dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimizing the DQM: cleaner plots, refresh rates and reference plots For production monitoring, we aim at plots requiring minimum visual processing by the shifter. Where available, plots also display reference data. The software automatically compares actual data with the reference, and canvas color is changed if a mismatch is found. Special efforts go into the development of DQM modes, such as cosmic acquisition, machine adjustment, detector tuning, or beam collisions with different conditions. For this, the DQM will get a database interface to allow straightforward and automated re-configuration. The PXD and SVD groups invest considerable effort into understanding machine background radiation at beam conditions. These efforts can only partially rely on simulations and require, instead, handy data summaries that are available over long times and different conditions.
DQM data seem to provide a useful tool for these observational studies. DQM data serve as data summaries also in the study of detector radiation damage: Slow changes in DQM data can be found in the historical record of DQM data and calibrations; also, the update frequency of reference data are an indication of changes in the detectors. All DQM plots are recorded on a dedicated web site with a refresh rate of ∼2 minutes. The web site provides access to the full history of all DQM plots by run and category, and also allows access to underlying raw datasets.
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreements No  644294  and  822070. This work is supported by  MEXT ,  WPI , and  JSPS (Japan) ;  ARC (Australia) ;  BMWFW (Austria) ;  MSMT, GAUK 404316 (Czechia) ;  AIDA-2020 (Germany) ;  DAE  and  DST (India) ;  INFN (Italy) ;  NRF-2016K1A3A7A09005605  and  RSRI (Korea) ;  MNiSW (Poland) ;  CNRS/IN2P3  (France),  Federal Ministry of Education and Research (BMBF, Germany)  and  MINECO  grant  FPA2015-71292-C2-1-P  (Spain).
A measurement of four branching-fraction ratios for three-body decays of B mesons involving two open-charm hadrons in the final state is presented. Run 1 and Run 2 pp collision data are used, recorded by the LHCb experiment at centre-of-mass energies 7, 8, and 13 TeV and corresponding to an integrated luminosity of 9 fb -1. The measured branching-fraction ratios are where the first of the uncertainties is statistical, the second systematic, and the third is due to the uncertainties on the D-meson branching fractions. These are the most accurate measurements of these ratios to date.
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. + B + K + D ( * )+ D ( * )- b c u u c u u s W + B + D 0 D 0 K + b c d d c u u s W + B 0 D 0 D ( * )- K + b c u u c s u u W + B + K + D 0 D 0 The datasets employed correspond to integrated luminosities of 3 fb -1 and 6 fb -1 , collected during LHC Run 1 (2011 and 2012) and Run 2 (2015-2018). The online event selection is performed by a trigger, which consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage at which the full event is reconstructed.
These ratios are measured to be where the first uncertainty is statistical, the second systematic, and the third one is due to the uncertainties on the D-meson branching fractions  [18]. The BaBar collaboration studied these decays previously  [5] , with a different set of D * 0 and D 0 channels, obtaining signal yields of 91 ± 13 B + → D * + D -K + candidates, 75 ± 13 B + → D * -D + K + candidates, and 1300 ± 54 B 0 → D * -D 0 K + candidates. The sizes of the signal yields obtained using the LHCb data are around twenty times larger for the first two decays, and over five times larger for the third. Significant increases are seen for the yields obtained in the normalisation modes, with respect to earlier studies using data from the Belle and BaBar experiments. Good agreement is seen with respect to the corresponding branching fraction ratios according to the Particle Data Group (PDG)  [18] , calculated to be 0.
Summary A data sample corresponding to an integrated luminosity of 9 fb -1 recorded with the LHCb detector is used to measure four ratios of branching fractions in B → D ( * ) DK decays. The ratios are consistent with previous measurements and are measured with the highest precision to date. Furthermore, this work represents the first published analysis at the LHC of b-hadron decays to two open-charm hadrons and a third, light, hadron. Large samples of B → D ( * ) DK decays are available, and can be isolated in the LHCb dataset with low background contamination. These are promising characteristics for these channels with future studies of their intermediate resonant structure in view.
