dataesr/scientific-paragraphs-categorization huggingface dataset:
reclinata samples, and were also misled by the size homoplasy between haplotype 1 and 7 (Figure  2 ). We therefore recommend that sequence data should be obtained before performing any study based on genotyping, in order to have a solid basis to interpret genotyping data. In total, considering all mutation types, our results allowed us to efficiently identify eight out of 13 species. This indicates that the locus psbZ-trnfM(CAU) has some potential to yield DNA barcodes that can be used for species identification within the genus Phoenix. This locus could also be useful to identify the female parent in many interspecific crosses, such as P. dactylifera × P. canariensis. Hybrids involving P. canariensis as female parents are particularly easy to track because this species is monomorphic with a private haplotype at the locus studied.
Acknowledgements We wish to thank the following persons and institutions for their valuable help during the various phases of our study:  Rita Bregliano † ,  Paolo Curir ,  Laura De Benedetti ,  Federica Nicoletti  ( CRA-FSO, Sanremo, Italy );  Frédérique Aberlenc-Bertossi ,  Natalie Chabrillange ,  Nora Scarcelli  ( IRD Montpellier, France );  Muriel Latreille ,  Sylvain Santoni  (AMM at  INRA SupAgro, Montpellier, France );  Patrizia Martini  ( IRF, Sanremo, Italy );  Mauro Roggero  ( Cooperativa "Il Cammino", Sanremo, Italy );  Giancarlo Pignatta  (" U Risveiu Burdigotu" Association, Bordighera, Italy ); the  Natta family (Bordighera, Italy) ;  Robert Castellana  ( CRP, Nice, France ),  Salwa Zehdi  ( University of Tunis, Tunisia ). We also thank the three anonymous reviewers and the scientific editor for their useful comments and suggestions.
Patients and samples By using our endoscopy database, we identified all patients in whom a nonampullary duodenal adenoma or adenocarcinoma was resected using endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) between August 2005 and February 2011, at Jichi Medical University Hospital. The diagnoses were confirmed by at least two pathologists. All of the samples were fixed by paraffin. Approval from the Institute Research Ethics Committee was obtained for the use of clinical materials described in the present study. Informed consent was obtained from all patients capable of completing the full diag-  nostic workup, and/or their parents, before the procedure, and the risk was explained to them in accordance with the Institutional Review Board guidelines.
ACKNOWLEDGEMENTS This work was supported in part by a  Grant-in-Aid for Scientific Research  (C) (21590793 to H. M. and  21590794  to H. S. ) and a  Grant-in-Aid for Scientific Research on Innovative Areas  ( 22114002  to H. M. ) from the  Japan Society for the Promotion of Science. This publication was subsidized by JKA through its promotion funds from  KEIRIN RACE.
Immunohistochemical Studies (IHC). Serial 3 m thick sections were cut from paraffin blocks and mounted on SuperFrost Plus slides (Menzel-Glaser, Germany). -smooth actin, CD68, CD3 (Dakopatts, Denmark), fractalkine (R&D System, Minneapolis, MN), and CX3CR1 (GmbH Gottingen, D) primary monoclonal antibodies were used. CD68 (alkaline phosphatase/anti-alkaline phosphatase) and CD3 (DAB detected) IHC was used to quantify the inflammatory infiltrate. For each antibody, positive cells, as a mean of 10 randomly selected fields per section, were counted at a magnification of 40X in the plaque shoulder region and in the cap itself. Using logistic regression analysis (SPSS 13. 0), the association of each polymorphism with the cardiovascular events (fatal AMI, survivors, and control group) was adjusted for age, gender, smoking cigarette, hypertension, diabetes mellitus, and hyperlipidemia.
Fractalkine and Expression. The Fractalkine expression was evaluated by immunohistochemistry in order to compare T cell population recruitment and chemokine release. Figure  2 (b) shows the expression of CD3, fractalkine receptor CX3CR1 Figure  2 (c) in T cells infiltrating the culprit lesion. In AMI plaques, immunostaining for fractalkine was strongly positive (Figure  2(d) ). The immunofluorescence in Figure  3  demonstrated the coexpression of these three proteins in the same cells of AMI culprit plaque confirming the existence of a T cell chronic active inflammation. Moreover, these data demonstrated the Th1 mediated immune response (IFN- production) involved in plaque inflammation and destabilization correlated with the presence of Th1 polarized T cells (CXCR1 expressing cells) recruited in the plaque by the interaction of fractalkine and the CX3CR1 receptor on the T cell membrane.
