dataesr/scientific-paragraphs-categorization huggingface dataset:
2A ,B and online methods). Then, this coarse alignment was followed by a more precise non rigid registration using the Coherent Point Drift algorithm  27  (Fig. 2C  and online methods). After this procedure, all detections over time for every single somata resulted in a more clearly identifiable cluster, even if they would not contain detection for all time steps. Each of these clusters in this 3D + time registered dataset corresponded to a single trajectory in the unregistered images. In a fourth and last step, the actual identification of each cluster was subsequently obtained by using DBSCAN on the 3D + time registered dataset (Fig. 2D )  28. DBSCAN is a clustering method that offers several advantages in our particular case. It automatically identifies the number of clusters, takes into account the local density and disregards isolated points considered as spurious detection or background noise without associating them to a group.
4 ). After this step, missing detections in each trajectory could be reconstructed by interpolation of close detections in time of the same somata (see reconstruction of trajectories and missing detections in the methods section). A quantitative comparison of our tracking algorithm using both manually annotated and synthetic datasets demonstrated that our approach outperforms currently available software for this task (see tracking evaluation in online methods and Supplementary Figs. 5 6 7 ). Once the tracks of individual neurons were obtained, it was possible to measure their individual signal through time. Comprehensive extraction of the raw signal intensity in the GCaMP channel was obtained by gathering the average intensity in non-intersecting volumes around all successive positions along each trajectory.
This step employed a discrete 3D Voronoi tesselation seeded by the tracked somata. Figure  2  shows the raw (Fig. 2F ) and normalized (Fig. 2G ) signals for all neurons tracked in a single fly (see online methods for signal normalization). Since the process was fully automated, it was possible to run the same analysis in parallel on many fly acquisitions using a computing cluster. As a validation step, 23 naïve flies that had never been exposed to octanol (OCT) were each exposed to a 5 s odor stimulus (OCT) or a control air puff stimulus as described in Fig. 2F-G  (see online methods for full details). The full sequence was simultaneously imaged and automatically analyzed. The results indicate that an average of 500 somata per fly were tracked (Fig. 2H ). From our evaluation on manually annotated and synthetic datasets, we could assess that most of them were true positives matching real neurons (see Supplementary Fig.
The solenoid valves were individually controlled by logic signals from a NI-USB (National Instruments) card to define the desired pacing configuration. image acquisition and odor presentation. To generate the flies for imaging, female Drosophila carrying the VT50339-Gal4 driver (3rd chromosome) were crossed to males carrying UAS-GCaMP6f/UAS-NLSmCherry (both 2nd chromosome) in order to express the calcium sensor and the nuclear marker in the mushroom body. In our experiments, a Zeiss Examiner Z1 Axio microscope was used for data acquisition, equipped with an EMCCD (Electron Multiplying Charge Coupled Device, Photometrics Delta Evolve). The light excitation was performed by two diode-pumped lasers that emit at 491 nm and 561 nm (maximum power: 50 mW, Roper Scientific). The sample was scanned using a CSUX1-M1N-E confocal head.
Two water immersion microscope objectives are available on this device: Zeiss 40x ON 1. 0 Vis-IR W apochromat 421462-9900 (working distance: 2. 5 mm) and Zeiss 63x ON 1. 0 Vis-IR W apochromat 421480-9900 (working distance: 2. 1 mm). Although the initial tests were made with the 63x objective, all of the acquired data used the 40x. Finally, the entire system was controlled by the VisiView 2. 1. 3 software (Visitron Systems GmbH). Sequences of 3D stacks were saved in a 5-dimensional. tiff file, with axis XYZTC and data recorded in a 16-bit format. Each plane was 256 × 512 pixels, as we only used half of the sensor for each channel (the full resolution of the camera is 512 × 512 pixels). The pixel size was 0. 16125 µm × 0. 16125 µm, and the total size of each 2D image was 41. 28 µm × 82. 56 µm.
This is because the affine registration already corrected for the coarse long term movements. On the other hand, note that CDP could not directly correct for large shifts on its own and the prior rigid registration enabled CDP to properly correct for local deformations. Overall, aligning all 3D detections overtime enabled to form clusters which the following step could detect and use to further identify, split and complete trajectories. annotated ground truth in 3D cannot be considered as a perfect. For tracking, manual annotations are typically time consuming and don't guarantee a high accuracy  51. In our case, it was impossible to annotate every individual object, even in one dataset in an exhaustive manner. Instead, 10 random nuclei were annotated by two experts along a sequence of 250 3D stacks for a total of 5000 data points.