Two strips of LED lights were installed on the ceiling along the treadmill to provide visible and infrared lighting during trials and intertrials, respectively (see below). The animals' position was tracked via a ceiling-mounted camera (Imaging Source, 25 fps). A custom-made algorithm detected the animal's body and recorded its centroid to approximate its position on the treadmill. After trial onset, the first interruption of the beam was registered as entrance time in the reward area (ET ). The entire setup was fully automated by a custom-made program (LabVIEW, National Instruments). The experimenter was never present in the behavioral laboratory during the experiments.
Locomotor Activity Test A group of animals with a striatal lesion (7 DLS, 2 DMS, and 3 DS), and another group of nonlesioned animals (n = 12) were used in this test to assess their general locomotor activity. Prior to this task, animals had full access to water and food for at least 3 days. Then, they were placed on an unfamiliar treadmill, with a different structure (slanted walls and covered reward port) compared to the treadmill in which they were trained, while their position was being recorded using a side-mounted high-speed camera (200 fps). During the first 10 min, the ambient light was turned off and the treadmill remained immobile. Their exploratory locomotor activity, i. e. , how much they moved along the treadmill, during this period, is presented in Figure  4A. Then, in a free-running task, they ran in trials of 30 s while the treadmill speed progressively increased across trials (5 trials at 0 cm/s, 2 trials at 10 cm/s, 3 trials at 15 cm/s and 5 trials at 20, 25, 30, 35, and 40 cm/s, data shown in Figure  4B ).
1, 1, 2, 5, 10}. Finally, to compute the cumulative cost for the 5 example trajectories in Figure  5B , we assumed that effort was proportional to the kinetic energy, that the spatial cost was diffuse and we set m = 1 and α = 10. It is worth to note that, combining Equation 5 and 7, the coefficient multiplying the effort term in the cost function is proportional either to αm or to αm 2 (respectively when the effort is proportional to the kinetic energy or to the force). Therefore, considering that the dynamics in Equation  2 is independent of the mass m, in the optimal control model, varying the mass is formally equivalent to varying the effort sensitivity. This equivalence explains the similarity of the results obtained in Figures  S4A  and S4B. Numerical Implementation. We used the CasAdi  [62]  software and direct collocation method to numerically find the optimal trajectories.
We assumed that effort was proportional to the kinetic energy, that the spatial cost was diffuse and we set m = 1, as in Figure  5A. Simulated trajectories were constrained to have the same initial position, final position and entrance time of the corresponding real trajectory. The minimization process was achieved via the Trust-Region Constrained Algorithm implemented in the SciPy optimize module. The effort parameter was constrained to values between 0 and 100. Note that the optimal trajectories for α = 0. 1 and for α = 100 correspond respectively to the blue line and the red line in Figure  5A. For each animal, once the best-fit α parameter for each session is obtained, we computed the difference ∆α between its median value after and before lesion (denoted respectively < α A > and < α B >).
QUANTIFICATION AND STATISTICAL ANALYSIS Data from each behavioral session was stored in separate text files, containing position information, entrance times, treadmill speeds, and all the task parameters. Position information was then smoothed (Gaussian kernel, σ = 0. 3 s). The entire data processing pipeline was implemented in Python, using open-source libraries and custom-made scripts. We used a series of Jupyter Notebooks to process, quantify, and visualize every aspect of behavior, to develop and run the optimal control simulations, and to generate all the figures in this manuscript.
0001, i. e. , less than one chance in 10000 resamples. On the other hand, in Figure  1K  (Before vs Stable), the CI is (-0. 04, 0. 03) which includes zero, and hence the difference between these two groups of data is reported to be non-significant. Statistical differences were reported using the following notation: *** for p < 0. 001, ** for p < 0. 01, * for p < 0. 05, and n. s. for not significant. Finally, the relation between lesion size and magnitude of behavioral impairment was quantified using the Pearson's correlation coefficient. For each coronal slide and hemi-striatum, the contour of the lesion was manually outlined using GFAP staining. The relative size of the lesion (compared to the full dS, manually outlined on the NeuN staining) and the coordinates of the lesion/striatum centroid was calculated.
