dataesr/scientific-paragraphs-categorization huggingface dataset:
We thank an anonymous referee for their comments to improve the manuscript and the helpful discussion with  Dr. Li Gang ,  Guo Zhao ,  Zhang Xianfei , and  Prof. Wei Xing. We acknowledge the support from the  National Natural Science Foundation of China (NSFC)  through grants  11833002 ,  11903005 ,  12090040 , and 12090042. W. Z. is supported by the  Fundamental Research Funds for the Central Universities. S. C. is supported by the  Agence Nationale de la Recherche  ( ANR, France)  under grant  ANR-17-CE31-0018 , funding the  INSIDE  project, and financial support from the  Centre National d'Études Spatiales (CNES, France). The authors gratefully acknowledge the Kepler team and all who have contributed to making this mission possible. Funding for the Kepler mission is provided by  NASAʼs Science Mission Directorate.
Hashiguchi and Naraoka (2019b). Criteria for identi cation of mass peaks from the sample surface was the intensity ratio > 10 at the sample surface (inside of the dotted line in Fig. 1b ) to the outside, which contained background ions from spray solvent and/or surrounding air. The obtained mass spectral data were converted to a text data le for imaging by Fire y software (Prosolia Inc. ) and the DESI images were visualized using BioMAP (maldi-msi. org). For the image analysis, the mass resolution of m/z of ± ~ 0. 001 was adapted. The chemical formulae of identi ed peaks were assigned using 12 C, 13 C, H, 14 N, 16 O, 23 Na, and 32 S, then ltered by ± 3 ppm mass precision. After the imaging, the ToF-SIMS measurement was performed on the same surface of A0080 using a TRIFT III spectrometer (ULVAC-PHI, Inc.
Acknowledgments We thank  Laetitia Laub  for a fruitful and enjoyable collaboration. This research is supported in part by the  Albert Einstein Center for Fundamental Physics at Universität Bern (NHT) , the  Swedish Research Council  grants contract numbers  2016-05996  and  2019-03779  (JB) and the  Agence Nationale de la Recherche (ANR)  under grant  ANR-19-CE31-0012  (project  MORA ) (ARS).
Results In the following we assume a dataset collected at the left-polarized (80% left-polarized electron beam, 30% right-polarized positron beam) ILC250 with 2000 fb -1 of integrated luminosity. Figure  3  gives the resulting values for a combination of the → + -and → + -primary Z boson channels. The left panel of Figure  3  shows the result in the SM case. The central panel supports the claim that the method is truly model independent. Here we assume a model with ( → ¯ ) decreased by 15%, and ( → + -) increased by 15%. While this is an unrealistically large deviation from the SM, this shows that the procedure still works far away from the SM hypothesis. The (absolute) 1 -uncertainties are listed in the table on the right side of Figure  3. These values assume BRs close to the SM expectation.
We bleached one area in L3 larval muscles and measured fluorescence recovery over 29 min (Figure  8A , B, Figure  8  -Video 1). We found little to no recovery during the observation period. This demonstrates that the Sls-Nano2 is indeed stably bound the Sls-Ig13/14 target and furthermore that Sls protein does not exchange significantly over a 30 min period in mature larval muscles. Together, these data verified that nanobodies against Drosophila proteins can indeed be expressed in vivo and thus can be used to investigate the dynamics of a chosen target domain. Hence, the here generated nanobodies will be invaluable tools to quantify the dynamics of Sls and Proj during muscle development and maintenance.
, 2022). Our data suggest that most of the Sls isoforms present in flight muscles do contain the C-terminal Sls-Ig51-Fn2 domains. This is consistent with developmental transcriptomics results that included splice isoform annotations  (Spletter et al. , 2015; 2018) , and the very low expression of a Sls isoform that uses an early alternative stop codon, which is rather expressed in leg muscles  (Sarov et al. , 2016). This is significant as the initially proposed short Sls isoform named Kettin is not supposed to contain the C-terminal Sls-Ig51-Fn2 domains and hence would not bridge across the thin I-band of flight muscles to the myosin filament  (Burkart et al. , 2007; Lakey et al. , 1993; Szikora et al. , 2020). Our new nanobodies now clarify that most Sls isoforms have at least the potential to bridge to the myosin filament in flight muscles  (Schueder et al.
