dataesr/scientific-paragraphs-categorization huggingface dataset:
Coupling With OASIS The model components are coupled using the OASIS3-MCT software  (Craig et al. , 2017) , which implements exchanges between multiple executables running concurrently using Message Passing Interface (MPI) communication. The OASIS3-MCT approach has the advantage of requiring a minimal amount of modifications in existing component codes. In CNRM-CM6-1, OASIS3-MCT transfers and interpolates coupling fields between SURFEX, CTRIP, and NEMO at a coupling frequency of 1 hr. In contrast ARPEGE-Climat and SURFEX models are coupled inline, that is, SURFEX is called as a subroutine of ARPEGE-Climat at each atmospheric time step, and are thus considered as one executable by OASIS3-MCT. Similarly, NEMO and GELATO are also coupled inline at a 1-hr frequency. NEMO sends the ocean and sea ice surface properties (sea surface temperature and currents, sea ice fraction and sea ice albedo) that are used in SURFEX to calculate surface turbulent and radiative fluxes, which are then sent back to NEMO.
Output Server XIOS CNRM-CM6-1 is used in a large number of MIPs, which request numerous diagnostics to be output following specific format norms. In order to ease their production, the model was interfaced with XIOS  (Meurdesoif, 2018) , an input/output parallel server software allowing for a declarative description of output files content and for the realization of online operations on fields, thereby almost fully removing the need for postprocessing. Once the distribution of the full model grid among processes is described, the XIOS library has a main interface routine (send_field), which allows each model MPI process to deliver easily its part of the full grid for any field. The routine send_field can be called from any model routine. XIOS postprocessing operations include time sampling and averaging, spatial remapping and reduction, vertical interpolation, and simple arithmetics.
An additional tool, called dr2xml (https://github. com/senesis/ dr2pub), translates the CMIP6 DR for each year of each experiment in a set of XIOS conformant XML definitions. These definitions are then used to activate the outputs in CNRM-CM6-1 components, which are all XIOS-enabled thanks to an alias table mapping model variable names and CMIP6 DR variable names. At run time, CNRM-CM6-1 then creates NetCDF output files, which are directly conformant with CMIP6 requirements and ready to publish on the Earth System Federated Grid (ESGF), except for some climatologies. This approach is applicable to any other model as long as it uses XIOS to produce its outputs. It is currently used in the IPSL climate model for its CMIP6 production.
As a consequence, given the major updates presented hereabove, the calibration of CNRM-CM6-1 has been a real challenge to achieve a reasonable present-day climate mean state and variability while ensuring energy and water conservation in the system. This can be viewed as replacing compensating errors by more physically based parameterisations. This effort will be pursued to further improve it. The CNRM-CM6-1 model has been used to perform the CMIP6 DECK experiments and many CMIP6 MIPs, including ScenarioMIP, DAMIP, OMIP, CFMIP, and RFMIP. Files compliant with CMIP6 requirements and straight ready to be published on the Earth System Federated Grid have been produced inline thanks to the use of XIOS, a parallel input/output data server, without any postprocessing step. Based on a general validation of both the mean climate and variability in DECK experiments, the present paper provides a general picture of CNRM-CM6-1 to the scientific community, which will analyze CMIP6 experiments.
Additional papers focusing on individual components performances are under way (see http://www. umr-cnrm. fr/cmip6/references for an updated list). In the course of CNRM-CM6-1 development, one of the main target was to reduce long-term drifts. These are strongly improved in comparison to those in the former CNRM-CM5. 1 version. The main improvements highlighted here are a general reduction of ocean biases in temperature and salinity, in particular in the southern ocean where the CNRM-CM5. 1 warm bias from the surface to the deeper ocean is much weaker. This is probably linked to the improvement ). Observations in black (see Appendix A for reference data sets) are compared to CNRM-CM5. 1 (blue) and CNRM-CM6-1 (red), including ensemble means (thick lines) and inter-member spread (±1,64 times the inter-member variance).
