dataesr/scientific-paragraphs-categorization huggingface dataset:
At lower percentage sequence identity, the query read was identified to genus (identity 95-96%) family (identity 90-94%) order (identity 80-88%) and class level (identity <80%). The quality of the BLAST-based taxonomic assignment of the OTUs was assessed using two approaches. First, GenBank was queried using the BLASTn algorithm for each sequence, and the top hits recorded. Blast results were inspected manually to remove inconsistencies: sequences were not considered when there was a contradiction in genus assignment in the reference database, since we suspected misidentification of the database sequence. Second, an alignment was generated for each genus using our query sequences, selected top BLASTn hits and the sequence of the first taxonomically named BLASTn hit, if the top hits were unidentified.
These alignments were assembled automatically in CLUSTALW  [17]  with manual editing. Neighbour joining (NJ) analysis, based on K2P distances and tested by 1000 bootstrap replicates (BS), were conducted for each alignment to confirm the identification of query sequence to the genus or species level, using PHYLIP 3. 6  [18]. Bayesian analysis was conducted on the same individual dataset used in the NJ analysis. The best nucleotide substitution model was evaluated by MrModeltest v 2. 5  [19]. Phylogenetic analysis was performed with Mrbayes v3. 1  [20]  applying a general time reversible (GTR) substitution model with gamma (G) and proportion of invariable (I) site parameters to accommodate variable rates across sites. Two independent runs of Markov chain Monte Carlo (MCMC) were performed using four chains for 2x10 6 generation and trees were saved each 1000 generations.
The initial 250 trees in each run were discarded as "burn-in". The remaining trees were used to construct majority-rule consensus trees. Bayesian post probabilities (PPs) for each clade were derived from trees remaining after the discarding the burn-in sample. Convergence of runs and parameters was controlled by the diagnostics implemented in MrBayes and in Tracer 1. 5  [21]. Trees were drawn using TREEVIEW  [22]. To assess sampling efficiency, a rarefaction curve was generated by means of the Analytic Rarefaction 1. 3 software  [23] , using 10 specimens as a step parameter for calculation. Statistical analysis of data was performed with Prism version 5. 00 software (GraphPad Software, California, USA) and Systat 11 (Systat Software, Inc. ).
3 ) indicating that the majority of taxa present in the site were covered by the analysis. HQ890367, HQ890366, HQ890365, HQ890364, HQ890363, HQ890362, HQ890369) (BS = 99 and PPs = 99); Six OTUs were assigned to Ascomycota OTU88 and OTU38 were ascribed to the genus Mycosphaerella (Fig. 4 ): OTU88, was detected on Q. petraea and Q. suber. Itforms a monophyletic clade with Cf. Ramichloridium (NCBI accession number GU214688), Mycosphaerella sp. M16 (NCBI accession number HM595520) and Mycosphaerella sp. C 2-2 (NCBI accession number JX290142) (BS = 100 and PPs = 100), isolated from Phellodendron amurense in South Korea Abies beshanzuensis in China and Toona sinensis in Korea, respectively  [24] ,  [25]. OTU38 was detected on Q. ilex, representing 3. 2% of the total reads, and, at lesser extent, on Q.
petraea and Q. suber. It has been identified as Mycosphaerella sp. Ponipodef 06 (NCBI accession number HQ731642) isolated from Populus nigra x P. deltoides in China (BS = 88 and PPs = 60). OTU25 was detected on Q. petraea and Q. suber (and other hosts, data not shown) and assigned to Strelitziana albiziae (NCBI accession number HQ599584) isolated from leaves of Albizia julibrissin infected with Camptomeris albiziae in South Korea  [26]. OTU102 was detected on Q. petraea and identified as Leptosphaeria sp. It formed a wellsupported clade (BS = 93 and PPs = 97) including only isolates from China (NCBI accession numbers:HM537061,KF143798,FI537121,KF143787) (Fig. 5 ). The blast and the phylogenic analysis assigned OTU4 to Erysiphe quercicola. No additional OTUs related to Erysiphales have been identified.
