dataesr/scientific-paragraphs-categorization huggingface dataset:
This is most pronounced in QGSJETII. 04. The PYTHIA8 CUET tunes describe the data slightly better, but have a tendency to undershoot the data towards |η| < 3. 5. The band around PYTHIA8 CUETP8S1 in Fig. 3  indicates the typical uncertainties due to the tune parameters. The best description of the data is provided by the PYTHIA8 tune CUETP8S1. When MPIs are switched off in PYTHIA8 more than half of the measured energy is missing, with a slight dependence on η. In Figs. 2 (middle) and 3 (middle) the energy density measurements are compared with predictions for the NSD-enhanced category. The differences between the model predictions are smaller compared with the INEL category. The EPOS-LHC and QGSJETII. 04 hadronic event generators overshoot the measurement only at |η| ≈ 4. 5 and otherwise show a good description of the data.
The PYTHIA8 tune CUETP8S1 at the upper limit of its uncertainties provides the best overall description of the data. Figure  2  (lower) shows a comparison of the energy density measurements as a function of η for the SD-enhanced category to predictions from PYTHIA8 MONASH, EPOS-LHC, and QGSJETII. 04. The comparison of the same data to the different PYTHIA8 tunes is shown in Fig. 3  (lower). For the SD-enhanced category the model spread becomes significantly larger. It is interesting that the EPOS-LHC and QGSJETII. 04 models are both compatible with the data only at the very lower limit of the systematic uncertainties, while all PYTHIA8 tunes are consistent with the data within the uncertainties. Furthermore, the shape of all the model predictions is very similar and, in contrast to the INEL and NSD-enhanced data, consistent with the data.
Summary The energy density, dE/dη, is measured in the pseudorapidity range -6. 6 < η < -5. 2 and 3. 15 < |η| < 5. 20. Special low-luminosity data recorded by the CMS experiment during proton-proton collisions at the centre-of-mass energy √ s = 13 TeV are analysed for this purpose. The data are presented at the stable-particle level to allow a straightforward comparison to any theory prediction or model simulation. The measurements are compared to models tuned to describe high-energy hadronic interactions (PYTHIA8) and to the predictions of models used in cosmic ray physics (EPOS-LHC, QGSJETII. 04) for inclusive inelastic (INEL), nonsingle-diffractive-enhanced (NSD-enhanced), and single-diffractive-enhanced (SD-enhanced) event selection categories. It is shown that the INEL and NSD-enhanced categories are extremely sensitive to multi-parton interactions, while the SD-enhanced category is essentially unaffected.
Acknowledgments 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:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  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) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  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).
Individuals have received support from the  Marie-Curie  programme and the  European Research Council and Horizon 2020  Grant, contract No. 675440  ( European Union ); the  Leventis Foundation ; the A. P. Sloan Foundation; the  Alexander von Humboldt Foundation ; the  Belgian Federal Science Policy Office ; the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the  Agentschap voor Innovatie door Wetenschap en Technologie  ( IWT-Belgium ); the  F. R. S. -FNRS  and  FWO (Belgium)  under the  "Excellence of Science -EOS" -be. h project n. 30820817; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Lend ület ("Momentum") Programme  and the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  New National Excellence Program ÚNKP , the  NKFIA  research grants  123842 ,  123959 ,  124845 ,  124850  and  125105  (Hungary); the  Council of Science and Industrial Research, India ; the  HOMING PLUS programme of the Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus programme of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2014/13/B/ST2/02543 ,  2014/15/B/ST2/03998 , and  2015/19/B/ST2/02861 , Sonata-bis  2012/07/E/ST2/01406 ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu , grant  MDM-2015-0509  and the  Programa Severo Ochoa del Principado de Asturias ; the Thalis and Aristeia programmes cofinanced by  EU-ESF  and the  Greek NSRF ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the  Welch Foundation , contract  C-1845 ; and the  Weston Havens Foundation (USA).