Acknowledgments This work has been partially funded by the  French ANR  project  CONVERGENCE  under grant  ANR-13-MONU-0008-02  and by the  H2020  project  CRESCENDO "Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach ," which received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  641816. The authors wish to thank  Céline Colombo  for her work on the model schematic. They also greatly acknowledge the  NEMO R&D team  for their support in implementing NEMO and its interface with XIOS. We would like to thank  Bodeker Scientific , funded by the  New Zealand Deep South National Science Challenge , for providing the combined NIWA-BS total column ozone database. The SURFEX-CTRIP code is available (Open-SURFEX) using a CECILL-C Licence (http://www.
, 2010; Teissier et al. , 2010). Transcriptome profiling in human fetuses  (BrainSpan, 2011)  at later stages of development showed postmitotic expression of Dbx1 in the cingulate MZ/cortical plate (http://brainspan. org/lcm/gene/ 78785), in agreement with our observations. We could not detect any Dbx1 expression in two additional human fetuses at GW5. 5 and GW6. 0 as well as in human datasets available (for in situ hybridization data, the Allen developing human and developing non-human primate brain atlases, the Marmoset gene atlas, and HuDSeN) or in a dozen single-cell RNA sequencing (scRNA-seq) datasets (human and non-human primates, tissues, and organoids), either accessible through web browser (UCSC cell browser or Shinycortex) or downloaded from the GEO repository. Collectively, these results suggest a low and very restricted Dbx1 expression in humans both spatially and temporally.
ACKNOWLEDGMENTS We acknowledge the  ImagoSeine facility , a member of the France BioImaging infrastructure supported by the  Agence Nationale de la Recherche  ( ANR-10-INSB-04 , ''Investments for the future''), for help with confocal microscopy; Animalliance for technical assistance and animal care;  N. Klaus ,  S. Goebbels ,  S. M. Barnat , and  S. Humbert  for providing the Nex-cre mouse line;  S. Karaz ,  M. Courgeon , and  F. Causeret  for the pCAGGS-Dbx1-ires-EGFP plasmid and sequence;  M. Moreau  and  F. Causeret  for interrogating public databases; and  J. Fei  for providing pCAGGS-mCherry and pCAGGS-loxP-stop-loxP plasmids. We thank  F. Matsuzaki ,  T. Suetsugu , and  A. Shitamukai  for training in exo utero electroporation;  E. Taverna ,  J. Pulvers ,  M. Barber ,  T. Nomura , and  J.
Sap  for critical reading of the manuscript; members of  Pierani's laboratory  for discussion; and  L. Vigier  for mouse maintenance. Y. A. was supported by the  ARC (Association pour la Recherche sur le Cancer) ,  FRM  ( Fondation pour la Recherche Medicale ), and  JSPS (Japan Society for the Promotion of Science) ; A. W. C. by the  FRM. A. P. is a  CNRS  ( Centre National de la Recherche Scientifique ) Investigator and the Team member of the  E ´cole des Neurosciences de Paris Ile-de-France (ENP). This work was supported by grants from the  Agence Nationale de la Recherche  ( ANR-2011-BSV4-023-01  and  ANR-15-CE16-0003-01 ),  FRM  (Equipe FRM  DEQ20130326521 ),  Fondation ARC pour la Recherche sur le Cancer  (ARC, Project  ARC SFI20111203674 ),  Ville de Paris  ( 2006 ASES 102 ), and  Federation pour la Recherche sur le Cerveau (FRC)  to A.
P. ; and state funding from the  Agence Nationale de la Recherche  under '' Investissements d'avenir'' program  ( ANR-10-IAHU-01 ) to the  Imagine Institute and Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS)  from the  Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT)  to H. O.
RNAseq data generation and de novo transcriptome assembly To obtain the ovary transcriptome, total RNA was extracted from the ovaries dissected from 60 B. anurus female pupae using the Qiagen RNeasy Mini Kit according to the manufacturer's protocol. PolyA-RNA-Seq Library construction and Illumina HiSeq 2000 sequencing (paired-end sequencing 2x100 bp) were performed by GATC Biotech AG company (GATC NG-7281 project). Upon reception of the sequences, the following analyses were performed: (i) quality assessment using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/), (ii) removal of sequencing adapters with Trimmomatic  (Bolger et al. , 2014) ; (iii) de novo transcript assembly with the Trinity suite version 2. 2. 0  (Haas et al 2013) ; (iv) calculation of transcript counts and fragments per kilobase per million (FPKM) using RSEM software  (Li et al.
