dataesr/scientific-paragraphs-categorization huggingface dataset:
While demonstrating an association between the extent of brain disconnectivity and a neuropathological marker of axonal damage, our results have to be interpreted in the context of their limitations. The patients were enrolled from different clinical centres in a real-world setting, thus any information regarding the use of DMT are due to clinical decisions outside the scope of this paper. Also, we did not have access to detailed information concerning recent disease activity besides information on new lesions in the brain. The mean serum NfL level across all patients was relatively low, and the skewed distribution with few cases with high serum NfL levels might have affected the results. Due to the relatively low levels of serum NfL for the MS subjects, we had to restrict our HCs dataset to the same sNfL batch only, since other HCs collected for other projects showed different levels of sNfL due to probable confounding batch-and population effects.
Acknowledgements We thank  Ingvild Sørum Leikfoss ,  Ingrid Mo  and  Fernanda Kropf  for their help with laboratory work at  Oslo University Hospital , MRI technicians  Susan Pikol  and  Cynthia Kraut  for their lesion segmentations and  Priscilla Bäcker-Koduah  for her laboratory work at  Charité-Universitätsmedizin Berlin , and all the participating MS patients and healthy individuals.
Infection by WT and I438V A524T HCVcc is expressed relative to their respective infection of parental cells (P). Example data from one representative transduction is shown. (E. & F) CHO cells were transduced to express exogenous human SR-B1 (left) or CD81 (right), to which WT and mutant sE2 binding was assessed by flow cytometry. Upper plots (E). provide representative cytometry histograms of sE2 binding to transduced or parental (-ve) CHO cells. Lower plots (F). display quantification of sE2 binding, with values normalised to WT binding at 40 µg/ml. Data points represent the mean of three independent experiments. In all plots, error bars indicate standard error of the mean, asterisks denote statistical significance (ANOVA, GraphPad Prism). Curve fitting (C. and F. ) performed with a hyperbola function and the curves compared to confirm significance difference (F-test, p<0.
T represents uncalibrated time and, therefore, cannot be converted to real time, but relative differences can be estimated. Comparison of time to 50% entry suggests that I438V A524T HCVcc completes entry ~3 times faster than WT. (D) Kinetics of WT and mutant HCV entry were experimentally measured by synchronised infection of Huh-7. 5 cells by HCVpp, followed by chase with a saturating inhibitory concentration of anti-CD81 mAb. Mutant HCVpp escaped the inhibitory effects of anti-CD81 ~3. 5 times faster than WT. Data points represent the mean of three independent experiments, the data is normalised to entry in the absence of anti-CD81 mAb. The data was fitted with a sigmoid function. In all plots, error bars indicate standard error of the mean, fitted curves were confirmed to be significantly different (F-test, p<0.
Circular dichroism spectroscopy Circular dichroism experiments were performed using the B23 nitrogen-flushed Module B end-station spectrophotometer at B23 Synchrotron Radiation CD BeamLine located at Diamond Light Source. They were performed by the BeamLine lab manager as a mail-in service. Four scans of protein samples in 50 mM NaF and 20 mM NaH 2 PO 4 and Na 2 HPO 4 pH 7. 2 were acquired in the far-UV region (175-260 nm) in 1 nm increments using an integration time of 1 sec, 1 nm bandwidth and pathlength of 0. 00146 cm, at 20 °C. Results obtained were processed using CDApps v4. 0 software  (Hussain et al. , 2015). The scans were averaged and spectra have been normalised using average amino acid molecular weight which was calculated for each sample. Spectra presented are difference spectra meaning the buffer baseline has been subtracted from the observed spectra, and zeroed between 253 and 258 nm.
Molecular dynamic simulations The complete model of the J6 E2 ectodomain was generated as previously described  (Stejskal et al. , 2020). For simulations of the mutant glycoprotein, the respective amino acid substitutions were modelled in using Modeller  (Webb and Sali, 2016). We performed MD simulations in explicit solvent using the Amber 16 GPU-based simulation engine  (Case et al. , 2005). The model was solvated in a truncated octahedral box using OPC water molecules. The minimal distance between the model and the box boundary was set to 12 Å with box volume of 4. 2 × 10 5 Å 3. Simulations were performed using the ff14SB force field on GPUs using the CUDA version of PMEMD in Amber 16 with periodic boundary conditions. CONECT records were created using the in-house MakeConnects. py script to preserve the disulphide bonds throughout the simulations.
