dataesr/scientific-paragraphs-categorization huggingface dataset:
When delving into the genetics of the virus, other information was revealed. Overall, 47 complete genome GPGV sequences (or near complete, covering at least all open reading frames) were assembled (Table  1 ), all submitted to GenBank (BK011089-BK011101, and the other sequences are available upon request). After a phylogenetic analysis (Fig. 1 ), three major clades of GPGV were found to infect these grapevines, each displaying a high intra-clade nucleic acid identity percentage ≥ 98. 10%. Interestingly, GPGV sequences seemed to cluster together very well by location (Fig. 1 , colors) however independently from cultivar. Fixation index (F ST ) analyses (Fig. 1 ) confirmed the genetic differentiation of the viral population according to location, showing a statistically significant high F ST value (F ST = 0.
The upper limit on the nested sum is the Up-Scale (us) factor used to define the high-resolution image. As an example, in this work we consider us = 4 and thus each cell of the LG detector is up-scaled to 16 cells in the high-resolution image. For some of the NN models, a different loss function was also studied and provided similar results: L 2 event = 1 N sc ∑ sc (e sc t -e sc d ) 2 where e sc t is the absolute value of the target neutral energy in the super-cell sc and e sc d is the corresponding predicted neutral energy. N sc is the total number of super-cells in an event that belongs to a topocluster. All the networks are trained with the Adam optimizer  [21]  and a learning rate of 10 -4. The training dataset consists of 80,000 images while the validation dataset has 20,000 images. The performance of the trained models are evaluated on a test dataset consisting of 6000 images.
The size and energy of the topocluster in isolated π + are systematically lower compared to the evaluation sample which features a large π 0 and π + overlap. As the energy of the π + increases, the size of the topoclusters in both, isolated and overlapped topologies becomes comparable thus reducing the bias in the high energy ranges. In contrast, for all NN methods and most energy ranges, the predictions yield a precise average value. In the low energy range, the UNet and CNN algorithms show small biases of approximately 4% and 2% respectively. These small biases are not observed for the GNN and Deepset algorithms. The GNN and DeepSet methods also perform up to approximately 20% better in terms of neutral energy response resolution. While these dif-ferences could be due to the tuning of the model or specific trainings that could benefit from different training strategies or larger training datasets.
Results for super-resolution In HEP experiments very large samples, providing a precise description of the development of electromagnetic and hadronic showers, can be simulated. Precise calorimeter images with arbitrarily high granularity can thus be produced, providing high-resolution images that can be used as targets in the training of NN algorithms. We use such a dataset with information of low granularity data and high granularity truth information to quantitatively establish a proof-of-principle NN based super-resolution method. The models used in Section 6 to predict the fraction of cell energy pertaining to neutral hadrons, in some cases supplemented with a broadcasting layer to perform changes in granularity, can also be used to predict calorimeter images with augmented granularity.
ACKNOWLEDGMENTS The authors would like to thank  Université Sorbonne Paris Cité  and the  National University of Singapore  for their support through the  USPC-NUS program  ( MagnoFlex  project, No. ANR-11-IDEX-0005-02  and  Nanoflex  project, No. 2016-04-R/USPC-NUS ). D. F. and F. Z. would like to thank  Labex SEAM (Science and Engineering for Advanced Materials and devices)  for financial support through the  MECAMAG  project (Nos. ANR-10-LABX-0096  and  ANR-18-IDEX-0001 ). The research was conducted within the context of the  CNRS-NUS International Research Project  " Heterogeneous stretchable systems, mechanical properties and associated functionalities at small scales"-StretchSmart.
Materials and Methods The clinical trial was approved by the ethics committee (Ärztekammer Schleswig-Holstein, Ethik-Kommission, DE/EKSH44) on 8 August 2012 and was prospectively registered at ClinicalTrials. gov (NCT01896154). The trial was conducted at Clinical Research Center Kiel (Kiel, Germany) in the period from 28 August 2012 to 27 March 2013. All subjects gave written informed consent for participation in the study.
Study Population Eligible subjects were community-dwelling men and postmenopausal women, aged 50-79 years, willing to have an influenza vaccination in season 2012/2013. Subjects were recruited from the database of Clinical Research Center Kiel and from advertisements in the Kieler daily newspaper. Among other conditions, suffering from influenza or influenza-like illness within the previous 10 months and usage of drugs altering the immune system (e. g. , antibiotics or corticosteroids) led to exclusion of subjects. The full list of eligibility criteria is available at ClinicalTrials. gov and in the supplement (Methods S1).
