dataesr/scientific-paragraphs-categorization huggingface dataset:
CHU Dataset Table  4  shows the results of evaluated approaches and their comparison with our results on CHU Nice Hospital dataset. In this dataset, as people tend to perform some of the activities in various regions (e. g. , preparing the drink at the phone desk), it is difficult to obtain high precision rates. However, compared to the online version of the supervised method in  [75] , our approach detects all activities and achieves a much better precision rate. The online version of  [75]  again fails to detect activities accurately and misses some of the "Prepare Drink", and "Reading Article" activities and produces lots of false positives for all other activities. It cannot handle the transition states in the boundary of the activity regions (e. g. , walking from telephone desk to DrugBox is detected as "Answer the Phone" activity).
DAHLIA Dataset Different from the two other datasets, the results on the DAHLIA dataset are compared with all the previous evaluations we could find in the literature. Meshry et al. [89]  exploits gesturelets extracted from skeleton data to compute geometrical features and detect the activities. The proposed method in  [90]  takes a graphical approach and poses the activity detection task as a maximum-weight connected sub-graph problem. Inspired by the Hough transformation that is successfully applied in object detection, Chan-Hon-Tong et al. [91]  proposes a method with discriminative features to globally optimize the parameters of Hough transform and utilize it for activity segmentation in videos. Finally, our results are compared with  [92]  that is a supervised method with a semi-supervised component to discover sub-activities.
Conclusions An online unsupervised framework is proposed for detection of daily living activities, particularly for elderly monitoring. To create the activity models, we benefited from the superiority of unsupervised approaches on representing global motion patterns. Then, discriminative local motion features were employed in order to generate a more accurate model of activity dynamics. Thanks to the proposed scene model, online recognition of activities can be performed with reduced user interaction for clipping and labeling a huge amount of short-term actions which are essential for most of the previously proposed methods. Our extensive evaluations on three datasets revealed that our proposed framework is capable of detecting and recognizing activities in challenging scenarios. The evaluations were intentionally conducted on the datasets recorded in nursing homes, hospitals, and smart homes to examine the implication of the method on ambient surveillance in such environments.
The ALBATROSS line consists of two 1000m long inductive cables connected to an embarked computer through an inductive modem. The line is instrumented with ten CTD and five currentmeters, allowing a more extended coverage of the water column (500 m to the bottom). Every 30 minutes, data from the instruments along the line are collected by the computer. A daily file is then sent to the interface module of the MII using acoustic data transmission, and from there to shore through the cable network. Presently data include pressure, temperature, conductivity, dissolved oxygen, suspended matter, current (velocity and direction), but the ALBATROSS mooring line is open to the community and it is available to host old/new or emerging sensors. This variability is mostly related to barotropic instabilities of the NC, but a part of it may also come from remote effects of deep-water convection and formation of deep waters in the Gulf of Lion, a major oceanic process for the entire Western Mediterranean Sea.
Acknowledements: The authors thank their colleagues from the  EMSO-ERIC, ANTARES  and  KM3NeT Collaborations  for supplying material:  C. Gojak ,  C. Tamburini ,  K. Mahiouz ,  K. Bernardet ,  Z. Hafidi ,  P. Coyle ,  V. Bertin ,  P. Lamare ,  V. Ciausu ,  A. Deschamps ,  Y. Hello ,  N. Lo Bue ,  G. Marinaro ,  A. Capone ,  J. J. Dañobeitia ,  P. Favali ,  M. Cannat ,  L. Coppola ,  G. Riccobene ,  L. Beranzoli ,  E. Richard ,  L. Vincent ,  A. Dolle ,  N. Bhairy ,  D. Malengros.
Other health assessments Participants were asked if they were currently under medical treatment. Individuals who reported medication intake had to specify the type of medication. In addition, when the participants completed the survey they were asked if they currently had cardiovascular disease or hypertension, chronic visceral disease or a somatic disorder (e. g. , eczema, asthma, headaches, somatoform disorders, colitis), or an eating disorder. Medication intake, cardiovascular diseases, somatic disorders, and eating disorders were measured on a binary scale coded as 1 (yes) and 0 (no). These data were treated in a qualitative way.
