dataesr/scientific-paragraphs-categorization huggingface dataset:
This framework is general and can be applied to model and characterize neuron types. Because effects of Imp knockdown and rescue with Profilin can be identified in the main axon as well as in the branch development or independently, we consider both structures separately. The four features we chose are: "main axon length", "main axon shape", "first order branch distribution along the main axon" and "branch length distribution". To measure these features, we segmented a set of images corresponding to each neuron type to obtain a numeric tree-shaped skeleton representing the morphology of each neuron. We then measured the features values using homemade software. The image segmentation as well as the measurement of each feature are described in the following sections. Neuron morphological automatic classification has already been addressed in the bibliography.
Imp Sh Prof Rescue WT Imp L Figure  1 : Representation of the morphology of each one of the groups under study (in order: Wild type, imp mutant and imp mutant rescued by Profilin). imp mutants are divided into short and long species (named Imp Sh and Imp L respectively) as both phenotypes are equally observed  (Medioni et al. , 2014). We used 3D images taken with a confocal microscope. Each set of images show the distal part of an axonal tree at adult stage (Figure  2 ). Single axons are labelled by GFP using the MARCM technique  (Wu and Luo, 2006) , which allows to image a single mutated (or wild type) neuron in a wild type environment. The database we used for this study consists of 46 wild type images, 48 imp mutants and 15 imp mutants rescued by Profilin. The voxel size varies among the images and is anisotropic in the Z axis.
Segmentation To avoid artificial jumps along the Z axis due to image anisotropy, we applied a simple quadratic interpolation algorithm included in FIJI (the open source image analysis software developed by NIH, Maryland, USA)  (Schindelin et al. , 2012). An automatic segmentation of the images is still not available in our case due to noisy background and poorly defined neuron trace. When observing the images, it can be not trivial even for experts to determine the correct 3D path followed by main axons and their branches. Their trajectories can be very complex as well as non-continuous and difficult to differentiate from background structures. Therefore we segmented the images with the open software Neuromantic  (Myatt et al. , 2012) , specially developed to segment 2 or 3D neurons manually or semi-automatically.
LIKELIHOOD ANALYSIS For a neuron n, we calculate the corresponding features and then compute the likelihood for each group of neurons X, (X ∊ {WT, Imp, Prof Rescue}). The neuron n is then classified in the group that maximizes the global likelihood. All the classifications present in this work were done using the leave one out technique, which consists in classifying a sample that has been removed from the database to perform the learning stage, i. e. the estimation of the distribution parameters. This maximum likelihood classification provides some assessment about the discriminative properties of the proposed models but is also used to analyse the mixture of feature values between the populations. Considering our four features to be independent from each other, the global likelihood is given as follows 𝐿({𝑙 𝑛 , # 1,𝑛 -# 150,𝑛 , 𝑘 𝑛 , 𝑏 ⃗ 𝑛 }|𝑛 ∊ X) = 𝐿 𝑙 (𝑙 𝑛 |𝑛 ∊ X)𝐿 𝑠ℎ (# 1,𝑛 -# 150,𝑛 |𝑛 ∊ X) 𝐿 𝑏𝑝 (𝑘 𝑛 |𝑛 ∊ X)𝐿 𝑏𝑙 (𝑏 ⃗ 𝑛 |𝑛 ∊ X), (12) and the maximum likelihood estimation results.
In situ hybridisation The in situ hybridisation (ISH) protocol for P. pileus was as described in  [50]. Following whole-mount ISH, specimens were microdissected. Tentacle roots were extracted from the body for separate observation. Transverse cryosectioning of tentacle roots after whole-mount ISH was performed using a Leica CM1860 cryostat at a thickness of 14 µm, as described in  [49]. In situ hybridisation of C. hemisphaerica medusae was performed as in  [47]. After post-fixation and Dapi staining, samples were mounted in Citifluor ®. All DIC images were obtained with an Olympus BX61 microscope using a Q-imaging Camera with Image Pro plus ® software (MediaCybernetics).