For each animal, the size and laterality were obtained by averaging data along the anteroposterior axis, for both left and right hemispheres. (C, D) Lesion size versus laterality for animals that underwent lesion before (Early, C) and after (Late, D) training. Lesion quantification was performed blindly relative to the behavioral analysis. In four animals with a dS lesion performed after learning the task (late lesion), the lesion size quantification could not be properly performed. These animals were classified according to their injection coordinates in the surgery (3 DLS and 1 DMS), however they were excluded from any analysis that required the lesion size (hence the difference between the number of "late lesion" animals in this figure, n = 53, and the total number of animals in Figure  1 , n = 57).
Acknowledgements We thank  Drs. I. Bureau ,  R. Cossart ,  J. Epsztein ,  E. Fino  and  T. Verstynen  for critical reading of earlier versions of this manuscript, and  Dr A. de Chevigny  for advice with histological verification of the lesions. This work was supported by the  European Research Council  (ERC-2013-CoG -615699 -NeuroKinematics; D. R. ), a  Centuri postdoctoral fellowship  (S. S. ), and a nEURo* AMU ,  ANR-17-EURE-0029  grant (M. S. ).
Exosomal miRNA Cargo Microarray analyses of exosomal miRNA cargo showed that 62 miRNAs were differentially expressed between the DS and NS conditions, based on early morning time points DS1 and NS5 matched for time of day (Figure  1 ). Of these, 10 miRNAs achieved statistical significance (p ≤ 0. 001). See Figure  2  (panel A) for a heatmap with clustering, where n = 5 in each condition met microarray experiment quality control criteria. Predictions used in silico tools, revealing 2976 putative target genes, which were assessed with gene ontology and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; see Tables  S1-S3. Among the top 10 differentially expressed miRNAs that reached statistical significance (Figure  2A ), one was selected as proof of concept. We selected hsa-mir-3614-5p, as it showed rhythmic expression with the clearest difference between the DS and NS conditions (Figure  2A ).
Effects of Exosomes on BMAL1 Binding Sites ChIP-Seq and bioinformatics were applied to generate genome-wide profiles of BMAL1 target genes that would be differentially affected by DS and NS exosome cargo. These profiles uncovered 42,700 BMAL1 binding sites in DS and only 5534 binding sites in NS, with 2481 overlapping between the two conditions (FDR < 0. 01). The DAVID Bioinformatics Database for associations with particular gene ontology terms was used to establish a systematic classification of the BMAL1 target genes (Table  S7 ), which revealed that BMAL1 target genes are related to rhythmic processes, metabolic pathways, transcription as a biological process, DNA binding or transcription as a molecular function, and nucleus as a cellular component. Genome binding sites were identified in 8.
DAPI staining was used to reveal co-localization with cell nuclei. The pipeline for characterization of miRNAs derived from plasma exosomes in blood samples drawn during constant routine is shown in Figure  5 (panel C ). Exosomal miRNA cargo was investigated with microarrays. Online databases were used for in silico prediction of putative target genes. Effects on gene expression were investigated with miRNA scramble, agomir, and antagomir transfected into plasma-derived exosomes, which were subsequently added into cultures of naïve differentiated human adipocytes. Gene expression was determined with quantitative reverse transcription polymerase chain reaction (qRT-PCR). Bmal1-dLuc U2OS human osteosarcoma cells containing a luciferase reporter driven by the Bmal1 promoter (U2OS cells)  [43]  were transfected with miRNA scramble, agomir, and antagomir and used as a reporter assay to investigate 24-h rhythmicity.
Chromatin immunoprecipitation-based sequencing (ChIP-Seq) and bioinformatics approaches were applied to generate genome-wide profiles of target genes of Bmal1 transcriptional activity. Online databases were used to establish a systematic classification of target genes and identify gene binding sites and pathways. Data mining analyses were performed to predict exosome-induced alterations in transcriptional binding in order to identify potential gene targets for intervention. The pipeline for characterization of miRNAs derived from plasma exosomes in blood samples drawn during constant routine is shown in Figure  5 (panel C ). Exosomal miRNA cargo was investigated with microarrays. Online databases were used for in silico prediction of putative target genes. Effects on gene expression were investigated with miRNA scramble, agomir, and antagomir transfected into plasma-derived exosomes, which were subsequently added into cultures of naïve differentiated human adipocytes.