Recombinant immunogens and Nanobody generation We screened existing transcriptomics data  (Spletter et al. , 2015; 2018)  and Flybase (http://flybase. org/reports/FBgn0086906; http://flybase. org/reports/FBgn0005666) to identify candidate domains of Sls and Proj that should be expressed in all muscle types. Next, we used Swissmodel  (Waterhouse et al. , 2018)  to predict domain borders for stably folding fragments. These fragments were then codon-optimised for expression in E. coli and cloned into a His14-bdSUMO fusion vector  (Frey and Görlich, 2014a). Expression was in E. coli NEB Express I q at 21°C, in 2YT + 50 µg/ml kanamycin with four hours of induction with 100 µM IPTG. Bacteria were pelleted by centrifugation, resuspended in 50 mM Tris/HCl pH 7. 5, 20 mM imidazole/ HCl pH 7. 5, 300 mM NaCl, lysed by a freeze-thaw cycle followed by sonication.
Biolayer interferometry (BLI) BLI experiments were performed using High Precision Streptavidin biosensors and an Octet RED96e instrument (ForteBio/Sartorius) at 25°C with phosphate-buffered saline (PBS) pH 7. 4, 0. 02% (w/v) Tween-20 and 0. 1% (w/v) BSA as assay buffer. Sls-Nano2, modified via an N-terminal and a C-terminal ectopic cysteine with two Biotin-PEG 3 -Maleimide molecules (Iris Biotech), was bound at 0. 6 µg/ml concentration to the sensors until a wavelength shift/binding signal of 0. 4 nm was reached. After one washing step in buffer, the biosensors were dipped into wells containing a concentration series of the Sls-Ig13/14 domains to measure the association rate and then incubated with assay buffer for dissociation. Data were reference-subtracted, and curves were fitted globally with a 1:1 binding model (Octet Data Analysis HT 12.
Acknowledgements We thank  Stefan Raunser  and  Mathias Gautel  and all their group members, as well as the Schnorrer and Görlich groups for their stimulating discussions within the  StuDySARCOMERE ERC  synergy grant. We thank  Metin Aksu  for help with the Octet measurements. We are grateful to the  IBDM imaging facility  for help with image acquisition and maintenance of the microscopes.
Funding This work was supported by the  Centre National de la Recherche Scientifique (CNRS, F. S. ) , the  Max Planck Society  (D. G. ),  Aix-Marseille University  (P. M. ), the  European Research Council  under the European Union's  Horizon 2020 Programme  ( ERC-2019-SyG 856118  to D. G. & F. S. ), the  excellence initiative Aix-Marseille University A*MIDEX  ( ANR-11-IDEX-0001-02 , F. S. ), the  French National Research Agency  with  ANR-ACHN MUSCLE-FORCES  (F. S. ), the  Human Frontiers Science Program (HFSP ,  RGP0052/2018 , F. S. ), the  Bettencourt Foundation  (F. S. ), the  France-BioImaging national research infrastructure  ( ANR-10-INBS-04-01 ) and the  Investissements d'Avenir, French Government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from  Excellence Initiative  of.
In order to investigate to what extend the localisation of this critical sarcomere component is conserved across evolution of bilaterian muscle, we aimed to re-investigate the localisation of the two Drosophila titin homologs Sallimus and Projectin by generating specific nanobodies. By carefully mining the Flybase expression database (http://flybase. org/reports/FBgn0086906; http://flybase. org/reports/FBgn0005666)wehave annotated the likely longest Sallimus (Sls) and Projectin (Proj, gene called bent, bt) isoforms expressed in larval body wall muscles (Figure1A, B). The longest Sls isoform contains 48 immunoglobulin (Ig)-domains of the total 52 Ig domains coded in the Sls gene (4 are selectively present in a short larval isoform).
Results and discussion In total, five sets of data were collected with one using 399 ms of accumulation time and four using 500 ms accumulation time. In each set the measurements of the cyclotron frequencies ν c of 131 Xe + and 131 I + were interleaved by switching ion species every ≈6 minutes. To obtain their cyclotron frequency ratio R, the values of ν c of 131 Xe + were linearly interpolated to the time of 131 I + measurement. The frequency ratio for each set was obtained by averaging the individual ratios. Fast switching between the two ion species ensured that the temporal fluctuations of the magnetic field had less than 10 -10 contribution to the uncertainty  [43]. Bunches with up to five detected ions were considered in the data analysis to reduce a possible cyclotron frequency shift due to ion-ion interaction  [39, 43].
