dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. All the data used in this paper are available upon request to the corresponding author. Acknowledgements. We wish to thank ESA/IDEAS program who supported this work. FEDER/Region Hauts-de-France and CaPPA Labex are acknowledged for their support for LILAS multiwavelength Raman LiDAR and MAMS system. 2020-ACTRIS-2/LiCAL Calibration center, ACTRIS-France, ANRT France, CIMEL Electronique and Service National Observation PHOTONS/AERONET from CNRS-INSU are acknowledged for their support. The development of Lidar retrieval algorithms was supported by Russian Science Foundation (project 16-17-10241). The authors would like to acknowledge the use of GRASP inversion algorithm (http://www. grasp-open. com) in this work. The SIRTA observatory and supporting institutes are acknowledged for providing IPRAL data.
Introduction Acquisition of data on animal movement when developing management strategies is a common challenge in species conservation. The field of movement ecology describes animal movement according to its attributes: the type of movement, i. e. nomadism, migration and residency, and the characteristics of the movement, i. e. migration distance, stopovers and the size and shape of the home range  (Allen and Singh, 2016). The recent growth of this field of research has highlighted supplementary challenges as obtaining data on available resources linking with position data, quantifying metabolic cost of movement or manages large spatio-temporal datasets  (Cagnacci et al. , 2010 , Hays et al. , 2016). The wide distribution of marine migratory species such as marine mammals, seabirds and sea turtles makes their protection especially difficult as it's require maintaining connectivity, combining logistical and political issues  (Runge et al.
A second diver held the foreflippers and helped to lift the turtle on to the deck of the boat for measurements and tagging. Each turtle was placed in a pen and standard morphometric data were then recorded, i. e. curved carapace length (CCL) and curved carapace width (CCW). A Passive Integrated Transponder (PIT) was inserted into the right triceps of each individual. The five turtles (3 juveniles and 2 males) were finally equipped with Argos-Fastloc GPS tags (10-F-296B, Wildlife Computers Redmond, WA, USA), then released.
Data collected from the tags To provide optimum location accuracy and increase the number of positions available in order to counterbalance errors caused by the close vicinity of the shore, the Argos GPS tags (n = 6) were set up to simultaneously record Argos and GPS locations  (Costa et al. , 2010; Rutz and Hays, 2009). The GPS sampling interval was set to 4 h but likely due to this limited sampling interval, no GPS locations was recorded. The tags provided diving data binned as 4-h period histograms, i. e. maximum dive depth, maximum dive duration and temperature. The SPLASH (Argos-Fastloc) tags deployed in 2013 were set up to collect maximum dive depths every 10 m from 10 to 100 m, and then every 50 m from 100 to 250 m (Table  1 ). Maximum dive durations were stored from 30 s to 1 min, then every minute from 1 to 5 min, every 5 min from 5 to 10 min, and finally every 10 min from 10 to 60 min.
Data pre-filtering Data were downloaded daily via Argos Message Retriever (WC-DAP, Wildlife Computers Data Analysis Programs). CLS (Collecte Localisation Satellites, Toulouse, France) enhanced Argos position estimates by applying Kalman-filtering algorithm to account for Argos errors. The altimetry product provided by the Hydrographic and Oceanographic Service of the French Navy (SHOM) at a 25 m 2 resolution was used to identify and discard erroneous locations on land. Class Z (i. e. insufficiently accurate) Argos locations and any locations associated with a travel speed of over 5 km. h -1 were also discarded  (Schofield et al. , 2013) , resulting in a total of 19. 8% of data being removed. The most of data remaining was class B (87. 8%) but a few were class A (7. 2%), 2 (1. 5%), 1 (1. 4%), 3 (1.