From the phylogenetic analysis it is clear that OTU4 is closely related to two isolates from Asia (Thailand and Japan). The division between OTU4 in one clade and the isolate from France on another clade is well supported (BB = 66 and PPs = 81) (Fig. 6 ). OTU77 showed 97% (E-value = 3e-95) pairwise similarity to Lalaria sp. JS-42 (NCBI accession number JF706660) isolated from flowers in Korea (BB = 100 and PPs = 97). In addition, OTU16 was assigned to Pestalotiopsis sp. (97% similarity; E-value = 3e-100). It accounts for about 2. 5% of the total reads on Q. petraea. Although the Asiatic origin of this OTU cannot be demonstrated, it probably refers to a new species. A graphical representation of OTUs abundance and distribution among hosts is showed in Fig. 7. In Table  1 , the information about identification of taxa and association with signs and symptoms are summarized.
and associated conidia were observed on Q. petraea leaf spots. Species of Pestalotiopsis commonly cause diseases on a variety of plants. They are also frequently isolated as endophytes or occur as saprobes  [38]. In such context the presence of conidiomata of Pestalotiopsis on necrotic tissues does not necessarily demonstrate the pathogenic behaviour of this taxa to Q. petraea. However, the low value of identity of OTU16 (97%) with the sequences of Pestalotiopsis spp. in the NCBI database and its considerable presence on Q. petraea (2. 5% of the total reads) support the hypothesis of a new species not previously recorded. Although the potential of sentinel plantations to intercept local plant pathogens seems high, several bottlenecks need to be resolved before this approach can be routinely utilized.
Modelling the sea level evolutions: tide and storm surge The simulation of the sea level evolutions has been conducted with the MARS model, developed by  Lazure and Dumas (2008). The calculations were applied to two regular nested grids having resolutions of 2 km and 400 m respectively (the calculation domains are depicted in Fig. 2 ). The larger grid was used to calculate the atmospheric storm surge. On the nested grid, the tide was simulated by the forcing on the boundaries of the calculation domain of this storm surge combined with 143 tidal components supplied by SHOM (database CST France, Le  Roy and Simon, 2003). To simulate the storm surge, the atmospheric conditions are derived from the CFSR-NOAA data set  (Saha et al. , 2010) : winds and atmospheric pressure, available at a 0. 5 • resolution, were exploited to calculate the non-stationary sea level over the whole studied area, and during a long enough period to attain an established situation.
For Gâvres, the LiDAR data characterization in terms of land use was procured semi-automatically with LasTools software  (Hug et al. , 2004) , completed by a field survey. The DEM interpolation was performed with ArcGIS, with a 1 m resolution, and includes all buildings and walls in the studied area. Ultimately, the 1 m resolution grid numbers 607 nodes (from south-east to north-west) by 663 nodes (from northeast to south-west). It is important to note that, as the collapse time of the wall over the dike is still unknown, this wall has been considered destroyed since the beginning of the simulation. Particular care has been taken with the representation of the dike and this wall, insofar as their topog- raphy strongly constrains the overtopping volume of water. This DEM is represented in Fig.
Acknowledgements. This study was carried out within the  JO-HANNA  project, supported by the  MAIF Foundation for Risk Prevention Research  and the  BRGM , and an applied research program supported by the French ministry in charge of the environment and the  BRGM. The authors would like to thank  IFREMER , for the availability of the MARS model, and the municipality of Gâvres and  DDTM (Direction Départementale des Territoires et de la Mer) of Morbihan  for providing data (respectively the list of flooded houses and the LiDAR data). They likewise are indebted to the French institutions that make several sets of necessary data publicly available: SHOM for the sea-level measurements REFMAR, CEREMA for the wave measurements of the Candhis database, and  Fabrice Ardhuin  for the simulations conducted in the  IOWAGA  project.