The autoclave is connected to an Ar pressure line used to apply a fixed confining pressure (up to 250 bar) and to an H 2 pressure line used to constrain experimental pressure (up to 50 bar) in the autoclave. The autoclave is installed in an oven allowing the temperature to be set and regulated up to 600°C. After air is evacuated out of the autoclave, Ar confining pressure is then fixed to 250 bar, and a continuous pressure of 10 bar of H 2 is applied to generate diffusion of H 2 into the FSCC. The total pressure of the autoclave, about 258 bar, is controlled using LabView software. The FSCCs were maintained at 250°C for 72 h in these conditions. After cooling down the autoclave, both FSCCs were transferred to the Linkam stage and heated back to 250°C, and Raman spectrometry was used to check the gas and dissolved species in the capillaries.
Raman and infrared microspectrometry Raman spectrometry analyses of the liquid and gas in the FSCC were performed using a Labram spectrometer (Horiba Jobin Yvon) using a 514. 5 nm radiation at 200 mW produced by an Ar + laser (Stabilite 2017, Newport Spectra Physics). Labspec software (Horiba Jobin Yvon) was used for spectral and baseline treatment. Methane presence was checked by infrared microspectrometry. The IR analyses were performed using a Bruker Equinox-55 microspectrometer. OPUS ( © Bruker) software was used for baseline treatment and correction for atmospheric CO 2 and water vapour. The FSCCs were analysed directly at the suited temperature (accuracy of AE 0. 1°C) using a Linkam MDS600 heating stage connected to the Raman and IR micro-spectrometer (note: the quartz window was replaced by a sapphire window for the Infared analyses in transmission mode).
ACKNOWLEDGEMENTS This work was carried out as part of a PhD Thesis supported by the  University of Lorraine Nancy, France , and by  TOTAL, Pau, France. Special thanks are expressed to  F. Walgenwitz  (former TOTAL, Pau) who initiated reflection on this research topic and to  D. Rives  and  C. Tomio  ( ENSG trainees, Nancy ) who provided help in conducting part of the experiments and data treatment. J. Bourdet  and an anonymous reviewer are warmly thanked for the helpful comments.
This paper describes a search for the pair production of top squarks using the full dataset collected at √ s = 8 TeV by the Compact Muon Solenoid (CMS) experiment  [7]  at the Large Hadron Collider (LHC) during 2012, corresponding to an integrated luminosity of 19. 5 fb -1. This search is motivated by the consideration that relatively light top squarks, with masses below around 1 TeV, are necessary if SUSY is to be the natural solution to the hierarchy problem  [8] [9] [10] [11] [12]. These constraints are especially relevant given the recent discovery of a particle that closely resembles a Higgs boson, with a mass of ∼125 GeV  [13] [14] [15]. Searches for top-squark pair production have also been performed by the ATLAS Collaboration at the LHC in several final states  [16] [17] [18] [19] [20] , and by the CDF  [21]  and D0  [22]  Collaborations at the Tevatron.
The SUSY MC events are generated without polarization. The effect of this choice on the final result is discussed in Section 9. Expected signal event rates are normalized to cross sections calculated at NLO in the strong coupling constant, including the resummation of soft gluon emission at next-to-leading-logarithmic accuracy (NLO+NLL)  [38] [39] [40] [41] [42] [43]. For both signal and background events, multiple proton-proton interactions in the same or nearby bunch crossings (pileup) are simulated using PYTHIA and superimposed on the hard collision. The simulation of the detector response to SUSY signal events is performed using the CMS fast simulation package  [44] , whereas almost all SM samples are simulated using a GEANT4-based  [45]  model of the CMS detector. The exceptions are the MADGRAPH tt samples used to study the sensitivity of estimated backgrounds to the details of the generator settings; these samples are processed with the fast simulation.