Naturally occurring botulinum toxin (BoNT) serotypes have different pharmacological features of therapeutic and aesthetic interest. This phase 1, double-blind, placebo-controlled study (EudraCT: 2016-002609-20) assessed safety, tolerability and pharmacodynamics (PD) of the first recombinant BoNT serotype E (rBoNT-E) versus abobotulinumtoxinA (Dysport®), administered to extensor digitorum brevis (EDB) of healthy males. Subjects were randomised 3:1 (n = 28) to single ascending rBoNT-E (0. 04-3. 6 ng) doses or placebo. A further 24 subjects received abobotulinumtoxinA (20, 40, or 70 U) or placebo. PD were assessed using compound muscle action potential (CMAP) amplitude. Demographics were similar between groups. All rBoNT-E doses were well tolerated (no severe treatment-emergent adverse events [TEAEs], serious adverse events, or treatment-related toxicities).
Study design This was a randomised, double-blind, placebo-controlled study (EudraCT: 2016-002609-20), performed at a single study centre in the UK. The centre was accredited by the Medicines and Healthcare products Regulatory Agency. The study was conducted in two parts, which were run in parallel (Fig. 1 ). Study cohorts and dose escalation were determined based on the percentage of the CMAP total amplitude relative to baseline (CMAP%), which was calculated as (CMAP total amplitude at the visit/CMAP total amplitude at baseline)x100, where baseline was defined as the average of CMAP values recorded at screening and day -1 (prior to dosing). In part A, healthy male volunteers were randomised to receive rBoNT-E or placebo (injected into the EDB) in a ratio of 3:1 (three receiving rBoNT-E, one receiving placebo).
Statistical analyses An appropriate sample size could not be determined statistically for part A, as no previous human data were available with rBoNT-E. A sample size of eight subjects (six treated with rBoNT-E and two with placebo) was chosen, allowing the demonstration of a statistical difference of 45% between rBoNT-E and placebo with a one-sided type 1 error rate of 0. 05 and a power of 80% (assuming a common standard deviation of 20% between rBoNT-E and placebo). Two different analysis populations were included in this study: the safety population and the PD population. The safety population consisted of all consenting subjects who received at least one dose of study medication. The PD population was all consenting subjects with available PD data and no protocol deviations with relevant impact on PD data.
Discussion This study was a FIH, single-centre, randomised, double-blind, placebo-controlled study to assess the safety, tolerability, and PD profile of rBoNT-E injected into the EDB of healthy male subjects (compared with established abobotulinumtoxinA treatment). Overall, subjects were well matched in terms of demographic characteristics, as all subjects were male, with similar mean ages across treatment groups. Furthermore, all randomised subjects received study treatment and completed the study. Mean EDB CMAP data from placebo-treated subjects was consistent with the literature  [18]. Single doses of rBoNT-E were well tolerated and rBoNT-E showed a good safety profile (with no dose-related effect) within the dose range investigated (up to 3. 6 ng). All TEAEs were of mild or moderate intensity (mostly considered unrelated to study treatment) and were consistent with those expected and/or events in individual subjects.
(7) , and two mate-paired libraries of 5-kb nebulized fragments were constructed to perform pyrosequencing as described by Aury et al. (8). 454 sequencing was used to remove low-quality sequences and generated 1,031,738 useful reads with an average size of 250 bp. In addition, genomic DNA was also randomly fragmented by sonication to produce an Illumina library according to Choulet et al. (9). An in-house quality control process based on the FastX package (http://www. genoscope. cns. fr/ externe/fastxtend/) was applied to the reads that passed the Illumina quality filters defined by Alberti et al. (10). The 44,119,481 obtained single reads with an average length of 100 bp were assembled with pyrosequencing data using Newbler version 2. 7  (11)  with default parameters. To improve genome assembly, total RNAs were extracted from Drosophila S2Rϩ cells after 12 hours and 6 days postinfection with T.
ratisbonensis using an RNeasy kit (Qiagen) according to the manufacturer's protocol. Transcriptome sequencing (RNA-seq) Illumina libraries were then obtained as described by Lee et al. (12) , and the 54,845,250 single-read sequences with an average length of 54 bp were assembled using the IDBA-UD assembler version 1. 1. 1 (13). Finally, T. ratisbonensis contigs obtained from whole-genome sequencing and RNA-seq approaches were assembled using the CAP3 program from the Geneious platform  (14). The resulting draft genome's total size is 7,555,974 nucleotides (nt) spread over 952 contigs with an overall GC content of 23. 2%. The N 50 contig length is 12,152 bp, and the largest contig is composed of 59,712 bp. From these assembly data, a total of 3,013 coding DNA sequences (CDSs) were predicted using a dedicated microsporidian annotation pipeline (E.