Microstates & Hubs We also evaluated the hubs' dynamic behavior over time. To do so, we segmented the EEG data in 5-s moving windows with 80% overlap. Following this, we inferred a time series formed by hubs-one for each type of hub. Finally, we established a threshold parameter defined by the standard deviation of each series. Using these thresholds, we performed a microstate analysis (described in Section 4. 6) triggered by the local maximum of each type of hub condition. In addition, we measured the correlation between the occurrence of these local maxima. In doing so, we observed a negative correlation between the divergent and neutral hubs across behavioral states. When we examined the correlation between H D and H C , we noticed that, during the cognitive states, when compared with resting-state, the negative correlation between them increases.
However, in addition to considering an imbalance to define sources (divergent hubs) and sinks (convergent hubs) nodes, we also introduced a range in which the incoming pathways are not representatively greater than the outgoing pathways or vice versa (neutral hubs). Furthermore, we evaluated the hubs' contributions, dynamic behaviors and roles in the network. Our data reveal that divergent hubs are more effective in characterizing the network; when they are removed, there is a significant loss of effective connectivity of the whole network (see Figure  1  and Table  2 ). We observed that the average percent variation in the characteristic path length when a single divergent hub is removed from the network is generally greater than when any other hub type is removed, across behavioral paradigms.
This effect was observed across all behavioral paradigms and supports a previous study by Kaiser et al. [14] , who evaluated the robustness of brain networks when removing their nodes and edges randomly or selectively. Similar to what our data reveal, the researchers concluded that, if structures with many connections (e. g. , an isolated hub) or the connective core are removed, the functional effects on the brain network should be substantial. After understanding the contribution of the connective core and the different hubs to the network effectiveness, we analyzed the connective core's composition. In doing so, we observed that the connective core is composed mostly of divergent hubs (see Table  3 ). Furthermore, we performed a tracking of the connective core across behavioral states. We observed that the connective core is composed of an invariant substructure, which is preserved between behavioral states, as well as a variant substructure that is characteristic of each paradigm (see Table  4 ).
EEG Data and Tasks For this study, we collected EEG data using a 32 channel set, with active electrodes from actiCAP and a BrainAmp amplifier (Brain Products, Gilching DE). Electrodes were placed according to the 10/20 standard system and their impedances were kept under 10 kΩ. We collected data at a sampling rate of 1 kHz. Pre-processing was done through the BrainVision analyzer 2. 1 (Brain Products, Gilching DE). Data were filtered through a Butterworth bandpass (0. 5-50 Hz) and a notch filter at 60 Hz. Ocular and muscular artifacts were further removed using independent component analysis and the semiautomatic correction feature of the program. Channels were referenced with respect to their average value. We collected EEG data from eleven volunteers during a resting-state (RS) period, a "Stroop color-word test" (ST) and a "2-back working memory test" (2B).
Chemical shifts were referenced to Tetramethylsilane (TMS) and AlðH 2 OÞ 3þ 6 for Si and Al, respectively. Semi-quantitative Nuclear Magnetic Resonance (NMR) data were obtained using the same mass of zeolite and by collecting spectra with the same number of scans. The acidity of the different zeolites was studied by adsorption/desorption of pyridine, followed by Fourier-Transformed Infrared (FTIR) spectroscopy. A zeolite wafer (approx. 20 mg of zeolite, diameter 16 mm) was placed in a FTIR quartz cell and outgassed at 450 °C for 10 h under vacuum. The temperature was then decreased to 150 °C and pyridine was adsorbed at that temperature for 10 min. Pyridine was then desorbed at increasing temperatures and the corresponding infrared spectra were collected at room temperature, using a Nexus 1 (ThermoOptek Nicolet) spectrometer.
Acknowledgements T. D. A. , V. T. , X. Z. , and L. L. acknowledge the support from  King Abdullah University of Science and Technology. V. T. acknowledges the support from User Proposals (#4420 and #5067) at the  Molecular Foundry ,  Lawrence Berkeley National Lab , supported by the  Office of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. We would also like to acknowledge the support from  Core Lab in KAUST.