Acknowledgements We acknowledge the support by the  Academy of Finland  under the  Finnish Centre of Excellence Programme 2012-2017 (Nuclear and Accelerator Based Physics Research at JYFL ) and projects No. 306980 ,  312544 ,  275389 ,  284516 ,  295207 ,  314733 ,  318043 ,  327629  and  320062. The support by the  EU   Horizon 2020 research and innovation program  under grant No. 771036  ( ERC CoG MAIDEN ) is acknowledged.
Acknowledgements The authors would like to thank  Csaba Tóth , and  Hann-Shin Mao  for help with the BELLA measurements,  Fabrice Reau  and  David Garzella  for operating the UHI-100 laser system, as well as  Elsa Baynard  and  Alok Kumar Pandey  for assisting measurements on the LASERIX laser system.
Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors  [25]. 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  [26]. The online event selection is performed by a trigger  [27] , 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 a data sample of pp collisions at √ s = 13 TeV recorded during 2016, corresponding to an integrated luminosity of about 1. 7 fb -1.
Roughly half of the data were recorded in each of the dipole magnet polarity configurations, resulting in a large degree of cancellation of charge-dependent curvature biases and their associated uncertainties. These data correspond to an average number of proton-proton interactions per bunch-crossing event of O(1). During Run 2 the LHCb detector was aligned and calibrated in real-time  [28]. The alignment of the tracking system is based on a χ 2 minimisation of the residuals of the clusters of tracker hits evaluated with a Kalman filter that takes into account multiple scattering and energy loss  [29]. The alignment algorithm also permits mass and vertex constraints  [30]. An optimised offline alignment, which includes Z → µµ events with a mass constraint that accounts for the natural width of the Z boson, is used to determine the track parameters.
Candidate QCD programs Five software programs, or combinations of these, are evaluated as potential candidates for the weighting of the simulated W and Z boson events. 1. Pythia: Events are generated using Pythia version 8. 235  [17]  with several values of the intrinsic transverse momentum (k intr T ) of the initial state partons and α s , closely following the work of Ref. [19]. The NNPDF23 lo as 0130 qed  [44]  PDFs are used in the event generation. The events are weighted using the methods described in Ref. [45]  to the NNPDF31 lo as 0118  [46]  and CT09MCS  [47]  PDFs. 2. POWHEGPythia: Events are generated using POWHEGBoxV2  [48]  with the NNPDF31 nlo as 0118 PDFs and are subsequently showered with Pythia version 8. 244  [17]. The event generation with POWHEGBoxV2 is repeated with different values of α s.
Acknowledgement I am thankful to  Clément Tauber ,  Antoine Venaille  and  Frédéric Faure  for inspiring discussions. This manuscrit was written partially from the lecture I gave at the  École Normale Supérieure de Lyon  from  2018 , and in a much less detailed version at Les Houches and Cargèse summer schools in 2018 and 2019, and partially from my Habilitation thesis [73].
ACKNOWLEDGMENTS We thank  L. Mazza  for useful suggestions during the writing of the manuscript. This work has been supported by the  Swiss National Science Foundation  under Division II. J. F. and M. F. acknowledge support from the  FNS/SNF Ambizione  Grant  PZ00P2_174038. We also thank  X. Turkeshi  and  M. Schiró  for making us aware, in the final phase of the writing of this manuscript, of their work [105] before publication, where a study of the dephasing model from the point of view of Green's function has also been performed.
Acknowledgments We would like to thank  Tim Adamo ,  Davide Billo ,  Andrea Cristofoli ,  Alok Laddha  and  Nathan Moynihan  for interesting discussions. TMCL was supported by  Science Foundation Ireland  through grant  15/CDA/3472. AP is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 852386 ). RG has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 764850  "SAGEX".
1-2. This analysis provides us with a unified view of metallic behaviour. As shown in Fig. 3a, b , the fitting of resistivity to all those metallic systems, reveals a clear evolution of coefficient A 1 and A 2 as a function of their room temperature resistivity (ρ 300K ). Interestingly, the data includes NdNiO 3 (NNO), which we consider in the present work as both, a test case and illustration of the utility of the formalism. In fact, this compound is attracting significant attention, since superconductivity was reported in the infinite layer system Nd 1-x Sr x NiO 2  [31] , and is a remarkable example of a material whose metallic behavior has been particularly difficult to classify  [25, 32, 33]. For the present study we have used high quality epitaxial NNO films grown on LaAlO 3 substrates, which have been characterized in detail in our previous work  [34, 35].