Acknowledgements We thank  Pierre-Yves Plaçais  and  Bruno Poggi  for critical reading of the manuscript. This work was funded by the  Agence Nationale pour la Recherche (ANR MemoMap   ANR-15-CE32-0008-01 ) (to A. G. and T. P. ), the  Labex MemoLife  (to A. G. and T. P. ), and the  European Research Council  ( ERC Advanced Grant  EnergyMemo, n° 741550) (to T. P. ). L. S. was funded by a postdoctoral fellowship from the  Deutsche Forschungs Gemeinschaft DFG  ( SCHE 1884/1-1  and  SCHE 1884/1-2 ). F. D. was funded by a doctoral contract from  Memolife  ( ANR-10-LABX-54   MEMOLIFE ) and  Paris Sciences et Lettres  ( ANR-11-IDEX-0001-02 PSL ).
Inventory of articles Bibliographic reference retrieval was carried out by searching methods related to BATs in international journals indexed in recognized databases and accessible from Mines Saint-Etienne. Searches were made in English and French on the Web of Science and BibCNRS, including in this latter researches on Google Scholar. As the word "technologies" is frequently used rather than "techniques" to designate BATs, an initial search using the terms "Best Available Techn*" was first made to cover as wide a field of research as possible. The number of results obtained being respectively 53,692 and 81,964 for the Web of Science and BibCNRS, revealing that the field of research was too broad, a second filter was added to the first one (Figure  2 ). The same entries were associated with keywords either related to the decision-making process or to BAT characteristics in order to narrow the scope of research (Figure  2  -Filter n°2 and 3) (Table  3 ).
As the number of rows needed to make a complex research using the "[or]" function would have been too high compared to the maximum of rows that the database can handle, the research corresponding to each cell of the table was made separately. For instance, the search carried out to obtain the result "39 (3)" for the shaded cell in Table  3  is given in Figure  3. Thus, duplication of results could not be avoided. To remedy this, all the articles were downloaded and sorted. If the full paper could not be found on one of the two databases, it was searched on Google Scholar or Research Gate. If it was still not available, a request was sent by Research Gate to the main author to get the full-text version. In Table  3 , the numbers in brackets refer to the number of articles from which the full-text version could not be found, either by searching on other databases or by requesting it to the author.
For certain methodologies, scope and objectives are defined based on expert judgment regarding the need to build a reference system for a given activity  (Carretero et al. , 2016; Dijkmans, 2000; Evrard et al. , 2018; Zanatta et al. , 2017)  or known environmental issues on a given territory  (Mavrotas et al. , 2009). This step can also be carried out via a MEFA based on scientific and technical literature  (Barros et al. , 2007)  or an  LCA (Laso et al. , 2017). In the study by Laso et al. (  2017 ), the information needed to conduct the LCA is gathered through questionnaires developed by a group of technical experts and completed by the industrial operators of the sector located in Cantabria region (Spain), or taken from life cycle inventory databases. However, the authors point out that the reported LCA results are rare in the BREFs, which makes this methodology difficult or, at least, time-consuming to reproduce.
The tag sequences were created with the oligotag command within the open-source OBITools software suite  (Boyer et al. , 2016)  so that all pairwise tag combinations were differentiated by at least five different base pairs  (Taberlet et al. , 2018). DNA amplification was performed in a 20 µL total volume solution containing 10 µL of AmpliTaq Gold 360 Master Mix (Applied Biosystems, Foster City, CA, USA), 0. 16 µL of 20 mg mL -1 bovine serum albumin (BSA; Roche Diagnostics, Basel, Switzerland), 0. 2 µM of each primer, and 2 µL of diluted DNA extract. DNA extracts from soil and filters were diluted eight times, while DNA extracts from leaves were diluted four times. Amplifications were performed using the following thermocycling program: an initial activation of DNA polymerase for 10 min at 95 • C; x cycles of 30 s denaturation at 95 • C, 30 s annealing at 53 • C and 56 • C for bacteria and fungi, respectively, and 90 s elongation at 72 • C; and a final extension at 72 • C for 7 min.