Acknowledgement: the support of the  European Regional Development Fund (FEDER)  in the frame of the  Interreg POCTEFA 2014-2020 Program Foodyplast  ( EFA099/15 ) and the EQUIPEX  MARSS  project ( 11-EQPX-0027 ) is acknowledged. AI acknowledges the co-financing of the  Communauté de Communes de Lacq-Orthez (CCLO).
Accepted Article This article is protected by copyright. All rights reserved. sequences of CVYV (GenBank NC_006941, JF460793 and KT276369 for Spanish (ALM32), Jordanian (Jord-gp1) and Israeli (ISM) isolates. respectively), as well as a sequence of coccinia mottle virus (CocMoV), a newly described ipomovirus closely related to CVYV (KU935732;  Desbiez et al. , 2016). For isolate SU12-10, Illumina MiSeq high-throughput sequencing was performed on small RNAs (Fasteris, Switzerland). The fragments were assembled de novo with CLC Genomics Workbench 7 (CLC Bio, Aarhus, Denmark.
Accepted Article This article is protected by copyright. All rights reserved. Full-length sequence of SU96-84 and worldwide isolates Complete sequences of isolates CVYV-Esp from Spain, CVYV-ISR from Israel, TN05-56 from Tunisia, OM04-06 from Oman, LI97-11 from Lebanon, IR02-106 from Iran, SU96-84 and SU12-10 from Sudan were obtained (GenBank accessions MK777988-MK777995). All isolates had the typical CVYV genetic organization, with no helper component and a duplicated P1 protein  (Valli et al. , 2008). All CVYV sequences except SU96-84 and SU12-10 shared at least 93% nt identity (96% aa identity), whereas SU96-84 and SU12-10 displayed only 83-85% nt identity (91-92% aa identity) with the 9 isolates from the Mediterranean Basin and Middle East and between each other. The sequences from the Israeli isolate was very similar to that of the mild CVYV-ISM (probably originating from the same 1960 isolate but with different life history in the lab), with only 40 mutations along the whole genome (99.
Accepted Article This article is protected by copyright. All rights reserved. Recombination signals were detected with at least 5 of the methods implemented in RDP4 between the full-length sequences of isolates LI97-11, Jord-gp1 and CVYV-Esp near positions 1500 (p = 10 -7 to 10 -25 depending on the method), between isolates TN05-56, Jord-gp1 and CVYV-Esp or ALM32 (p = 10 -2 to 10 -6 ) near position 1800, and between SU96-84, SU12-10 and an unknown minor parent at position 8286 (p=10 -6 to 10 -20 ) in the dataset excluding the 5' and 3' non-coding (NC) regions. Two significant recombination signals (p<0. 001) were found with GARD at positions 1521 and 2876. Using SplitsTree4, phylogenetic relationships between the 11 CVYV sequences appeared network-like, suggestive of recombination events (data not shown), and the phi-test for recombination was significant (p= 3.
5 10 -12 ). In order to determine the parental and recombinant sequence(s), the same analyses were performed on the dataset excluding each of the putative recombinants one by one or two by two. Recombination signals were still detected with RDP4, GARD and SplitsTree when only one isolate was removed. Recombination signals disappeared with GARD when LI97-11 and TN05-56 were removed, and with all methods when LI97-11, TN05-56 and SU12-10 were removed simultaneously (p=0. 37 for SplitsTree Phi-test). This suggests that LI97-11 and TN05-56 are indeed recombinant isolate, and it is also probably the case for SU12-10.
) and the sequence was completed by defining primers on the different contigs obtained, RT-PCR and Sanger sequencing. Comparisons of full-length sequences and phylogenetic analyses Sequences were assembled with CAP3 (Huang & Madan, 1999)  with manual corrections when needed. The best substitution model was selected withMEGA6 (Tamura et al. , 2013). Nucleotide and deduced amino-acid sequences were aligned using ClustalW and MAFFT included in MEGA6. For divergent sequences, nucleotide alignments in the coding regions were performed based on amino-acid alignments. Percentages of identity (p-distances) were calculated for whole sequences and for the different regions using MEGA6. Genetic comparisons were also performed including the 3 complete CVYV sequences available in GenBank (see above) and the complete sequences of CocMoV and SqVYV.
