dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability. The current version of the models are freely available. The exact version of the model used to produce the results used in this paper is archived (i) on Zenodo for NterGeo at https://doi. org/10. 5281/zenodo. 3733278 under the GNU General Public License v3. 0 or later, as are input data and scripts to run the model and produce the plots for all the simulations presented in this paper. The BACKPLUMES model is an open-source code and is available on the CHIMERE model web site https://www. lmd. polytechnique. fr/~menut/backplumes. php.
Yet, a rather stable amount of the final protein is needed for the good operation of the cell. The processes that regulate noise levels and maintain cell homeostasis have been scrutinized for a long time. Recently, micro RNAs (further referred to as µRNAs) have occupied the front of the scene. These are very short RNAs which do not code for proteins. Many different sorts of µRNAs are involved in various epigenetic processes. But one of their roles seems precisely the reduction of noise level in DNA transcription. In this scenario, the µRNAs are synthesized together with the mRNAs. Then, some of the synthesized µRNAs bind to the mRNAs and de-activate them. These µRNA-bound mRNA become unavailable for protein synthesis. It has been proposed that this paradoxical mechanism which seems to reduce the efficiency of DNA transcription may indeed have a role in noise regulation (see  [9, 19, 10]  and the review  [24] ).
We refer to  [29]  and references therein for the definition of the coefficients ω N i and quadrature points x N i. The truncation order N is chosen such that the numerical error between the approximation at order N and N + 1 is inferior to the given precision 10 -8 when p ≤ 1. For p ≥ 1, the function f k may take large values and it is harder to get the same numerical precision. In the numerical results below the mean error for the chosen sets of parameters with large values of p is around 10 -4 and the maximal error is 10 -2. This is good enough to comment on qualitative behavior. The code used for these numerical simulations is publicly available on GitLab  [17]. We plot the relative cell to cell variation CV(ρ δ,γ,p fast )/CV(ρ δ 0 ) with respect to γ and p for two different values of δ.
Remark 5. 2 (Implementation). Observe that the matrix which has to be inverted in order to solve the scheme is not a square matrix because of the mass constraint (which is necessary to ensure uniqueness of the solution). In practice, in order to solve the corresponding linear system M F = B where F = (f ij ) ij and B = (0,. , 0, 1) ∈ R NrNµ+1 and M ∈ R (NrNµ+1)×NrNµ we use the pseudo-inverse yielding F = (M t M ) -1 M t B. Finally the use of a sparse matrix routine greatly improves the computation time. Our implementation was made using Matlab. The code is publicly available on GitLab  [17].
(ANR-11-LABX-0007-01). SM acknowledges support by the CNRS-Royal Society exchange projects "CODYN" and "Segregation models in social sciences" and the Chaire Modélisation Mathématique et Biodiversité of Véolia Environment -École Polytechnique -Museum National d'Histoire Naturelle -Fondation X. All authors would like to thank Prof. Matthias Merkenshlager from Imperial College Institute of Clinical Sciences for bringing this problem to their attention and stimulating discussions.
Acknowledgements PD acknowledges support by the  Engineering and Physical Sciences Research Council (EPSRC)  under grants no. EP/M006883/1  and  EP/N014529/1 , by the  Royal Society  and the  Wolfson Foundation  through a  Royal Society Wolfson Research Merit Award  no. WM130048  and by the  National Science Foundation (NSF)  under grant no. RNMS11-07444  ( KI-Net ). PD is on leave from  CNRS ,  Institut de Mathématiques de Toulouse, France. MH acknowledges support by the  Labex CEMPI.
e. , TICI < 2B vs. TICI ≥ 2B), but with conflicting results  (12, 13). Given the lack of data from a randomized controlled trial, an online survey regarding post-EVT BP management was performed across institutions in the United States (StrokeNet Sites)  (15). In recanalized patients after EVT, most institutions (36%) seemed to target an SBP in the range of 120-139 mmHg and allow permissive hypertension in nonrecanalized patients. Taken together, these data underline the need of a randomized controlled trial assessing different BP targets after EVT reperfusion. The BP TARGET trial aims to assess the efficacy of a "tight" BP lowering control (with a target 100-129 mmHg for SBP) versus a more conservative BP control (SBP 130-185 mmHg) following 24 h after reperfusion by EVT in AIS patients. 1  and carried out using an electronic case report form (eCRF), developed using Cleanfile R software.
