dataesr/scientific-paragraphs-categorization huggingface dataset:
In our work, we observe that such hierarchy exists as well for BERT models that are not trained using the standard language modelling objective. Goldberg (2019)  shows that the BERT model captures syntactic information well for subject-verb agreement. We build on this work by performing the test on each layer of BERT controlling for the number of attractors and then show that BERT requires deeper layers for handling harder cases involving long-distance dependency information. Tenney et al. (  2019 ) is a contemporaneous work that introduces a novel edge probing task to investigate how contextual word representations encode sentence structure across a range of syntactic, semantic, local and long-range phenomena. They conclude that contextual word representations trained on language modeling and machine translation encode syntactic phenomena strongly, but offer comparably small improvements on semantic tasks over a non-contextual baseline.
Data source and population The Registre Électronique des Arrêts Cardiaques (RéAC) registry is a French, nationwide, prospective OHCA registry that has been described in detail elsewhere  [1]. Briefly, RéAC was initiated in 2009 and deployed nationally in June 2012. Almost 90 percent of French emergency medical aid services participate in RéAC. The registry prospectively includes OHCA victims of any age managed by a physician-manned MICU team, regardless of the CA's aetiology and whether or not CPR was performed. The RéAC's report form is structured according to the Utstein reporting framework. It collects sociodemographic data, time intervals, details of the CA, descriptions of BLS and ALS procedures, and the immediate post-CA outcomes. These data are collected on site. An additional form is used to report on the 30-day follow-up for discharged survivors.
Associations were tested using mixed logistic regression model taken into account the matched nature of the data. Associations were estimated using crude and adjusted odds ratios (cORs and aORs, respectively) and their 95% confidence intervals (CIs). Missing data were not imputed. Unless otherwise stated, the threshold for statistical significance was set to p<0. 05. All tests were two-tailed, and were performed using Stata/MP software (version 13. 0, StataCorp LLC, College Station, TX, USA).
Cells were immunostained with the primary mouse anti-HA antibody (clone 16B12; BioLegend), followed by secondary staining with Alexa Fluor 647-conjugated goat anti-mouse IgG (Invitrogen). After surface HA staining, cells were PFA fixed and analyzed for surface HA and GFP by flow cytometry. In selected experiments, surface HA staining was followed by Triton-mediated permeabilization and intracellular MXA/B staining using rabbit anti-MXA/B (Santa Cruz Biotechnology) as a primary antibody and Alexa Fluor 488-conjugated goat anti-rabbit IgG (Invitrogen). Flow cytometry analysis was performed using a FACSCalibur system with BD CellQuest Pro 4. 0. 2 software (BD Pharmingen) and FlowJo V10 software (FlowJo). TZM-bl infectivity assay. The virus-containing supernatant was transferred to TZM-bl cells stably expressing long terminal repeat (LTR)-driven luciferase.
Briefly, each sample was loaded with a 100-l solution containing 0. 2 l CCF2-AM dye (Thermo Fisher Scientific), 0. 8 l solution B (Thermo Fisher Scientific), 1 l of probenecid (2. 5 mM; Thermo Fisher Scientific), and 98 l CO 2 -independent medium. Cells were incubated in the dark at room temperature overnight. The percentage of cells undergoing fusion with BlaM-Vpr-containing virus particles was monitored by analysis of the change from green (520 nm) to blue (450 nm) fluorescence, using the fluorescenceactivated cell sorter (FACS) LSRIII system with DIVA software, and analyzed using FlowJo V10 software (FlowJo). Internalization assay. Infected T cells were immunostained on ice with the primary anti-HA antibody (clone 16B12; BioLegend). Cells were then shifted to 37°C for the indicated durations, placed back on ice, and immunostained on ice with secondary Alexa Fluor 647-conjugated goat anti-mouse IgG (Invitrogen), followed by PFA fixation.
ACKNOWLEDGMENTS We thank  Oliver Dittrich-Breiholz  for granting access to the StepOne Plus real-time PCR system. We thank the  Flow Cytometry Core Facility at University Hospital Bonn  for help with FACS analysis. We thank  Heinrich Göttlinger  for providing the SERINC5(iHA)encoding pBJ5 plasmid. We are grateful to  Thomas Pietschmann  for constant support. We thank the  NIH AIDS Reagent Program  for providing essential reagents. TWINCORE is a joint venture between the  Medical School Hannover (MHH)  and the  Helmholtz Centre for Infection Research (HZI), Hannover, Germany. Vânia Passos  and  Sergej Franz  are supported by the  Infection Biology International Ph. D. Program of the Hannover Biomedical Research School. Vânia Passos is supported by the  GABBA Ph. D. Program and the Fundação para a Ciência e Tecnologia (FCT).