, 2011). A total of 46,530 assembled transcripts (contigs) were obtained. The transcripts assembly obtained statistics were: a mean size of 990 bp, a median size of 470 bp, a N50 length of 1,942 bp, a L50 length of 6,588 bp and 42,46% of GC content. For annotation, all 46,530 transcripts were used as a query for a BLASTx search  (Altschul et al. , 1997)  in the 'National Center for Biotechnology Information' (NCBI) non-redundant (nr) database, considering all hits with an e-value<1E-08. Following BLAST search, we obtained 24,851 transcripts with "no hits" (53,4%). We also used BUSCO  (Simão et al. , 2015)  to test for the presence of reference genes in this transcriptome. Over the 1,658 Total BUSCO groups searched, we obtained 1,477 complete BUSCOs (87. 6%), 1,063 complete and single-copy BUSCOs (64%), 414 complete and duplicated BUSCOs, 101 fragmented BUSCOs and 80 missing BUSCOs.
Finally, after filtering with FPKM >1 and isoform percentage (isopct) > 15, a total of 41,467 transcripts were kept for further analyses (list in Supplemental Table  1 ). A selected set of transcripts was manually analyzed to identify the open reading frames, then the predicted protein sequence was used in blastP similarities searches against NCBI nr protein database to assess amino acid identities between the B. anurus predicted protein and the best protein match.
A number of genes similar to IVSPER genes are highly transcribed in B. anurus ovaries "Ichnovirus Structural Protein Encoding Regions" (IVSPERs) were first described in the campopleginae H. didymator  (Volkoff et al. , 2010)  then in the banchine Glypta fumiferanae  (Cusson et al. , 2012). The IVSPER genes are specifically transcribed in the calyx tissue and 2/3 rd of them encode proteins found associated with IV particles by mass spectrometry analyses. IVSPERs remain in the wasp genome and are not packaged within the IV particles. These regions hypothetically correspond to fingerprints of the ancestral ichnovirus which integrated into the ancestral wasp genome. Except for their involvement in the IV particle constitution, the precise function of all the IVSPER genes remains today unknown.
Finally, we also identified a homolog of the G. fumiferanae U28, which has for now not being identified in the campoplegine H. didymator. Among all the obtained transcripts, the most highly transcribed one in the B. anurus calyx is a gene homolog to the H. didymator IVSPER gene U13 (Table  1 ; Supplemental Table  1 ). Note that this gene is absent from G. fumiferana genome  (Darboux et al. , 2018). The B. anurus U13-like gene encodes a predicted protein which is similar in size with the H. didymator protein and with which it shares 44% identity (Table  1 , Supplemental Figure  1 ). The second most transcribed is a long transcript matching in a short region with H. didymator IVSPER gene U20 which is also specific to this species. U20 encodes a very short protein sharing 48% amino acid identity with the B.
Acknowledgements. We thank the quarantine insect platform (PIQ), member of the  Vectopole Sud network , for providing the infrastructure needed for insect maintenance. This work was in part supported by the  Agence Nationale de la Recherche (ABC-PaPoGen , ref. ANR-12-ADAP-0001 ) and by the " Département Santé des Plantes et Environnement " from  INRA. The B. anurus transcriptomic dataset is available in the Sequence Read Archive (SRA) database under the accession number  PRJNA508369. This Transcriptome  Shotgun Assembly  project has been deposited at DDBJ/EMBL/GenBank under the accession  GHFB00000000. The version described in this paper is the first version,  GHFB01000000.
Outcomes (Schedule in Table 2) Baseline assessment After inclusion, data on medical history including main medical history with compulsory recording of diabetes, respiratory condition, hypertension, neurological disorders and surgical history will be collected. Specific AC history with main events, time of occurrence and previous treatments received (medications, injections etc. ) will be also recorded. After checking that the patient is not under steroids or strong opioids (morphine, oxycodone, fentanyl, hydromorphone or buprenorphine), medications will be quantified (nature and daily dose) and recorded.
