dataesr/scientific-paragraphs-categorization huggingface dataset:
BCI devices for inducing neural repair mechanisms and BCI-controlled neuroprostheses are not yet well defined, and work is needed to develop appropriate device designs and operating protocols for clinical deployment. Nevertheless, the BCI research community should consider these clinical science gaps, as well as regulatory and commercialization challenges, while developing BCI systems. Incorporation of regulatory and deployment strategies in long term BCI research plans will speed adoption into clinical practice. This workshop brought together various research groups currently conducting clinical trials of implanted BCIs with the goal of restoring upper limb function lost after injury or disease. Most are operating under Investigational Device Exemptions (NCT00912041, NCT01849822, NCT01964261, NCT01364480, and NCT01894802).
However, some end-users (e. g. people with cerebral palsy) may exhibit other types of artifacts that are more challenging to remove  [270]. Similarly, real-life applications are generally noisier than laboratory conditions  [271]. The creation of repositories archiving datasets from different types of end-users could advance the state-of-the-art in robust methods for real-life BCI systems. A crowd-sourcing approach for labeling signal contamination may provide a valuable asset to benchmark approaches for artifact management. Another issue concerns the metrics used to estimate the BMI performance  [272]. Performance assessment should go beyond machine learning/classification metrics, and include efficiency/effectiveness metrics in the human-computer interaction sense  [273]. Strong evidence shows that human factors influence performance.
Individual workshop acknowledgements and funding The workshop 'Algorithms and performance using implanted devices' was supported in part by  NSF-NCS  grant  BCS1533672. The workshop 'Brain-computer interface based motor and cognitive rehabilitation after stroke' was supported by the  Italian Ministry of Healthcare  ( RF-2010-2319611 ) and  European ICT   Program  Project  FP7-224631  ( TOBI ) and  FP7-287320 (CONTRAST). The workshop 'BCI research and development for children' was supported by the  Clinical and Translational Science Center  ( UL1 TR000457-06 )  Weill Cornell Medicine Medical College of Cornell University  and the  Eisenberg Ahsen Foundation  Grant. The workshop 'BCIs for assessment of locked-in and patients with disorders of consciousness (DOC)' was supported by the  H2020  project  ComaWare.
The workshop 'BCIs for stroke rehabilitation' was supported by the  H2020  project recoveriX. The workshop 'Clinical applications of brain-computer interfaces in neurorehabilitation' acknowledges support from the  National Institutes of Health  grant  RNS094748A  and  National Science Foundation  award  1446908. The workshop 'Exploiting cognitive processes for brain-machine interaction' was supported by the  Swiss National Center of Competence in Research (NCCR) Robotics  and the ' EPFL Fellows' fellowship program  co-funded by  Marie Curie ,  FP7  Grant agreement no. 29771. The workshop 'Improving BCI usability through transfer learning methods' presented work that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement NO 657679 The workshop 'Non-invasive  BCI-control of FES  for grasp restoration in high spinal cord injured humans' is supported by the  European ICT   Program  Project  H2020-643955  ' More-Grasp ' and the  ERC Consolidator Grant 'Feel Your Reach' 681231.
The workshop 'Novel application fields for auditory BCIs' was (partly) supported by  BrainLinks-BrainTools , Cluster of Excellence funded by the  German Research Foundation  (DFG, Grant No. EXC 1086 ). The workshop 'Out of the lab -acquiring high quality EEG during mobile application' was supported in part by the  Land Steiermark project rE(EG) map (grant  ABT08-31142/2014-10 ) and the  Cluster of Excellence Hearing4all, Oldenburg, Germany.
These techniques combine extensions of the previously developed agent-based simulation software platform (FLAME) with autotuning capabilities and optimisation strategies for the current tumour model. Development of such a platform could advance the development of novel therapeutic approaches for the treatment of CRC which can also be applied to other solid tumours. Hamie et al. show in "Scaling Machine Learning for Target Prediction in Drug Discovery using Apache Spark" the implementation of the traditional pipeline for identification of candidate molecules that affect proteins associated with diseases in drug discovery by using Apache Spark, which enabled them to lift the existing programs to a multinode cluster without making changes to the predictors. Evaluations show almost linear speedup and a reduction in the number of intermediate files while allowing easier checkpointing and monitoring.