Primary antibodies used were: anti-XPF (mouse monoclonal, Ab-1 219 Thermo Fisher Scientific) 1:200 and anti-Actin (rabbit polyclonal, ab1801, Abcam, Cambridge, UK) 1:1000. The following day, membranes were incubated in secondary antibodies conjugated to peroxidase during 1 h at RT and revealed with Pierce ECL Western Blotting Substrate (Pierce). Digital images of the membranes were captured with a GeneGnome apparatus (Syngene Bio imaging, Bangalore, India). GeneTools analysis software (Syngene Bio imaging) was used to quantify the amount of protein per band.
Then, cells were washed in PBS 0. 05% Tween-20 for 40 min, fixed with 100 µL Formalin/PBS (1:10) for 20 min and image acquisition and data processing achieved using a high content screening (HCS) system, the ARRAY SCAN VTI (Thermo Fisher Scientific). Plates were scanned with a CCD camera-equipped fluorescence microscope and the images were processed with the software Cellomics Scan (Thermo Fisher Scientific). At least three independent UDS assays were performed and results are represented as fluorescence intensity of treated and non-treated cells.
Dieopxybutinate-Induced G2/M Cell Cycle Arrest A total of 1 × 10 6 cells of each cell line were seeded in 3 × 25 cm 2 flasks (F25) in 5 mL of complete medium. Then, 24 h afterwards, DEB was added at doses of 0, 0. 005, 0. 01, 0. 025 and 0. 05 µg/mL. Then, 48 h after the treatment, cells were trypsinized, PBS washed and resuspended in 200 µL of PBS. 2 mL of ice-cold ethanol 70% was added and cells were placed for 30 min on ice. Ethanol was removed by centrifugation and substituted with 2 mL of Staining solution (40 µg/mL Propidium Iodide, PI, Invitrogen, Carlsbad, CA, USA; Pure Link RNasa A 0. 1 mg/mL, Invitrogen; PBS 1x). Cell cycle population distribution depending on DNA amount was analyzed by flow cytometry with FACSCalibur (BD Biosciences, Allschwil, Switzerland) as in  [35]. A total of 15,000 events were registered per sample and data was analyzed by FlowJo VX software.
Later, a second lysis step was done by adding 250 µL of solution lysis 2 (85. 6 mg/mL sucrose, 15 mg/mL citric acid and 0. 2 µM Sytox green in deionized water) for 30 min at RT. After lysis, samples were stored at 4 • C until being processed by flow cytometry (up to two days). Data acquisition was performed by flow cytometry with FACSCalibur; Sytox-associated fluorescence was detected by FL1 channel while EMA-associated fluorescence was detected by FL3 channel. Collected data was analyzed by Flow Jo VX software. The data of micronuclei (MN) presented in this work represents results from five independent experiments each one in duplicate.
To further discern among XPF variants, functional analysis of GG-NER and TC-NER were performed with the whole set of variants. Figure  2C  shows UDS assay data representing the DNA repair ability by measuring DNA synthesis in the G1 phase of the cell cycle after DNA damage induced by UVC. This assay allowed a clear distinction from the XPF mutations that conferred UV sensitivity and the FA mutations. Despite this, all the variants showed a certain grade of impairment in UDS, the two FA associated variants, XPF-L230P and XPF-R689S, retained around 30% to 40% of their UDS activities when compared to the XPF-WT cells (Figure  2C ). XPF-R153P showed the lowest UDS value and the CS and the XP associated variants, XPF-C236R, XPF-R589W and XPF-R799W, showed UDS levels similar to the XPF-KO. These observations are in line with the UDS levels of the patients-derived cells bearing these mutations  [21, 23, 26, 27].
It has been shown that a replication-independent repair (RIR) of DNA interstrand crosslinks exists and that it works outside the S-phase. This system depends on both branches of NER and translesion synthesis polymerases  [51]  and defects in this mechanism are additive with the defects in ICLs repair during the S-phase  [52]. One hypothesis to explain DEB sensitivity even of the XPF variants that are not causing FA, could be their inability to participate in RIR. The accumulation of chromosome alterations caused by exposure to ICL agents is associated with a delay of the cell cycle to enter into mitotic phase. A DEB-induced G2/M cell cycle arrest assay was performed to test the different XPF variants. Again, for a better understanding of results, data have been plotted in two different graphs sharing the same controls (Figure  3B, C ).