Fascin-dependent focal adhesion assembly requires fascin-MT binding We next asked whether the direct interaction between fascin and MT functionally contributed to adhesion dynamics. To this end, adhesion disassembly following nocodazole washout was quantified in fascinKD cells suggesting that stabilising the fascin-MT complex also results in more stable focal adhesions. We postulated that the reduced focal adhesion dynamics in these cells might correlate with altered microtubule stability. In support of this, analysis of acetylated tubulin levels by western blot under basal conditions demonstrated higher levels in MT1 fascin expressing cells and less stable MT in MT1 fascin cells in both HeLa and MDA MB 231 cells  (Fig 2I and data not shown). This data combined demonstrates that fascin-MT binding plays a role in MT stability and that this contributes to focal adhesion disassembly.
More recently, the LHCb experiment also reported a measurement of the χ b1 and χ b2 production cross section ratio using combined √ s = 7 and 8 TeV data  [12]. This Letter presents a measurement of the χ b2 /χ b1 production cross section ratio. The χ b1 and χ b2 states are reconstructed by detecting their radiative decays χ b1,2 → Υ(1S) + γ, which is the dominant decay mode, with the Υ(1S) decaying into two muons. An accurate measurement of the photon energy (typically in the range 0. 5-2 GeV) is obtained from the reconstruction of the momentum of the electron-positron pair originating from the photon conversion in the beam pipe or in the inner layers of the CMS silicon tracker. The resulting mass resolution of the χ b candidates, around 5 MeV, is sufficient to resolve the two χ b1,2 peaks at the expense of a limited yield, given the small reconstruction efficiency for such low-energy photons.
Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
e. (σ π +σ K )/2 or (σ K +σ p )/2. A separation at 3σ level in θ ckov is achieved up to p T = 3 GeV/c for K-π and up to p T = 5 GeV/c for K-p. The separation at 6 GeV/c for K-p can be extrapolated from the curve and it is about 2. 5σ. The HMPID geometrical acceptance is about 5% for tracks with high momentum. Therefore the analysis of HMPID required to analyze a larger data sample with respect to the other PID methods, as reported in Table  1. The total efficiency is the convolution of the tracking, matching and PID efficiencies. The PID efficiency of this method is determined by the Cherenkov angle reconstruction efficiency. It has been computed by means of Monte-Carlo simulations and it reaches 90% for particles with velocity β ∼ 1. As a cross check, the PID efficiency has been determined using clean samples of protons and pions from Λ and K 0 s decays.
5 GeV/c to subtract the contamination due to secondary e ±. This contamination is about 16% for p T = 0. 5 GeV/c. The resulting spectra are corrected for the detector acceptance and for the reconstruction and selection efficiencies. This correction is specific to each analysis and accounts for the acceptance of the detector used in the PID procedure, the trigger selection and the vertex and track reconstruction efficiencies. They are evaluated by performing the same analyses on simulated events generated with PYTHIA 6. 4 (Perugia0 tune)  [26]. The particles are propagated through the detector using the GEANT3 transport code  [34] , where the detector geometry and response as well as the data taking conditions are reproduced in detail. In Fig. 11  the efficiency ε(p T ), specific to each analysis, accounting for PID efficiency, acceptance, reconstruction and selection efficiencies are shown.
All presented PYTHIA tunes use a color reconnection mechanism  [1]  which can mimic effects similar to that induced by collective flow in Pb-Pb collisions  [45]. In both PHOJET and EPOS, which are microscopic models that utilize the color-exchange mechanism of string excitation, the hadronic interactions are treated in terms of Reggeon and Pomeron exchanges. PYTHIA6-Z2 tune is based on the first measurement of multiplicity distributions in minimum-bias pp collisions at √ s = 900 GeV at the LHC. In the CentralPerugia2011 tuning both LEP fragmentation functions and minimun-bias charged particle multiplicity and underlying event data from the LHC are used. Both PYTHIA8 and EPOS LHC are tuned to reproduce the existing data available from the LHC (e. g. multiplicity and, for EPOS, also identified hadron production up to 1 GeV/c for pions and kaons and up to 1.