Spatial analysis All analyses were carried out using R software version 3. 3. 3 (R Core Team, 2017). The straight-line distance traveled was calculated between the first and the last locations for each female using the track-Distance function from the trip package  (Sumner, 2016). The overall distance between two successive locations was calculated for each turtle. The travelling speed was then derived from the overall distance and time elapsed between paired locations. The date of migration departure was determined for each adult female tracked in 2015 and 2016 by performing a spatial query in ArcGIS  (Baudouin et al. , 2015; Chambault et al. , 2015; Chambault et al. , 2016a & b). Potential foraging grounds were identified by analyzing the post-nesting migratory routes of the 11 females, measuring the proportion of time the turtles spent in each grid cell (resolution of 33.
4 × 33. 4 km) using the tripGrid function from the trip package  (Sumner, 2016). Areas where a female remained at least three consecutive days were considered as a stopover. First location recorded in the potential foraging ground identified was consider as the end of the migration. Based on dates of departure and arrival of migration, the data sets for the females were then split into three groups according to the phase, namely the inter-nesting, migration and final foraging.
Kernel analysis In order to display the residency patterns of juveniles, males and females during the inter-nesting season and at foraging grounds, home ranges were estimated using the kernel density distributions (KDE) based on the Argos positions  (Worton, 1989) , as no GPS data was recorded by the tags. To minimize autocorrelation in spatial analysis, the median daily locations were calculated for each turtle  (Schofield et al. , 2013)  and implemented in the kernelUD function from the adehabitatHR package  (Calenge, 2006). Kernel densities were estimated for the juveniles (n = 3), the males (n = 2) and the females at their inter-nesting (n = 9) and potential foraging habitats (n = 8). The minimum smoothing parameter was used to prevent fragmentation of home range estimates, as suggested by  Kie (2013).
The 50% and the 95% kernel contours were used to identify the core use area and the broad home range, respectively. The overlap between the home ranges of juveniles and males was measured by extracting the kernel contours using the getverticesHR function from the adehabitatHR package  (Calenge, 2006). The intersection between the extracted contours was then estimated using the gIntersection function from the rgeos package  (Bivand and Rundel, 2017).
Acknowledgements This study was carried out within the framework of the  Plan National d'Action Tortues Marines de Martinique (PNATMM). This study was co-financed by the  FEDER Martinique (European Union ,  Conventions 2012/DEAL/0010/4-4/31882 & 2014/DEAL/0008/4-4/ 32947 ),  DEAL Martinique  ( Conventions 2012/DEAL/0010/4-4/31882 & 2014/DEAL/0008/4-4/32947 ), the  ODE Martinique  ( Convention 014-03-2015 ), the  CNRS (Subvention Mission pour l'Interdisciplinarité) , the  ERDF  fund (Convention  CNRS-EDF- juillet2013 ) and the  Fondation de France  ( Subvention Fondation Ars Cuttoli Paul Appell ). The authors also appreciate the support of the  ANTIDOT  project ( Pépinière Interdisciplinaire Guyane, Mission pour l'Interdisciplinarité, CNRS ). The authors would like to thank the  DEAL Martinique , the  ODE Martinique , the  ONCFS Martinique , the  ONEMA Martinique , the  SMPE Martinique , the  ONF Martinique , the  PNR Martinique , the  Surfrider Foundation, Aquasearch, CREOCEAN, Carbet des Sciences, Aliotis plongée, Plongée-Passion , the  Collège Cassien Sainte-Claire  and the  Collège Petit Manoir  for their technical support and field assistance.
e. parallel to the [001] direction in which the current flows, to in-plane parallel to [100] direction. In-plane anisotropy was also explored by rotating the magnetic field in the sample plane while keeping it perpendicular to the tunneling current. Theory. Density functional theory (DFT) calculations have been carried out within the local density approximation (LDA) as implemented in the SIESTA-GREEN package  43, 44  , employing norm-conserving Troullier-Martins pseudopotentials  45. The electron-electron interaction is introduced through the LDA+ U approach  46, 47  , which describes electron correlations through a single effective parameter U eff = U -J. We set U eff to 3. 0 eV for Co and Mn d shells, as usually employed in the literature for similar compounds  [48] [49] [50]. SOC was taken into account in the calculations following the self-consistent fully-relativistic pseudo-potential formalism implemented within the SIESTA framework  51.