Spectra were acquired using the WinSpec software; further data treatment was performed with the IgorPro for Windows software package. The details of the raw Raman spectra treatment have been described elsewhere. 51 In this work, for Raman kinetics measurements on the basis of spectral band areas, an additional step of automatic background correction using spline functions has been introduced. DNA isolation and characterization. 20 µl of EV preparation obtained as described above from rat hepatocytes were purified using AllPrep DNA/RNA Mini Kit (Qiagen). Quantification was performed using Qubit™ (Thermo Fisher). To examine the size profile, 1 µl of extracted DNA was loaded into a chip of DNA 12 000 kit (Agilent) and run in 2100 Bioanalyzer (Agilent). The electrophoretic profile and gel representation were obtained using 2100 Expert Software (Agilent).
Second, forced aggregation of nanovesicles in the optical trap may occur at very high C p , giving rise to very strong Raman bands that dominate the spectrum and do not correspond to vesicle chemical composition, but rather to the structure of an aggregate (so-called morphology-dependent resonances  35  ). Data analysis of Fig. 5  allows the following main tendencies to be formulated. First, larger vesicles of the same chemical composition produce a stronger Raman signal at the same C p and are easier to detect at smaller C p. For example, at C p = 10 12 ml -1 , the Raman signal from liposomes "200 nm" is approximately 3. 3 and 4. 9 times stronger than those from liposomes "100 nm" and "50 nm", respectively. Second, heavier (filled with biomaterials) vesicles of approximately the same size can be detected at smaller C p.
In such cases, the RTM diagnostics using EVs is undoubtedly feasible. [83]  Intra-sample biomolecular heterogeneity of EVs Nevertheless, the almost perfect reproducibility of the data in Fig. 6 (curve d ) was surprising to us. Therefore, we performed numerous RTM experiments on exosomes from various rat hepatocyte preparations to acquire larger statistics. As a result, we indeed observed strong intra-sample chemical heterogeneity in some preparations. The result of one such experiment is presented in Fig. 7A. Raman spectra (a-c) correspond to 3 independent vesicle sets of the same sample containing exosomes from rat hepatocytes without any treatment. From the analysis of Raman markers (see below), we conclude that this sample contains very different types of exosomes. Vesicle set (a) is characterized by a major contribution from non-saturated lipids, low protein content and the absence of nucleic acids.
Note that the widely used methods of multivariate statistical analysis,  [81] [82] [83]  in particular principal component analysis,  52, 55  provide a robust separation of Raman data into clusters, but information about individual biomolecular composition is usually not obtained. Therefore, for a qualitative, and sometimes quantitative, estimation of global biomolecular content, we employ the method of biomolecular component analysis based on separate Raman markers. The idea is not newthe particularity lies in the choice  121  of Raman markers. For example, two strong dominant Raman contourswithin the range of 1435-1465 cm -1 due to backbone δ(CH 2 , CH 3 ) deformations in proteins and lipids and within the range of 1640-1690 cm -1 due to overlapping contributions from ν(CvC) and ν(CvO) stretches in proteins, lipids and nucleic acidswere found to be non-optimal as they cannot provide a reliable separation of individual biomolecular contributions.
Acknowledgements S. G. K. would like to thank  Raphael Voituriez  for the helpful discussion of the dynamics of single-vesicle trapping. We acknowledge the donation of the MLP-29 cell line by  Dr Enzo Medico. This work has been supported by the  Health Basque Government  ( 2015111149  to JMF), the  Movember Foundation  ( GAP1  to JMF), the  Ramón Areces Foundation  (to JMF), the  Instituto de Salud Carlos III  ( PI12/01604  to JMF), and the  Spanish Ministry of Economy and Competitiveness MINECO  ( SAF2015-66312  to JMF), with all of them co-financed by the  ERDF (FEDER)  funds from the  European Commission , "A way of making Europe".