MolProbity software was used to generate physiologically relevant protonation states. Minimisation and equilibration: The systems were minimised by 1,000 steps of the steepest descent method followed by 9,000 steps of the conjugate gradients method. Sequential 1ns relaxation steps were performed using the Lagevin thermostat to increase the temperature from 0 to 310 K, with initial velocities being sampled from the Boltzmann distribution. During these steps, pressure was kept constant using the Berendsen barostat. All atoms except for the modelled residues, hydrogen atoms and water molecules were restrained by a force of 100 kcal/mol/Å 2. The restraint force was eventually decreased to 10 kcal/mol/Å 2 during subsequent 1ns equilibration steps at 310 K. A further minimisation step was included with 1000 steps of the steepest descent method followed by 9,000 steps of the conjugate gradients method with all backbone atoms restrained by a force of 10 kcal/mol/Å 2.
To achieve independent repeat simulations, we performed steps to decorrelate the output from the equilibration process. Initial velocities were generated from the Boltzmann distribution using a random seed. The coordinates, but not velocities, from the final equilibration step were used as input for a short (40ns) production run. The coordinates, but not velocities, from this run were used for a second 4ns production run. This was followed by a 1μs production run. This process was repeated for each independent simulation. The MD trajectories were analysed using scripts available in cpptraj from Amber Tools 16. For RMSF/RMSD analyses, the average structure generated from the given trajectory was used as the reference structure. The analyses were performed using the backbone Cα, C and N atoms unless otherwise stated.
Acknowledgements We thank  Prof. Greg Towers ,  Dr. Clare Jolly ,  Prof. Richard Milne ,  Dr. Clare Bennett  and  Prof. Ben Seddon  for scientific and academic support, and to colleagues who provided resources (see Methods). We are grateful to  Dr. Colman ,  Dr. Jackson  and  Dr. Louis  for providing encouragement. For incisive criticism, we also thank  Prof. Kermode ,  Prof. Mayo  and HTJI. This work was supported by  Sir Henry Dale Fellowships  to J. G. ( 107653/Z/15/Z ) and  CJRI  ( 101239/Z/13/Z );  Medical Research Council CVR Core  funding to JG ( MC_UU_12014 ); a  Wellcome Trust  studentship to LS ( 109162/Z/15/Z ); a  Medical Research Council  studentship to MDK (https://www. ukri. org/councils/mrc/); CJRI was also supported by  Deutsche Forschungsgemeinschaft (DFG , Grant  SFB 1310 ), the  Medical Research Council  (ref:  MC_UU_00002/11 ) and the  Isaac Newton Trust.
org: VMF Data Server 2021). The global ocean tide model applied in the GPS data processing is FES2014b, and the ocean loading parameters have been retrieved from the Onsala Space Observatory website (M. S. Bos and H. -G. Scherneck, http://holt. oso. chalmers. se/loading/). To enhance the coordinate solutions, the daily global wide lane phase bias (wlpb) files from JPL are used to resolve the phase cycle ambiguities  (Bertiger et al. 2010). Although each kinematic position has a higher uncertainty and is affected by biases which usually cancel out over long periods of measurements, the instantaneous coseismic displacements at PALP are much higher than the highfrequency noise of around 1 cm and 2-3 cm for, respectively, the horizontal and vertical positions. Finally, the GPS time tags are corrected to UTC time by subtracting 18 s.
The coseismic displacement of the station simply follows from epoch-to-epoch coordinate differences. The standard available script has been modified to properly weigh the phase/code measurements of the stations and also to output the correlations. The X-Y-Z Cartesian component positions are then converted to the north-east-up positions along with their formal standard deviations. They are scaled using the weighted-rootmean-square of all the positions up to the time of the earthquake and generally reach a relative precision (3σ ) of about 30 mm on the horizontal components. The resulting displacement field is then differentiated by computing adaptive linear fits adapted to satisfy an error to fit criteria. The slope of the linear fit then gives the local velocity. The resulting data is then resampled again at 1 Hz by linear interpolation.
A C K N O W L E D G M E N T S FA would like to thank  N. M. Pahlevan  at  USC  for encouraging this work. HSB acknowledges the  European Research Council  grant  PERSISMO (grant 865411 ) for partial support of this work. AS would like to acknowledge the  European Research Council  grant REALISM ( 2016-grant 681346 ). AJR was supported by the  Caltech/MCE Big Ideas Fund (BIF). AE acknowledges support by the  National Science Foundation  ( CAREER Award Number   1753249 ). The continued (long-term) operation of the GPS stations in Central Sulawesi, Indonesia data has been co-facilitated by the  EU-ASEAN SEAMERGES  ( 2004SEAMERGES ( -2006 ) ) and  GEO2TECDI-1/2  projects (2009)(2010)(2011)(2012)(2013) in cooperation with the  Geospatial Information Agency of Indonesia (BIG). GPS data acquisition and research was partly funded by grants from the  Dutch NWO User Support Programme Space Research  (2007Research ( -2018)).