Gaussia luciferase activity was quantified using the Luciferase Reporter Assay System from Promega and normalized against co-transfected firefly luciferase expressing plasmid (ptkLUC+). For Fig. 2c, [α],  [α]-MUT, and [β] fragments (same as used for the ChIP assay shown in Fig. 2a ) were PCR amplified and cloned into ptkLUC+ plasmid (accession number AF027128) between HindIII and BamHI restriction sites.
Funding This work was supported by the  Deutsche Forschungsgemeinschaft  by a grant ( SCHA 408/12-1 ;  HE 7135/2-1 ) to AH and MS as well as Crédits Incitatifs Phase 2015/Emergence to AH. AH was additionally funded by the project  AquaCRISPR  ( ANR-16-COFA-0004-01 ). This publication was funded by the  German Research Foundation (DFG)  and the  University of Würzburg  in the funding program Open Access Publishing.
Availability of data and materials Datasets generated and analyzed during the current study are included in this published article and its Additional files 1, 2, 3, 4, and 5. The Sox5 medaka mutant strain (N541S) has been deposited with the National Bioresource Center (http://www. nbrp. jp/localeAction. do?lang=en). All materials generated during the current study are available from the corresponding author on reasonable request.
In-vivo human brain hyperspectral image database A set of five in-vivo brain surface HS images, captured using the previously described acquisition system, has been used for this research. These images belong to adult patients undergoing craniotomy for resection of intra-axial brain tumor. Images have been obtained at the University Hospital Doctor Negrin of Las Palmas de Gran Canaria (Spain) and at the University Hospital of Southampton (United Kingdom) from five different patients with confirmed grade IV glioblastoma tumor on histopathology. The study protocol and consent procedures were approved by the ComiteE ´tico de Investigacion Clı ´nica-Comitede E ´tica en la Investigacion (CEIC/CEI) for the University Hospital Doctor Negrin and the National Research Ethics Service (NRES) Committee South Central-Oxford C for the University Hospital of Southampton.
The resected tissue is sent to the pathologists in order to confirm the presence or absence of tumor, and obtain the specific histopathological diagnosis (grade and type of tumor). Since this technology cannot penetrate into the tissue (in case of near infrared it can be 1 mm at most), the average size of the resected sample of the tumor for pathological analysis is 0. 5x0. 5 mm and 0. 2 mm depth. Normal tissue markers are only used as a reference for the labelling process performed after the operation has finished. It is not ethic to perform a biopsy of the tissue that is known to belong to normal brain (it can produce damages in the undergoing patient outcomes). Employing the histopathological information (from the tumor tissue samples) and the knowledge of the operating surgeon (from the normal tissue samples), the labeling of the HS cubes is performed to generate a gold standard dataset for the supervised classification stage of the brain cancer detection algorithm.
The manual labeling of the HS data consists of visual identification of each sample by a specialist, which is time-consuming task and can introduce errors due to human intervention. For this reason, a methodology for extracting the gold standard information from the HS cubes, based on the Spectral Angle Mapper (SAM) algorithm, has been developed. The tool developed for sample labeling has been designed using Matlab 1 GUIDE application and measures the angle between two high dimensional vectors. This SAM classification is an automated method for comparing the spectra of the pixels of a HS image with a well-known spectrum obtained from a reference pixel. The tool was used by the corresponding operating surgeon after the operation conclusion in order to generate the gold standard map for each image.
Furthermore, the same technique combined with a directed acyclic graph method has been used to diagnose unilateral hearing loss in structural MRI  [42] , demonstrating that the SVM algorithm is a reliable candidate to work with medical images. In medical HSI, SVMs have been already used to classify several types of cancer, including prostate  [14] , lung tissue and lymph nodes  [21]  skin tumors  [43, 44] , tongue  [45]  and colon  [46]. On the other hand, during the development of this research project, some studies have been carried out using SVMs to classify hyperspectral in-vivo images of human brain affected by cancer  [26, 30]. For the research presented in this paper, LIBSVM  [47]  has been used for support vector classification. SVM algorithm requires a confident labeled dataset in order to train the model that will be used to classify the input data.
