dataesr/scientific-paragraphs-categorization huggingface dataset:
General Motor Performance To assess motor performance, tapping rate (mean ITI) and motor variability (CV of ITI) in the synchronization and continuation phases were analyzed for all trials. Data were entered in two independent 2 × 2 × 2 mixed-design ANOVAs. Congruency (congruent vs. incongruent) and Phase (synchronization vs. continuation) were the within-subject factors, while Language (French vs. German) was the between-subject factor. For tapping rate, the triple interaction Congruency × Language × Phase was highly significant [F (1, 61) = 16. 15, p < 0. 001]. This interaction was decomposed by holding Language constant, and by running two additional 2 × 2 ANOVAs. As can be seen in Figure  4  (right panel), German participants tapped in the vicinity of the expected interval (mean ITI 599 ms, 95 %-CI = ±3 ms) without showing any differences between the synchronization and continuation phase, nor between congruent and incongruent alignments.
ACKNOWLEDGMENTS This work was supported by the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement No. 327586  to SF, the  LMU Munich's Institutional Strategy LMUexcellent  within the framework of the  German Excellence Initiative  to SF, and the  Institut Universitaire de France (IUF)  to SDB. Both authors were financially supported by a bilateral travel grant from the  DAAD and Polish NCN  (no. 2011/01/M/HS6/04158 ). We thank the  IPS (LMU Munich ),  Franziska Holzheimer ,  Elena Maslow ,  Karine Valette , and  Virginie Epting  for help with stimulus recording and testing, and  Amandine Michelas ,  Pauline Welby ,  Christine Meunier , and  Mireille Bastien-Toniazzo  for helpful discussions.
Flow cytometry Mice were infected with 500 PFU of IAV and treatment with DF2162 or vehicle started 3 days after infection (twice a day until day 6). At day 14 post-infection, mice were euthanized and the lungs were collected to access T cell number and subtypes. Lungs were homogenized, and leukocytes were labeled with appropriate dilutions of Pacific Blue-conjugated anti-CD45, PerCP-conjugated anti-CD3, APC-conjugated CD8, and APC-Cy7-conjugated CD4. Events were acquired using a LSRFortessa cytometer (Becton Dickinson Biosciences, Rungis, France) running FACSDiva software and were then analyzed with the FlowJo software.
acKnOWleDgMenTs We would like to thank Frankcinéia Assis and Ilma Marçal for technical assistance. We thank Dra. Maria Leonor Sarno de Oliveira (Instituto Butantã-São Paulo/Brazil) for providing the S. pneumoniae strain. We also thank Dr. Amber Smith and Dr. Jon McCullers for all the help during the standardization of the model of secondary pneumococcal infection.
For the synthesis of the polyesters, 1 H NMR spectra were recorded using a Jeol 400 spectrometer at an operating frequency of 400 MHz. For the synthesis of 20, 22, and 23, 1 H NMR spectra were recorded using a VNMRS 600 spectrometer at an operating frequency of 600 MHz. 13  C NMR spectra were recorded on the same instrument at an operating frequency of 150 MHz. All NMR data was processed using MestReNova (version 10. 0) desktop NMR data processing soware. 27.
| DNA sequence and phylogenetic analysis The nucleotide sequences of the purified products were obtained by automated sequencing at Inqaba biotech TM (Pretoria, South Africa) using the Big Dye terminator kit (Applied Biosystems, USA) and the same primer sets used for amplification. Nucleotide sequences were edited and aligned using Sequencher TM Gene Codes Version 4. 8 (Gene codes Corporation, MI, USA) and Mega 7. 0  (Kumar, Stecher, & Tamura, 2007)  software packages, respectively. The Kimura 2-parameter model was selected as the best-fit model using ModelTest  (Posada & Crandall, 1998)  implemented in Mega 7. 0  (Kumar et al. , 2007). Neighbour-joining (NJ) and Maximum likelihood phylogenetic trees were constructed using Mega 7. 0  (Kumar et al. , 2007)  with nodal support being assessed by 1,000 bootstrap replicates.
