dataesr/scientific-paragraphs-categorization huggingface dataset:
Software requirements The following additional software and libraries are required: the bowtie2 mapper  [26] , R and the BioConductor packages RColorBrewer, ggplot2, grid, Samtools (>0. 1. 19) , Python (>2. 7) with the pysam, bx. python, numpy and scipy libraries, and the g++ compiler. Note that a bowtie2 version > 2. 2. 2 is strongly recommended for allele-specific analysis, because, since this version, read alignment on an N-masked genome has been highly improved. Most of the installation steps are fully automatic using a simple command line. The bowtie2 and Samtools software are automatically downloaded and installed if not detected on the system. The HiC-Pro pipeline can be installed on a Linux/ UNIX-like operating system.
Conclusions As the Hi-C technique is maturing, it is now important to develop bioinformatics solutions which can be shared and used for any project. HiC-Pro is a flexible and efficient pipeline for Hi-C data processing. It is freely available under the BSD licence as a collaborative project at https://github. com/nservant/HiC-Pro. It is optimized to address the challenge of processing high-resolution data and provides an efficient format for contact map sharing. In addition, for ease of use, HiC-Pro performs quality controls and can process Hi-C data from the raw sequencing reads to the normalized and ready-to-use genome-wide contact maps. HiC-Pro can process data generated from protocols based on restriction enzyme or nuclease digestion. The intra-and inter-chromosomal contact maps generated by HiC-Pro are highly similar to the ones generated by the hiclib package.
In addition, when phased genotyping data are available, HiC-Pro allows the easy generation of allele-specific maps for homologous chromosomes. Finally, HiC-Pro includes an optimized version of the iterative correction algorithm, which substantially speeds up and facilitates the normalization of Hi-C data. The code is also available as a standalone package (https://github. com/hiclib/iced). A complete online manual is available at http://nservant. github. io/HiC-Pro. The raw and normalized contact maps are compatible with the HiTC Bioconductor package  [28] , and can therefore be loaded in the R environment for visualization and further analysis.
HiC-Pro performance and comparison with hiclibHiC-Pro was run on the IMR90 Hi-C dataset from Dixon et al. and Rao et al. in order to generate contact maps at resolutions of 20 kb, 40 kb, 150 kb, 500 kb and 1 Mb. Contact maps at 5 kb were also generated for the IMR90_CCL186 dataset. The CPU time for each step of the pipeline is reported and compared with the hiclib Python library. The reported results include time of writing contact maps in text format. Times are minutes:seconds.
Acknowledgements We would like to thank  Felix Krueger  for useful discussion about allele-specific analysis, and  Jesse Dixon  and  Neva Cherniavsky  for their advice in defining the best GM12878 phasing data. This work was supported by the  France Genomique National infrastructure  ( ANR-10-INBS-09 ), the  Labex Deep , the  European Research Coucil  ( SMAC-ERC-280032 ), the  ERC Advanced   Investigator award  ( ERC-250367 ), the  European Commission  ( HEALTH-F5-2012-305626 ), the  ABS4NGS  project ( ANR-11-BINF-0001 ), the  National Human Genome Research Institute  ( R01 HG003143 ), and the  Paris Alliance of Cancer Research Institutes  ( PACRI-ANR ). J. D. is an Investigator of the  Howard Hughes Medical Institute.
Observational and experimental data will be combined to analyse the effects of nutrients, contaminants, degraded physical habitat and water scarcity on river biodiversity and ecosystem functioning. Models will be used to forecast further declines in biodiversity and associated changes in ecosystem functioning. • To analyse the effects of water scarcity on ecosystem services in the study basins. The implications of socio-economic development will be analysed to provide an economic and social valuation of ecosystem services, taking into account the user perspective. Thresholds will be identified beyond which water scarcity and multiple stressors would threaten ecosystem services and make it impossible to satisfy water demands. • To explore how to adapt management and policies to minimise the ecological, economical and societal consequences of ongoing global change.