Gene phylogenies Data sets were built by blasting (BlastP) human proteins on NCBI against predicted proteins from the complete genomes of C. owczarzaki (Ichthyosporea), M. brevicollis (Choanoflagellata), Amphimedon queenslandica (Porifera, Demospongiae), Sycon ciliatum (Porifera, Calcarea) T. adhaerens (Placozoa), N. vectensis (Cnidaria, Anthozoa), Hydra magnipapillata (Cnidaria, Hydrozoa), Lottia gigantea (Lophotrochozoa, Mollusca), D. melanogaster (Ecdysozoa, Arthropoda), Strongylocentrotus purpuratus (Deuterostomia, Echinodermata), Branchiostoma floridae (Deuterostomia, Chordata, Cephalochordata), Ciona intestinalis (Deuterostomia, Chordata, Urochordata), Danio rerio (Deuterostomia, Chordata, Vertebrata, Teleostea), Xenopus tropicalis (Deuterostomia, Chordata, Vertebrata, Amphibia) and Homo sapiens.
e. which WW domain of a given gene in a given species corresponds to which WW domain in other genes). When several WW domains were present in the same protein, they were labelled alphabetically according to their position, starting from the N-terminal extremity (i. e. for Yorkie: Yki a and Yki b correspond to the first and second WW domains, respectively; for Nedd4, WW domains labelled from a to e). Results of WW domain analyses were taken into account to build combined alignments; for Yorkie, of the TEAD-binding (TBD) domain and the two WW domains (TBD domain replaced with missing data for Nedd4 proteins); for Salvador, of the SARAH domain and the two WW domains. Alignments were constructed automatically using MUSCLE  [53]. They were visually checked, and ambiguously aligned parts were removed in BioEdit  [54].
Alignments used to build the trees (in FASTA format) are provided as Additional file 1. Phylogenetic analyses were carried out from the amino-acid alignments by maximum likelihood (ML) using the PhyML 3. 1 program  [55]  with the LG model of amino-acid substitution and a BioNJ tree as the input tree. A gamma distribution with four categories was used, and the gamma shape parameter and the proportion of invariant sites were optimised during the searches. Statistical robustness was estimated by computation of bootstrap values (100 replicates).
Acknowledgements We thank the director and staff of the  "Station Biologique" in Roscoff (France ), notably  Gilles Maron  and  Laurent Lévêque  for their help in animal collection. The initiation of this project resulted from stimulating discussions with Franck Bourrat. We thank  Karen Pottin  for useful technical advice concerning cryosectioning. We are grateful to  Hervé Le Guyader  for insightful comments on a previous version of the manuscript. This work was supported by a grant from the  "Agence Nationale de la Recherche "  ANR-09-BLAN-0236-01 DiploDevo  and a grant by the  COMUE   Sorbonne Universités "INDIBIO" (Convergence program ).
The Integrated Carbon Observation System Atmosphere Thematic Centre (ICOS ATC) automatically processes atmospheric greenhouse gases mole fractions of data coming from sites of the ICOS network. Daily transferred raw data files are automatically processed and archived. Data are stored in the ICOS atmospheric database, the backbone of the system, which has been developed with an emphasis on the traceability of the data processing. Many data products, updated daily, explore the data through different angles to support the quality control of the dataset performed by the principal operators in charge of the instruments. The automatic processing includes calibration and water vapor corrections as described in the paper. The mole fractions calculated in near-real time (NRT) are automatically revaluated as soon as a new instrument calibration is processed or when the station supervisors perform quality control.
GHG mole fractions in 2009. The centralized data processing aims to reduce inter-laboratory differences and facilitate the production of a coherent dataset in near-real time (NRT). The NRT processing chain was built on the expertise gained during previous European projects including CARBOEUROPE, Infrastructure for Measurements of the European Carbon Cycle (IMECC) and Global Earth Observation and MONitoring (GEOMON). NRT is defined here as on a daily basis. NRT data production is more demanding but brings several benefits. In terms of station management, it allows station principal operators and investigators to get a fast feedback on the data; it improves reactivity in case of disruption in the data flow and thus limits data gaps. NRT data are also useful for campaign-based measurement setups.