Patient satisfaction assessed by the VAS and physiotherapist evaluation assessed by the Likert scale will be compared (mean) by Student t test if conditions of normality and equal variance are satisfied at each time. The adverse events and the security of the RCT will be presented and described in each arm. To provide an unbiased assessment of the intervention efficacy, all effectiveness data will be analyzed according to the intent-to-treat principle. The analyst will be blinded to patient data during the analysis.
Acknowledgments Jean-Yves Sanchez  acknowledges the  CONEX Programme , funding received from  Universidad Carlos III de Madrid , the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration (Grant agreement nº  600371 ),  Spanish Ministry of Economy and Competitiveness  ( COFUND2013-40258 ) and  Banco Santander. Amadou Thiam  acknowledges  ANR  for his fellowship. Yannick Molméret acknowledges KICINNO Energy for the granting of his post-doc fellowship, in the frame of the project PENLiB coordinated by  Prof. Jean-Yves Sanchez.
In the past few years, the technology of automated guided vehicles (AGVs) has notably advanced. In particular, in the context of factory and warehouse automation, different approaches have been presented for detecting and localizing pallets inside warehouses and shop-floor environments. In a related research paper [1], we show that an AGVs can detect, localize, and track pallets using machine learning techniques based only on the data of an on-board 2D laser rangefinder. Such sensor is very common in industrial scenarios due to its simplicity and robustness, but it can only provide a limited amount of data. Therefore, it has been neglected in the past in favor of more complex solutions. In this paper, we release to the community the data we collected in  [1]  for further research activities in the field of pallet localization and tracking.
Data In this article, we present a dataset of 2D range data obtained from a laser scanner moving inside an industrial workshop environment, where EUR standard pallets (see Fig. 3 (a)), people, robots and other equipment are present. Each frame of the sensor trajectory corresponds to: (i) a 2D range scan (see Table  1 ) obtained from a SICK S3000 Pro CMS laser rangefinder (see Fig. 3(b )); (ii) a 2D image obtained by processing the 2D range scan (see Fig. 4 ); (iii) a tag attached by a human, indicating whether the scan includes a pallet or not; and (iv), the region of interest of the pallet in the image (if any), also defined by a human. The laser rangefinder has a resolution of 0. 25 deg and a maximum field of view of 190 deg, leading to scans made of 761 ranges. It operates at 16 Hz frequency, and the scans are averaged every 4 frames during the static data acquisition phase in order to reduce noise.
Environment We performed our experiments for data acquisition in the indoor environment represented in Figs. 1 2 , with the sensor moving in the 40 m 2 area highlighted in Fig. 1. Such environmennt has been fitted to reproduce a typycal industrial workshop, featuring industrial pallets, furniture, robots and equipment (e. g. , a conveyor belt). People were also included in the scene and allowed to move during data acquisition, which lead to temporary occlusions of the objects in the environment. Between acquisition sessions, the position of several objects was modified to better simulate a dynamic environment. The 2D laser rangefinder was positioned close to the floor, in a way that was both realistic with real world mounting position and able to perceive a pallet laying directly on the ground.
25 deg respectively. Keep in mind that the sensor employed runs at 16 Hz, which would rapidly lead to a unmanageable amount of data, especially considering the manual labelling steps ahead. For this reason, we decided to effectively reduce the operating frequency to 4 Hz in the static data acquisition phase, thus every R i is actually the result of the average of 4 raw consecutive frames from the sensor. This also helps reducing noise on the data. An example of such process as well and the structure of the raw range data are reported in Table  1. In our experiments, we are focusing on the detection of pallets in the environment, hence, the set R of all raw range data readings R i , consisting of 565 2D range scans, has been manually divided into two classes: 1. Pallet class represents the case of having a pallet located somewhere in the environment with a free operating face, i.
A selection of ROIs not including a pallet is also included in the dataset repository. We will not further delve here into the details of our specific solution to the problem of pallet localization and tracking, which we present instead in the related research paper  [1]. We just point out that the data was indeed employed for pallet localization and tracking and that the proposed architecture was tested using 4 additional continous trajectories, which are also made available on the dataset repository. In particular, localization was performed using the aforementioned Region Proposal Network, cascaded with a Faster Recurrent Convolutional Neural Network classifier that took as input the set of manually labelled ROIs  [6]. On the other hand, tracking was performed using a Kalman Filter  [7].