Bioinformatic analyses of raw reads The Illumina raw sequence reads were processed separately for each library using the OBITools software suite  (Boyer et al. , 2016) , specifically dedicated to metabarcoding data processing. First, the raw paired-ends were assembled using the illuminapairedend program, and the sequences with a low alignment score (fastq average quality score <40) were discarded. The aligned sequences were then assigned to the corresponding PCR replicates with the program ngsfilter by allowing zero and two mismatches on tags and primers, respectively. Strictly identical sequences were dereplicated using the program obuniq, and a basic filtration step was performed with the obigrep program to select sequences within the expected range length (i. e. , longer than 65 and 39 bp for fungi and bacteria, respectively, excluding tags and primers), with-out ambiguous nucleotides, and observed at least 10 times in at least one PCR replicate.
The remaining unique sequences were grouped and assigned to molecular operational taxonomic units (MOTUs) with a 97 % sequence identity using the Sumatra and Sumaclust programs  (Mercier et al. , 2013). The Sumatra algorithm computes pairwise similarities among sequences based on the length of the longest common subsequence, and the Sumaclust program uses these similarities to cluster the sequences  (Mercier et al. , 2013). An abundance of sequences belonging to the same cluster was summed up, and the cluster center was defined as the MOTU representative of the cluster  (Mercier et al. , 2013). The taxonomic classification of each MOTU was performed using the ecotag program  (Boyer et al. , 2016) , which uses full-length metabarcodes as references. The ecoPCR program  (Ficetola et al. , 2010)  was used to build the metabarcode reference database for each marker.
Briefly, ecoPCR performs an in silico amplification within the EMBL public database (release 133), using the Fung02 and Bact02 primer pairs and allowing a maximum of three mismatches per primer. The resultant reference database was further refined by keeping only sequence records assigned at the species, genus, and family levels. After taxonomic assignment, data sets were acquired, and further processing with the open-source R software (RStudio interface, version 3. 4. 1) was performed to filter out chimeras, potential contaminants, and failed PCR replicates. More specifically, MOTUs that were highly dissimilar to any reference sequence (sequence identity <0. 95) were considered chimeras and discarded. Secondly, MOTUs whose abundance was higher in extraction and PCR negatives were also excluded.
Data analysis Unless specified otherwise, all exploratory statistical analyses were achieved with R. Rarefaction and extrapolation curves were obtained with the iNEXT 2. 0-12 package  (Hsieh et al. , 2016)  to investigate the gain in species richness as we increased the sequencing depth for each sample. Alpha diversity estimators including Shannon and Chao1 were calculated with the phyloseq 1. 22-3 package  (McMurdie and Holmes, 2013)  on data rarefied to the same sequencing depth per sample type (see Table  S2  for details on the rarefaction depths). Nonmetric multidimensional scaling (NMDS) ordination analysis was performed to decipher the temporal patterns in airborne microbial community structures (phylum or class taxonomic group) in air samples. These analyses were achieved with the metaMDS function within the vegan package  (Oksanen et al.
Microbial characterization of samples, richness and diversity The structures of bacterial and fungal communities were generated for the 62 collected samples, consisting of 36 aerosol, 18 surface soil, and 8 leaf samples. After paired-end assembly of sequence reads, sample assignment, filtering based on sequence length and quality, and discarding rare sequences, we are left with 2 575 857 and 1 647 000 reads respectively for fungi and bacteria, corresponding to 4762 and 5852 unique sequences, respectively. After the clustering of highquality sequences, potential contaminants, and chimeras, the final data sets (all samples pooled) consist respectively of 597 and 944 MOTUs for fungi and bacteria, with 1 959 549 and 901 539 reads. The average number of reads (average ± SE) per sample is 31 607 ± 2072 and 14 563 ± 1221 for fungi and bacteria, respectively.
Our findings are a first step in the understanding of the processes leading to the emission of these important chemical species and the large OM fraction of PM in the atmosphere, and in the parametrization of these processes for their introduction in CTMs. They could also be used for planning efforts to reduce both the PBOA source strengths and the spreading of airborne microbial and derivative allergens such as endotoxins and mycotoxins. However, it remains to be investigated how well different climate patterns and sampling site specificities, in terms of land use and vegetation cover, could affect our main conclusions. Data availability. The sequencing data files are available from the DRYAD repository (https://doi. org/10. 5061/dryad. 2fqz612m4, Samaké et al. , 2020). All relevant chemical and environmental data sets are archived at the IGE (Institut des Géosciences de l'Environnement) and are available upon request from the co-author (Jean-Luc Jaffrezo).