Follow-up examination will be done at 3 months to assess the mRS. This assessment will be performed by certified research nurses, unaware of the group assignments, during face-to-face interviews, or via telephone survey. For the primary outcome, a central imaging core laboratory, not involved in subjects' management and blinded to treatment allocation, will review the CT imaging. In cases of disagreement between the two assessors (JS, FD), a centralized neuroradiologist (JCS) will review the CT imaging and will decide on the primary endpoint value. Serious adverse events (SAEs) and procedurerelated complications (e. g. , symptomatic ICH and hypotensive events below 80 mmHg) will be adjudicated by three members of the Data and Safety Monitoring Board (DSMB), blinded to treatment arm.
Data Collection The entire study is conducted using electronic case report forms (eCRFs), where all clinical data on enrolled subjects are data entered (single-keyed) by the site personnel. The eCRF was developed using Cleanfile software. The essential data necessary for monitoring the primary and secondary endpoints are identified and managed at regular intervals throughout the trial. Data are monitored by the data management team of the data-management department of University Hospital of Lille by using the predefined rules, and queries will be automatically edited. Finally, an overall automated monitoring is done by the data manager at the end of the data entry. In case of discrepancies, queries will be edited to resolve the problems encountered.
Sample Size Estimates Based on the literature  (19)  and the data from the Endovascular Treatment in Ischemic Stroke (ETIS) registry, we expect an ICH rate of 40% in reperfused subjects with a conventional BP control during 24 h following reperfusion (SBP between 130 and 185 mmHg). We assume that a tight BP control during 24 h following reperfusion (SBP between 100 and 129 mmHg) will be associated with an absolute decrease of 15% (corresponding to a hemorrhage rate of 25%). To detect this effect size, with a twosided test at the 0. 05 level of significance and a power of 80%, 152 subjects per arm will be required. To account for an anticipated rate of 5% of uninterpretable follow-up CT scan, we planned to include a total of 320 subjects (160 per arm).
Statistical Analyses Statistical analyses will be independently performed by the Biostatistics Department of Lille University. Data will be analyzed using SAS software (SAS Institute Inc. , Cary, NC, USA), and all statistical tests will be performed with a 2-tailed alpha risk of 0. 05. Baseline characteristics will be described for each treatment group; categorical variables will be expressed as frequencies and percentages and quantitative variables will be expressed as means ± standard deviation or medians (interquartile range) for non-Gaussian distribution. Normality of distributions will be assessed graphically and by using the Shapiro-Wilk test. No formal statistical comparisons of baseline characteristics will be done; clinical importance of any imbalance will be noted. All analyses will be performed using all randomized participants based on their original group of randomization, according to the intention-to-treat principle.
The imputation procedure will be performed using the main baseline characteristics and treatment group. Missing values will be imputed under a missing at random assumption by using the regression switching approach (chained equation with m = 10 imputations) with the predictive mean matching method for quantitative variables and logistic regression models (binary, ordinal, or polynomial) for categorical variables  (21). Treatment effect estimates obtained in multiple imputed datasets are combined using Rubin's rules  (22). As exploratory analyses, heterogeneity in treatment effect size on primary outcome across key subgroups will be evaluated by including the corresponding multiplicative interaction terms in the multivariate mixed logistic regression models. From these models, treatment effect sizes (adjusted OR) will be estimated in each subgroup.
Secondary Outcomes Secondary binary outcomes (symptomatic ICH, parenchyma hematoma type 2, favorable outcome, 90-day all-cause mortality) will be also analyzed similarly to the primary efficacy analysis. The secondary ordinal outcome [distribution of 90-day after combining scores of 5 and 6 (23)] will be described by the median (interquartile range) for each treatment group and compared using a mixed ordinal logistic regression model including the same fixed and random effects that is used for primary efficacy model. The adjusted common OR per 1-point improvement will be calculated as the treatment effect size. The change in NIHSS score at 24 h and infarct volume will be calculated and compared between the two treatment groups using the constrained longitudinal data analysis (cLDA) model proposed by Liang and Zeger  (19, 20, 24)  including the same fixed and random effects as in the primary efficacy model.
to be completed by December 2019. The data collection phase of study is expected to be completed by January 2020. The study, which is registered with ClinicalTrials. gov (ClinicalTrials. gov Identifier: NCT03160677), is conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. Research ethics approval was obtained from the Comité de Protection des Personnes Sud Méditerranée IV on March 14, 2017. Data collection during the study is reported in Table.