Test data interpretation Test data were interpreted based on World Health Organization (WHO) criteria  [14] , stating that: mortality < 90% is indicative of resistance, mortality levels from 90 to 97% is suggestive of probable resistance and needs further investigation, and mortality ≥98% is indicative of susceptibility. The mortality of a test sample was calculated by summing the number of dead mosquitoes across all four exposure bottles and expressing this as a percentage of the total number of exposed mosquitoes. The Abbott formula  [15]  was used to correct test mortality if mortality in the control was 5-20%. The test was repeated if mortality in the control was more than 20%. The time necessary to allow 50% of test mosquito populations to be knocked down (KDT 50 ) was determined using the PoloPlus software 1.
A C Fig. 1  Knockdown and resistance factor of wild Anopheles coluzzii populations from non-agricultural (Vitre in column a) and agricultural areas (Tiassale in column b and Gagnoa in Column c) and exposed to three neonicotinoids. We were unable to generate a regression curve for clothianidin as only two time-points knockdown data were recorded after 30 min exposure with more than 90% mosquito's dead in less than 15 min. The resistance ratio (RR) was calculated as the time necessary to allow 50% of test mosquitoes to be knocked down (KDT 50 ) of the wild strain divided by the KDT 50 the susceptible Kisumu strain. We could not calculate the RR for imidachloprid in Gagnoa because only one mosquito was knocked down after exposure. This was referred here as +++ (extremely high) appeared to be resistant to acetamiprid with mortalities less than 85% even 5 days post-exposure.
The first incidences of resistance to neonicotinoids appeared 5 years after their introduction around 1996, in a worst case scenario in glasshouse production systems where multiple insecticide applications occurred within a finite pest population that also had a high reproductive rate. Today, resistance to these products is present in a substantial number of pests and the Arthropod Pesticide Resistance Database lists more than 500 cases of resistance to neonicotinoids  [17]. Mutations on the nAChR responsible for neonicotinoid resistance appear to be very rare in nature  [2] , but when individuals are subjected to extensive selection pressures under laboratory conditions, mutations on these receptors associated with resistance are observed. Cloning of nAChR subunits of Nilaparvata lugens, the brown planthopper, after selection pressure with imidacloprid over 35 generations in laboratory conditions revealed that a replacement of tyrosine with a serine was associated with a 250-fold resistance level to imidacloprid  [18].
The precise tracking system allows the separation of "prompt" charm hadrons, which are directly produced in pPb collisions, from "nonprompt" charm hadrons coming from decays of b hadrons. The excellent particle identification capabilities of the LHCb detector allow measurements of various species of charmed hadrons. Finally, prompt open-charm hadrons can be measured down to low transverse momentum (p T ) at forward rapidity (y) owning to the LHCb's geometric coverage. These measurements provide sensitive probes of the nPDF in the low parton fractional longitudinal momentum (x) region down to x ≈ 10 -6 -10 -5 , where the nPDF is largely unconstrained by experimental data. Prompt D 0 meson production has been measured by the LHCb collaboration in pPb collisions at √ s NN = 5. 02 TeV with data recorded in 2013  [10].
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. The average efficiency for kaon identification for momenta between 2 and 100 GeV/c is about 95%, with a corresponding average pion misidentification rate around 5%. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. The online event selection is performed by a trigger, which consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. This analysis uses the data sample of pPb collisions at √ s NN = 5.
02 TeV taken with the LHCb detector in 2013, with a proton beam energy of 4 TeV and lead beam energy of 1. 58 TeV per nucleon in the laboratory frame. Since the LHCb detector covers only one direction of the full rapidity acceptance, two distinctive beam configurations were used. In the 'forward' ('backward') configuration, the proton (lead) beam travels from the VELO detector to the muon chambers. The rapidity y in the laboratory rest frame is shifted to y * = y -0. 4645 in the proton-nucleon rest frame. Here, y * is the rapidity of the Λ + c baryon defined in the centre-of-mass system of the colliding nucleons, and it is defined with respect to a polar axis in the direction of the proton beam. During data taking, the hardware trigger operated in a 'pass-through' mode that accepted all bunch crossings, regardless of the inputs from the calorimeter and muon systems.