In brief, 50 µL magnetic beads were dispersed in a plate together with 50 µL of supernatant or standard and incubated for 30 min at room temperature while shaking 300 rpm in the dark. Subsequently, the beads were incubated with 25 µL detection antibodies for 30 min at room temperature while shaking 300 rpm in the dark followed by 50 µL streptavidin-PE for 10 min at room temperature while shaking 300 rpm in the dark. The beads were resuspended in 125 µL assay buffer and read by Bio-Plex ® MAGPIX™ Multiplex Reader (Bio-Rad). Data processing was performed using Bio-Plex Manager 5. 0, and concentrations (in pg/mL) were interpolated from standard curves. In the few cases that data points were below or above the standard curves, the lower or upper limits of the standard curve were used for data points, respectively.
Differences in dietary intake over time within the complete study population were tested by Wilcoxon signed rank test. Occurrences were tested using a Chi-square test with post-hoc Fisher exact test. For all outcomes, available case analyses were performed. No data were imputed. All statistical analyses were performed using IBM SPSS Statistics for Windows (version 25. 0, Armonk, NY, USA) and microbiota results were visualised with Graphpad Prism (version 5. 03, San Diego, CA, USA). Normally distributed data are presented as mean ± standard deviation (SD) and medians with interquartile ranges (IQR) for data that were not normally distributed. Two-sided p-values ≤ 0. 05 are considered significant. If significant, uncorrected p-values for post-hoc tests were corrected for multiple testing by the false-discovery rate (FDR) of Benjamini-Hochberg.
Study Subjects In this study, a total of 239 subjects aged 50-79 years were included, of which 231 completed the entire study protocol. Five subjects dropped out due to an infection prior to vaccination (common cold, bronchitis, influenza-like illness, urinary tract infection), one subject due to gastrointestinal complaints, and two subjects withdrew consent (Figure  2 ). Baseline characteristics of the study population are presented in Table  1. The six intervention groups did not differ in sex, age, or BMI. Overall, adherence to the study product consumption was high. The mean compliance by product counting was 98. 9%, with 94. 6% of the study subjects having a compliance exceeding 95%, which did not differ between intervention groups (p = 0. 888; data not shown). According to the Morisky score  [39] , 95.
v. E. , Y. S. writing-review and editing; C. L. , J. S. and J. J. M. supervision. J. J. M. funding acquisition. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the European Community's Seventh Framework Programme [FP7/2007-2013] 'EU FibeBiotics' under Grant Agreement number 289517. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee Ethik-Kommission der Ärztekammer Schleswig-Holstein, DE/EKSH44, AZ 094/12 (I) (protocol code: Fib-P-2012, 8 August 2012).
Acknowledgments: Clinical Research Center Kiel greatly acknowledges  Maria Gatzmange  and  Tahereh Dezhahang  for their excellent medical technical assistance in the study conduct and data management. The team at  WFBR ,  Monic Tomassen ,  Renata Ariens ,  Shanna Bastiaan-Net ,  Dianne Somhorst , and  Coen Govers  is acknowledged for preparation of the SBG. Thanks to  Andrea Liesegang  at tecura  GmbH Medizin & Biotechnik, Kiel  for generating the randomisation list for the trial. Great thanks to  John Blocher  for in depth discussions on possible mechanisms of action and  Jan Oddvar Johansen ,  Frederic Prothon , and  Hans van der Saag  for providing NPS materials.
B. contributed to the draft manuscript; M. v. E. , Y. S. writing-review and editing; C. L. , J. S. and J. J. M. supervision. J. J. M. funding acquisition. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the European Community's Seventh Framework Programme [FP7/2007-2013] 'EU FibeBiotics' under Grant Agreement number 289517. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee Ethik-Kommission der Ärztekammer Schleswig-Holstein, DE/EKSH44, AZ 094/12 (I) (protocol code: Fib-P-2012, 8 August 2012).