5 GeV/c for protons). The PHOJET parameters are not retuned using the LHC data. The measured pion p T spectrum is reproduced by EPOS within 15% over the whole p T range. PYTHIA6-Z2, PYTHIA6-CentralPerugia2011 and PYTHIA8 show similar trends. They correctly predict the shapes of the pion spectra for p T > 500 MeV/c, overestimating the data by about 10%, 20% and 25%, respectively, while the shapes differ from data for p T < 200 MeV/c (the ratios are not flat) and the yields are underestimated by up to 30%. The PHOJET generator does not provide a satisfactory description of the measured spectrum shape for any of the particle species. The deviations from the data show a maximum for p T ∼ 1. 2 GeV/c and are more pronounced for kaons and protons than for pions. All the tested Monte-Carlo generators underestimate the kaon yield by about 20-30% for p T > 600 MeV/c while for p T < Pion, kaon and proton productionin pp collisions at √ s = 7 TeV ALICE Collaboration 400 MeV/c they overestimate the data by up to 30%.
A similar deviation is observed by the ALICE collaboration also for other strange particle species with a hierarchy depending on the strangeness content  [46]. The proton yield is well described by EPOS only at low transverse momenta (p T < 1 GeV/c), while the generator tends to overestimate the data by up to 30% at higher p T. None of the three PYTHIA tunes describes the shape of the proton spectrum in the full p T range. All of them give a reasonable description of the yield in the range 1 < p T < 2 GeV/c, but they overestimate the data at lower and higher p T by up to 40%. The comparison of the p T -dependent particle ratios with models allows the hadronization and soft parton interaction mechanisms implemented in the event generators to be tested. In the left and right panels of Fig. 19 , the measured (K + +K -)/(π + +π -) and (p+p)/(π + +π -) ratios as a function of p T are compared with the same event generators shown in Fig.
18. The measured (K + +K -)/(π + +π -) ratio increases from 0. 05 at p T = 0. 2 GeV/c up to 0. 45 at p T ∼ 3 GeV/c with a slope that decreases with increasing p T. All the models underestimate the data at high momenta, with EPOS exhibiting the smallest deviation. The measured (p+p)/(π + +π -) shows an increase from 0. 03 at p T = 0. 3 GeV/c up to 0. 25 at p T ∼ 1. 5 GeV/c while above this p T it tends to flatten. The data are well described by PYTHIA6-Z2 while PYTHIA6-CentralPerugia2011, PHOJET and EPOS show a large deviation at high momenta. PYTHIA8 shows a smaller deviation over the whole momentum range even if, as seen in Fig. 18 , it overestimates both pion and proton spectra. The comparison between data and Monte-Carlo calculations shows that the tunes of the generators based only on few global observables, such as the integrated charged hadron multiplicity, allow only for a partial description of the data.
OS muon and electron taggers look for leptons originating from semileptonic b → cW transitions of the b hadron, and an OS kaon tagger looks for kaons coming from b → c → s transitions. A vertex-charge tagger reconstructs the decay vertex of the OS b hadron and predicts its charge by weighting the charges of its decay products according to their transverse momentum. The OS taggers employed by LHCb are described in Ref. [2]  and the SS taggers in Refs. [3, 4]. This paper reports a new flavour tagging algorithm for the LHCb experiment that relies on reconstructed decays of charm hadrons produced in the OS b hadron decay. For the development and evaluation of the tagging algorithm, signal B meson and charm hadron candidates are reconstructed using data from 3 fb -1 of integrated luminosity collected by LHCb at 7 TeV and 8 TeV centre-of-mass energies in 2011 and 2012, respectively.
The signal candidates are selected by combining pairs of oppositely charged muons, with invariant mass consistent with the known J/ψ mass, with charged kaons, and are required to pass a set of cuts to obtain a good signal to background ratio  [2]. When multiple candidates are present for a single event, that with the best decay vertex fit is kept. A fit to the reconstructed B + mass distribution is used to separate signal and background via the sPlot procedure, which computes signal and background weights for each candidate  [25]. The empirical model for the signal is a sum of two Crystal Ball functions  [26] , while background is modeled by an exponential distribution. A total of 1. 1 × 10 6 signal candidates in this channel are found in the full dataset. The parameters p 0 and p 1 are determined by splitting the data into 13 bins of η between 0.