Furthermore, the achieved results enable to conclude that the radical improvement in performance does not impair tree accuracy, which is a key issue in phylogenetic inference. Energy efficiency of software tools for BioComputing is not a usual topic of research. In the paper "Energy Efficiency of Sequence Alignment Tools -Software and Hardware Perspectives", Kierzynka et al. compare the energy efficiency of the most established software tools performing exact pairwise sequence alignment on various computational architectures: CPU, GPU, and Intel Xeon Phi. The results show that the energy consumption may differ as much as nearly 5 times. Substantial differences are reported even for different implementations running on the same hardware. Moreover, they present an FPGA implementation of one of the tested tools -G-DNA, and show how it outperforms all the others on the energy efficiency front.
Up-to-date meta-databases are vital for the analysis of biological data. Pedersen et al. present in the paper "Large-scale Biological Meta-Database Management" a solution for biological meta-database management. It provides efficient storage and runtime generation of specific meta-database versions, and efficient incremental updates for biological data analysis tools. The approach is transparent to the tools, and it also provides a framework that makes it easy to integrate GeStore with biological data analysis frameworks. An evaluation of the performance characteristics of the system plus an evaluation of the benefits for a biological data analysis workflow are presented. the University of Notre Dame. Sandra Gesing research interests include science gateways, bioinformatic applications, grid and cloud computing, parallel programming, and GPU programming.
Acknowledgements We would like to thank all the authors, reviewers and editors involved in the elaboration of this special issue, including also the reviewers that were involved in the CCGrid 2015 conference, where short versions of the papers were previously selected. We are especially grateful to  Peter Sloot , editor in chief of the  The International Journal of eScience (Future Generation Computer Systems ), for approving this special issue and for his help along the process of its preparation.
The Needs Assessment Analysis We targeted several members working on 13 different use cases involved in HUBBLE project through either by contacting them through a survey or an interview about their visualization needs. A use case, as defined in HUBBLE project, can be the study of data gathered during a learning environment or a pedagogical scenario that involve produced by a set of users (teachers, students, researchers, and so on. ). Each use case includes a detailed documentation that would allow describing a sequence of events, activities, and users involved in such platform. Among 13 use cases, we received 10 feedbacks which represent 10 different application of contexts. As a result, we noticed a diversity of users' profiles that could be interested in data visualization, represented in Figure  2.
ILLUSTRATIVE CASE STUDY In HUBBLE project, we had to work on various datasets; each dataset is associated with a use case. A use case is composed of a data and individuals description such as the teacher, researchers, analysts, who were involved in a real learning session. In order to build our first prototype, we did focus and used data from the MOOCAZ case study; it is a MOOC which concerns the creation of a MOOC from A to Z. We received data and observational needs from researchers of the French ENS Cachan. We had twocourse sessions with approximately 5000 students in each one. Each course session has a start date, set by the pedagogical teams, and an end date. The learners of the same session follow the MOOC with proposed deadlines. These learners had to work on several proposed chapters that may include videos, quiz, and assessments.
We applied the proposed dashboard generator process for designing and deploying dashboard for HUBBLE project's use cases where the results and the experiment demonstrate the feasibility of this process. However, users often have just a slight idea about their preferences, and they are not able to express them precisely. In this case, our aim in the future is that users will not be asked directly for their preferences, but this information will be derived from their behavior while interacting with the dashboard. It requires from the dashboard generator to take into account and to adapt dynamically to this user's context evolution. Therefore, user's visualization preference and the change of context will be captured through the study of their user's interaction history. For example, through the combination of visual activities or the repetitive choice of data representation.
Methods Detection of repair systems and CRISPR-Cas systems. NHEJ and type II CRISPR-Cas systems were detected using MacSyFinder (default parameters)  6. The published models were used for the detection of type II CRISPR-Cas systems  6. To detect NHEJ, we retrieved protein profiles from TIGRFAM: Ku (PF02735) and ligD (TIGR02777, TIGR02778, TIGR02779). We built a MacSyFinder model where the presence of Ku was defined as mandatory and that of LigD as accessory (Supplementary Note 2). Other ligases can indeed be recruited by Ku  18. With this method, 74% of the systems detected encoded both Ku and LigD; 26% encoded only Ku. We compared these results to a previous analysis using other methods  30. Only one out of 113 genomes was discordant (we identified a NHEJ system in Sinorhizobium meliloti were none had been found previously)  30.