In this work, the labeled dataset of in-vivo brain samples that is used to train the SVMs has been created by combining the efforts of neurosurgeons and pathologist, as it has been previously described. Before explaining the methodology employed for performing a supervised classification over the available HS data, some considerations have to be taken into account. Due to the impossibility of having a way to extract the labeled information from all the pixels in a HS cube of brain tissue, there are two ways of measuring the performance of the generated supervised models. For the available labeled dataset, it is possible to use standard metrics in order to measure the accuracy provided by the model when classifying unseen data. Nevertheless, for evaluating a supervised model applied to a whole HS cube (where not all pixels have been labeled) only the visual evaluation of an expert is possible.
The methodology for evaluating the supervised classifiers in a quantitative way is as follows: first, we use the labeled information corresponding to the dataset, and then we apply a 10-fold cross validation in order to measure the performance of the model. The quantitative evaluation metrics used for this purpose has been sensitivity, specificity and overall accuracy metrics, and will be defined later in this paper. Once the quantitative metrics have been obtained, the previously trained SVM classifier is used to classify a whole HS cube, and then it is evaluated by neurosurgeons in order to analyze the quality of the algorithm in distinguishing different types of tissues, materials or substances. In order to include the spatial features of the HS images, a spatial homogenization is applied to improve the supervised classification results by incorporating the neighborhood information of each pixel into the classification chain.
produce some misclassifications, as it will be explained later. However, it is possible to see that the differences between the normal class and the tumor class are remarkable. These differences will ensure a successful classification of the normal and tumor pixels by the supervised classifier. In order to demonstrate that the use of a supervised classifier will achieve a reliable differentiation between the labeled pixels that conforms the golden standard database, these pixels have been spectrally analyzed employing an SVM classifier. Afterwards, the SVM model generated using the golden standard database for each patient was employed to classify the entire HS cube of this patient. As it was previously mentioned, the golden standard information was extracted from the HS data using a specific tool developed to this end.
Hyperspectral imaging can distinguish between tumor and normal tissue pixels by their spectra In order to measure the supervised classifier performance and to select the optimal configuration of the SVM model, a three-way cross validation has been employed. Linear, Radial Basis Function (RBF), polynomial and Sigmoid kernels have been tested and compared. Fig  8A  shows the overall accuracy classification results obtained in the experiments comparing the four SVM kernels with the default parameters, using the labeled dataset for each patient individually and performing the three-way cross validation. S1-S4 Tables present the confusion matrix results of the different classifications for each patient and type of kernel. Linear kernel provides the best accuracy results for this type of sample having a lower computational cost than the other kernels exceeding 99% of overall accuracy.
HSI offers a new possibility to address these issues, being a non-contact, non-ionizing and non-invasive technique. In this research work, a methodology to develop a surgical tool for identifying and delineating the boundaries of the tumor tissue using HS images has been described. For processing these data, an active interaction between medical doctors and engineers was required. On the one hand, medical doctors generated the HS image database and the selection of the images where tumor tissue was present. They were also involved in the identification of certain types of tissues, materials and substances that appear in the captured HS cubes. On the other hand, engineers performed the digital processing of the images, developing a brain cancer detection algorithm that exploits the spatial and spectral features of the HS images.
The use of an increased database to generate the supervised classification model, where the inter-patient variability is taken into account, is expected to produce better classification results. The inclusions of more labeled samples of normal tissue will reduce the occurrence of false positives in the results. Furthermore, an extensive clinical validation is required to validate if the boundaries of the tumor area represented in the TMD map are accurately identified. Several biopsies of the boundaries of tumor area must be obtained and analyzed by the pathologists to certify the brain cancer algorithm results. On the other hand, there are some misclassifications between different tissues with high vascularization. In some cases, extravasated blood and normal tissue affected by edema were classified as blood vessel/hypervascularized tissue (blue color).