Primary antibodies were diluted 1:1000 in saturation solution composed by Tris buffer saline (10-mM Tris, 150-mM NaCl, pH 8. 0) supplemented with 0. 1% Tween20 and 5% non-fat dry milk. Revelation was performed using secondary antibodies conjugated to horse-radish peroxidase (GE-Healthcare, Chicago, IL, USA) diluted 1:2000 in saturation solution and Opti-4CN™ colorimetric kit (Bio-Rad, Hercules, CA, USA). ImageJ software was used to measure the relative intensity of protein bands.
3 s) of a 1 µm × 1 µm area with a power of 110 (±5) mW. 512 × 512 images were acquired at 1. 3 s intervals with pinhole set to 1 Airy unit. Membrane rupture and repair processes were monitored by measuring variations in fluorescence intensity of FM1-43 as previously described  [26, 33]. The FM1-43 was excited by the 488-nm laser line (intensity set at 30% of maximal power) and fluorescence emission was measured between 520 nm and 650 nm. For quantitative analysis, the fluorescence intensity was integrated over the whole cell surface and corrected for the fluorescence value recorded before irradiation, using ImageJ software.
We had therefore to slightly modify the way to analyze membrane repair. Control and AnxA6-deficient myotubes were cultured on glass bottom dishes equipped with a square-patterned coverslip that enabled cell tracking during different stages of the experiment (Figure  3 ). A field identified by means of the alphanumeric code was imaged and myotubes present in the field were successively damaged by laser ablation exactly as in the standard membrane repair assay. In the case of control LHCN myotubes, which are able to rapidly reseal sarcolemma damage, we observed that a large majority (about 90%, Table  S2A ) of damaged myotubes were still adhered on the coverslip 1 h after laser ablation (Figure  3A ). A similar result was observed when LHCN myotubes were transduced with a scrambled shRNA (data not shown).
Endogenous AnxA6 exhibited a similar distribution (Figure  S7 ). kinetics were observed from one myotube to another. As an example, one typical experiment with 20 cells is presented in (D). When the presence of a plateau and a maximal fluorescence intensity lower than 20 a. u. were observed, the cell was classified as "repaired cell" (lower part of the graph, as indicated). When the kinetics of fluorescence changes showed a continuous increase with values higher than 90 a. u. , the cell was classified as "unrepaired cell" (upper part). Otherwise, cells were classified as "undetermined cells". Data analysis is presented in Table  S1. Red dashed lines divide the three types of response. Cells 2020, 9, x 9 of 22 Data analysis is presented in Table  S2. Scale bars = 100 µm.
In contrast, red arrows point out myotubes that disappeared. About 100 cells over 5 independent experiments were analyzed for each condition. Data analysis is presented in Table  S2. Scale bars = 100 µm. When LHCN myotubes were damaged on their surface, accumulation of membrane material at the disruption site was visible on several sections (Figure  6E ). A tangle of membranes was observed in sections located above the plane of the damaged sarcolemma, which may correspond to the cap subdomain that is localized outside the cell (Figure  6E , Section 1). Deeply in the cytoplasm we observed that an accumulation of membrane material filled the entire area damaged by laser ablation. We can hypothesize that the laser penetrated deeply in the cytoplasm and created a significant membrane damage, which required greater mobilization of intracellular material to plug the tear (Figure  6E , Sections 2 and 3).
Acknowledgments: The authors thank the  AFM-Telethon  for their financial support. The platform for immortalization of human cells of the  Center of research in Myology (Paris, France)  is acknowledged for access to human immortalized LHCN myoblasts. The authors thank  Volker Gerke  and  Sophia Koerdt  from the  University of Muenster  for the gift of pAnxA6-GFP-N1 plasmid. The fluorescence microscopy was done in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France BioImaging. The help of  Christel Poujol  and  Sébastien Marais  is acknowledged. The authors thank the staff of Vect'UB, the vectorology platforms (INSERM US  005-CNRS UMS 3427-TBM-Core ,  Université de Bordeaux, France ) for technical assistance in shRNA strategy and production of lentiviral particles.