The selected basins encompass a rich set of socio-ecological conditions (forested mountainous areas, highly populated regions relying on water transfers, agricultural areas and industrial clusters), and a wide geographic coverage (Fig. 2 ), but are all affected by water scarcity due to either climatic or societal factors. Each basin focuses on a specific set of stressors to illustrate different management scenarios. In four of them (Adige, Sava, Ebro and Evrotas) extensive field work will be done, while in two of them (Anglian River basin district and Souss Massa) the existing data will be used to evaluate different management scenarios. The Ebro is the largest Mediterranean Spanish river, 928 km in length and with a drainage basin of 85,550 km 2  (Sabater et al. , 2009). Hydrology is markedly seasonal, with high flow peaks and long periods of low flow, and is severely affected by water scarcity.
Detailed structure of GLOBAQUA GLOBAQUA is organised in fourteen work-packages (WPs), grouped in five main modules (Fig. 3 ). All WPs will be implemented in all six case-study river basins, although fieldwork will not be performed in all of them to the same extent. • Module 3, IMPLICATIONS (WPs 8 to 10): it analyses the socioeconomic implications of the effects of impacts on water quality and availability, as well as on biodiversity and ecosystem functioning, by translating the information gathered by modules 2 and 3 into ecosystem services, and investigating the effects on the socio-economic development. • Module 4, ENVIRONMENTAL MANAGEMENT (WPs 11 and 12): it integrates the results of the other modules to define a manageable perspective of water scarcity for the studied river basins. It is, thus, an interface between scientific results of the project and their policy implications.
• Module 5, PROJECT COORDINATION AND DISSEMINATION (WPs 13 and 14): it is devoted to the communication of the results to target groups (researchers, policy makers, water managers, land planners, etc. ), and to stimulating the use of results through relations with stakeholders and end-users. It also seeks to coordinate project activities. WP1-DATA manages data either internally generated by the project or acquired from external sources, and conveys them to a Hub platform to share the data among project members, and eventually among potential users, either scientists or managers. WP2-SCENARIOS draws data stored by WP1-DATA to produce spatially explicit, integrated scenarios of environmental change for each case study, including climatic, socio-economic and land use factors. The primary climate model data set will be an ensemble of 1960-2100 transient runs performed with the RCA4 model at 50 km and 12.
5 km. Comparisons will focus on the 2050 time horizon (2041-2070) versus the reference climatology. It encompasses numerous GCMs (~10) and RCMs (~6), as well as 3 of the CMIP5 Reference Concentration Pathways (RCPs 2. 6. , 4. 5 and 8. 5). The RCA4 data set will be complemented with data from other RCMs in EURO-CORDEX  (Kjellström et al. , 2013). The climate model data will further be biascorrected to sufficiently reproduce the reference climatology. Finally, the climate data is spatially scaled to account for fine scale heterogeneities in complex terrain, using a model interface that superimposes stationary, topography-induced patterns of the hydrological model scale (≤1 × 1 km 2 ) on the RCM fields. The outputs of these exercises will be used by the modules STRESSORS and RECEPTORS. They will also be used to define the boundary conditions for the impact on ecosystem functioning and for interactions with the modules IMPLICATIONS and ENVIRONMENTAL MANAGEMENT.
WP3-HYDROL uses existing modelling approaches to link surface and subsurface hydrological fluxes and the impact of water scarcity on the ecological status of streams, including hyporheic, riparian and floodplain zones. Flow and transport processes are upscaled from point to basin and regional scales, accounting for the sources of uncertainty of the model outputs. Historical data series are analysed to disentangle the effect of multiple human impacts on water resources along time and across space. This WP uses global change scenarios built in WP2-SCENARIOS and data gathered in WP1-DATA, and provides information to WP4-GEOMORPH, WP6-BIOL, WP7-ECOSYSTEM and WP8-SERVICES. WP4-GEOMORPH analyses the effects of changing hydrology, land use, and climate on sediment transport, channel morphology, physical habitat, and pollutant fluxes in rivers.