Peyretaillade, M. Reichstadt, S. Rimour, I. Wawrzyniak, and F. Delbac, unpublished data) combining homology search, transcriptional regulatory motif detection, and GLIMMER gene prediction  (15, 16). Functional annotation conducted using Blast2GO software  (17)  with default parameters revealed that 2,099 CDSs (70%) present at least one ortholog in microsporidia, and 44. 5% can be assigned to putative functions consistent with other microsporidium genomic data  (18). Noncoding rRNA and tRNA genes were detected using RNammer  (19)  and tRNAScan-SE version 1. 23  (20) , respectively. Thus, six 5S rRNAs, a contig including the 16S-23S rRNA unit, and 55 tRNA genes were annotated. Additional noncoding RNAs were identified using a comparative analysis following a previously described approach  (21)  and Anncaliia algerae genomic data.
Transposable elements (TEs) were obtained from tblastx analysis (E value cutoff of 1e-5) using TE sequences previously identified in numerous microsporidian species  (22). An exhaustive search of TEs has also been carried out with TransposonPSI software (http://transposonpsi. sourceforge. net/) to identify highly variable subclasses of TEs. TE family affiliation was also checked using RepeatMasker (http://www. repeatmasker. org/). A total of 160 sequences were identified as TEs and were classified among the following 3 major families: long terminal repeat (LTR) retrotransposon (50. 6%), DNA Mariner (20. 6%), and DNA Merlin (20. 6%). Among the 95 complete transposase sequences, 67. 4% present a CCC/GGG-like signal upstream of the transcriptional initiation signals  (16) , suggesting that these sequences were under the control of T.
ACKNOWLEDGMENTS This work was supported by the  BEELOSS   ANR  project ( ANR-12-BSV3-0020 ). We thank the  AuBi platform group  (https://www. france-bioinformatique. fr/fr/ plateformes/aubi) and the  Mésocentre of Clermont Auvergne University  (https:// mesocentre. uca. fr/), C. Franzen  ( University of Regensburg, Germany ) for the gift of the T. ratisbonensis strain used in this study, and  L. Troxler  for preprocessing the RNA-seq data.
Results and Discussion Metabolic network reconstruction of the host (Drosophila melanogaster) and its two commensals, Ap WJL and Lp NC8 , was automatically generated using the CycADS pipeline  [59]. The resulting BioCyc metabolism databases are available at http://artsymbiocyc. cycadsys. org for annotation and analysis purposes. We generated the enriched functional annotations of all the predicted proteins from the complete genomes of D. melanogaster (Drosophila, RefSeq GCF_000001215. 4 release 6), A. pomorum strain DM001 (Ap WJL , accession NCBI Bioproject PRJNA60787) and L. plantarum subsp. plantarum NC8 (Lp NC8 , NCBI Bioproject PRJNA67175). From the genomic analyses, we inferred all pathways allowing production of the organic compounds that are present in the exome-based FLYAA holidic diet (HD) developed by Piper et al  [40] : fly's essential and non-essential amino acids (EAA Fly (n=10) and NEAA Fly (n=10)), B-vitamins (n=7), cholesterol (n=1), and nucleic acids and lipid precursors (NAL, n=4).
Drosophila melanogaster biosynthetic capabilities inferred from genome-based metabolic network reconstruction Although a BioCyc metabolic reconstruction of D. melanogaster is already publicly available https://biocyc. org/FLY, we constructed an improved BioCyc database using a recent genome version and annotation  [59]. This metabolic reconstruction identified 22,189 protein-encoding genes including 5,061 enzymes and 156 transporters associated with 1,610 compounds assembled in a network of 331 pathways (versus the 227 pathways found in BioCyc). Like other metazoans, Drosophila possesses the gene repertoire to produce all the NEAA Fly , but is unable to produce the EAA Fly   (Fig 1A and   S2 ). Hence, this enzyme can switch between its aerobic condition activity (EC 1. 3. 5. 1) towards its anaerobic condition activity (EC 1.