ACKNOWLEDGEMENTS The project was supported by The  Wellcome Trust  (Project number  210183. 183 AHDW03/04 ). We thank the GNP authorities for permission and relevant information, the field staff at the Agricultural District Directorate in Gorongosa and The  Carbon Sequestration Project in Nhambita at GNP  for their support during the field work. The following people were involved in the laboratory analysis:  Ricardo Gomes Souto  from the  Faculty of Veterinary Medicine ,  Eduardo Mondlane University, Mozambique ,  Dr. Rahana Dwarka  and  Ms. Nontobeko Ntshali  of the TADP,  OVI. Ms Heloise Heyne  was responsible for the soft tick identification. Dr Penrith  reviewed the manuscript. Dr Carlos Lopes Pereira  helped with the warthog sampling and  Dr. Elisabeth Specht , the former head of  Regional Veterinary Laboratory , provided unfailing support.
† From the Fig. S1 , † the diffusion coefficient of diluted asphaltene in toluene is 3. 41-5. 73 Â 10 À10 m 2 s À1 which is consistent with the experimental results 2. 2-6. 3 Â 10 À10 m 2 s À1. 31 Moreover, the interlayer distance value from DPD simulations is about 5. 05 Å in our calculation, which is slightly larger than the experimental results (about 3. 55 Å). 32  On the basis of these results, it is believed that the bead-bead interaction parameters used in the simulations could well reect the authentic physical properties of petroleum systems. In this study, DPD simulations were performed using the Mesocite module embedded in the Materials Studio 6. 1 package from Accelrys, Inc. 33  All the simulations were performed in a cubic box with a size of 100 Â 100 Â 100R c 3 with periodic boundary conditions at three direction.
Acknowledgements This study was supported by  Faculty of Earth Resources, China University of Geosciences (Wuhan)  and  Ecole Central Nantes and Université Laval  and the project of integrated exploration project from  Chinese Development Research Center of Geological Survey, key fundamental geological study on the integrated exploration area of Baiyinchang polymetallic district, Gansu province  ( 12120114050101 ). This study was also supported by  Special  fund for public welfare of the  Ministry of Land and Resources : application demonstration and scientic research base construction of three dimensional integrated exploration technology of metal mineral resources ( 201011002-6 ). Besides, we acknowledge the supports from the  Henan coalbed methane development and utilization company Ltd.
The structuring element is a geometric figure, simple to form, known or arbitrary, and can be a circle, segment, square, or triangle. Results: We show the different results obtained after testing carried out in algorithmic processing using MATLAB: To ameliorate the visualization of the abnormal blood cells, we have applied the elements basis morphological operations in a different way. We have performed an opening by reconstruction and a closing by reconstruction. The obtained result show that we have obtained an efficient detection of the targeted objects (abnormal blood cells or leukemia).
This study is multicenter, prospective, randomized, and controlled with a group of patients following conventional reha bilitation (level of evidence I). All participants gave written informed consent before the randomization. The study was approved by the French Medical Ethical Committee (Comité de Protection des Personnes de Lorraine), realized in structures (Nancy-Thermal) agreed for research (Agence Régionale de Santé), and registered in ClinicalTrials. gov with the identifier: NCT02225613.
acKnOWleDgMenTs The authors thank the orthopedics surgeons of the Médipôle Gentilly: Dr. Nicolas Hummer, Dr. Nicolas Paris, Dr. Frank Wein; of Louis Pasteur Clinic: Dr. Benoît Goudot, Dr. Frank Wein; of Saint-André Clinic: Dr. Maurice Morel; of the University Hospital: Dr. Manuela Barla, Dr. Rémi Belleville, Dr. Jean-Baptiste Gross, Prof. Didier Mainard, Dr. Michael Mangin, Dr. Vincent Seivert. The authors thank the physiotherapists: Mr. , Ms. Johnny Afonso, Patrick Boisseau, Xavier Celli, Jessy Courtier, Suzy Diller, Jean-Philippe De Jesus Goncalves, Antoine Fourcaulx, Alexandre Ferry, Laureline Grajon, Elodie Jussy, Angélique Labeys, Thibaut Lombardet, Aurélie Muller, Camille Pons, Thomas Wysocki. The authors acknowledge Prof. Michel Boulangé, Dr. Jean-Marc Geoffroy, and Prof. Gisèle Kanny for their advices and help in this study.