Event selection 4. 1 Object definition and event preselection The data used for this search were collected using high transverse momentum (p T ), isolated, single-electron and single-muon triggers with p T thresholds of 27 and 24 GeV, respectively. The electron (muon) trigger efficiency, as measured with a sample of Z → events, varies between 85% and 97% (80% and 95%), depending on the η and p T of the leptons. Data collected with high-p T double-lepton triggers (ee, eµ, or µµ, with p T thresholds of 17 GeV for one lepton and 8 GeV for the other) are used for studies of dilepton control regions. Events are required to have an electron (muon) with p T > 30  (25)  GeV. Electrons are required to lie in the barrel region of the electromagnetic calorimeter (|η| < 1. 4442), while muons are considered up to |η| = 2.
These processes are collectively referred to as the "rare" processes. The fourth and final background contribution is from the production of W bosons with jets (W + jets). The multijet contribution to the background is negligible in the signal regions due to the requirement of a high-p T isolated lepton, large M T , large E miss T , and a b-tagged jet. Here, "multijet" refers to events composed entirely of jets, without a lepton, W or Z boson, or top quark. Backgrounds are estimated from MC simulations, with small corrections (see below). The simulation is validated in control regions (CRs) designed to enrich the data sample in specific sources of background while maintaining kinematic properties that are similar to those in the signal regions (see Section 6). In the CRs the kinematic variables used in the cut-based and BDT selections are examined to verify that they are properly modeled.
B Monte Carlo modeling of initial-state radiation The experimental acceptance for signal events depends on initial-state radiation (ISR). As the simulation is not necessarily expected to model ISR well, we validate the simulation by comparing MADGRAPH MC predictions with data. The predicted p T spectrum of the system recoiling against the ISR jets is compared with data in Z + jets, tt, and WZ final states. These processes can be measured with good statistical precision in data and cover a variety of masses and initial states. Z + jets events are selected by requiring exactly two opposite-sign, same-flavor leptons (ee or µµ) with an invariant mass between 81 and 101 GeV. These events, as well as the tt and WZ samples discussed below, are collected with dilepton triggers. Events with at least one best signal region     b-tagged jet or with additional lepton candidates are vetoed to remove contributions from tt and diboson (WZ/ZZ) production, respectively.
Given the MC overprediction observed in the high-statistics Z + jets events, and the consistency of the other final states with this result, weights are derived to correct the MC prediction as a function of the p T of the system recoiling against ISR jets. These weights are applied to the MADGRAPH signal samples used in this analysis, and the full values of the corrections are taken as a systematic uncertainty. The values of the weights range from 0-20% depending on the p T of the system recoiling against ISR jets. The shaded bands shown on the ratio plots in   Figure  25 : Comparison of data to MC prediction for the jet recoil system p T in tt events. The MC prediction is normalized to the total data yield. The ratio of data/MC is also shown. The shaded band shows the weights derived for MC simulation and the variation to assess systematic uncertainties.
Acknowledgements We thank  Ian Low  for assistance with polarization issues in top squark decays and  Jiayin Gu  for help in implementing the code for the variables of Ref. [55]. 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:  BMWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MEYS (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES (Croatia) ;  RPF (Cyprus) ;  MoER ,  SF0690030s09  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NKTH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Republic of Korea) ;  LAS (Lithuania) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MSI (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MSTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  NSC (Taipei) ;  ThEP-Center, IPST  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Individuals have received support from the  University of California Institute for Mexico  and the  United States ; the  Marie-Curie  programme and the  European Research Council and EPLANET (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  Ministry of Education, Youth and Sports (MEYS) of Czech Republic ; the  Agence Nationale de la Recherche   ANR-12-JS05-002-01  (France); the  Council of Science and Industrial Research, India ; the  Compagnia di San Paolo (Torino) ; the  HOMING PLUS programme of Foundation for Polish Science , cofinanced by  EU, Regional Development Fund ; the Thalis and Aristeia programmes cofinanced by  EU-ESF  and the  Greek NSRF.