Exosome Markers Purified exosomes were incubated with flow cytometry (Exo-Flow kits; System Biosciences, Mountain View, CA) and analyzed using FACS analysis (FACSCalibur; BD Biosciences, San Jose, CA, USA). Different exosome markers were used based on their functions: tetraspanins (#EXOFLOW150A-1; CD9, CD63 and CD81), targeting/adhesion (#EXOFLOW200A-1; CD31), and membrane transport and fusion (#EXOFLOW500A-1; Rab5a). Negative controls were carried out in the absence of exosomes. Labeled exosomes markers were analyzed on a flow cytometer (FACSCanto II, FACSCalibur; BD Biosciences, San Jose, CA, USA) using FACSDiva software 2. 56 (BD Biosciences, San Jose, CA, USA). Data analyses were performed using FlowJo software (Tree Star, Ashland, OR, USA).
The quality of miRNAs was determined using Agilent Small RNA Kit  [25]. The miRNA expression analyses were performed using human miRNA microarray for one-color technique (Agilent Technologies, Santa Clara, CA, USA) consisting of 60-mer DNA probes synthesized in situ that represent 2006 mouse miRNAs and 39 viral miRNAs from the Sanger database (version 21). Total RNA (100 ng) was labeled and hybridized on a microarray (miRNA complete labeling and hybridization kit) and afterwards scanned using DNA Microarray Scanner (Agilent Technologies, Santa Clara, CA, USA). Total RNA including enriched miRNA was dephosphorylated with calf intestine alkaline phosphatase (Agilent Technologies, Santa Clara, CA, USA), denatured with dimethyl sulfoxide, and labeled with pCp-Cy3 using T4 RNA ligase (Agilent Technologies, Santa Clara, CA).
The labeled RNAs were hybridized to custom 8×60K human miRNA microarrays (Agilent Technologies, Santa Clara, CA, USA). After hybridization and washing, the arrays were scanned with an Agilent microarray scanner using high dynamic range settings as specified by the manufacturer (Agilent Technologies, Santa Clara, CA, USA). Microarray results were extracted using Agilent Feature Extraction software (v12. 0; Agilent Technologies, Santa Clara, CA, USA). The total gene signal was normalized to the 75th percentile of the signal intensity.
Computational Target Predictions and Functional Annotation Gene targets for differentially expressed miRNAs were initially computationally predicted using established miRNA target-prediction programs: MicroInspector, miRanda, PicTar, RNA22, RNAhybrid and TargetScan. Provided they were reproducibly detected in at least four of the six prediction programs, the predicted genes of individual miRNAs were uploaded to the online Database for Annotation, Visualization and Integrated Discovery (DAVID 6. 7, http://david. abcc. ncifcrf. gov) as well as the Ingenuity Pathway Analysis tool version 2. 2. 1 (IPA, https://www. qiagenbioinformatics. com) for functional annotation and clustering analysis. DAVID software was used to identify the most relevant (overrepresented) biological terms associated with a given gene list.
Its cluster tool grouped genes based on their associated gene ontology annotations, and the related terms were clustered into groups with enrichment scores calculated from their EASE Score, the modified Fisher exact p value  [108]. The web server hosts a continuously updated version of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database release 82. 1 (https://www. genome. jp/kegg/kegg1. html), which provided a relevant search module based on KEGG pathway descriptions. IPA was used to characterize clinical relevance, and molecular and cellular functions related to the identified genes. A cut-off false discovery rate (FDR) of 0. 05 was set to identify differentially expressed genes. The right-tailed Fisher's exact test was performed in IPA to calculate p values determining the probability that each biological function assigned to the data set was due to chance alone.
