dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Evaluation An index tree is constructed through recursively merging neighboring sub-regions when forwarding messages of the same size in between is minimum in energy consumption. An example is shown in Figure  3 , where the skewness degree is set to 60%. Leaf nodes are denoted by the IDs of correspond grid cells, as mentioned in Section 3. 2, and R i (i = 1, 2,. ) represents non-leaf nodes, which are (sub-)regions composed of several neighboring grid cells. For instance, R 5 represents a sub-region whose grid cells are gc 22 and gc 23 , while R 2 is composed of R 3 and grid cell gc 24. Query answering is performed leveraging this index tree for data gathering, aggregation and routing to the SN. Our technique is evaluated with respect to four types of queries  [33]  leveraging the sub-region and attributes of interest, and a query is denoted as q = <tm, qr, AT R q >: • Single-attribute query in the whole region (SAQWR): q retrieves sensory data of all sensor nodes in the whole network region (i.
e. , q. qr = nr) for a certain attribute (i. e. , |q. AT R| = 1). nr specifies the whole network region. Sensory data of the attribute q. AT R at nr are gathered and routed to the SN at a certain time slot q. tm. • Single-attribute query in a sub-region (SAQSR): The difference between SAQSR and SAQWR lies in the query region. SAQSR retrieves sensory data of sensor nodes in a sub-region of the network (i. e. , q. qr nr) with a certain attribute q. AT R (i. e. , |q. AT R| = 1). • Multi-attribute query in the whole region (MAQWR): q. qr is the whole network region (i. e. , q. qr = nr). q. AT R contains some, but not all, attributes. Generally, sensory data of the attributes in q. AT R at nr are gathered and routed to the SN at q. tm. • Multi-attribute query in a sub-region (MAQSR): The difference between MAQWR and MAQSR lies in the query region where MAQSR retrieves sensory data of sensor nodes in a sub-region of the network (i.
e. , q. qr nr) with multiple attributes. respectively. The gradient of the curves represents the ratio of energy consumption for query answering. This figure shows that when the cache size in the SN is relatively small and is not capable of caching all sensory data requested by a certain query, more sensory data should be replaced in the SN according to our data replacement mechanism, as presented in Section 4. 2, and more energy is required for answering the query. For instance, in comparison with the case when the number of attributes is three, 198% (or 355%) more energy is consumed for the case when the number of attributes is seven (or nine). Experiments are conducted to evaluate the performance of these four kinds of queries leveraging our two-tier cooperative caching mechanism. Figure  4  compares the energy consumption of MAQWR, where the number of attributes varies as 1, 3, 5, 7 and 9, respectively.
It is evident from this figure that our PCC is more energy efficient than MQP, especially when the query attributes are relatively large in number. [33]  for the query types of MAQWR and SAQWR, where the number of attributes is set to 1, 3, 5, 7 and 9, respectively. The energy consumption at various time points (TP2, TP4, etc. ) are illustrated. Generally, the energy consumption of our PCC is much smaller than that of MQP (roughly 30% less on average for PCC than MQP), especially when the number of query attributes is relatively large. Note that the energy consumption of PCC is quite large at the first time point, since no sensory data have been cached in the SN for reducing the data gathering from the network in real-time to facilitate the query processing. [33]  for the query types of MAQWR and SAQWR, where the number of attributes are set to 1, 3, 5, 7 and 9, respectively.
This two-tiered cooperative mechanism, which caches sensory data at the sink node and the head nodes of grid cells, is efficient for facilitating the query answering, as evidenced by the experimental evaluation in Section 5. 3. A cluster-based cooperative caching mechanism is developed by Chauhan et al. [47]  for supporting query processing. The network is divided into non-overlapping clusters, and each sensor node is assumed to have some cache space. Sensory data are stored in the cache space of sensor nodes that are near the sink node. When a query request is to be responded to, sensory data are retrieved through a cache discovery process. Generally, a sensor node that is responsible for this query is examined for determining whether the required sensory data are saved in its cache space.
It is argued that in certain WSN applications, query requests come periodically with stringent delay constraints  [10]. Therefore, a periodic aggregation query scheduling is performed by the designed routing strategy along with packet scheduling protocols. The quality of queries in WSNs is discussed by Brayner et al. [56] , which aims to deliver a reasonable level of data quality as expected, while ensuring the intelligent consumption of limited network resources. Generally, these methods mainly explore the strategies of supporting (periodic) query processing in WSNs, in order to consume less resources while prolonging the network lifetime. The sharing of sensory data retrieved at a certain time slot and between concurrent queries and the reuse of sensory data gathered by recent queries for answering the forthcoming queries are not discussed extensively.