7 with either MDA-MB-231 or SUM-159PT were plated in medium with 3% Matrigel in 96 wells ultra low attachment plates for three days to allow the formation of the hetero-spheroids formed. Then medium was added to each condition containing the apoptosis indicator (fluorescent caspase 3/7 substrate) supplemented with medium containing or not the different EVs (3 × 10 8 pp/mL) as indicated in the figure. The size of the spheroids and appearance of green fluorescence (apoptosis) were recorded every 2 h on an INCUCYTE apparatus for 4 days. Data are represented as the AUC of the spheroid size (upper panels) and apoptosis (lower panels) over 96 h. Data from one representative experiment of three independent experiments is shown, all data are shown as mean ± SEM (n = 3 technical replicates; ***p < 0.
The size of the spheroids and green fluorescence (apoptosis) was recorded every 2 h on an INCUCYTE apparatus for 4 days. Data are represented as the AUC of the spheroid size (upper panels) and apoptosis (lower panels) over 96 h. Data from one representative experiment of three independent experiments is shown, all data are shown as mean ± SEM (n = 3 technical replicates; ***p < 0. 001, compared to the untreated cells). the EV-based therapy studies already published, our results emphasize the potential possibility of using EVs with enhanced anti-tumor capacities in cooperation with the immune system, to counteract tumor cell proliferation and subsequent distant invasion of aggressive cancers.
The pellet, containing the EVs fraction, was then resuspended in a minimal amount of cold PBS. The final EVs solution was aliquoted in siliconized polypropylene microcentrifuge tubes (Sigma, #T3281-500EA) and aliquots were kept at -80 °C until use. EVs were characterized by NTA (Nanoparticle Tracking Analysis) using the Nanosight (Malvern Instruments, Software version NTA 3. 1 Build 3. 1. 46). PBS used for sample dilution was filtered and checked for vesicles absence prior to use. Calibration was performed using silica microspheres of 0. 10μm sold at a known concentration of 4,897 × 10 13 pp/mL (particles/mL) (Biovalley, #24041). Each sample was analyzed twice (using two different dilutions and aliquots) and 5 videos of 60 seconds were made per analysis. Final sample concentration was calculated as a mean of the different concentrations measured per analysis and as a proportion of the difference of concentration of the silica microspheres given by the Nanosight and the known concentration informed by the manufacturer.
Red and green fluorescent images of the cells were acquired every 2 hours for 4 days on the INCUCYTE device (Essen Bioscience). To evaluate proliferation and apoptosis, Areas Under the Curves (AUC) were calculated with the GraphPad Prism software for each tested condition for 4 days. For spheroids growth and apoptosis detection, in a 3D cell system culture (spheroids: 5 × 10 3 MDA-MB-231 or 5 × 10 3 SUM-159-PT and hetero-spheroids: 5 × 10 3 MDA-MB-231 + 2. 5 × 10 3 RAW264. 7 or 5 × 10 3 SUM-159-PT + 2. 5 × 10 3 RAW264. 7), cells were seeded in a 96-well ultra-low attachment plates (Corning, #7007) in 100 μL in their corresponding medium containing 3% FBS and 3% Matrigel, and let for 3 days in the incubator to allow the spheroids to form. Then medium containing the fluorescent marker of activated caspase 3/7 (Green INCUCYTE caspace-3/7, Essen Bioscience) was added for 6 h to the wells in order to obtain a homogeneous labeling of the cells.
Six hours later, medium containing EVs or not was added to the wells as described in the figures. Green fluorescent images were acquired on the INCUCYTE device (Essen Bioscience) every 2 hours for 4 days. AUC of spheroids, hetero-spheroids growth and apoptosis were calculated with the GraphPad Prism software for each tested condition for a total time of 4 days. In vivo experiments. Six-week old Hsd: Athymic Nude-Foxn1nu female mice were purchased from Charles Rivers company. Mice were hosted during 1 week at IRSL animal facility before they were manipulated. Animals were handled according to the guidelines of institutional animal care committees using protocols approved by the Comité d'Ethique en Experimentation Animale Paris-Nord. Mice were maintained in a 12-h light-dark cycle animal facility under specific pathogen-free conditions with free access to water and food.
Acknowledgements We thank the members of  Dr. Lehmann-Che  and  Dr Jauliac  laboratory for insightful discussions. We thank members of the technological platform of IRSL for technical advice. This work was supported by the  SATT INNOV IDF, INSERM  and  University of Paris. We thank the  Conselho Nacional de Pesquisa e Desenvolvimento (CNPq)  that granted  Lívia Cardoso Bueno de Camargo a PhD  scholarship through the program  Ciência Sem Fronteiras.