Additionally, whole-river metabolism is reconstructed from historical data on oxygen concentration to determine the effects of climatic variation and new human stressors of known occurrence on river ecosystem functioning. The historical perspective of human occupancy and land use change is assessed through analyses of historic adjustment of river channels. WP8-SERVICES uses the information on ecosystem functioning to establish cause-effect relationships between stressors and ecosystem services. Thus, it provides objective tools for decision systems to evaluate the effects of planned management interventions and to help planners manage the river basin as a whole, taking decisions to counteract the effects of multiple stressors on ecosystem attributes such as ecological or chemical status. The transversal component of WP8-SERVICES within GLOBAQUA requires a close relationship to WP6-BIOL, WP7-ECOSYSTEM, WP9-SOCIOECON, WP10-VALUATION and WP11-INTEGRATION.
Acknowledgements This work has been supported by the  European Communities 7th Framework Programme Funding  under Grant agreement no. 603629-ENV-2013-6. 2. 1-Globaqua  and by the  Generalitat de Catalunya  (Consolidated Research Groups "2014  SGR 418 -Water and Soil Quality Unit"  and  2014 SGR 291 -ICRA ). Damià Barceló  acknowledges support from the  Visiting Professor Program of King Saud University. Special thanks are due to all partners of the GLOBAQUA consortium and the peer review panel for ensuring quality results and fruitful collaboration within the project.
(ii) A significant, universal decrease of the radii with pair momentum has been observed in A-A collisions, while the analogous trend in pp depends on the considered direction (out, side, or long) and event multiplicity. A transverse momentum dependence of the radii similar to A-A was observed for the asymmetric d-Au collision system at RHIC  [19, 20]. The one-dimensional radii extracted from the ALICE 3-pion cumulant analysis were also investigated in pp, p-Pb, and Pb-Pb collisions at the LHC  [21]. For the p-Pb system, at a given multiplicity, the radii were found to be 5-15% larger than those in pp, while the radii in Pb-Pb were 35-55% larger than those in p-Pb. The A-A pion femtoscopy results are interpreted within the hydrodynamic framework as a signature of collective radial flow. Models including this effect are able to reproduce the ALICE data for central collisions  [22, 23].
Attempts to describe the pp data in the same framework have not been successful so far and it is speculated that additional effects related to the uncertainty principle may play a role in such small systems  [24]. In p-A collisions, hydrodynamic models which assume the creation of a hot and dense system expanding hydrodynamically predict system sizes larger than those observed in pp, and comparable to those observed in lower-energy A-A collisions at the same multiplicity  [24, 25]. However such models have an inherent uncertainty of the initial state shape and size, which can also differ between pp and peripheral A-A collisions. Alternatively, a model based on gluon saturation suggests that the initial system size in p-A collisions should be similar to that observed in pp collisions, at least in the transverse direction  [26, 27].
At that stage both systems are treated in the same manner in the Color Glass Condensate model (CGC), so that their subsequent evolution should lead to comparable radius parameter at freeze-out. Ref. [28]  suggests a (small) Yang-Mills evolution in addition. The observation of a larger size in the p-A system with respect to pp would mean that a comparable initial state evolves differently in the two cases, which is not easily explained within the CGC approach alone. The d-Au data measured at RHIC suggest that hydrodynamic evolution may be present in such a system, while the ALICE 3-pion analysis at the LHC  [21]  leaves room for different interpretations. The pion femtoscopic radii as a function of pair transverse momentum from p-Pb collisions at √ s NN = 5. 02 TeV, which are reported in this paper, provide additional constraints on the validity of both approaches.
The Gaussian source provides a commonly used approximation of the source size and was used to compare to other experimental results, especially the ones coming from A-A collisions, where the source shape is more Gaussian than in small systems. While pursuing the standard procedure with the Gaussian assumption, we also carefully look for any deviations between the fit function and data which might suggest a significantly non-Gaussian shape of the source, which would be an important similarity to the pp case. In the analysis of pp collisions by ALICE  [16] , a Gaussian is used together with other source shapes, exponential and Lorentzian  [16]. A Lorentzian parametrization in the out and long directions and a Gaussian parametrization in the side direction were found to fit the data best according to χ 2 /ndf.