C. Fit to various metals In the present work, a parallel resistor formalism  [8] : 1/ρ(T )=1/(ρ 0 +AT+BT 2 )+1/ρ sat , was employed to describe the electrical resistivity of NdNiO 3 film. For comparison, the resistivity data of various metallic systems from simple metals to strongly correlated metals were extracted from previous works. In the following, we will discuss in detail the fits to all the materials using the same approach.
For this, we rely on EUV imaging data from the recently launched Solar Orbiter  (Müller et al. 2020 ) spacecraft, in particular from the Extreme Ultraviolet Imager (EUI;  Rochus et al. 2020). This particular EUI data set has a spatial resolution of about 420 km (2 pixels) and a cadence of 5 s. This is significantly better than earlier EUV observations covering polar regions. As a result, we are able to study how these blobs are associated with the initiation of flows along the base as well as along the jet spire. This paper is organized as follows: Sect. 2 describes the data and the results are presented in Sect. 3. Finally, we conclude with a discussion in Sect. 4.
Data In this study, we use the EUV imaging data of the north polar coronal hole taken with EUI on Solar Orbiter. In particular, we use data from the High Resolution Imager at 174 Å (HRI EUV ) which samples plasma with a temperature of around T ≈ 1 MK. The EUI data 1 were obtained on 2021 Sep. 14 between 04:11 and 04:30 UT. To extend the time covered by EUI, we also use data from the Atmospheric Imaging Assembly (AIA;  Lemen et al. 2012 ) onboard the Solar Dynamics Observatory (SDO;  Pesnell et al. 2012). The AIA images were taken through the 171 Å (just below 1 MK) and 193 Å (T ≈ 1. 5 MK) passbands, between 03:20 UT and 04:40 UT on the same day 2. At the time of this observation, Solar Orbiter, and with it EUI, was at a distance of 0. 59 AU from the Sun and its position with respect to the solar longitude was about 47 • west from the Sun-Earth line.
A schematic diagram outlining the relative position of these two spacecraft is shown in Fig. 1a. The plate scale of EUI in this dataset is ∼210 km per pixel. For AIA, this is ∼400 km per pixel. Moreover, the image cadence in EUI is 5 s, while the AIA data cadence is 12 s. The time stamps we quote in this paper are Earth times, that is to say the times measured at a distance of 1 AU, taking into account the difference in the observing time due to the difference in Sun-EUI and Sun-AIA light travel times.
The signals are weaker in this case and hence, only a few ridges can be identified with conviction. The speeds of these transients lie between 109 km s -1 and 136 km s -1. Although these values are fairly comparable to the blob speeds, they are significantly lower than the outflow speeds that we had measured earlier from the AIA data. Further, the association between a propagating blob in Slit 1 (Fig. 5b ) and a transient seen in Slit 2 (Fig. 5d ) cannot be established conclusively due to a weaker signal in the Slit-2 map. Nonetheless, these outflow speeds are significantly higher compared to the speeds of the micro-jet-like events that were reported by  Hou et al. (2021) , using similar or coarser spatiotemporal EUI data covering a quiet-Sun region 4. Therefore, the higher flow speeds that we observed here could be facilitated by ambient open magnetic fields in the coronal hole region.
Acknowledgements. We thank the anonymous reviewer for the encouraging comments and helpful suggestions. Solar Orbiter  is a mission of international cooperation between  ESA  and  NASA , operated by  ESA. The EUI instrument was built by  CSL ,  IAS ,  MPS , MSSL/UCL, PMOD/WRC, ROB,  LCF/IO  with funding from the  Belgian Federal Science Policy Office  ( BELSPO/PRODEX PEA 4000134088 ); the  Centre National d'Etudes Spatiales (CNES) ; the  UK Space Agency (UKSA) ; the  Bundesministerium für Wirtschaft und Energie (BMWi)  through the  Deutsches Zentrum für Luft-und Raumfahrt (DLR) ; and the  Swiss Space Office (SSO). The AIA data used here is courtesy of the  SDO (NASA)  and  AIA consortium. The authors would also like to acknowledge the  Joint Science Operations Center (JSOC)  for providing AIA data download links.