A forecast road surface temperature (RST) helps winter services to optimize costs and to reduce the deicers environmental impacts. Data from road weather information systems (RWIS) and thermal mapping are considered inputs for forecasting physical numerical models. Statistical models include many meteorological parameters along routes and provide a spatial approach. It is based on typical combinations resulting from treatment and analysis of a database from measurements of road weather stations or thermal mapping, easy, reliable, and cost effective to monitor RST, and many meteorological parameters. A forecast dedicated to road networks should combine both spatial and time forecasts needs. This study contributed to building a reliable RST forecast based on principal component analysis (PCA) and partial least-square (PLS) regression.
To do so, partial least-square (PLS) regression could indeed contribute to such a goal. A similar study based on these multivariate data analysis tools was performed by Kršmanc et al. [13]  but only on specific locations. This study will be divided in two parts. The first one will consist of a description of a RST forecast on a specific urban location based on PLS. The variables consisted in air temperature obtained from measurements with the thermal mapping vehicle, along with other meteorological and anthropogenic data. This will contribute and therefore provide a more comprehensive picture of RST variation for a broader range of atmospheric stability than that conventionally considered by such surveys. A second part will be dedicated to a comparison between forecast results and field data.
Methodology 2. 1. Equipment, Route Investigated. DTer Est in Nancy (France) has developed and used a thermal mapping vehicle from the beginning of the existence of the technique. Along the years, a substantial quantity of thermal data for analysis has been accumulated. For this investigation, a whole route going around and within this town was chosen (Figure  1(a) ). Then a stretch of a French urban street in Nancy (France) was selected for detailed analysis on RST. It is roughly several hundred meters long with a typical urban canyon configuration (Figure  1(b) ). The thermal dataset is available for this route contains thirty-four of thermal fingerprints, each considered a sample, obtained under extreme and intermediate weather conditions over years 2012 and 2013, at least once a month. RST was obtained by using an infrared radiometer operating in the 7-14 m spectral range, for a range from -30 ∘ C to +70 ∘ C, an uncertainty of 2 ∘ C (at 23 ∘ C), fitted to the bottom of a survey vehicle, and located in a compartment at controlled temperature.
Partial Least-Square Regression. PLS is a statistical method that enables data reduction when dealing with large datasets  [17]. It consisted in computing a calibration using a training set between air temperature matrix  air and another one of variables. As pointed out by Almkvist et al. [18, 19] , the ground heat flux is one variable affecting RST. This work detailed in this paper focused on a selection of variables easy to obtain, and that therefore already included this ground heat flux. It includes RST at the chosen urban location, relative humidity, wind force global radiation, nebulosity, the existence of precipitations within the previous 24 hours, the moment of the day (before midday a. m. , or after midday p. m. ), and the traffic intensity (weak, moderate, and high) as an anthropogenic variable.
Traffic data was obtained from the appropriate service and each qualitative appreciation corresponds to a range of hours within the day. A weak traffic occurs during the night, a moderate one roughly between 10:00 a. m. and 4:00 p. m. , and the highest appears during peak hours such as the ones of 8:00 a. m. and 6:00 p. m. Although RST for winter months is around 0 ∘ C, this situation contributes to make difficult the decision to apply deicers with respect to very negative RST values, and this is the reason why they were studied. This training set contributes to the generation of a statistical forecast model of RST with  air. Since noise is contained in both variables and  air data, this computation will conduct to the generation of a calibration matrix  and a minimized error matrix , to obtain the relationship: Variables = 𝑃 ⋅ 𝑇 air + 𝐸.
Search of the Optimum Number of Measurements Sets for the PLS for RST Forecast. PCA was conducted with Unscrambler X 10. 1 package software on  air datasets to identify potential specific thermal fingerprints. Results indicated that over 99% of the variance could be explained with the first principal component, indicating the data homogeneity. These results are consistent with a previous published study  [12]. In the case of  air , an average offset of -2. 3 ∘ C was identified and this correction was then applied to  air profiles calculated from PCA. Generic  air profiles over this route using PCA outputs (scores and loadings) were then calculated and illustrated in Figure  4. PLS was then performed on the urban stretch, considering, respectively, first the whole dataset and then only on some samples corresponding to temperatures below 10 ∘ C considered as representative of winter in the Nancy (France) area.