Systematic uncertainties on the radii The analysis was performed separately for positively and negatively charged pions. For the practically zero-net-baryon-density system produced at the LHC they are expected to give consistent results. Both datasets are statistically consistent at the correlation function level. The main contributions to the systematic uncertainty are given in Table  2  for GGG radii and in Table  3  for EGE radii. We used two alternative representations (Cartesian and spherical harmonics) of the correlation function. The same functional form for both of them was used for the fitting procedure. However, the implementation of the fitting procedure is quite different: log-likelihood for Cartesian vs. regular χ 2 fit for SH, 3D Cartesian histogram vs. three 1D histograms, cubic or spherical fitting range in the (q out , q side , q long ) space.
Interestingly, it is very similar in data and the models in all directions, which suggests that the system dynamics might be correctly modeled by hydrodynamics. Also in the data the source shape is distinctly non-Gaussian. Further studies would require examination of the source shape in p-Pb collision models to see if similar deviations from a Gaussian form are observed. (GeV/c) T k 0. 2 0. 4 0. 6 0. 8 (fm) long E R 1 2 3 = 35. 5 〉 η /d ch N d 〈 0-20% , = 23. 2 〉 η /d ch N d 〈 20-40% , = 16. 1 〉 η /d ch N d 〈 40-60% , = 8. 2 〉 η /d ch N d 〈 60-90% , The CGC approach has provided a qualitative statement on the initial size of the system in p-Pb collisions, suggesting that it is similar to that in pp collisions  [27, 28]. The measured radii, at high multiplicities and low k T , are 10-20% larger than those observed at similar multiplicities in pp data.
For lower multiplicities the differences are smaller. These differences could still be accommodated in CGC calculations. Furthermore, the evolution of the slope of the k T dependence is similar between pp and p-Pb collisions in the side direction. Another similarity is the distinctly non-Gaussian shape of the source, which in pp and p-Pb is better described by an exponential-Gaussian-exponential form. It appears that data in p-Pb collisions still exhibit strong similarities to results from pp collisions. However some deviations, which make the p-Pb more similar to A-A collisions, are also observed, especially at high multiplicity. The differences between small systems such as pp and p-Pb and peripheral A-A data are most naturally explained by the significantly different initial states in the two scenarios.
Comparison to the world data In Fig. 9  the results from this analysis of the p-Pb data from the LHC (red filled circles) are compared to the world heavy-ion data, including results obtained at lower collision energies, as well as to results from pp collisions from ALICE and STAR. It has been observed  [13]  that the 3-dimensional femtoscopic radii scale roughly with the cube root of the measured charged-particle multiplicity density not only for a single energy and collision system, but also across many collision energies and initial system sizes. The pp and A-A datasets show significantly different scaling behavior, although both are linear in dN ch /dη 1/3. The p-Pb radii agree with those in pp collisions at low multiplicities. With increasing multiplicity, the radii for the two systems start to diverge.
Conclusions We reported on the three-dimensional pion femtoscopic radii in p-Pb collisions at √ s NN = 5. 02 TeV, measured in four multiplicity and seven pair momentum intervals. The radii are found to decrease with k T in all cases, similar to measurements in A-A and high-multiplicity pp collisions. The radii increase with event multiplicity. At low multiplicities they are comparable to the pp values, while at higher multiplicities and low pair transverse momentum they are larger by 10-20%. However, they do not reach the values observed in A-A collisions at lower energies. The high multiplicity data are compared to predictions from two models, both of them incorporating a fast hydrodynamic expansion of the created medium. They overpredict the values of the R out and R long parameters, however the introduction of a smaller initial size results in a better description.
Similar results were obtained for three other longer probes. C. IGV visualization of the same region as shown in A for the targeted exome MyoPanel2. Shorter HaloPlex probes cover exon 7, producing a number of reads. D. Characteristics of MyoPanel1 and 2 designs. Touch 2 instrument (Life Technologies, CA, USA), followed by an enrichment of the ion spheres particles (ISPs) using an Ion One Touch ES (Life Technologies, CA, USA) enrichment module according to the manufacturer's instructions. ISPs were loaded on a 318v2 chip and sequenced using an Ion PGM Sequencing 200 Kit v2 (Life Technologies, CA, USA) on a PGM sequencer system (Life Technologies, CA USA). Raw data generated by the PGM sequencer were processed by Torrent Suite Software v. 4 and aligned using TMAPv. 3 (https:// github. com/iontorrent/TMAP, June 2015).