It allows us, for example, to adjust the campaign setup and observation plan or to place more emphasis on a specific phenomenon. On a more scientific level, NRT data allow for early-warning monitoring systems, for example, in the case of extreme GHG events (e. g. , drought, high-pollution event). NRT is a necessity to perform data assimilation for operational systems (e. g. , Monitoring Atmospheric Composition & Climate -MACC) in which NRT data are either used as a diagnostic or ingested in assimilation mode to improve operational forecasting (http://www. gmes-atmosphere. eu/d/ services/gac/verif/ghg/icos). NRT data are, however, less precise than so-called consolidated data. In ICOS, consolidated data are expected to be produced on a 6-month basis. They contain additional data treatment steps ensuring increased precision and confidence in the dataset.
This allows full traceability and transparency of the consolidated dataset, which will be the basis for elaborated products and services. This article describes the computing facility dedicated to the ICOS ATC at LSCE, the different steps of the automatic processing of CO 2 and CH 4 mole fractions, including the automatic quality control of the raw data, and the corrections due to water vapor interference and calibration (WMO scale). Most of the processing protocols and parameters are illustrated with a few examples from instruments currently providing raw data to the ICOS ATC as part of the ICOS extended demonstration experiment. Because the paper is fo- cused only on CO 2 and CH 4 , only analyzers deployed in the monitoring network that measure these gases have been considered. To date, for these species, only cavity ring-down spectroscopy (CRDS) analyzers commercialized by the Picarro company meet the ICOS requirements, but other instruments may be added in the future.
Server organization and data archive at ICOS ATC The instrumental raw data are transferred at least once a day from the monitoring sites to an ATC server using the secure file transfer protocol (SFTP). The files are first archived, and the data are automatically processed by the ICOS database. Three dedicated servers (Fig. 1 ) are installed and maintained at ICOS ATC to fulfil automatic data collection from measurement stations, processing and distribution to users. Data collection (icos-ssh server): ICOS network stations upload raw data from the instruments to the ATC on a daily basis. This upload can be managed by upload software developed at ATC. All collected data are centralized on the icos-ssh server and upon receipt are copied to a dedicated server for processing and archival. Data are kept on the icos-ssh server for 1 month after their upload.
The amount of data to archive would then be approximately 230, 780 MB day -1 and 1. 3 GB day -1 , respectively, for models ESP100/G1301, G2301 and G2401 of CRDS Picarro analyzers. Data processing (icos-data server): Upon reception, data are processed. The processing is performed on the icosdata server, a dedicated internal (inaccessible from outside the ATC) server at ATC that also hosts the ICOS atmospheric database. The icos-ssh server (accessible from outside the ATC) also hosts the QA / QC applications developed at ATC, used by principal investigators (PIs) and authorized persons to carry out the measurement control. Data distribution (icos-web server): The distribution of data and data products are served by the icos-web server. This server hosts the ATC website and uses an open-source content management system framework (Drupal).
For security purposes, only read-only access is allowed to some partitions on the icos-data server. Access to the ICOS atmospheric database hosted on icosdata from icos-web is prohibited. Traceability of the data downloads and long-term archival, which are not described here, are being implemented in col-laboration with the Carbon Portal of ICOS, which is hosted and operated at Lund University in Sweden (https://www. icos-cp. eu). 3 Processing: automatic filtering of raw data Specific processing chains have been developed for each type of trace gas analyzer, but the general framework remains the same. Here, we describe the processing chain and associated parameters defined for the treatment of continuous measurements of CO 2 and CH 4 atmospheric mole fractions. Similar chains have also been developed for measurements of other ICOS parameters such as meteorological variables or radon but are not described in detail in this article.
Automatic quality control flags imply that no expert has manually inspected the data yet, whereas manual quality control flags imply that an expert has manually inspected the data. The backward propagation of manual quality control flags implies that an expert has performed manual inspection of the corresponding aggregate data but not the data directly. through the whole air inlet, and possible bias due to a contamination in the inlet upstream the connection of the target gas is not considered. As recommended by WMO, two target tanks, with a significant range in the mole fractions of the measured species, are required at ICOS stations (WMO, 2012). Short-term target gases are analyzed at least once per day, whereas long-term target gases are measured only once every 2 to 4 weeks (after each calibration sequence).