Genome dataset. We analyzed 5563 complete genomes retrieved from NCBI RefSeq (ftp://ftp. ncbi. nih. gov/genomes/, last accessed in November 2016) representing 2437 species of Bacteria. These can be found in Supplementary Data 1. Phylogenetic analyses. We built persistent genomes for 245 Firmicutes genomes smaller than 5 Mb available in GenBank RefSeq (Dataset). The persistent genome of each clade was defined as the intersection of pairwise lists of orthologs that were present in at least 90% of the genomes. A list of orthologs was identified as reciprocal best hits using end-gap free global alignment, between the proteome of a pivot and each of the other strain's proteomes. Bacillus subtilis strain 168 was used as pivot for each clade. Hits with <37% similarity in amino acid sequence and more than 20% difference in protein length were discarded.
Acknowledgements We thank  Philippe Horvath  for providing S. thermophillus strain DGCC7710 and phage Phi2972 and  Sylvain Moineau  for providing plasmid pNZ123. We also want to thank them both for their very helpful advice. We thank  Matt Deyell  for plasmid pMD021. A. B. is a member of the " Ecole Doctorale Frontière du vivant (FdV) ". This work was supported by the  European Research Council (ERC)  under the Europe Union's  Horizon 2020 research and innovation programme  (grant CRISPAIR agreement no. 677823 , and grant EVOMOBILOME no. 281605 ); the  French Government 's  Investissement d'Avenir program ;  Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'  ( ANR-10-LABX-62-IBEID ); the  Pasteur-Weizmann consortium.
Acknowledgments We thank  Marie Se  ´mon for her comments on the manuscript. This work was supported by the  Centre National de la Recherche Scientifique  and the  Agence Nationale de la Recherche  ( Ancestrome :  ANR-10-BINF-01-01 ,  DaSiRe :  ANR-15-CE12-0010-01 ,  Convergenomics :  ANR-15-CE32-0005 ). This work was carried out using the computing facilities of the  CC LBBE/PRABI.
Availability mapdNdS has been implemented in the Bioþþ suite  (Gueguen et al. 2013). The maximum likelihood program is called bppml, and is available at the address http://bioweb. me/bppsuite. The stochastic mapping program is called mapnh, and is available at the address http://bioweb. me/ testnh. A short tutorial about model inference and stochastic mapping as described in this article is available here: http:// bioweb. me/mapdNdS.
MUSE observations in the UDF-10 The MUSE observations of the udf-10 in the HUDF (see Fig. 1 , right panel), cover an area of 1. 15 arcmin 2 at a depth of ≈31 hours of exposure time and were acquired during Guaranteed Time Observations between September 2014 and December 2015. The final data cube is of much better quality than the HDFS thanks to an improved observational strategy and data reduction scheme; see B17-paper I for the detailed data reduction processes and quality assessment. The effective spatial resolution of the final data cube ranges from 0. 57 in the red (at 9350 Å) to 0. 71 in the blue (at 4750 Å). The 1σ emission-line surface brightness limit is 2. 8 × 10 -20 ergs s -1 arcsec -2 in the red (6500-8500 Å) between OH sky lines. The redshift of 252 objects were securely measured and span the range of 0.
e. that corresponds to about 1. 5 times the PSF FWHM size of the data cube. The S/N ratios were estimated between 4150 -4350 Å (in the object rest frame), representative of the continuum level (i. e. almost free of absorption and emission lines) based on the variance cubes produced by the MUSE pipeline. For galaxies that ended up in the final sample, the S/N ratio of the central 16 spaxels was typically between 5 (e. g. for galaxy HDFS-ID #10) to 10 (e. g. for galaxy HDFS-ID #4) per pixel. A subsample of 30 objects (15 in the HDFS and 15 in the udf-10) was thus obtained. To ensure a robust extraction of the stellar kinematics, we then excluded 13 galaxies (5 in the HDFS, 8 in the udf-10) for which we could not obtain a spatially binned data cube with more than six bins with a S/N greater than 8 (see §3.