Statistical Analysis All statistical analyses were performed with pvalue. io software  (18). Data were expressed as the median [interquartile range] or the frequency (percentage). Intergroup differences groups were probed with a Mann-Whitney test (for quantitative variables) or a chi-squared test or Fisher's exact test (for qualitative variables). The threshold for statistical significance was set to p<0. 05. As this pilot study was exploratory, no calculation of the sample size was carried out.
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 758826 & 756962). Z. L. acknowledges funding from the Swedish research council, Vetenskapsrådet 2018-06610. S. D. S. acknowledges funding from the Royal Society and Tata Group (UF150033). G. D. acknowledges the Royal Society for funding through a Newton International Fellowship. We acknowledge financial support from the EPSRC and the Winton Programme for the Physics of Sustainability.
Some Numerical Details The user-defined material subroutine UMAT, which was developed by Huang  [17]  for the commercial finite-element program ABAQUS, was applied in this study. This subroutine assumed that the plastic shear on single slip systems was determined by the elastic shear stress acting on the corresponding slip system. The plastic shear rates. γ pl on a single slip system were calculated by means of the following rate dependent flow law  [18] :. γ pl =. a τ τ f m (1) where. a is the reference value of the plastic shear rate, which is reached when the shear stress τ equals the frictional shear stress τ f. By means of the power law exponent m, the course of the function. γ pl = f (τ) is determined. An exponent value m→∞ corresponds to rate-independent material behavior  [18]. The plastic shear was determined by means of numerical integration of the plastic shear rate over the time increment ∆t as follows: ∆γ pl = ∆t (1 -θ).
Results and Discussion In order to reconstruct the microstructure of the investigated duplex stainless steel, the advantages of both X-ray tomography techniques described in Section 2 were combined. The reconstructed 3D-microstructure of the austenite phase (Figure  7a ) and of the ferrite phase (Figure  7b ) were generated by combining the DCT volume data with the PCT volume data. Before doing this, the overlapping grain volume of the DCT reconstruction data was deleted. A fatigue crack at the surface of a miniature sample, to which ultrasonic fatigue testing at 20 kHz was applied, is shown in Figure  8a. The fatigue sample was fatigued at a stress amplitude of 400 MPa. The test was stepwise interrupted in order to monitor the short fatigue crack propagation by means of optical microscope images, which were taken after different loading cycles.
The fatigue crack presented in Figure  8  initiated at two nucleation sites, i. e. , (i) at the phase boundary between the austenite grain γ 3 and the ferrite grain α 2 and (ii) at the phase boundary between the austenite grain γ 4 and the ferrite grain α 3. The transcrystalline crack nuclei grew together and subsequently propagated in both directions as a consequence of further cyclic deformation. The crack growth was decelerated, when a crack tip approached a phase boundary, such as in the case of the phase boundary between the austenite grain γ 7 and the ferrite grain α 4. An acceleration of crack propagation was always detected when the crack overcame the boundary. At the phase boundary between the austenite grain γ 1 and the ferrite grain α 1 , the crack was even temporarily stopped. By means of the phase contrast tomography technique and the commercial 3D data visualization software AVIZO FIRE, the crack shown in Figure  8a  was examined nondestructively and represented three-dimensionally after different numbers of load cycles.
The geometry information was discretized by tetrahedral finite elements by means of the commercial image processing software AVIZO FIRE. The experimentally determined phase affiliation of each grain and the individual crystallographic grain orientation were considered. Moreover, anisotropic elasticity and lattice-type-specific crystal plasticity were taken into account in the stress distribution calculations (Section 3). The volume fraction of both phases was confirmed to be about 50% each. In order to consider the behavior of the surrounding of the depicted volume of the microstructure in a reasonable approach, this section was embedded in a frame consisting of finite elements with isotropic-elastic material behavior (Young's modulus E = 197 MPa and Poisson's ratio ν = 0. 3). This surrounding frame is not shown in Figure  9  for the sake of clarity.