The steps and the flag will be described in the following paragraphs. Because ways and conditions to automatically validate raw data may differ from an instrument model to another, the list of internal flags are instrument dependent. If these flags are important for the traceability of the process, they are inconvenient for the majority of the data users who request a simple and unambiguous way to separate the valid and invalid data. For this reason, we have defined another flag scheme named "user flag", as described in Table  1. It is instrument independent and allows easy differentiation of the data that have been validated/invalidated either through NRT data processing or after the requested inspection of the data by an expert. This flagging scheme is completed with a third type of flag named "descriptive flag", which allows the PI to provide codified reasons for invalidating data or useful information for validating data.
System configuration The objective of ICOS is to develop a standardized European monitoring network for greenhouse gases with centralized data processing. Technical discussions about the measurement protocols have been organized during the ICOS preparatory phase through seven working groups. This process has resulted in the first version of the ICOS Atmospheric Station Specifications  (ICOS, 2015). Because the monitoring stations have specific local constraints, it has been required that the processing chains can be parameterized to handle some of the station specificities. A dedicated application, called ATCConfig, has been developed to allow the station PIs to configure the stations of which they are in charge. This application enables the following key points to be described in detail: contact persons and institutes in charge of the station and instruments; geographic coordinates, postal address and description of the monitoring station including the different measurement setups with plumbing schemes; instrument description: category, model, firmware, location to trace instrument movements (e.
g. , for reparation) and various related metadata; calibration/target tanks: model, tank inspection date, valve and regulator description, filling date and mole fractions values; description of the sampling line connections and tank connections to the instruments; description of the measurement sequences (in situ air, calibration and target gases; see Table  3 ); definition of the measurement processing parameters (control, correction and data filtering; see Table  4 ). Each registered instrument is assigned a unique identifier used to reference it (preceded by "no. " in this article). A key aspect of the designed system is to ensure a high level of traceability that leads us to keep track of the history of all configurations provided by station PIs. Regarding the configuration of the measurement processing, we consider three types of sequences: calibration, ambient/target and inter-comparison.
Control based on analyzer ancillary data The first step of the processing consists of the evaluation of instrumental parameters (e. g. , temperature, pressure, flow rate). In the case of the CO 2 /CH 4 analyzers currently used in the ICOS network, each raw data point is scanned for three Table  4. List of the parameters used for the automatic processing of CO 2 and CH 4 mole fractions by CRDS analyzers. The humidity filtering applied to the tank measurements consists of checking the absolute difference between the wet value and the computed dry value against the defined threshold. The parameters are specific to the instrument considered.
41 at Mace Head, is provided in Table  4. The valid intervals and thresholds are instrument and location dependent at this point, but discussions are ongoing between the scientists in charge of the instruments to evaluate the possibility to standardize these criteria for a given instrument model. This decision depends on whether the setup of the station has an influence on the instrument performance. For each GHG data measurement, all selected parameters are tested against their valid interval or threshold. If at least one parameter fails, the GHG data are flagged as invalid. Each failure is traced in the internal cumulative flag (Table  5 ). Table  6  shows all internal flags that have been attributed to three analyzers continuously measuring the CH 4 mole fractions during 2014. From this list, it appears that raw data may be rejected for a combination of reasons.
Quality control Manual rejection flag set up by the station PI -Backwards quality control Propagation of a manual flag set up by the station PI on an aggregated value (e. g. , the hourly mean) to all data used for averaging (e. g. , the 1 min means and raw data) ous raw data. For a multiple sampling level site, such as the Observatoire Pérenne de l'Environnement (identified by the three-letter code OPE) high tower in France, this percentage of rejected data can increase to 16 % (Table  6 ) because of the frequent changes from one sampling level to another.