Recombination and selection analyses for CVYV Eleven full-length or nearly full-length CVYV sequences, including the GenBank sequences of isolates ALM32, Jord-gp1 and ISM and the 8 sequences obtained during this work, were analysed for recombination using different methods implemented in RDP4 (Martin et al. , 2015): RDP, GeneConv, MaxChi, Bootscan, SiScan and Chimaera. Only recombination signals Comment citer ce document : Desbiez, C. (Auteur de correspondance), Caciagli, P. , Wipf-Scheibel, C. , Millot, P. , Ruiz, L. , Marian, Dafalla, G. , Lecoq, H. (2019). Evidence for longterm prevalence of cucumber vein yellowing virus in Sudan and genetic variation of the virus in Sudan and the Mediterranean Basin. Plant Pathology, 68 (7), 1268-1275. , DOI : 10. 1111/ppa. 13055.
detected by at least 5 methods and with a p-value of less than 0. 001 were taken into consideration. The same dataset was analysed with GARD included in the HyPhy package (Kosakovsky Pond et al. , 2005). Recombination signals were also analysed with SplitsTree (Kloepper& Huson, 2008)  on the alignment of the 11 CVYV sequences. Selective pressures acting on CVYV genomes were estimated by calculating the dN/dS ratio of nonsynonymous to synonymous mutation rates along the whole coding region of the genome, for the 11 complete coding sequences available. Search for positive selection (i. e. dN/dS>1) was performed with the FEL, MEME and FUBAR methods implemented on the HyPhy platform (www. datamonkey. org). The analyses were performed on the whole dataset in a first approach, then separately for the different fragments where no recombination signals were detected and for each protein.
Acknowledgements We thank  Drs Y. Abou-Jawdah ,  A. Al-Matrooshi  and  K. Bananej  for their help in collecting the isolates used for this study. We thank the  Experimental Infrastructure team of INRA-Montfavet  for their help in the greenhouse experiments, and the  Microscopy Platform at INRA-Montfavet  for the electron microscope observations. The authors declare that they have no conflict of interest.
( detected by at least 5 methods and with a p-value of less than 0. 001 were taken into consideration. The same dataset was analysed with GARD included in the HyPhy package (Kosakovsky Pond et al. , 2005). Recombination signals were also analysed with SplitsTree (Kloepper& Huson, 2008)  on the alignment of the 11 CVYV sequences. Selective pressures acting on CVYV genomes were estimated by calculating the dN/dS ratio of nonsynonymous to synonymous mutation rates along the whole coding region of the genome, for the 11 complete coding sequences available. Search for positive selection (i. e. dN/dS>1) was performed with the FEL, MEME and FUBAR methods implemented on the HyPhy platform (www. datamonkey. org). The analyses were performed on the whole dataset in a first approach, then separately for the different fragments where no recombination signals were detected and for each protein.
Cd distribution and isotope fractionation in the soils The variable Cd concentrations of the soils are in accord with previously reported total Cd concentrations for soils from different locations in Ecuador. 5, 8, 15 Notably, the new soil Cd data show no systematic variation with soil type or soil properties and, for the soil proles from Farm A, with depth. The eight soil samples from the two proles of Farm A are, however, in accord with a rough positive correlation of d 114/ 110 Cd with Cd concentration, and two of the three 0-20 cm soil samples from Farms B and C are also in accord with such a trend (Fig. 2 ). The small dataset obtained here does not allow a rigorous evaluation of the inferred correlation but some interpretations can be developed. Notably, the majority of soils cluster at Cd concentrations of 0.