In this work we quantitatively explore the idea that variations in species abundances between different sites can be explained mainly through the local variations in resource availability. Concretely, we reanalyze data from the Human Microbiome Project (HMP)  [3] [4] [5] 12]  on the species composition of different bodysites (i. e. , gut, skin, vagina, and oral cavity;  Fig 1). To develop the analysis method, we start from a theoretical model that assumes maximal diversity of species and derives a relationship between species abundance and resource availability. We then use the results of this model to guide the joint analysis of the datasets from different body sites in the HMP project, introducing a new Common Component Analysis (CoCA) method. The general idea is that if the same species exist in different body sites, the same resources must also be present at these body sites.
Adapting resource models to relative abundances with the maximum diversity hypothesis. Our first step to analyze the HMP microbiome dataset is to build a theoretical model for species abundance covariation that will guide our inference. Metagenomic survey experiments typically provide measures of relative, rather than absolute, species abundances. That is, the experiments do not provide an accurate measure of the overall size of the population. Therefore, it is necessary to develop a theory that directly models relative abundances. Our theory is inspired by two aspects of MacArthur's consumer resource model  [1, [13] [14] [15] : first, species are described by vectors of resource utilizations that act as a basis of a linear niche space and, secondly, that the equilibrium relative abundances correspond to the point where the consumers make the most effective use of the resources.
Real systems, however, are much more complex than these deterministic models and there exist many well-known mechanisms (e. g. spatial structure, stochasticity  [16] ) that lead to communities that are more diverse than predicted from classical models. In our statistical inference models used to analyze the microbiome data, we set M = N -1 and let the weights of the inferred effective resources determine the dimension of the niche space. Effective resource models are factor models that can be trained with Common Components Analysis (CoCA). The maximum diversity hypothesis can be implemented as a statistical inference model, which we present on the example of the HMP dataset. The Human Microbiome Project data are grouped into four bodysites (gut, skin, vagina, and oral cavity). There are two standard approaches to analyzing the data in such a situation: first, the data can be lumped together and principal components analysis (PCA) can be performed on all of it, and second, individual PCAs can be performed for each bodysite.
Future analyses of larger, and more diverse, datasets will further elucidate the relationship between this underlying niche space and the functional properties of the organisms in the microbiota. Given that CoCA identifies features that separate the bodysites with high fidelity, we believe that it is a useful technique for identifying microbiota based biomarkers that discriminate between host phenotypes. Extending our results to include data from unhealthy subjects will be an important avenue for future work. A recent paper by Bashan et al  [41]  developed an approach to analyzing microbial dynamics based on a Dissimilarity-Overlap Curve. They found that communities with a high overlap in the species that were present also have a low dissimilarity in their relative abundance profiles. They argue that this relationship is evidence of "universality" where interspecies interactions are essentially the same across a population of human subjects.
47. https://doi. org/10. 1371/journal. pcbi. 1005435. g005 CoCA uses simultaneous diagonalization to identify processes that are shared between communities. Covariance matrices from communities without shared drivers of variation cannot be simultaneously diagonalized, as we showed with randomized data. Consequently, we would expect that CoCA would fail on datasets from clearly different ecological environments (e. g. , hot springs compared to body sites). In this case, failure is not a bad thing: it can be easily diagnosed from the poor agreement between the predicted and observed covariances and it provides an ecologically meaningful result be ruling out the hypothesis that the environments have shared drivers of variation. CoCA does not explain 100% of the variation in the HMP data, nor do taxonomic relationships explain 100% of the variation in the inferred resource utilizations.
These data are likely to be noisy, but the degree of uncertainty is difficult to quantify. Moreover, the use of OTUs defined by 97% sequence identity, and subsequent reduction of the communities to 100 highly abundant species, leads to a coarse grained representation that may smooth out relevant features. It will be important to revisit our analyses on additional datasets, and with additional tools for generating highly accurate pictures of community composition. On the theoretical side, it will be important to examine the validity of the maximum diversity hypothesis across communities with different properties.