The determination of specific coefficients for one instrument requires laboratory experiments to be performed as described by  Rella et al. (2013). Such experiments are now performed systematically for each ICOS instrument at the ATC ICOS Metrology Laboratory. A technical paper describing these tests and associated results is in preparation. In the ICOS data processing, the water vapor correction is applied in the same way to all analyzed samples (calibration and target gases, ambient air). Figure  5  shows a comparison of the water corrections applied to CO 2 and CH 4 measurements on two instruments running in parallel at the Mace Head station. One instrument (G1301 model, no. 41) is directly measuring the wet air, whereas for the other one (G2301 model, no. 54) the air is preliminary dried with a cryogenic dryer using a "cold trap" immersed in an ethanol bath cooled at -50 • C.
Statistics of the comparisons of hourly means over the year are summarized in Fig. 7. The water vapor corrections shown in Fig. 7  correspond to the difference between data with and without the H2O correction (amount of water vapor correction) and not to a measurement or correction bias. These corrections are needed to convert humid air mole fractions in dry air mole fractions. However, any error in the water vapor correction would introduce a bias in the resulting dry air mole fractions, whose amplitude would depend on the H 2 O concentration. The determination of a specific correction for each instrument by the ATC will minimize the bias associated with humid air measurements. Conversely, drying the air (e. g. , using a Nafion membrane) may also cause a measurement bias by contamination of the sampled air.
Calibration correction All CO 2 and CH 4 measurements that are intended to be added to the international monitoring networks database must be calibrated relatively to the WMO mole fraction scale for gas mole fractions in dry air maintained by WMO/GAW Central Calibration Laboratories (CCL). The current scales used for CO 2 and CH 4 are "WMO CO 2 X2007" (http://www. esrl. noaa. gov/gmd/ccl/co2_scale. html) and "WMO CH 4 X2004" (http://www. esrl. noaa. gov/gmd/ ccl/ch4_scale. html). Updates of the WMO scales will be taken into account by the CAL and the time series will recalculated by the ATC. As explained previously (see Sect. 3. 1), the station PIs are in charge of the configuration of the calibrations performed at their site (number of calibration tanks, frequency of calibrations and duration of the gas injections).
Because the calibration correction is essential for the final in situ or target data value determination, the calibration data are filtered through a set of specific controls to determine whether all expected data are present and the quality is sufficient for use in the computation of the calibration equation (see below). All controls made on the calibration sequences are instrument, location and species dependent. If there are enough valid data, the calibration is accepted and the calibration equation is determined. The equation coefficients are stored in the database, making them available for the calibration of the other types of samples (ambient air and target gases). The controls applied to the calibration data are currently the following: 1. The expected number of cycles with their associated number of calibration standards is checked along with the minimum duration of the tank injection.
41 at the Mace Head station on 10 December 2014 is shown in Figs. 8 and 9. The set of parameters defined by the PI for this instrument are given in Tables  3  and 4. Four calibration tanks are used and are analyzed four times (cycles) for 20 min in each calibration, including 15 min dedicated to the flushing of the inlet lines and analyzer cell (stabilization time). Overall, the calibration lasts for 320 min. Figure  8  shows the different steps of the calibration process from analyzing the raw data and aggregating to the minute to calculating the cycle and calibration sequence averages. A fitting function (see Fig. 9 ) is then applied to the results of the calibration to define the coefficients of the correction, which will be applied to in situ air and target gas measurements to ensure the data are compatible with the WMO reference scales.
The data are corrected with the closest calibration equation in time existing before the data. As soon as there are calibration episodes before and after the considered data, the correction is made with a linear interpolation of the enclosing calibration equations. It is important to note that NRT data provided after 24 h will be automatically modified after a few weeks once the next calibration is available to estimate the temporal drift of the analyzer. If no calibration equation is available within a period of 180 days to correct the data, the data are flagged as incorrect, and the explanation is added to the internal cumulative flag. We have analyzed, for 11 monitoring stations, the differences in the CO 2 and CH 4 mole fractions processed in nearreal time with the same dataset after calibration drift correction and manual validation by the PI.