Waterman matrix provided by the SIMAP (Similarity Matrix of Proteins) project  (Arnold et al. 2014). This project proposed to compute similarity metrics between well-established and stable gene models and organize them in a shared database. This approach offers the advantage of avoiding to re-compute distances between gene models that remain unchanged between genomes releases, and appears as an alternative strategy that could respond to growing concerns related to the increasing environmental footprint of Big Data computations  (Lucivero 2020). The critical point of genome counts and scalability was also thoroughly discussed by Mateus Patricio via the example of Ensembl Compara  (Herrero et al.
2016). At the time of the meeting (Ensembl release 96, 199 genomes), Ensembl Compara had about 50 different pipelines summing up to around 59. 2 million jobs per release and about 51. 8 CPU-years in total, run four times a year. However, the Darwin Tree of Life project (DToL, see www. darwintreeoflife. org) will generate a data deluge of 66,000 high-quality annotated genomes from all eukaryotic species found in the British Isles. Consequently, Ensembl Compara has been enhancing its release capabilities and introduced new ways for quantifying and monitoring database changes between releases. By using new statistics such as Jaccard Index  (Jaccard 1901 ) and Gini Coefficient  (Gini 1921 ) based on gene counts, Ensembl Compara can ensure that the amount of changes the database undergoes between releases falls within the expected range.
Another metric that has been heavily used to guide the impact of changes is the Gene Order Conservation (GOC), a score which indicates how many of the four closest neighbours of a gene match between orthologous pairs and are in the same relative order (synteny). Better scalability has also served as motivation to look into innovative ideas such as validating predicted homologies via Deep Learning algorithms. This led to the creation of a Google Summer of Code (GSoC) project (github. com/EnsemblGSOC/compara-deep-learning) to evaluate the feasibility of such an approach. Implementation and large-scale tests are currently ongoing in Ensembl Compara (personal communication). Another major goal of the Quest for Orthologs consortium continues to be facilitating comparisons among different methods-emerging and established-and the meeting featured several talks touching on standardisation and benchmarking.
2019). Tarcisio Mendes de Farias presented improvement of the Orthology Ontology  (Fernández-Breis et al. 2016; Anon)  , which now supports Hierarchical Orthologous Groups (HOGs). He also noted semantic differences among current Resource Description Framework (RDF) interfaces among orthology databases, which hampers data exchange and querying  (Sima, Dessimoz, et al. 2019 ). On a related topic, Ana Claudia Sima introduced BioQuery, a system to enable semantic queries across federated bioinformatics databases  (Sima, Mendes de Farias, et al. 2019). Lastly, also relevant to standardisation are tools for the visualisation and analysis of HOGs, e. g. sets of genes that are inferred to have descended from a common ancestral gene within a species clade. These tools are implemented in the iHam and pyHam libraries  (Train et al.
Phylogenetic profiling was one of the main topics of this 6th edition of the meeting. Approaches to infer phylogenetic profiles based on the identification of orthologs have a substantially lower false positive rate  (Altenhoff et al. 2016)  , which increases the resolution of the analysis. Yet, the computational complexity of the ortholog searches, as most algorithms scale exponentially with the number of taxa and sequences, is substantial. It leaves the generation of phylogenetic profiles that make comprehensive use of the currently available genomes to institutions with a dedicated computer infrastructure. However, many orthology databases, e. g. OrthoDB, OrthoInspector, EggNOG, OMA provide options to query and/or visualize phylogenetic distribution across thousands of species. In particular, a large panel of tools dedicated to phylogenetic profiling has been introduced in the new version of OrthoInspector  (Nevers et al.
Orthology beyond the gene unit It has been noted many times that performing orthology analysis with entire protein sequences will inevitably lead to problems for multi-domain proteins  (Sonnhammer et al. 2014). For instance, only one domain out of several may be orthologous to another protein while the rest of both proteins are made up of different domains. The other domains may even be orthologous to a third protein, manifesting different evolutionary histories of the domains in a protein. This could happen even if the domain architectures are the same. Despite these issues, most ortholog databases and algorithms ignore them and only consider complete proteins. However, at the QfO 6 Meeting, a number of presentations on domain-level orthology analysis show that this topic is actively being researched and that progress has been made.
For instance, for an example dataset, 21% of the domain architectures were found to have domain insertions (three domains on average). The MBGD database is constructed using the domain-aware algorithms DomClust  (Uchiyama 2006)  and DomRefine (Chiba and Uchiyama 2014) that separate orthologous domain clusters based on an ab initio score optimization and refinement procedure. Hirokazu Chiba presented an analysis of proteins in MBGD with domains that belong to different ortholog groups. By connecting ortholog groups when a protein is found in both groups and analysing the resulting domain fusion network they found that proteins involved in signal transduction and secondary metabolites were particularly prone to domain fusions. Comparing different species indicated that extremophiles had unusually few domain fusions.
Ingo Ebersberger introduced FAS (github. com/BIONF/FAS), an approach to compare domain architectures between pairs of proteins and to score their similarity. Integrated with an ortholog search, FAS scores can be used to screen for lineage-specific changes in the domain architecture of orthologs indicative of a change in function. He showed an application of this approach to the phylogenetic profile of the proteome from Acinetobacter baumannii , a nosocomial human pathogen, across more than 2,500 species. This reveals a subset of evolutionarily old proteins for which a change in domain architecture coincides with an increased capability to infect the human host. Another subject that had been discussed was the relationship between orthology and gene context. How function and gene clustering relate, and how gene clustering evolves across species, has been well studied in prokaryotes, but its significance in Eukaryotes remains understudied.