Conclusions Advanced diffraction contrast tomography (DCT) and phase contrast tomography (PCT) were combined and successfully applied to an austenitic-ferritic duplex stainless steel. It was shown that a three-dimensional microstructure of a metallic material that contained two phases with different crystal structures could be reconstructed nondestructively. Besides the information on the phase distribution and grain microstructure of the material, this sophisticated technique additionally provided crystallographic orientation data of the grains. The obtained data were used to create realistic three-dimensional finite-element models of the microstructure. By means of a crystal plasticity finite-element analysis, experimentally determined first and second order residual stresses caused by the manufacturing process of the material were simulated.
cerevisiae counterparts (Sup. Data 1) using the MODELLER software package  58. Model fitting and rigid body refinement allowed interpretation of both negative stain and cryo-EM densities and later aided by AlphaFold predictions  59. To the knowledge of the authors, this is the first example for the de novo reconstruction of a previously unknown, non-symmetric macromolecule obtained with a CryoARM 200 electron microscope. Details of data collection and handling strategies are similar to recent reports  [60] [61] [62]  and are described in the methods section.
We found well-defined cryo-EM density on the downstream face (front) of hPol I subunit RPABC1 (Rpb5) that is closely connected to the foot insertion site. Domain prediction using the HHPRED package  79    Furthermore, a loop between HMG-box helices one and two directly interacts with DNA and contributes to sequence specificity  82. In RPA1, we observed an insertion between the corresponding helices α27d and α27e that contacts loop T56-V60 of subunit RPABC1 (Fig. 4 ). In contrast, a basic surface patch is found on the opposite face (Sup. Fig. 7 ). To test whether DNA-interaction is possible, we recombinantly expressed MBP-tagged versions of the domain (full length and minimal) and tested their ability to bind an unspecific dsDNA-fragment. Significant DNA-binding was not observed, although the full-length fragment may retain some very low affinity in vitro.
For the 3D model a Z-stack was imaged using the internal GaAsP-PMT detectors from 490-668 nm for sfGFP and 410-473 nm for DAPI in a two-track process. Image processing was done using the Zeiss AxioVision software. The 3D Volume images were created in Imaris 9. 6. Analysis of Pol I subunits RPA1, RPA34, RPA43 and A14 Data sets from Pol I subunits were generated using their corresponding InterPro 114 entries (RPA1: IPR015699, RPA34: IPR013240, RPA43: IPR041901 and IPR041178, A14: IPR013239 downloaded on 07. 06. 2021). A common dataset of RPA1, RPA34 and RPA43 was generated by searching for common species within the three InterPro families. To each obtained species the concatenated sequence of RPA1, RPA34 and RPA43 was assigned.
Reanalysis of previously published ChIP datasets Raw data was handled, mapping coordinates exacted, and the data displayed as previously published  90. The used data was: Top2A GSE99197_SRR5585950_TOP2A-MEF  87. ArrayExpress E-MTAB-5839 data sets were: ChIP-seq_UBF_MEFs_UBFfl_Rep1; ChIP-seq_RPI_MEFs_UBFfl_Rep1; ChIP-seq_Rrn3_MEFs_UBFfl_Rep1; ChIP-seq_TBP_MEFs_UBFfl_Rep1; ChIP-seq_TAF68_MEFs_UBFfl_Rep1  46. Taf1c is not included in the Figure since it is identical to the Taf1b mapping but data is also available in E-MTAB-5839 as ChIP-seq_TAF95_MEFs_UBFfl_Rep1. Negative stain EM hPol I samples were centrifuged (4°C; 15,000 rpm; Eppendorf table top centrifuge) for 5 min. Five µl of the samples were then applied to glow-discharged 400-mesh copper grids (G2400C; Plano) with a self-made carbon film of ~7 nm thickness (Pilsl, et al, Methods Mol Biol, in   press).
Homology models of the hPol I subunits RPA1, RPA2, RPA49, RPA34, RPA12 and RPA43 were generated based on sequence and secondary structure alignments with the crystal structures of their S. cerevisiae counterparts (Sup. Data 1) using the MODELLER software package  58. The models were adjusted in COOT  125  and realspace refined using Phenix  126. At later stages, released AlphaFold 59 models were used to guide chain-tracing in poorly resolved areas. A model of the stalk subunit RPA43 is included in some figures, but was not deposited due to poor or absent cryo-EM density resulting from flexibility.
Acknowledgements The authors especially thank  Philip Gunkel  for his contribution. We thank all past and present members of the  Engel lab ,  Achim Griesenbeck ,  Colyn Crane-Robinson ,  Christophe Lotz ,  Marlene Vayssieres ,  Klaus Grasser ,  Herbert Tschochner , and  Philipp Milkereit  for help and discussion,  Gerhard Lehmann  and  Nobert Eichner  for IT support,  Joost Zomerdijk  for UBFconstructs,  Volker Cordes  for the Hela P2 cell line,  Remco Sprangers  for shared cell culture,  Dina Grohmann  and the  Archaea Center  for fermentation, and  Thomas Dresselhaus  for access.