3). This criterion results in a subsample of 17 galaxies (10 in the HDFS and 7 in the udf-10) with a spatially resolved continuum in the two MUSE data cubes. This sample is presented in Table  1. The 10 galaxies selected in the HDFS MUSE data cube were all included in the sample of C16, who studied their spatially resolved gas kinematics based on the same data set. We use their analyses as a reference for comparison with our stellar and gaseous kinematics. We also use the HDFS catalogue of  Bacon et al. (2015) , which comprises C16 selected objects whose strongest emission line (i. e. [OII], [OIII], or Hα) covers an area larger than 20 spaxels (i. e. twice the seeing disk) with a S/N per pixel above 15. These authors thus obtained a sample of 28 galaxies. No prior selection on magnitude was applied, as opposed to the sample defined here, but a redshift cut at z ≈ 1.
06) for a stellar mass of ∼10 10. 5 M , for example it has no ionised gas detected in the MUSE data cube, except for a weak [OII] emission line. We used the deep HST images obtained in the HDFS and udf-10 fields to assess the morphology and close environment of our galaxy sample. The morphological types are dominated by late-type disk galaxies (14 of 17). Eight of these galaxies have clear spiral arms visible in high-resolution HST images (see Table  1 ) and are either disturbed, asymmetric, or exhibit a bar. Two other disk galaxies appear very inclined (i. e. i ≈ 75 • ; HDFS-ID #1 and HDFS-ID #4) and elongated, whereas three others are rather compact (i. e. 1. 5 in diameter). The last late-type disk galaxy, HDFS-ID #8, potentially has a satellite in the north-east side, clearly visible in its HST image (see also C16, their Appendix B).
Characterisation of the MUSE spectral resolution A critical ingredient in a robust measurement of the intrinsic stellar kinematics of a galaxy using the full spectral fitting technique, and in particular its velocity dispersion (i. e. the broadening of the spectral lines), accounts for the instrument spectral resolution, also called the line spread function (LSF). Indeed, the stellar (and gas) templates used to fit the observed spectra are spectrally convolved to match the instrumental spectral resolution to separate the instrumental broadening of the lines from the intrinsic velocity dispersion of the observed galaxy. The MUSE pipeline provides a measure of the LSF that is achieved on single calibration files (e. g. arc exposures). The MUSE HDFS and udf-10 data cubes consist of a combination of more than 50 single dithered exposures rotated by multiples of 90 degrees.
This observational strategy (i. e. rotation and dithering) significantly helps build master flats and lower systematics and leads to a spatial homogeneisation of the LSF of the MUSE data cube towards an average of the instrumental resolution. We thus decided to accurately characterise the LSF by relying on a direct measurement on the final combined data cubes, following the same methods as in the first paper of this series (B17-paper I). We used the sky emission lines of the combined non skysubtracted MUSE data cubes, not corrected for heliocentric velocity, to characterise the instrumental LSF. At first order, we assumed the shape of the LSF to be Gaussian and measured the FWHM of 19 groups of 1 -10 bright sky emission lines spread as uniformly as possible over the MUSE wavelength range. These lines were selected from the UVES catalogue.
e. averaged over the MUSE FOVs) as the inferred MUSE spectral resolution function for each respective MUSE data set 5. The derived instrumental LSFs FWHM are shown in Fig. 4  and correspond to a spectral resolution of R 1600 -3600, and σ LS F = 70 -40 km s -1 from the blue to the red end, consistent with the nominal instrument characteristics 6. We underline the high stability of MUSE LSF from these two data cubes, observed at different periods, as detailed in B17-paper I.