5. The total cross-section is calculated by integrating the double differential cross-section over a given kinematic range. The forward-backward ratio R FB measures the Λ + c production asymmetry in the forward and backward rapidity regions. It is defined as R FB (y * , p T ) ≡ d 2 σ(y * , p T ; y * > 0)/dy * dp T d 2 σ(y * , p T ; y * < 0)/dy * dp T , (2) where σ(y * , p T ; y * > 0) and σ(y * , p T ; y * < 0) correspond to the cross-sections of the forward and backward rapidity regions symmetric around y * = 0, respectively. The R FB ratio is measured in the common rapidity region of the forward and backward data 2. 5 < |y * | < 4. 0. The baryon-to-meson cross-section ratio R Λ + c /D 0 ≡ σ(Λ + c )/σ(D 0 ) is calculated as the ratio of Λ + c and D 0 production cross-sections R Λ + c /D 0 (y * , p T ) = d 2 σ Λ + c (y * , p T )/dy * dp T d 2 σ D 0 (y * , p T )/dy * dp T , (3) where σ Λ + c and σ D 0 are cross-sections of Λ + c and D 0 hadrons in pPb collisions at √ s NN = 5.
The final reconstruction and selection efficiency in general increases with p T. The efficiency is below 1% for the lowest p T values and reaches 4-5% at p T > 8 GeV/c. The PID efficiencies of the Λ + c decay products are assessed separately with a data-driven method  [24]  using high-purity samples of D 0 mesons from D * (2010) + decays for kaons and pions, and Λ baryons for protons. The samples are taken from the same pPb data set as used in the present analysis. The single-track PID efficiencies are mostly above 80% (90%) for protons (pions and kaons) for track momenta in the range of 3 < p < 100 GeV/c and pseudorapidities in the range of 2 < η < 5, although the efficiencies at the edge of the acceptance are generally lower. The single-track PID efficiencies are convolved with Λ + c → pK -π + decay kinematic distributions obtained from simulation to produce the total PID efficiency for Λ + c baryons in each (p T , y * ) bin.
Conclusion Prompt Λ + c production cross-sections are measured with pPb collision data collected by the LHCb detector at √ s NN = 5. 02 TeV. The forward-backward production ratios R FB are presented, and are compared to theoretical predictions. A larger production rate in the backward-rapidity region compared to the forward region is observed. The forward-backward production ratio R FB shows consistency with HELAC-Onia calculations with the three nPDFs EPS09LO, EPS09NLO  [43]  and nCTEQ15  [44]. In addition, the production cross-section ratio R Λ + c /D 0 between Λ + c baryons and D 0 mesons, which is sensitive to the hadronisation mechanism of the charm particles, is measured. The result is consistent with theory calculations based on pp data. The Λ + c measurements in classes of event multiplicity can be anticipated with the pPb dataset at √ s NN = 8.
16 TeV recorded by the LHCb collaboration in 2016, which is about 20 times larger than the 5. 02 TeV dataset. An improvement in precision is also achievable with the increased sample size and an improved simulation. In addition, a dataset of pp collisions at √ s = 5. 02 TeV corresponding to a luminosity of 0. 1 fb -1 was collected in 2017. The nuclear modification factor for the Λ + c baryons can be directly measured using this dataset. MOST    [5, 6]  0. 300 ± 0. 045 ± 0. 047 0. 501 ± 0. 048 ± 0. 063 0. 684 ± 0. 054 ± 0. 076 1. 199 ± 0. 112 ± 0. 145  [6, 7]  0. 123 ± 0. 025 ± 0. 029 0. 203 ± 0. 023 ± 0. 026 0. 258 ± 0. 026 ± 0. 030 0. 377 ± 0. 045 ± 0. 049  [7, 8]  0. 034 ± 0. 015 ± 0. 011 0. 080 ± 0. 014 ± 0. 013 0. 146 ± 0. 018 ± 0. 019 0. 152 ± 0. 026 ± 0. 021  [8, 9]  -0. 049 ± 0. 011 ± 0.
Acknowledgements We are grateful to  H. Shao  for providing theoretical calculations of prompt Λ + c production in pPb collisions in the LHCb acceptance. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;.
The genome of the more deeply sequenced An. fontenillei specimen was assembled at the Bioinformatics Unit, CRG (Barcelona) (Tables  S2, S3A ). Reads were trimmed and filtered using Skewer, version 0. 2. 2  73  , to remove the adapter sequence and the low quality part. A FastQC analysis was performed to check the quality of the trimmed reads. Analysis of contaminants using a Kraken database that includes complete bacterial, archaeal, and viral genomes in RefSeq 74 highlighted the presence of the enterobacteria phage phiX as the only contaminant (Table  S3B ). Then, the trimmed reads were assembled with the Platanus software version 1. 2. 4  75  to produce contigs and scaffolds using the paired-end information. To join the contigs within the same scaffolds, stretches of N needed to be added.