A dynamic method with a fixed cycle time of 3 s was applied via the Compass 1. 7 acquisition and processing software (Bruker Daltonics). Prior to database searching with Protein Scape 3. 1. 3 (Bruker Daltonics) connected to Mascot 2. 5. 1 (Matrix Science), raw data were processed in Data Analysis 4. 2 (Bruker Daltonics). Swiss-Prot Homo sapiens database (release-2020_01, 220420 entries) was used for database search with the following.
II. 1 Computational details First principles calculations have been carried out within the DFT framework, using the Vienna Ab initio Simulation Package (VASP),  52, 53  in combination with an evolutionary algorithm (EA), as implemented in the USPEX code,  54, 55  to resolve the crystal structures, calculate the electronic structure and assess the thermodynamics of the ionic insertion/release processes. The pristine disodium benezenediacrylate (Na 2 BDA), its sodiated phases and their corresponding Li-ion homologues have been investigated. The first population of the EA was composed of about 300-500 randomly generated structures, built up as follows. The molecular units were split into two components, namely either Na or Li atoms and the organic BDA moiety, which were combined to form random geometries.
For Na 2 BDA, where the crystals were larger than for Li 2 BDA but smaller than for Li 2 BDAÁ1H 2 O, electron crystallography was employed. Due to the stronger interaction of the electrons as compared to X-rays, smaller crystals can be analysed. The 3DED is collected using a Transmission Electron Microscope (TEM). The 3DED data were collected using the continuous rotation electron diffraction (cRED) technique which collects data applying a continuous tilt on the goniometer. In order to verify the structure, it was combined with refinement based on powder X-ray diffraction (PXRD) data. The 3DED was performed using a JEOL JEM-2100 TEM with a LaB6 filament operated at an accelerating voltage of 200 kV. A Timepix hybrid electron detector (ASI) was used to gather the data. Details of the 3D ED data collection and analysis can be found in the ESI.
75 Å, 5. 21 Å, 16. 40 Å, 901, 95. 51, 901 and the space group P2 1 /c (Table  S1 , ESI †). The coordination around the Li ion appears to form a tetrahedron. As expected, the Li and Na compounds possess the same space group but different unit cell parameters. The crystal data for Li 2 BDA were collected from a twin crystal at room temperature, and may hence be less optimal, but nevertheless display the main structural features. There exists a strong contrast in coordination around the different ions in the Li and Na compounds, where it is tetrahedral around Li ions and trigonal prismatic for Na ions. This can lead to different types of structural changes during the ion insertion process. A summary of the experimental and theoretical results regarding the crystal structure of both compounds can be found in Tables  S1-S3  (ESI †).
Acknowledgements We acknowledge support from the  Swedish Research Council  (Grant number:  2018-04506 ,  349-2014-3946  and  2019-05465 ), the  Swedish Energy Agency  (Grant number: 45420-1),  Formas  (Grant number:  2016-00838 ) and STandUP for Energy. The computational infrastructure has been provided by the  Swedish National Infrastructure for Computing (SNIC) at the PDC Center for High Performance Computing and National Supercomputer Centre (NSC)  at  Linko ¨ping University.
Expression profiles of wheat ASN genes during development Raw sequencing reads from public RNA-seq datasets were mapped to the IWGSC RefSeq v1. 1 genome assembly to provide a comprehensive overview of the expression profile of each TaASN gene. The expression values for each dataset are provided in Additional file 2 as mean Transcripts Per Million (TPM) values. In a dataset encompassing roots, leaves, stems, spike and grain, each sampled at three developmental stages  [24] , TaASN1 transcript levels were highest in young roots and leaves, whereas the three homeologues of TaASN3. 1 and TaASN3. 2 showed a broader expression profile, with transcripts detected in all assayed tissue types across different stages of development (Fig. 5a ). TaASN4 homeologues were also broadly expressed, with TaASN-A4 transcript levels highest in root and spike tissues, and TaASN-B4 and TaASN-D4 more highly expressed during stem development (Fig.