Spatial binning and kinematics uncertainties After extracting the best template that fits the stacked galaxy spectrum, we spatially binned the reduced MUSE sub-cubes of each of the sample galaxies to increase and homogenise their spectral S/N. We used the adaptive spatial binning software developed by  Cappellari & Copin (2003)  based on Voronoi tessellation. We estimated the original S/N of each individual spectrum by taking the median S/N on the spectral range 4150 -4350 Å (in the rest frame of the object), which is representative of the continuum level, i. e. almost free of absorption and emission lines. We used the variance cubes associated with the MUSE data cubes, produced by the MUSE pipeline, as a measure of the original noise. The choice of the target S/N is dictated by the fact that one wants to recover the galaxy kinematics with minimum systematic biases while maximizing the number of individual binned spaxels.
e. HD 120136) and broadened it to 2000 different velocity dispersion values, σ in , uniformly spread between 10 and 100 km s -1. We then ran pPXF with the same settings as described in Section §3. 2 and analysed the obtained stellar kinematics. This test was performed for three different values of input S/N, i. e. 8, 12, and 15, similar to the typical highest values of the stellar continuum S/N of the MUSE HDFS and udf-10 data cubes. The results are shown in Fig. 5. This test only probes the systematics of the method but does not account for potential errors due to template mismatch or correlated noise. We find no systematic biases in the recovery of V when the MUSE data is binned to a minimum S/N of 8 pixel -1 and find an associated systematic error smaller than ≈5 km s -1 for any input velocity dispersion σ in (see top panels of Fig.
The kinematics analysis (see section §3. 2) has been performed on these spatially binned sub-cubes and the results are presented in the next section, Section §4. We do not bin the data spectrally as this would lower the sampling of the LSF (native MUSE sampling is 1. 25Å per spectral pixel) and increase the uncertainties on the extracted kinematics. Finally, we estimated the uncertainties of the derived kinematics by performing 500 fits to each of the individual binned spaxels of the sample galaxies to which we previously added random noise. The level of this noise has been constrained to follow a Gaussian distribution with a sigma equal to the standard deviation of the respective residuals of the original fit. We used the same fitting procedure as described in section §3. 2 and found typical variations of ∆V s ≈ 10 km s -1 and ∆ σ s ≈ 20 km s -1 for the stellar kinematics, and ∆V g ≈ 4 km s -1 and ∆ σ g ≈ 6 km s -1 for the gas kinematics.
Acknowledgements. This work has been carried out thanks to the support of the  ANR FOGHAR  ( ANR-13-BS05-0010-02 ), the  OCEVU Labex  ( ANR-11-LABX-0060 ), and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the  "Investissements d'avenir" French government  programme managed by the  ANR. BE  acknowledges financial support from the  "Programme National de Cosmologie et Galaxies" (PNCG)  of  CNRS/INSU, France. RB acknowledges support from the  ERC  advanced grant MUSICOS. JR acknowledges support from the  ERC  starting grant  CALENDS-336736. JS acknowledges support from the  ERC  grant  278594 -GasAroundGalaxies. RAM acknowledges support by the  Swiss National Science Foundation.
A data analysis method allowing to detect cells within a droplet at high density becomes essential. Only few approaches have been described regarding this issue. One solution is to set up two thresholds to detect the droplet and its cell occupancy respectively  7, 13, 15  or even three thresholds 22 , one for the droplets, and two to indicate respectively the rising edge and falling edge of cell signals. Moreover, as all these methods are solely based on thresholding, their degree of accuracy is limited. Their use is not suited for instance to detect cells in close proximity or cell aggregates. This is a strong limitation as this situation often occurs for cell lines growing as aggregates or during mitosis. Here we describe a LIF-based procedure to accurately count cells in droplets by overcoming counting errors caused by signal noise, cells in close proximity and cell clumping.
Thus, a careful analysis and Dashed black rectangle encloses a signal sample corresponding to a droplet chosen as an example to illustrate the signal processing analysis. (c) The signal processing method is schematized in the black box. Briefly, each droplet is identified by applying a droplet threshold on the red fluorescence channel. The green fluorescence channel was then filtered within each droplet, and a first order differential is applied to identify the local maximal values. A cell threshold (grey line) is eventually applied to identify cells. The number of cells per droplet is then enumerated as signal peaks (orange) within the interval of each droplet. An exhaustive description of the process can be found in Supplementary Fig. S1. (c) Cell density was adjusted to 2 × 10 6 cells/mL such that expected theoretical cell to droplet ratio treatment of data allows an optimal filtering of noise (see data analysis section and Fig.