Indeed, no electrostatic field apart from the one in LiNbO 3 will contribute to the EO effect. The same structure was modeled to solve the Poisson equation when a voltage of 1 V is applied between the two consecutive electrodes. Thus, COMSOL multiphysics software was used to this end by integrating periodic boundary conditions in the x-direction and Dirichlet boundary conditions for the up and down limits of the calculation window. In addition, a very fine mesh, based on Delaunay triangulation, was applied in order to minimize the numerical errors when interpolating the 𝐸 es (x,z) values over the regular square grid used in the FDTD simulations. The x-component of the electrostatic field presented in Fig. 2   and -𝑉 𝑊 2 = -1 260 × 10 -9 = -3. 85 × 10 6 𝑉/𝑚, (4) respectively. This difference comes from the presence of electrode corners (90 • ) that induces an enhancement of the electric field due to a well-known "antenna effect" that was widely studied both theoretically  [27, 28]  and experimentally  [29, 30].
Acknowledgements Several computations have been performed on the supercomputer facilities of the "Mésocentre de calcul de Franche-Comté". This work has been partially supported by the EIPHI Graduate School (contract ANR-17-EURE-0002) and by the ANR-CNRS Astrid Maturation EsencyaIMAT under the number ANR-19-ASMA-0003-01.
We investigate the strong-lensing cluster Abell 370 (A370) using a wide Integral Field Unit (IFU) spectroscopic mosaic from the Multi-Unit Spectroscopic Explorer (MUSE). IFU spectroscopy provides significant insight into the structure and mass content of galaxy clusters, yet IFU-based cluster studies focus almost e xclusiv ely on the central Einstein-radius re gion. Co v ering o v er 14 arcmin 2 , the new MUSE mosaic extends significantly beyond the A370 Einstein radius, providing, for the first time, a detailed look at the cluster outskirts. Combining these data with wide-field, multi-band Hubble Space Telescope ( HST ) imaging from the BUFFALO project, we analyse the distribution of objects within the cluster and along the line of sight. Identifying 416 cluster galaxies, we use kinematics to trace the radial mass profile of the halo, providing a mass estimate independent from the lens model.
g. Bergamini et al. 2019 Bergamini et al. , 2021 ) ). Despite these benefits, MUSE observations of clusters have up to now largely focused on their very central regions, with data usually extending only to the line which separates the singly imaged weaklensing regime from the multiply-imaged strong-lensing regime. In some ways this is understandable: as the optically densest region, the core benefits the most from IFU co v erage, while the multiplyimaged galaxies found there place the tightest constraints on the mass model. Nevertheless, the more extended 'outskirts' regions can also benefit from MUSE spectroscopy. Specifically , cluster members lying in these re gions liv e in relativ ely less dense environments when compared to those in the core, and many have not been exposed as long to the harsh intracluster medium.
This allows them to retain a larger fraction of their gas (e. g. von der  Linden et al. 2010 ; Haines et al. 2015 ; Oman & Hudson 2016 )  and maintain greater star formation rates  (SFR). With MUSE data extending from the core to the outskirts, changes in these parameters can be tracked directly as a function of position and infall radius, revealing a clearer picture of baryonic and dark matter mass evolution as galaxies traverse through the cluster. Since an increased SFR also tends to produce bluer galaxies that can be missed by a standard (e. g. Gladders & Yee 2000 )  cluster member colour cut, the MUSE data provide an additional benefit of capturing these galaxies automatically, without needing to invoke a colour evolution model to target them specifically. At the same time, identifying additional (bluer) cluster members places tighter constraints on the substructure mass distribution within the cluster, and with a robust spectroscopic sample of cluster members at larger radii it becomes more feasible to employ additional analysis (including the use of cluster kinematics) to complement the lensing data alone.
This work, which we are calling Pilot-WINGS (the Pilot Wide-area INtegral-field Galaxy Surv e y) also sets the stage for a larger proposed study (BUFFALO-WINGS), targeting several clusters out to still greater radii. The paper is organized as follows: in Section 2 we highlight the data used for our analysis including the acquisition and reduction techniques used. In Section 3 we present the MUSE redshift catalogue, pro viding o v erall statistics as well as a specific breakdown of foreground, cluster, and background galaxies. We use these redshifts to investigate mass distributions within the cluster and along the line of sight in Section 4. We summarize and conclude in Section 5 , where we also lay out future plans for the BUFFALO-WINGS surv e y. Throughout this work, we assume a flat cosmological model with = 0.