Acknowledgements. We acknowledge the work of many engineers in the lab at the  Institut des Géosciences de l'Environnement  for the analyses ( Anthony Vella , Vincent Lucaire). The authors would like to kindly thank the dedicated efforts of many other people at the sampling site and in the laboratories for collecting and analyzing the samples. The PhD of Abdoulaye Samaké is funded by the government of Mali. We gratefully acknowledge the  LEFE-CHAT  and  EC2CO  programs of the  CNRS  for financial supports of the  CAREM-BIOS multidisciplinary project , with ADEME funding. Chemical and microbiological analytical aspects were supported at IGE by the  Air-O-Sol  and MOME platforms, respectively, within  Labex OSUG@2020  ( ANR10 LABX56 ). Review statement. This paper was edited by  Alex Huffman  and reviewed by two anonymous referees.
Acknowledgments The authors thank  Editor Christina Patricola  and two anonymous reviewers for their comprehensive and insightful comments, which have led to improved presentation of this manuscript. We acknowledge support by the  Blue-Action Project (European Union 's  Horizon 2020 research and innovation program ,  727852 , http:// www. blue-action. eu/index. php?id = 3498). The  WHOI-NCAR group  is also supported by the  US National Science Foundation (NSF)   Office of Polar Programs Grants   1736738  and  1737377 , and their computing and data storage resources, including the Cheyenne supercomputer (doi: 10. 5065/D6RX99HX ), were provided by the  Computational and Information Systems Laboratory at NCAR. NCAR  is a major facility sponsored by the  U. S. NSF  under Cooperative Agreement 1852977.
Bayesian inverse problem Several Bayesian methods have been developed within the context of STE problems:  Rajaona et al. (2015)  develop an adaptive scheme based on importance sampling (IS), enhancing its estimation process by learning from the previous sampling rounds. Their method is tested afterwards on the Fusion Field Trials 2007 experiment. Tichý et al. (2016)  propose an iterative variational Bayes method with no need of tuning and, as a result, suitable for operational use. The algorithm is then tested using real data from the European Tracer Experiment (ETEX). However, the most common ways to estimate the posterior distributions of the source's variables is through the use of Markov chain Monte Carlo (MCMC) algorithms. These methods are described by Delle  Monache et al. (2008)  who apply MCMC techniques for a stochastic estimation of the Algeciras incident source location.
Objectives of this study The objective of this study is to apply a sophisticated MCMC method to the reconstruction of the source of unknown origin of the Ruthenium-106 detected in Europe in the autumn of 2017. Uncertainties are integrated into the MCMC modelling. In Section 2, we provide context, describing recent works and findings about the 106 Ru detection event studied in this paper. Furthermore, the observation dataset, meteorological fields, and atmospheric dispersion models are presented and discussed. In Section 3, the Bayesian framework is introduced and a full probabilistic description of the problem is given. Notably, discussions on the choice of the distributions are provided. Thereafter, a classic MCMC method -the MH algorithm -is presented and applied to retrieve the longitude, the latitude, the magnitude and the time of the release.
Observation dataset The full observation dataset (> 1000 air concentration measurements of 106 Ru in September and October 2017) is described in the work of  Masson et al. (2019). 106 Ru was observed at levels between a few microBecquerel per cubic meter of air to more than 170 milliBecquerel per cubic meter of air in Romania, where it achieved its maximum. Only stations located further west in Europe (Portugal, Spain, and Ireland) did not report detections of 106 Ru. In total, 31 countries on the European continent have reported concentration levels of 106 Ru  (Saunier et al. , 2019). Note that the air sampling period is not consistent over the different countries. It ranged between 11 h (in Romania) to 3 months (in Russia). There is also a considerable variability in the detection limit of the measurements; it is very low in France (under 1 μBq/m3) but higher in Germany or in Romania.