Expert automated genome annotation and reconstruction of the biosynthetic potential of D. melanogaster, A. pomorum WJL and L. plantarum NC8 We used the Cyc Annotation Database System (CycADS)  [59] , an automated annotation management system, to integrate protein annotations from the complete genomes of Drosophila melanogaster (RefSeq GCF_000001215. 4 release 6), A. pomorum strain DM001 (accession NCBI-Bioproject PRJNA60787) and L. plantarum subsp. plantarum NC8 (NCBI Bioproject PRJNA67175). CycADS collects protein annotation results from different annotation methods including KAAS  [95] , PRIAM  [96] , Blast2GO  [97, 98] , and InterProScan  [99]  in order to obtain Enzyme Commission numbers and Gene Ontology annotations. All annotation information was then processed in the CycADS SQL database and automatically extracted to generate appropriate input files to build the three BioCyc databases using the Pathway Tools software v22.
5  [100]. The BioCyc databases and their associated metabolic networks are available in the EcoCyc database  [101]. From the genomic analyses, we inferred the biosynthetic capabilities of the three organisms and manually inspected all pathways allowing production of the organic compounds that are present in the exome-based FLYAA holidic diet (HD)  [40]. For each gap found in biosynthetic pathways or non-conventional enzymatic catalysis, TBLASTN  [102]  searches were performed in the three genomes to look for unpredicted protein activities. Alternative pathways were searched in the literature or using the BioCyc "Metabolic Route Search" tool  [103].
Acknowledgements We would like to thank  Dali Ma  for critical reading and editing of the manuscript and valuable suggestions, members of the  Ribeiro and Piper labs  for advice on how implementing effectively holidic diets preparations,  Edern Cahoreau ,  Maud Heuillet  and  Floriant Bellvert  from the  Metatoul platform of Genopole Toulouse  for NMR and LC/MS analysis, the  ArthroTools platform of the SFR Biosciences  ( UMS3444/US8 ) for fly equipment and facility and the  Bloomington Stock Centre  for fly lines. Research in F. L's lab is supported by the  "Fondation pour la Recherche Médicale " (« Equipe  FRM DEQ20180339196 ) and the  Scientific Breakthrough Project  from  Université de Lyon  "Microbehave". Research in P. d. S and F. C labs are supported by  INRA  and  INSA Lyon. J. C is funded by a postdoctoral fellowship from the  "Fondation pour la Recherche Médicale " (FRM,  SPF20170938612 ).
NC8 biosynthetic capabilities inferred from genome-based metabolic network reconstruction. CC-BY-NC 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under it is able to synthesize all amino acids, DNA bases and the seven B-vitamins (biotin, folate, pantothenate, riboflavin, thiamine and intermediates of nicotinate and pyridoxine) present in HD. However, we found no genomic support for the synthesis of choline and myo-inositol in the Ap WJL genome. bioRxiv preprint doi: ; https://doi. org/10. 1101/728774 this version posted January 17, 2020. The copyright holder for this preprint (which was not analysis predicts that Lp Metabolic reconstruction from Lp NC8 genome generated a database that includes 2,868 protein-encoding genes, consisting of 973 enzymes and 74 transporters associated with 1,154 compounds, all assembled in a network of 246 metabolic pathways.
Snow accumulation is the main positive component of the mass balance in Antarctica. In contrast to the major efforts deployed to estimate its overall value on a continental scale -to assess the contribution of the ice-sheet to sea-level rise -knowledge about the accumulation process itself is relatively poor, although many complex phenomena occur between snowfall and the definitive settling of the snow particles on the snowpack. Here we exploit a dataset of near-daily surface elevation maps recorded over three years at Dome C using an automatic laserscanner sampling 40-100 m 2 in area. We find that the averaged accumulation is relatively regular over the three years at a rate of +8. 7 cmyr -1. Despite this overall regularity, the surface changes very frequently (every 3 days on average) due to snow erosion and heterogeneous snow deposition that we call accumulation by "patch".