Data collection We analyzed data from the Human Microbiome Project (HMP) on person-to-person variability in relative species abundances in four bodysites (gut, oral cavity, vagina, and skin; Fig  1A )  [3] [4] [5] 12]. The species-level relative abundances derived from the HMP whole genome sequencing data were obtained from MG-RAST (Project 385) through the MR-RAST API  [42]. Only the processed data as provided on the MG-RAST server were extracted. Thus, these species counts were constructed using the default MG-RAST pipeline  [43]. Briefly, this pipeline identifies putative rRNA fragments and clusters them at 97% identity to define operational taxonomic units, which are assigned species labels using a search against the M5rna database  [44]. We eliminated any lowly abundant species and selected for further study 100 species (Fig 3A ) that were highly abundant across all bodysites, as described in S1 Text.
Data challenges and RAMP Advances in machine-learning technology is moving towards a new paradigm in data processing design, in which the role of the individual will have to be redefined. In order to integrate the human creativity with the computational resources in an efficient way, various data-challenge platforms have been emerging, which provide a competitive and/or collaborative environment to crowd-source the analysis expertise. Rapid Analytics and Model Prototyping (RAMP)  [1]  is an online data challenge platform, primarily motivated by the need to achieve a more systematic integration of the development. It aims to incorporate specific types of interactions between developers during the design process, and seeks insight into the evolution of the design. What distinguishes RAMP among various data challenge platforms is its focus on the progress rather than the result.
The RAMP includes many different types of data challenges, in addition to the track reconstruction challenge, to each of which any user can subscribe and submit several "solutions" for the formulated "problem". The problems can be of different types, such as classification, regression or clustering, in a variety of contexts such as chemistry, biology or climate research  [1]. The distinguishing feature of RAMP is that the submission is the "code" to solve the problem, which runs remotely on a fully modularized workflow. For a given problem the users can implement methods for training and predicting, with any level of intermediate processes, as long as the methods return the expected format for the prediction. A screenshot of the submission sandbox is shown in Figure  1. The submissions are written in python and can use common libraries such as numpy  [2] , pandas  [3] , scikit-learn  [4].
No additional random background hits have been simulated. The data provided to the participant is one csv file with one line per hit corresponding to 10000 events, while a separate 10000 event file is kept private for the evaluation of the submissions by the RAMP platform. Hits are grouped per event in a random order. The following features are provided: event number : arbitrary index, unique identifier of the event particle number : arbitrary index, unique identifier of the ground truth particle having caused that hit. This number is hidden from the prediction method. hit identifier : index of the hit in the event (implicit variable) layer number : index of the layer where the hit occurs, from 1, the innermost layer, to 9, the outermost layer phi number index of the pixel corresponding to the hit, counting clockwise, from 0, at φ = 0 to the maximum, which depend of the layer x : horizontal coordinate of the hit y : vertical coordinate of the hit It should be noted that although one can easily translate layer and phi information into x and y (and vice versa), both are provided in order to help participants to gain time.
Conclusions and outlook The problems for the CTDWIT 2017 hackathon were designed in a simplified way in 2D in order to allow the users to experiment on novel approaches, instead of being confined to regular methods of track reconstruction, within a 36 hours hackathon. In particular, the 2-dimensional setup simplified the visualisation of both the problem and solution, and the high average momentum and low multiplicity setup of the input samples made it easier to find a starting point for the development. With about 30 submissions in two days, the hackathon established a proof of principle for a wider scale data challenge for a more realistic experimental setup. The post-hackathon test of the analysis in much more challenging physics conditions illustrated that it is possible to think ahead of the available data and implement robust solutions even if some features of the data are unknown.
COMPUTATIONAL RESULTS We assess the performance of FastCNP on the synthetic benchmark 1 largely tested in the literature  [1, 2, 4]. FastCNP was implemented in C++, and complied using GNU gcc 4. 1. 2 with '-O3' option on a 2. 5 GHz Intel E5-2670 processor with 2 GB RAM under Linux. Due to the stochastic nature of FastCNP, each instance was solved 30 times independently. For each run, we used a time limit t max = 3, 600 seconds (this condition is comparable to the stopping conditions used by the reference algorithms  [1, 2, 4] ). To report our results, we indicate the best objective value among the 30 results ("f best "), average objective value ("f avд "), average time to find the best value for the 30 trials ("t avд ") and number of successful trials to find the best value ("#succ") of the 30 trials.