Acknowledgments This work was supported partially by the  National Natural Science Foundation of China  (Grant Nos. 61379126  and  61401107 ), by the  Scientific Research Foundation for Returned Scholars, Ministry of Education of China , by the  Guangdong University of Petrochemical Technology's Internal Project  (Grant No. 2012RC106 ), by the  Educational Commission of Guangdong Province, China  (Grant No. 2013KJCX0131 ), and by the  Fundamental Research Funds for the Central Universities.
5, as implemented in Fastjet  [44]. Information from all the detector subsystems is used to create charged and neutral particle inputs to the jet-clustering algorithm using a particle flow approach  [20]. During 2011 and 2012, LHCb collected data with a mean number of pp collisions per beam crossing of about 1. 7. To reduce contamination from multiple pp interactions, charged particles reconstructed within the vertex detector may only be clustered into a jet if they are associated with the same pp collision. Signal events are selected by requiring a muon candidate and at least one jet with ∆R(µ, j) > 0. 5. For each event the highest-p T muon candidate that satisfies the trigger requirements is selected, along with the highest-p T jet from the same pp collision. The high-p T muon candidate is not removed from the anti-k T inputs and so is clustered into a jet.
Background determination Contributions from six processes are considered in the W +jet data sample: W +jet signal events; Z+jet events where one muon is not reconstructed; top quark events producing a W +jet final state; Z → τ τ events where one τ lepton decays to a muon and the other decays hadronically; QCD dijet events; and vector boson pair production. Simulations based on NLO predictions show that the last contribution is negligible. The signal yields are obtained for each muon charge and center-of-mass energy independently. The p T (µ)/p T (j µ ) distribution is fitted to determine the W +jet yield of each data sample. To determine the W +b and W +c yields, the subset of candidates with an SV-tagged jet is binned according to p T (µ)/p T (j µ ). In each p T (µ)/p T (j µ ) bin, the two-dimensional SV-tagger BDT-response distributions are fitted to determine the yields of b-tagged and c-tagged jets, which are used to form the p T (µ)/p T (j µ ) distributions for candidates with b-tagged and c-tagged jets.
These p T (µ)/p T (j µ ) distributions are fitted to determine the SV-tagged W +b and W +c yields. Finally, to obtain σ(W b)/σ(W j) and σ(W c)/σ(W j), the jet-tagging efficiencies of tag (b) ≈ 65% and tag (c) ≈ 25% are accounted for. In all fits performed in this analysis, the templates are histograms with fixed shapes. The p T (µ)/p T (j µ ) distributions are shown in Fig. 1  (in this and subsequent figures the pull represents the difference between the data and the fit, in units of standard deviations). The W boson yields are determined by performing binned extended-maximum-likelihood fits to these distributions with the following components: • The W boson template is obtained by correcting the p T (µ)/p T (j µ ) distribution observed in Z+jet events for small differences between W and Z decays derived from simulation.
• The template for Z boson events where one muon is not reconstructed is obtained by correcting, using simulation, the p T (µ)/p T (j µ ) distribution observed in fully reconstructed Z+jet events for small differences expected in partially reconstructed Z+jet events. The yield is fixed from the fully reconstructed Z+jet data sample, where simulation is used to obtain the probability that the muon is missed, either because it is out of acceptance or it is not reconstructed. • The templates for b, c and light-parton jets are obtained using dijet-enriched data samples. These samples require p T (j µ +j) < 10 GeV and, for the heavy-flavor samples, either a stringent b-tag or c-tag requirement on the associated jet. The templates are corrected for differences in the p T (j µ ) spectra between the dijet-enriched and signal regions.
2 (see Fig. 2 ) are fitted simultaneously. The templates used in these fits are obtained from data in the same manner as the SV-tagger BDT templates. After correcting for the efficiency of requiring BDT(bc|udsg) > 0. 2, the b and c yields determined from the fits to M cor and SV track multiplicity and from the two-dimensional BDT fits are consistent. The mistag probability for W +light-parton events in this sample is found to be approximately 0. 3%, which agrees with the value obtained from simulation. From the SV-tagger BDT fits, the b and c yields are obtained in bins of √ s, muon charge, and p T (µ)/p T (j µ ). The p T (µ)/p T (j µ ) distributions for muons associated with b-tagged and c-tagged jets are shown in Figs. 5  and 6. These distributions are fitted to determine the W +b and W +c final-state yields as in the inclusive W +jet sample.