Acknowledgements This study was funded by the  Olga Triballat Institute , the  French Cooperative Ethiquable  and by the  Isotopes Axe of the Observatoire Midi-Pyrénées (OMP-Toulouse). The analytical work in the UK was supported by  STFC-GCRF  funding to MR. Rémi Freydier  from the  Platform AETE-ISO, OSU OREME  at the  Montpellier University  is thanked for the Q-ICP-MS measurements. We are grateful to  Nicolas Eberhart from Ethiquable  for his help with sample collection in Esmeraldas and the cacao farmers for their support in the eld, especially to  Belia Vaca  and  Galo Rodriguez. The critical but constructive comments by four reviewers of an earlier manuscript version and two referees of the current text were immensely helpful in craing a signicantly improved article.
S. A. ) was used for all experiments. 36 Data were acquired in the positive-ion mode at a mass resolution setting of 120000 or 240000 when mentioned (fwhm @ m/z 400) and an ion injection time of 250 ms. Automatic gain control was disabled in all experiments. Laser postionization (MALDI-2) was performed as previously described. 35 Unless reported otherwise, all experiments were performed using a repetition rate of 100 Hz, a MALDI-2 wavelength in the 260-280 nm range, a pulse energy of 500 μJ and a pulse delay time of 5 μs. Further details are provided in the Supporting Information. Data Acquisition. Drug Profiling. Each drug standard and mixture was analyzed sequentially with MALDI-2 (100 Hz repetition rate) and conventional MALDI (100 and 1000 Hz repetition rate). Data were acquired by setting up a rectangular scan area (50 μm step size) within which MALDI-2 data were acquired for three rows after which the MALDI-2 laser was disabled and MALDI data acquired.
For data analysis, 50 scans in each row were averaged to compare the signal intensity from each experiment, while error bars were determined from the standard deviation acquired from three consecutive rows. In the case that analyte signals were unresolved from isotopes of lower m/z fragments (e. g. , [the M -H 2 + H] + ion of terfenadine) isotope correction of ion intensities was performed. The sensitivity evaluation only took in consideration the concentrations presenting a signal-to-noise (S/N) superior to 3 when averaging 50 scans. The S/N values were provided by Thermo Xcalibur 3. 0. 63 software. To calculate the concentration density per surface area, the values were extracted from the HTX imaging software and converted in ng/mm 2. Imaging Experiments. MSI data from human cartilage was acquired across half the tissue using MALDI-2, after which the MALDI-2 laser was disabled and MALDI-MSI data acquired.
For the dog liver sample, first 1 cm 2 of tissue was acquired with both MALDI and MALDI-2 to evaluate the postionization benefits. Then, a consecutive full section was acquired at a pixel size of 30 × 30 μm 2. Data from cartilage was acquired at a pixel size of 50 × 50 μm 2. Tandem mass spectrometry (MS/ MS) was performed directly on tissue to identify triamcinolone acetonide using resonance collision-induced dissociation in the ion trap with a normalized collision energy of 30. An isolation window of 1 m/z unit was employed. Data Analysis. Spectral analysis and visualization were performed using Thermo Xcalibur 3. 0. 63 and mMass 5. 5. 0. 37 MSI data was reconstructed and visualized using in-house developed software written in MATLAB (Matlab; Mathworks, Natick, MA, U. S. A. ; R2014a) as previously described.
Of these 10 compounds, four compounds produced more abundant [M + Na] + ions than [M + H] + ions using conventional MALDI (doxorubicin, paclitaxel, kynurenine, and TAA). Thus, we compared the [M + Na] + intensity of MALDI with that of the [M + H] + ion formed by MALDI-2. MALDI-2 still yielded overall higher intensities for these compounds, as demonstrated in Supporting Information, Figure  6A. We acknowledge the high concentration of drug standards used to generate the data shown in Figure  2. For well-ionizable compounds, this high concentration could potentially result in signals that exceed the single-scan dynamic range of the Orbitrap analyzer. 41 We therefore analyzed five of the same drug compounds following dilutions of between 10-and 500fold to evaluate the gain in signal for lower concentrations.