P-values based on other distributions were very close to the reported P-values based on the lognormal distribution. They were adjusted for all known characteristics of the woman and/or the study sample; see table footnotes for details specific to each comparison. We compared the following populations: (1) abnormal vs normal cytology in all samples (N=4997), (2) abnormal vs normal cytology in primary screening samples (N=2846), (3) primary screening samples (N=2864) vs referral population samples with abnormal cytology (N=367) and (4) primary screening samples with normal cytology (N=2719) vs referral population samples with normal cytology (N=516). Analyses were performed with SAS Ver. 9. 3 (SAS Institute, Cary, NC, USA). Finally, we evaluated the differences in the assays' ability to detect HPV infections and ≥CIN2 by determining the degree of their concordance in baseline testing results.
All cytology was read by the same laboratory under routine conditions, which were not influenced by the study. Moreover, unlike in some other split-sample studies evaluating several HPV assays, 10 cytology-normal/HPV-positive women were invited for repeated testing. Approximately 60% of these women had a follow-up, regardless of the assay that detected the HPV infection. 27 This proportion was consistent with the data from randomised controlled trials comparing HPV testing to cytology in primary cervical screening. 32 Finally, management according to CIN, cervical intraepithelial neoplasia; HC2, Hybrid Capture 2; HPV, Human Papillomavirus; IQR, interquartile range; Md, median. Note: In the Horizon study, there were in total 516 women with normal cytology in samples taken for follow-up of a recent abnormality.
ACKNOWLEDGMENTS The authors would like to thank women for the participation in the study follow-up. The authors would, furthermore, like to acknowledge the  Danish Genomica, Hologic ,  QIAGEN , and  Roche  representatives for enabling the collaboration between their parent companies,  Copenhagen University Hospital Hvidovre  and the  University of Copenhagen. CONF LICT OF I NTEREST M. R. and her former employer received honoraria from QIAGEN for lectures on her behalf. S. N. declares no conflict of interest. E. L. participated in meetings with  Roche  and  Astra-Zeneca  with fees paid to the  University of Copenhagen  and was an unpaid advisor to GenProbe and NorChip. S. P. received honoraria from  Hologic  for lectures. D. E. attended sponsored meetings and symposia, received honoraria from  BD  and  Qiagen  for lectures and is project manager on studies funded by  BD Diagnostics.
Three methods to estimate genetic distances were investigated: 1) M1: a ratio fixed across the whole genome of one Megabase per centiMorgan; 2) M2: the ratios of genetic distance (using M3, below) over physical distance fixed for each chromosome; and, 3) M3: a genetic map of inter-SNP distances estimated using CRIMAP software (v2. 503). Results: The estimates obtained with M2 and M3 showed much less variability between autosomes than those with M1, which tended to give lower N e results and higher LD decay. The results suggest that N e has decreased since the development of sheep breeds in Europe and this reduction in N e has been accelerated in the last three decades. The N e estimated for five generations in the past ranged from 71 to 237 for Texel and Romney breeds, respectively. A low level of genetic kinship and inbreeding was estimated in those breeds suggesting avoidance of mating close relatives.
The genetic gain resulting from breeding programs has resulted in significantly increased efficiency of sheep production. However, the management of genetic variability between and within breeds is one of the major challenges that will determine the success of future selection  [6]. Therefore, it is crucial to understand the genetic structure of the selected populations and to assess its genetic diversity. This can be achieved using genomic information and for this purpose, we utilized a dataset of 12,597 animals genotyped with the Illumina OvineSNP50 BeadChip; a SNP array developed by the International Sheep Genomics Consortium (ISGC) in conjunction with Illumina  [4, 7]. Estimating the effective population size (N e )  [8]  is a useful way to evaluate the loss or gain of diversity across time and allows identification of selection events, such as bottlenecks  [9].
Breed groups were assigned using Sheep Improvement Limited (SIL, http:// www. sil. co. nz/) recorded breed. When the recorded breed included a composite breed containing a known proportion of Romney, Coopworth, Perendale or Texel, it was replaced using those proportions so that animals subsequently meeting a composite group definition were included in the analysis. The dataset description, which includes the number and breed proportion of each pure and crossbred animal in the dataset is shown in Table  1. Pedigree information for all the animals was available from SIL and a description of the pedigree information is shown in Additional file 1.