The Z +b and Z +c yields are obtained by fitting the SV-tagger BDT distributions in the fully reconstructed Z+jet data samples and then correcting for the missed-muon probability. The fits are shown in Figs. 5  and 6  for each muon charge and center-of-mass energy. The yields obtained still include contributions from top quark production and Z → τ τ. The Z → τ τ background, where one τ lepton decays into a muon and the other into a hadronic jet, contaminates the W +c sample due to the similarity of the c-hadron and τ lepton masses. The p T (SV)/p T (j) distribution, where p T (SV) is the transverse momentum of the particles that form the SV, is used to discriminate between c and τ jets, since SVs produced from τ decays usually carry a larger fraction of the jet energy than SVs from c-hadron decays.
The cross-check fits of Sect. 4, using the corrected mass and track multiplicity, remove information associated with jet quantities, such as p T , from the yield determination and produce yields consistent at the 5% level. This is assigned as the uncertainty for the SV-tagged yield determination. The W boson template for the p T (µ)/p T (j µ ) distribution is derived from data, as described in Sect. 4. The fit is repeated using variations of this template, e. g. using a template taken directly from simulation and using separate templates for W + and W -, to assess a systematic uncertainty. The dijet templates are obtained from data in a dijet-enriched region. The residual, small W boson contamination is subtracted using two methods: the W boson yield expected in the dijet-enriched region is taken from simulation; and the p T (µ)/p T (j µ ) distribution in the dijet-enriched region is fitted to a parametric function to estimate the W boson yield.
In this context, bacteriophages may offer an interesting perspective and recent calls have been made to promote their use  (14) (15) (16). Bacteriophages are viruses that infect specifically bacterial cells. They are unique among antibacterial agents in their ability to increase their number in presence of the bacterial target. During the last decade, a renewed interest for phage therapy has led to publication of several encouraging in vitro and in vivo studies  (17) (18) (19) (20). Several clinical trials (http://clinicaltrials. gov) have been initiated and the European Union has recently funded a large randomized controlled trial to test the efficacy of phage therapy to treat infected burn wounds (http://www. phagoburn. eu). In this article, we investigated the use of bacteriophages to treat pneumonia induced by highly virulent extra-intestinal pathogenic E.
Ceftriaxone sodium (TEVA, France) was suspended in normal saline. A 50 mg/Kg dose (1250 µg, 200 µL) was injected intraperitoneally 2 hours post-infection and every 12 hours during 2 days (i. e. a total of 5 injections). In parallel, control group received 200 µL injections of normal saline. Ceftriaxone minimal inhibitory concentration (MIC) for the 536-lux strain was inferior to 0. 03 mg/L (according to standardized disk diffusion method). Photon emission of the luminescent bacteria was recorded and analyzed as previously described  (20)  using an Ivis Spectrum imaging system (PerkinElmer, California, USA) and a dedicated software (Living Image, PerkinElmer). Amount of photon emitted was expressed as photon/sec/cm²/steradian and normalized by subtracting the background luminescence noise using a non-infected control mouse included in each recording.
Statistical analysis All statistical analyses were performed by using GraphPad Prism version 5. 00 (GraphPad Software, California, USA). The normal distribution of all variables was checked using the Kolmogorov-Smirnov test and results are then expressed as means ± standard deviation. In case of non-Gaussian distribution, results are expressed as median [25th ; 75th percentile]. Statistical tests (Student's t-test or Mann-Whitney test) were chosen accordingly. Survival curves were compared by using the Logrank test. Comparison of proportion was performed using Fisher's exact test. A p value less than 0. 05 was considered as statistically significant.
Acknowledgments We thank  Pr. James Kaper  ( University of Maryland School of Medicine, Baltimore ) for the gift of pCM17 and  Jonathan Messika  for the PDP302 strain. We thank  Marie-Anne Nicola  ( Imagopole, Institut Pasteur ) for her technical assistance and all members of the animal housing facility for their help in mice management. We also express our gratefulness to  Chantal Archambeau  and  Estelle Atse  for logistics support and media preparation.