We assume that the release occurred at ground-level. The domain will be restricted to a domain extending from the west of Europe to the east of Kazakhstan. This follows previous work by two of the authors, which excluded the possibility of the source being located anywhere else  (Saunier et al. , 2019). Inverse modelling involves the comparison of the observation dataset with a corresponding set of predictions produced by the atmospheric dispersion model given a characterised release. As explained in more detail by, e. g. Winiarek et al. (2011) , the observation equation y ¼ Hx þ ε (1) expresses this relationship, where H is the observation operator, x is the vector of variables describing the source of the release (evolution of the release, nature of the radionuclides, location, etc), and ε is the observation error.
Acknowledgements The authors would like to thank the reviewer for very complete comments and suggestions, and  M� et� eo-France  and the  European Centre for Medium-Range Weather Forecasts (ECMWF)  for the meteorological fields used in this study. The authors also acknowledge the support provided by  CEREA, the joint laboratory of � Ecole des Ponts ParisTech  and  EdF R&D. CEREA is a member of  Institut Pierre Simon Laplace (IPSL).
0 can model the physical dynamics of storage changes in the unconfined aquifer  34  , which is an essential part of terrestrial water storage  37, 38. It should be noted that the simple groundwater model in CLM4. 0 has its limitations at local-scale processes. However, owing to its coupling with the atmospheric and ocean models, CESM is a suitable tool for resolving large-scale multiple interactions and feedbacks of the groundwater system within the hydrological cycle under a fully coupled Earth system framework  23, 39, 40. In this paper, to estimate the future climate-driven GWS changes, we used the simulation output data of CESM-LE (2006 -2100) under the RCP 8. 5 (Representative Concentration Pathway) high-emission scenario. Without considering the anthropogenic effects of water use and management (such as groundwater pumping, dam impoundment, and water transfer), the CESM-LE only accounts for natural physical processes that act on the water budgets, which allows us to investigate the mechanisms and projections of anthropogenic warming-induced GWS changes.
While our explanations of fundamental mechanisms followed a one-way approach, the results shown here considered all known feedbacks in the coupled system. For example, in the Southern Plains, a decrease in soil moisture influences moisture supply for ET, and an increase in rainfall is the result of both regional and atmospheric circulation changes such as the strengthening effect of ENSO teleconnection  53. A key finding is that future changes in GWS are not only governed by the projected changes in precipitation but also modulated by other hydrological processes (e. g. , ET and snowmelt). It is noteworthy that certain mechanisms affecting groundwater recharge in this study are already occurring at present. For instance, changes in timing and the magnitude of spring snowmelt have been reported in observational datasets  54, 55  and likely influence the groundwater recharge  56.
3. The contributions of three climate-driven factors to groundwater recharge is quantified by the following coefficient ranging between 0 and 1: Contribution i ¼ a i j j P 3 i¼1 a i j j : ð2Þ The results are plotted as RGB (Red: snowmelt, Green: ET, Blue: rainfall) for triplet color in Fig. 3. For example, for the Canning Basin (aquifer 7), they are 0, 0. 36, and 0. 64, and for the Southern Plains (aquifer 2), they are 0. 30, 0. 24, and 0. 46, respectively. R 2 for the regression model and the contribution for each factor (the splitting channel) is shown in Supplementary Fig. 11  with a single-color scale. Estimating GWS changes from satellite datasets. For estimating "true" and total (climate-driven and anthropogenic) GWS in the past decade, recent releases of GRACE data  70  from the NASA Jet Propulsion Laboratory with mascon solution and the NASA Global Land Data Assimilation System 1.
0 (GLDAS) were used (Fig. 4a ). GLDAS forces several offline land surface models with the same atmospheric forcing from observation-based datasets to simulate the water and energy cycle fluxes. To estimate GWS, we used the total water storage from GRACE and removed the storages of canopy water, snow, and soil moisture from GLDAS (multimodel average). This method is commonly used with GRACE-related studies  [42] [43] [44] 46. The trends are calculated after removing the seasonal. Climate-driven and anthropogenic-pumping GWS changes. To compare the climate-driven and the anthropogenic-pumping effects, we use the twentiethcentury CESM simulation from a previous study  40  (orange bar in Fig. 4b ), which included global groundwater pumping (the same model configuration as in a previous study  9  ) for industrial, irrigation, agricultural, and domestic water demand (Supplementary Fig.