Conclusions This study develops a brain cancer detection algorithm to classify HS images of brain tumor in surgical-time during neurosurgical operations. It has been demonstrated that the use of HSI as a new non-invasive surgical-time visualization tool can improve the outcomes of the undergoing patient, assisting neurosurgeons in the resection of the brain tumor. The identification of the tumor boundaries and the tumor infiltration into normal brain is highly relevant in order to avoid excessive resection of normal brain and to avoid unintentionally leaving residual tumor. Currently, further investigations are being carried out by the research team in order to generalize the results obtained, to optimize the algorithms and validate their findings, as well as to increase the image database and optimize the acquisition system.
Acknowledgments We thank  J. Morera ,  C. Espino ,  M. Marquez ,  D. Carrera ,  M. L. Plaza  and  R. Camacho , neurosurgeons and pathologists of the  University Hospital Dr. Negrin. We also thank  Paul Grundy  and  Victoria Wykes , neurosurgeons of the  University Hospital of Southampton  who helped in this research.
This research was totally funded by the  European Commission  (https://ec. europa. eu/)through the  FP7 FET Open (Seventh Framework Programme ) programme ICT-2011. 9. 2,European Project  HELICoiD "HypErspectral Imaging Cancer Detection " under Grant Agreement 618080 (GMC). There was no additional external funding received for this study. The funder had no role in the study.
Importantly, substitution of R540 with A abolished both Gag association with, and modification by, PRMT1. In addition, PFV Gag C-terminus fused to GFP (GFP-GRs) is enriched in nucleoli when PRMT1 expression is reduced by RNA interference, mimicking the phenotype of the R540 mutation. These data are consistent with a model according to which PRMT1-mediated modification of PFV Gag antagonize its nucleolar accumulation. Although we do not provide direct evidence of PRMT1-mediated methylation of R540, it is tempting to speculate that reversible modification of this amino acid might contribute to finely tune the distinct functions of Gag at different stages of the replication cycle. Nevertheless, finding that Gag mutants where R540 is mutated to A or F, which mimics constitutive methylation  [53, 54] , have a similar phenotype argues that methylation at this site is neither required for localization in nucleoli nor for tethering to chromatin.
Statistic testing Graphical representation and statistical analyses were performed using the GraphPad Prism software (Graph-Pad Software, San Diego, CA, USA). Differences were tested for statistical significance using ne-way ANOVA statistical test with a Bonferroni Multiple comparison post-test. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Acknowledgements We thank  Axel Rethwilm  and  Dirk Lindemann  for FV reagents;  Mark Bedford  for GFP-PRMTs plasmids;  Christelle Doliger ,  Sophie Duchez  and  Niclas Setterblad at the Imaging, Cell selection and Genomics Department of the Institut Universitaire d'Hématologie  for confocal microscopy;  Claudine Pique  for critical reading of the manuscript.
Electrocardiogram Electrocardiogram (ECG) was performed in RA patients and controls. Resting 12-lead ECGs (25 mm/s paper speed and 10 mm/mV amplitude) were recorded using a three-channel direct writing machine (ASPEL-Mr Gold). ECGs were reviewed by an independent cardiologist. For each ECG, data on the following parameters were recorded: heart rate, QRS interval, QT interval, heart rate-corrected QT (QTc) calculated using Bazett's formula.
AtG3BP1 is a homolog of the human HsG3BP1 Human G3BP1 protein contains two well-characterized domains, namely the nuclear transport factor 2 domain and an RNA recognition motif. We searched the TAIR10 database for all proteins with these two conserved domains. The Arabidopsis genome codes for eight proteins which satisfy these criteria. The eight Arabidopsis G3BPs are named AtG3BP1 to AtG3BP8 as indicated in Fig S1A. The independent identification of different members of the AtG3BP family by several groups and the use of different naming systems have presented conflicts in the available literature, and a unified classification of AtG3BPs is desirable for further studies. A multiple alignment and phylogenetic tree for the relationship of G3BPs in Arabidopsis and human was generated using Molecular Evolutionary Genetics Analysis Version 7 (MEGA7) based on the amino acid sequences (Fig  1A )  (Kumar et al, 2016).