Acknowledgments We would like to thank  Jay Armas ,  Eric Bergshoeff ,  Matthias Blau ,  Jan de Boer ,  Jean-Pierre Derendinger ,  Troels Harmark ,  Kristan Jensen ,  Cindy Keeler  and  Jan Rosseel  for many valuable discussions. The work of JH is supported by the advanced  ERC  grant 'Symmetries and Dualities in Gravity and M-theory' of  Marc Henneaux. The work of EK was supported in part by  European Union 's  Seventh Framework Programme  under grant agreements ( FP7-REGPOT-2012-2013-1 ) no  316165 ,  PIF-GA-2011-300984 , the  EU program  "Thales" MIS  375734 , by the  European Commission  under the  ERC Advanced Grant   BSMOXFORD 228169  and was also co-financed by the  European Union (European Social Fund, ESF)  and  Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)  under "Funding of proposals that have received a positive evaluation in the 3rd and 4th  Call of ERC  Grant Schemes".
Satellite products Altimeter and ocean color products used in this study were specifically produced for the Kerguelen region by Ssalto/Duacs and CLS with support from CNES from mid-May 2011 to July 2012. Altimetry maps were generated from Jason-1, Jason-2 and Envisat along-track observations with procedures analogous to those used for AVISO data (SSALTO/DUACS User Handbook, 2014). Some of the parameters (i. e. , mean dynamic topography, correlation scales and measurements errors) were specifically tuned for the region. These products are based on altimetric measurements at 10 day frequency, and interpolated to provide higher temporal frequency information. In particular, a high resolution (1/8 • ) regional mean dynamic topography (MDT) was computed  (Rio et al. , 2012) , applying the same two-step methodology used for the calculation of the global CNES-CLS09 MDT available at that time  (Rio, 2012) , but with an updated input data set and refined in situ measurements processing.
First, a geoid model based on 1 year of GOCE data was used  (Pail et al. , 2011)  together with the CNES-CLS11 Mean Sea Surface  (Schaeffer et al. , 2012)  to derive an MDT at a spatial scale larger than 125 km. This first guess was then improved by using synthetic mean geostrophic velocities and synthetic mean heights, calculated by subtracting the ocean temporal variability measured by altimetry from instantaneous in situ measurements of the ocean current velocities and dynamic heights. We used velocities from SVP (Surface Velocity Program)-type drifters available from 1993 to 2011 from the GDP (Global Drifter Program) together with 183 000 velocities from buoys launched during the KEOPS2 sea campaign and spanning the period from 15 October 2011 to 31 May 2012. Ekman currents were subtracted from the buoy velocities.
Since a significant number of GDP buoys have been affected by undetected drogue loss since 2004, a wind slippage corrective term was removed  (Rio et al. , 2012). Also, a 3 day low pass filter was applied to get rid of the other ageostrophic high-frequency currents (stokes drift, inertial oscillations, tidal currents). The data set of in situ dynamic heights was calculated using the temperature and salinity profiles from CTD (conductivitytemperature-depth) casts (Cora3. 2) and Argo floats for the period 1993-2010, together with 75 CTD profiles measured from 15 October 2011 to 19 November 2011 during the KEOPS2 campaign. Maps of absolute dynamic topography at a resolution of 1/8 • were produced daily in real time (RT-MADT) by CLS/CNES. Near-real-time (NRT-MADT) maps were produced after 6 days by further correcting the real-time products.
Maxima (ridges) of Lyapunov exponents are used as a way to extract from altimetry data candidates for frontal regions, as a largescale tracer gradient can be amplified by this mechanism. The same Runge-Kutta scheme used to compute the backward FSLE was applied to retrieve additional diagnostics fundamental to the real-time analysis. Firstly, the ability of mesoscale eddy cores to retain water parcels was quantified by backward advection of particles released within regions with negative OW values, and their duration within these regions was measured. In effect this yields an estimate of how long ago a water parcel entered a given eddy, yielding a retention value expressed in days. Similarly, backward advection was used to identify water parcels originally above the Kerguelen Plateau, defined as the region where ocean depths are shallower than 700 m.