Benchmark data sets The G3PO benchmark contains 1793 proteins from a diverse set of organisms (Additional file 1: Table  S1 ), which can be used for the evaluation of gene prediction programs. The proteins were extracted from the Uniprot  [34]  database, and are divided into 20 orthologous families (called BBS1-21, excluding BBS14) that are representative of complex proteins, with multiple functional domains, repeats and low complexity regions (Additional file 1: Table  S2 ). The benchmark test sets cover many typical gene prediction tasks, with different gene lengths, protein lengths and levels of complexity in terms of number of exons (Additional file 1: Fig. S1 ). For each of the 1793 proteins, we identified the corresponding genomic sequence and the exon map in the Ensembl  [35]  database.
Phylogenetic distribution of benchmark sequences The protein sequences used in the construction of the G3PO benchmark were identified in 147 phylogenetically diverse eukaryotic organisms, ranging from human to protists (Fig. 1a  and Additional file 1: Table  S3 ). The majority (72%) of the proteins are from the Opisthokonta clade, which includes 1236 (96. 4%) Metazoa, 25 (1. 9%) Fungi and 22 (1. 7%) Choanoflagellida sequences (Fig. 1b ). The next largest groups represented in the database are the Stramenopila (172), Euglenozoa (149) and Alveolata (99) sequences. More divergent species are included in the 'Others' group, containing 57 sequences from 6 different clades, namely Apusozoa, Cryptophyta, Diplomonadida, Haptophyceae, Heterolobosea and Parabasalia.
Quality of protein sequences The protein sequences included in the benchmark were extracted from the public databases, and it has been shown previously that these resources contain many sequence errors  [22] [23] [24] [25]. Therefore, we evaluated the quality of the protein sequences in G3PO using a homologybased approach (see Methods), similar to that used in the GeneValidator program  [23]. We thus identified protein sequences containing potential errors, such as inconsistent insertions/deletions or mismatched sequence segments (Additional file 1: Fig. S3  and Methods). Of the 1793 proteins, 889 (49. 58%) protein sequences had no identified errors and were classified as 'Confirmed', while 904 (50. 42%) protein sequences had from 1 to 8 potential errors (Fig. 3a ) and were classified as 'Unconfirmed'.
Quality of genome sequences The genomic sequences corresponding to the reference proteins in G3PO were extracted from the Ensembl database. In all cases, the soft mask option was used (see Methods) to localize repeated or low complexity regions. However, some sequences still contained undetermined nucleotides, represented by 'n' characters, probably due to genome sequencing errors or gaps in the assembly. Undetermined (UDT) nucleotides were found in 283 (15. 8%) genomic sequences from 58 (39. 5%) organisms, of which 281 sequences (56 organisms) were from the metazoan clade (Additional file 1: Fig. S5 ). Of these 283 sequences, 133 were classified as Confirmed and 150 were classified as Unconfirmed. We observed important differences between the characteristics of the sequences with UDT regions and the other G3PO sequences, for both Confirmed and Unconfirmed proteins (Additional file 1: Table  S5 ).
Evaluation of gene prediction programs We selected five widely used gene prediction programs: Augustus, Genscan, GeneID, GlimmerHMM and Snap. These programs all use Hidden Markov Models (HMMs) trained on different sets of known protein sequences and take into account different context sensors, as summarized in Table  1. Each prediction program was run with the default settings, except for the species model to be used. As the benchmark contains sequences from a wide range of species, we selected the most pertinent training model for each sequence, based on their taxonomic proximity (see Methods). The genomic sequences for the 1793 test cases in the G3PO benchmark were used as input to the selected gene prediction programs and a series of tests were performed (outlined in Fig. 4 ), in order to identify the strong and weak points of the different algorithms, as well as to highlight specific factors affecting prediction accuracy.
Computational runtime We also compared the CPU time required for each program to process the benchmark sequences (Additional file 1: Table  S7 ). Using the gene sequences with 150 bp flanking regions (representing a total length of 51,699, 512 nucleotides), Augustus required the largest CPU time (1826 s), taking > 3. 4 times as long as the second slowest program, namely GlimmerHMM (540 s). GeneID.