The aforementioned pioneering modelling studies are limited by the scarcity of observations providing detailed information on the accumulation process. Here, we exploit a new dataset of daily surface elevation maps obtained with an automatic ground-based laserscanner at Dome C  (Picard et al. , 2016)  scanning a surface area of 40-100 m 2 and which operated during three years. The aim is to answer three main questions: 1) what are the spatial and temporal characteristics of the accumulation patterns, 2) how long does snow remain on the surface before being eroded or definitely incorporated in the snowpack, and 3) what is the impact of the accumulation process on the snowpack's upper internal structure ? The study focuses on the meter-scale and daily to annual time scale and follows a statistical approach to describe the accumulation and erosion patterns and their dynamics.
The RLS was operating at Dome C (Fig. 1 ) over 2 periods with different configurations (Table  1 ). From 1 January 2015 to 17 January 2016, it was set up at a height of 2. 8 m (period 1). A major failure occurred during this period between 17 October 2105 and 5 December (49 days). After the maintenance during the summer campaign, it was reinstalled at a height of 4. 5 m (period 2) on 1 February 2016. Because of the difference of height during period 1 and 2, the scanned area is different: 40 m 2 and 110 m 2 respectively, and the effective spatial resolution was accordingly adjusted to 2 cm and 3 cm respectively. Although the area scanned during period 1 was located inside the area scanned during period 2, no attempt to co-register the two was done. The two time-series are interpreted as independent datasets.
mlab. griddata Python function (version 2. 2). The grid spacing is set to 2 and 3 cm respectively for the periods 1 and 2, in relationship with the different setup heights. To avoid filling large gaps with the bilinear interpolation, a grid point was attributed a valid z value only if at least one measurement was taken within 6 cm around it (independently of the period and grid space). If the final map contains less than 100,000 valid grid points, it is completely discarded. All these operations yield a time-series of elevation maps on a common grid. The grid orientation was determined by observing the shadow of the RLS mast in the scanned area. We found that the x-axis lies towards 116 • (East-South-East), which allowed us to draw the South direction on the maps. Various additional datasets are then derived from the generated maps.
The accumulation between every successive scan is calculated as the difference between the elevation for each point of the grid. In most cases, both scans are acquired one day apart (90% over the period 2a) so the computed accumulation is representative of the daily variation. However, due to scan failure or rejection during the processing, longer time intervals are present in the time-series (2 days in 7% of the cases, and 3-9 days in the remaining 3%). We nevertheless interpret the accumulation time series as if it was daily accumulation only. The age of the snow on the surface is another dataset derived from the elevation maps. The algorithm is applied independently for each point as follows. For each point, the age is initialised to 0. For each date i the accumulation a i since the last detected deposition or erosion event (or the beginning of the time-series) is calculated.
Meteorological data Meteorological data are used to relate surface changes and weather. We use precipitation forecast provided by the ERA Interim reanalysis (ERA-I)  (Dee et al. , 2011)  near Dome C. Due to the absence of reliable methods to record in-situ precipitation in Antarctic conditions, ERA-I is one of the most reliable sources of information to date  (Bromwich et al. , 2011; Wang et al. , 2016). For wind speed and direction, we use data from the Weather station at Concordia station ( 75. 1 • S, 123. 3 • E, 3230 m a. s. l. , http://www. climantartide. it/). Over the three years, the mean wind speed at 3 m is 7 ms -1 which is higher than the ERA-I prediction of 5. 2 ms -1 at 10 m. Nevertheless, both sources agree on the temporal variations, with a correlation of 0. 8, which is the most important point for our comparison.
Results The RLS dataset is studied first by addressing the general characteristics of the elevation changes (Sec. 3. 1) and annual accumulation over the whole area (Sec. 3. 2), to then focus on the smaller temporal and spatial scales  (Sec. 3. 3, 3. 4 and 3. 5). Lastly, we investigate the internal structure of the snowpack deduced from the elevation changes (Sec. 3. 6).
Surface elevation changes The elevation of the surface averaged over the scanned area generally increases with time at a mean rate of 8. 7 cmyr -1 (Fig. 2 ). However, different dynamics are observed for the different years. As noted by  Picard et al. (2016)  for the first year, a unique and marked accumulation event occurred in the period 4-17 July 2015 which accounted for most of the annual accumulation during that year. In contrast, the second and third years of the dataset feature a fairly regular increase of the surface elevation suggesting the absence of dramatic accumulation events. Interestingly, this regularity in the cumulated precipitation is also found in the ERA-I forecast (Fig. 2 , green curve) with a remarkable absence of seasonal signal despite the huge variations of temperature between summer (typ.