Other 2012-2014 SVP historical drifter trajectories were also included in the analysis. The time since having left the plateau and the latitude of departure from the shelf break also were estimated for each SVP drifter trajectory in analogy with the altimetry-based calculation. Results and comparison are shown in Fig. 7  (first column: altimetry; second row: SVP drifters). As SVP drifters are anchored to a fixed, shallow layer (nominally 15 m) under the effect of Ekman drift, the comparison is performed by including the Ekman component in the altimetrybased advection scheme (see Data and Methods). Not surprisingly, the SVP plot is much noisier than the altimetryderived one due to its asynopticity. However, qualitative analogies can be readily seen. In particular, the presence of a "fast lane" stemming from the northern part of the Kerguelen Plateau and contouring the polar front, a recirculation region centered at 74 • E, 49 • S enclosing older water, and a latitudinal mixing frontier at 85 • E, where old water parcel encounters are dispersed in thin filaments by strong mesoscale activity.
Acknowledgements. The authors would like to thank the  Marion Dufresne  crew and  B. Queguiner. The altimeter and color/temperature products for the Kerguelen area were produced by  Ssalto/Duacs  and  CLS  with support from  CNES. The authors would also like to acknowledge  AVISO/CLS ,  Météo-France  and the  Global Drifter Program/NOAA/AOML ,  Miami, Florida , and both the  Drifter Operations Center and Data Assembly Center  for arranging drifter deployments and data assembly, quality control and distribution of the data. A. Della Penna  was supported by a conjoint Frontières du Vivant (Paris 7) and  CSIRO-UTAS Quantitative Marine Science PhD  scholarship. This work was supported by the  French Research program  of  INSU-CNRS LEFE-CYBER (Les Envéloppes Fluides et l'Environnement-CYcles Biogéochimiques, Environnement et Ressources ), the  French ANR (Agence Nationale de la Recherche ,  SIMI-6 program ,  ANR-10-BLAN-0614  and  ANR MYCTO-3D-Map) , the  French CNES  ( Centre National d'Etudes Spatiales ), the  French Polar Institute IPEV  ( Institut Polaire Paul-Emile Victor ) and the  NASA/CNES OSTST   ALTIMECO  project.
Microarray experiment After validation of RNA quality (RIN score ≥8), 50 ng of total RNA was reverse-transcribed using the Ovation PicoSL WTA System V2 (NuGEN Technologies, West Cumbria, UK), according to the manufacturer's instructions. Biotin-labelled cDNA was then hybridized to GeneChip® Human Gene 2. 0ST microarrays (Affymetrix, Santa Clara, CA, USA) at 45°C for 17 h. The microarrays were washed on the fluidic station FS450 according to the specific manufacturer's protocols and scanned using the GCS3000 7G (Affymetrix). The scans were then analyzed with Expression Console software (Affymetrix) to obtain raw data (. cel files) and metrics for quality controls. Examination of these quality-control metrics and of raw-data distribution showed no outlier samples. Data were normalized using the Robust Multi-array Average (RMA) algorithm in R software with the custom chip description file (CDF) version 17.
0. 0 [27]. Data are available on the NCBI Gene Expression Omnibus (GEO) via the accession number GSE61905  [28]. Differentially expressed gene enrichment analysis was carried out using the DAVID bioinformatics resources (NIH), based on gene ontology (GO) biological processes, and Ingenuity Pathways Analysis (Ingenuity® Systems, USA)  [29] [30] [31]. To identify a transcriptomic signature predicting thiopurine resistance, we applied the 'nearest shrunken centroids' method using the Prediction Analysis of Microarrays (PAM) R package, which identifies predictive classifier genes  [32].
Microfluidic-based RT-qPCR assay Microfluidic-based quantitative PCR assay was performed to validate the differential microarray expression patterns of the molecular signature genes. Transcripts were quantified for 40 genes of interest and four reference genes (GUSB/GAPDH/RPL13A/B2M). The RT-qPCR assay is detailed in the Additional file 1: Data S1 and Table  S2.
Changes are expressed in resistant cell lines, using sensitive cell lines as reference. The P value of overlap was used to rank the significance associated for each upstream regulator. The P value indicates the significance of the overlap between the genes targeted by the upstream regulator in the database and the data from micro-arrays, without taking into account the regulation direction. The activation state makes predictions about potential regulators by using information about the direction of gene regulation and can be used to infer the activation state of a putative regulator. Results with a P value <0. 01 are presented in this table. toxicity  [13, 38]. However, HPRT activity does not vary widely within the general population and probably has little influence on clinical outcomes  [39].