The filter was also used to implement a Sequantial Classification procedure, i. e. , accepting a ROI as an actual pallet was deferred till it was detected and tracked for a predefined amount of time, eventually reaching a sufficient confidence threshold. Finally, note that the dataset can be used for multi-pallet detection, but that was not part of our data collection experiment. Indeed, in the related research paper  [1]  we ran a preliminary study on the subject by generating artificial data. We want to stress that given that the EUR-Pallet is an official standard with strict tolerances, differences between any two pallets are not perceivable by the the sensor, due to its characteristics and margin of error. This leads to two major consequences: • It is not possible with this sensor and with the dataset we provide to univocally identify a pallet, yet it is possible to distinguish them from each other if appropriate tracking techniques are put in place, like we did in the related research paper  [1].
• Artificial 2D images including two or more pallets in every image are easy to generate. This can be achieved by taking an original image and adding the pallet ROI from another image, possibly changing position, orientation, and/or adding noise, and consequently deleting any reading in the original image that would now be occluded by the new pallet. Such artificial images are not provided here, but can easily be generated with the provided materials and tools. Nevertheless, future work on our related research paper will include real world multi-pallet testing, thus an extended dataset will be made available too. 1 : An example of the raw range data provided by the laser rangefinder. As soon as the data is visualized using the standard ROS package rviz, four sequential frames are stored in a text file.
2 MV dc for 1 h; 1. 06   The beam line vessel is not shown. 14). In a second phase, a mock-up of the electrostatic shield, with holes for gas flow, can be introduced in the test set-up. In a reasonable time (of the order of some months), this approach is expected to provide reliable data on voltage holding with large electrodes, under realistic conditions, up to more than 1 MV, in both vacuum and low-pressure gas, with and without an intermediate shield. The results will allow us to confirm the present design of the MITICA BS insulation or to design an intermediate electrostatic shield if necessary for the operation at 1 MV.
Summary Realization of the NBTF is progressing well thanks to the commitment of all the participants in the project. Although some components of the PS and of the cooling system are still unavailable, SPIDER has entered into operation and is providing the first data on the source plasma. This corresponds to the attainment of an important ITER milestone: as the NBTF is part of the ITER project it also represents the start of experimentation for the first part of ITER. During a long shutdown scheduled to start at the end of 2019 a leaking grid segment will be substituted, the RF drivers will be modified, according to recent numerical and experimental results and other interventions will be performed as indicated by the experimentation. All MITICA auxiliaries are being installed or commissioned; most of the PSs were installed at the beginning of 2019.
The current study aimed to further investigate the rate of HTT in the Jockey superfamily, focusing on the occurrence of its families in drosophilids. For a definition of family, we followed Wicker et al. [15] : a family is a group of highly conserved TEs with a sequence similarity of ≥80% in at least 80% of their coding region in an aligned region of at least 80 nucleotides. The term clade (instead of superfamily) is used by the Repbase database  [16] , the most commonly used database for TE classification. Repbase also uses a somewhat different criterion from the TE hierarchical classification of Wicker et al. [15] , based on studies of enzymology, structural similarities and relationships between sequences  [17]. As an example, the 123 Jockey sequences within the Jockey clade are distributed in up to 15 families in each Drosophila species (named Jockey-1 to Jockey-15) in Repbase, and four Jockey families were recorded in D.
Results A broad homology-based search using 111 downloaded consensus sequences of Jockey families deposited in the Repbase database allowed us to identify Jockey sequences in 44 genomes of Drosophilidae species (Additional file 1: Table  S1 ) belonging to the genera Drosophila (subgenera Sophophora, Drosophila, and Dorsilopha), Zaprionus (subgenus Zaprionus) and Scaptodrosophila (Additional file 1: Table  S2 ). Sequences not yet catalogued in Repbase (Additional file 1: Table  S3 ) were annotated in 28 species. In the subgenus Sophophora, new Jockey sequences were annotated in 12 genomes: five in species in the melanogaster group (D. biarmipes, D. mauritiana, D. malerkotliana, D. serrata, and D. suzukii) and seven in the obscura group (D. athabasca, D. guanche, D. lowei, D. miranda, D. obscura, D.