Results For this study, 27 matK and 37 rbcL barcode sequences where generated from 37 morphologically identifiable specimens (Table  1 ; GenBank KF746442-KF746505). Median sequence quality (B 30 )  22  of the newly generated sequences was 0. 891 (IQR 5 0. 829-0. 928) for matK and 0. 909 (IQR 5 0. 756-0. 939) for rbcL. Trimmed and edited matK sequences were 840 bp in length for A. wrightii and 837 bp in length for all other species examined (A. wrightii has a lysine  (AAG)  inserted at nucleotide position 306). All Trimmed and edited rbcL sequences are 607 bp in length. When the newly generated sequences were analyzed in concert with publicly available sequences (Table  2 )  3, 4, [23] [24] [25] [26] [27] [28] [29] [30] [31]  , no unambiguous matK sequence variation was detected within S. repens (n 5 12) or A.
wrightii (n 5 15). Variation was detected at two rbcL nucleotide positions in S. repens (n 5 18): GenBank sequence AJ621936  25  had a 'C' at nucleotide position 60 whereas all other sequences examined had an 'A' at that nucleotide position and GenBank sequence M81815  23  had a 'C' at nucleotide position 234 whereas all other sequences examined had a 'T'. Both nucleotide substitutions are predicted to result in amino acid substitutions. Neither nucleotide substitution has been detected in more than one individual. No rbcL sequence variation was detected in A. wrightii (n 5 17). Serenoa repens and A. wrightii can be consistently distinguished from Brahea, Livistoninae, and Rhapidinae by a combination of matK nucleotide positions 802 and 818 (Fig. 1 ). Serenoa repens, A. wrightii, and Pholidocarpus majadum Becc.
PCR products were treated with ExoSAP-IT (USB) and bidirectionally sequenced on a 3730 automated sequencer (Life Technologies) using the amplification primers and BigDye v3. 1 (Life Technologies; High-Throughput Genomics Unit, University of Washington). Data analysis. Raw chromatograms were processed with KB (version 1. 4; Life Technologies) and contigs were created and edited with Sequencher (version 4. 10; Gene Codes). Sequence quality was evaluated using B (version 1. 2)  22  with the quality threshold (q) set to 30. Publicly available reference sequences where analyzed along with the sequences generated for this study (Tables  1  and 2 )  3, 4, [23] [24] [25] [26] [27] [28] [29] [30] [31]. Diagnostic nucleotide positions were located in multiple sequence alignments constructed with MUSCLE (version 3.
The spoligotype patterns were uploaded and assignment of the Spoligo International Type (SIT) designations was done using the international SITVIT2 database of the Institute Pasteur of Guadeloupe, which is an updated version of the SITVITWEB database  [10]. When a spoligotype was not found in the SITVIT2 database, it was classified as a unique spoligotype. The term "orphan spoligotype" refers to an isolate having a spoligotype that was present only once in our dataset. When multiple isolates (i. e. 2 or more) were obtained with a unique spoligotype pattern, a new SIT number was assigned by Institute Pasteur. For the unique patterns, lineage designations were given manually by expert-based interpretations using revised SpolDB4 rules. A Finnish designation "F-type" was assigned to the Finnish unique orphan spoligotypes.
Isolates Genotypic lineages (i. e. clades or families) were obtained from the SITVIT2 database (accession date: May 16 th 2013), designated in the database as: the Beijing clade, the Central Asian (CAS) clade and 2 sublineages, the East African-Indian (EAI) clade and 9 sublineages, the Haarlem (H) clade and 3 sublineages, the Latin American-Mediterranean (LAM) clade and 12 sublineages, the ancestral "Manu" family and 3 sublineages, the S clade, the IS6110-low-banding X clade and 3 sublineages, an ill-defined T clade with 5 sublineages, as well as the recently described Ural lineage (with 2 sublineages Ural-1 and Ural-2).
Statistical analysis Data regarding patients, clinical presentation, and M. tuberculosis complex isolates were obtained from the laboratory database and from the National Infectious Diseases Registry, and analyzed in STATA 12 (Statacorp LP, USA). When multiple samples were collected from the same patient, the first isolate with a genotype result was used. Spoligotypes in binary format and MIRU results were entered into BioNumerics software version 6. 6 (Applied Maths Inc. , Sint-Martens-Latem, Belgium) for cluster analysis. Minimum spanning tree (MST) was constructed by permutation resampling (200x) with highest resampling proportion. Fischer exact and Pearson Chi 2 test were performed to assess the similarity between lineages.