Each sample was hybridized to an Agilent oligonucleotide microarray for all of the independent experiments (Agilent Technologies, Santa Clara, CA, USA). The microarray slides were scanned using Agilent dual-laser Microarray Scanner and the digitized images were acquired and processed using Agilent Feature Extraction software (v12. 0; Agilent Technologies, Santa Clara, CA, USA). Background-subtracted intensities were normalized using the quantile method across all remaining microarray experiments. We first corrected microarray expression intensity background using the norm-exp algorithm with an offset value of 50  [109]. The background-corrected data were normalized between arrays using the cyclic loess method  [110]. We applied limma moderated t-tests to detect differentially expressed genes, considering the batch effect caused by different chips as covariates in a linear model.
Statistical Analyses Comparisons between conditions employed unpaired Student t-tests, or a non-parametric equivalent when data were not normally distributed, one-way analysis of variance (ANOVA), or two-tailed pairwise comparisons with Student-Newman-Keuls tests. Linear regression was applied to estimate differences between conditions and reference values. Cosinor analyses to investigate 24-h rhythmicity were performed using non-linear mixed-effects regression  [113, 114] , implemented in SAS version 9. 4 (SAS Institute, Cary, NC, USA). Comparisons between conditions for cosinor analysis were based on planned contrasts, implemented as t-tests of the difference in parameter estimates between conditions against zero. Controls for non-specific effects of time awake, implemented by adding time awake as a regression term and testing the regression coefficient against zero using t-test, were performed and found to be non-significant.
The version with DA is based on the dual 4D-Var technique  (Bajo et al. , 2017)  and assimilates sea-level data from nine tide gauges along the Italian Adriatic coast. ARPAE is the Regional Agency for Prevention, Environment and Energy of Emilia-Romagna (Italy). The Hydro-Meteo-Climate Service of Arpae (Arpae-SIMC) actively works in numerical model forecasting, both deterministic and probabilistic, at different time scales from very short-range, in case of extreme events, to seasonal scale. Arpae-SIMC is aCentre of 440 Competence for the Italian National Civil Protection system as well as the Support Centre for the Civil Protection Agency of Emilia-Romagna. The integrated modelling system developed by Arpae-SIMC for its marine and coastal activities (Fig. 4 ) consists of a waveforecasting operational chain called MEDITARE  (Valentini et al.
copernicus. eu/). The surface boundary conditions are the most important for a storm surge model and consist of wind and pressure fields. Due to the orographic conformation around the Adriatic Sea, atmospheric models tend to have large errors and local-scale models with high resolutions may be required. Deterministic models can include the tidal potential in their equations and thus simulate the propagation of the astronomical tide. However, many operational systems prefer to simulate only the storm-surge component and locally add the astronomical 535 tide, calculated through harmonic analysis. In this way the accuracy on the total level is not affected by the error in the tide modeling. Others possible problems in the Adriatic Sea concern the complexity of the coasts and the need to adequately represent them (for this purpose it is better to use models with unstructured grids) as well as the lack of high resolution and accurate bathymetric datasets in the continental shelf border.
Statistical forecast models The statistical models represent a fundamental part of CPSM (Centro Previsione e Segnalazione Marea) tidal forecasting modelling system. The model structure has been designed and calibrated using a database of tide and atmospheric data, over a 545 period exceeding 50 years (from 1966 to 2016). The database contains the values of tide level, atmospheric pressure, baric gradients and wind in the Adriatic and Tyrrhenian Sea. These values were used to obtain regression coefficients for different versions of the models inspired by the autoregressive scheme, with expert type, from the ARMAX family. (Goldmann and Tomasin, 1971; Sguazzero et al. , 1972; Tomasin, 1972)  https://doi. org/10. 5194/nhess-2020-361 Preprint. Discussion started: 17 November 2020 c Author(s) 2020.
CC BY 4. 0 License. The actual operational structure of the system of statistical models is illustrated in Table  1. The engineering processes of the 550 expert structure divided the database into subsets of similar cases, considering: meteorological situation, seasonality, and moment of forecast. With this process, many classes of coefficients have been created. Every single class corresponds to an appropriate set of coefficients used by the basic model for statistical interpretation of the evolution of sea level in the Venetian area (lagoon and sea). Another important development for the forecast of sea level with statistical models consists in using the expected pressure and 555 wind fields. CPSM is using the forecast fields issued by ECMWF and COSMO-I. It allows us to make projections into the future, with an hourly step, using observations and predictions simultaneously.