Sequence variants were identified using the VariantCaller tool from the Ion Torrent package using default "germline high stringency" parameters. For MyoPanel2, the initial DNA extraction and library preparation steps were similar to that of MyoPanel1, except that the adaptors used for creating the libraries of DNA samples with the Haloplex Target Enrichment System Kit (Agilent Technologies, CA, USA) were specific of Illumina sequencing technology. The libraries were then separated in three different pools for the step of clonal amplification by bridge PCR on the Next Seq 500 followed by paired end sequencing using the NextSeq 500 Mid Output kit (300 cycles) and the Illumina two-channel sequencing by synthesis (SBS) technology (Illumina, CA, USA). Sequencing reads were aligned using BWA-MEM 0.
7. 12  (Li and Durbin, 2009)  with default parameters and Hg19 as the reference genome. Sambamba 0. 51 (http://lomereiter. github. io/sambamba/index. html, June 2015) was used to convert SAM to BAM format. GATK 3. 3 (McKenna et al. , 2010)  was used for local realignment around indels and for base quality score recalibration. Variant calling was performed using Haplotype Caller (GATK 3. 3), followed by standard hard variant filtering using VariantFiltration module of GATK 3. 3 with cut-offs depth b10 and quality score b50 according to GATK Best Practices recommendations (Van der  Auwera et al. , 2013; DePristo et al. , 2011).
Mutation coverage analysis A set of 11,467 published mutations was obtained from Locus Specific Mutation Databases (http://grenada. lumc. nl/LSDB_list/lsdbs, June 2015) for target regions of 78 genes present in both MyoPanel1 and Myopanel2. This set was then used to calculate coverage of individual mutation positions for each sample using VarAFT tool. Number of mutations with no coverage, less than 6× coverage and less that 20× coverage was calculated.
Acknowledgements We thank  Pr. Jean Pouget ,  Pr. Shahram Attarian ,  Dr. Emmanuelle Campana-Salort ,  Dr. Amandine Sevy ,  Pr. Jorge Bevilacqua  and  Pr. Meriem Tazir  for providing clinical samples. We also thank  Amira Cherrallah  and  Gaby Luciani  for their help with library preparation as well as  Arnaud Lagarde  for his help with PGM sequencing. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  n°2012-305121  " Integrated European -omics research project for diagnosis and therapy in rare neuromuscular and neurodegenerative diseases (NEUROMICS) ".
e. , sensing environmental data or event detection) requested by the precision agriculture monitoring application. Namely, this metric can hold the features in a routing path, particularly the role that can mark important retrievable resource information. In addition, as most of A-LLNs nodes have similar environmental sensing mission workloads, the RDC value is the most influential factor of the affordable workload metric and it will only be taken into account when the DODAG meets routing oscillations, and also when the weight value change between two ETX and RE composite routing metrics is bigger than a threshold. In A-LLNs, restarting is a common operation when the system meets a fault or exception at the software or hardware level. A specific component like NanoRisc  [71]  is able to provide counter information with a defined sampling frequency.
Simulation Setup and Designated Scenarios In this section, the configuration of the COOJA simulator, MSPsim emulator and wireless channel model will be presented. Then, the validation of SCAOF in RPAL model will be carried out for different network topologies and different scenarios. Furthermore, the SCAOF algorithm is tested under enhanced simulation scenario scripts which enable runtime adjustments of radio medium, node state and behavior. To the best of our knowledge, this is the first work to evaluate the RPL-based protocol with dynamic simulation and runtime fault injection.
Simulation Parameters All the nodes in this set of experiments run Contiki OS on an emulated platform built by MSP430x MCU with enough RAM memory and IEEE 802. 15. 4 compliant radio CC2520 module. The MSPSim hardware emulator is able to introduce hardware constraints into this simulation. Powertrace model is adopted to monitor the energy consumption of each node. To observe the comparison results, the configurations of different layers of Contiki uIPv6 protocol stack are set to the default parameter states. See Table  6  for a detailed description of the network and the simulation configurations. The simulation output is recorded in the raw logging files for statistical analysis. For each simulated scenario of different network size, five simulations are applied repeatedly then five random topologies per network scenario and the final results are computed as average values.