Bacterial isolates Of the 1048 M. tuberculosis complex isolates, 1044 (99. 6%) were M. tuberculosis, three were Mycobacterium africanum, and one was Mycobacterium bovis. With MIRU typing, 722 distinct profiles were obtained of which 149 (20. 6%, 475 isolates) were recognized and 573 (79. 4%) were not recognized in the MIRU-VNTR plus website  [11]. From the 273 distinct spoligotypes that were identified, 215 (78. 7%) were recognized in the SITVIT2 database while 58 (21. 2%) were not recognized (unique). Out of the 58 unique spoligotypes, 23 new SITs (41 isolates) were created using revised SpolDB4 rules. Of the new SITs and unique orphanspoligotype patterns, 68 isolates were only detected in Finland (F-type), 67. 6% (46/68) of these were obtained from Finnish born patients. 51 strains remained orphan.
M. tuberculosis genotypic lineages The most common TB lineages found from the Finnish isolates were T (n=346, 33. 0%) and Haarlem (n=237, 22. 6%) (Table  1  and Figure  1 ). For 130 isolates (12. 4%) the lineage could not be defined (i. e. was unknown). Out of the 130 isolates, 103 (79. 2%) were obtained from Finnish born patients. Based on phylogeographical mapping, the distribution of lineages closely resembles the distribution seen in Sweden (Figure  2 ). The main difference was for the Ural lineage, which according to the SITVIT2 database, was not regularly found in Sweden. Both Ural-1 and Ural-2 (42 and 18 isolates respectively) were predominantly found among Finnish born cases (50/60, 83. 3%). Additionally, isolates not belonging to any known lineage (i. e. undefined clade) were found more commonly among Finnish born (n=103, 79.
In-depth cluster analysis and epidemiological research is warranted to investigate this conclusively. Although the majority of our isolates could be genotyped and classified successfully, many of the spoligotypes and MIRU profiles found in this study were not recognized in the international databases. We identified 68 isolates with spoligopatterns that were exclusively encountered in Finland (F-type), and of those 51 were orphan spoligotypes. This could be due to information lacking in the databases or could reflect the unique nature of Finnish M. tuberculosis isolates. These strains were genetically distinct from the more common SIT strains, but more research is needed to explore this in-depth. In conclusion, a large proportion of the M. tuberculosis isolates seen in Finland originated from Finnish born elderly patients.
Filamentous growth is only seen extracellularly, and this difference is highly significant (p,0. 001) in both DMSO (n = 11) and DPI (n = 12), as measured by Fisher's exact test. Data are pooled from three independent experiments. (B-E) CAF2-dTomato-infected Tg(mpeg1:GAL4/UAS:Kaede) fish were treated with DMSO (n = 11) or DPI (n = 7) from two hours pre-infection and throughout imaging. At 4 hpi, only macrophages at the infection site in the hindbrain were photoswitched green-to-red by exposure to 405 nm laser light. Photoswitched fish were imaged every two hours for an additional 18 hours. (B) Representative images of macrophage movement during the time-lapse, with a schematic above depicting the movement of macrophages out of the hindbrain and acquisition of yellow color as fresh green-fluorescent Kaede protein is produced.
Scale bar = 50 mm. (C) Representative images of DMSO-(left) and DPI-treated (right) fish at 18 hpi, showing filamentous growth in the DPI-treated but not the control fish. Scale bar = 50 mm. (D) Macrophages at the site of infection were enumerated at each time-point. Green and red bars represent green-fluorescent (native Kaede) and red or yellow-fluorescent (photoswitched Kaede). Solid bars represent averages from control fish and lightly shaded bars from DPI-treated fish. Although all the infected, DPI-treated, fish died by 18 hpi, uninfected, DPI-treated, fish do not die due to this treatment alone (data not shown and Fig. 5D ). (E) Movement of photoswitched macrophages from the hindbrain was tracked for two classes of Kaede+ macrophages: those that had internalized fungi and those that did not phagocytose fungi.