1A , the hierarchy of processing from distinct unimodal regions to integrative heteromodal regions in Fig. 1B , and the resulting gradients one to three in Fig. 1C ). This type of decomposition has revealed highly similar key dimensions of connectivity difference within the cortex across multiple datasets (e. g. , in the same dataset as used in the present study  (McKeown et al. , 2020) , the Human Connectome Project (HCP; dataset for  Margulies et al. , 2016; Hong et al. , 2020) ; openlyshared Autism Brain Imaging Data Exchange iInitiative (ABI-DEI and II; http://fcon_1000. projects. nitrc. org/indi/abide; for  Hong et al. , 2019) ; the Chinese Color Nest Project (CCNP; for  Dong, Margulies, Zuo, & Holmes, 2021) ) and across research groups (e. g. ,  Dong et al. , 2021; Hong et al. , 2019 Hong et al.
Methods This study includes analysis in a sample of participants who performed a resting-state scan and behavioural testing in separate sessions. We determined our sample size based on participant availability (i. e. , tested as many of the participants with resting-state data as were willing), and report all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study. No part of the study procedures or analyses was pre-registered in a time- stamped, institutional registry prior to the research being conducted.
Association strength task Participants were asked to select the target word that was most strongly associated with the probe (a picture). The semantic association that linked probes and targets was either strong (e. g. , dog-bone) or weak (e. g. , dog-ball). There were 60 strong and 60 weak association trials, presented in four blocks of 30 trials each. The order of trials within each block was randomized across subjects. The strong and weak trials were selected from a large database used in previous experiments  (Davey et al. , 2015; Krieger-Redwood, 2012). The strength of association between probe-target pairs was assessed using a 7-point Likert scale, along with the familiarity and imageability for each member of the pair. The association rating between probes and targets differed significantly between conditions, but there were no differences between conditions in word length, familiarity, imageability or lexical frequency (Table  1 ).
Although the semantic network does not extend to the primary sensory/motor cortex, there is still variation along the principal gradient, from DMN apex regions to secondary visual and motor regions. G2 also still shows some separation within the semantic mask, suggesting that regions of the semantic mask have sensory-motor properties. Gradient maps one to ten from the group-averaged dimension-reduction analysis of the whole brain and within the semantic network are publicly available on NeuroVault in a collection with the title of this article (https://neurovault. org/collections/10931/). The conditions of our ethics approval do not permit public archiving of the raw data and code supporting this study. Readers seeking access to this data should contact the PI Professor Beth Jefferies, or the local ethics committee at the Department of Psychology and York Neuroimaging Centre, University of York.
Relationship between semantic performance and gradient similarity scores within the semantic network To examine the relationship between semantic performance and gradient similarity scores, we used a multivariate linear regression (SPSS; version 26). In the first stage of our analysis, we used semantically-relevant gradient similarity scores (for gradients 1e3) to predict performance on semantic association strength and modality tasks within the same model. The gradient similarity scores were based on correlation coefficients between individually-derived gradients and grouplevel gradients within semantically-relevant cortex (see Methods). In order to include both tasks in the same model, we used difference scores (i. e. , weak minus strong RT and word minus picture RT), to capture the effects of semantic control demands and modality of presentation for each participant.
The initial pattern rotates at an angular 1 http://github. com/jobovy/galpy Fig. 3. Tidal spiral arms of Model 1hf. We show the time evolution between 0 and 1 Gyr (indicated in the panels of the first column) of the disc in the X -Y space, in density (left column), coloured by average radial velocity V R (middle) and average azimuthal velocity V φ -V φ (R) (i. e. with respect to the mean at the given radius, third column). The dashed and dashed-dotted lines show the prediction of the spiral arms location following the winding with ω -1 2 κ. The sector of the disc marked with dotted lines is a region whose dynamics we examine later in Fig. 6. speed of ω -1 2 κ, which depends on radius R, and thus its locus follows: φ(R, t) = ω(R) - 1 2 κ(R) t + φ 0. (5) Because of the radial dependence of the rotation speed, the pattern wind (shears) with time.
Acknowledgements. We thank the anonymous referee for the insightful and helpful comments. Work produced with the support of a 2021 Leonardo Grant for Researchers and Cultural Creators,  BBVA Foundation. The Foundation takes no responsibility for the opinions, statements and contents of this project, which are entirely the responsibility of its authors. We thank  Prof. Dr. Curtis J. Struck  for useful discussions. TA acknowledges the grant  RYC2018-025968-I  funded by  MCIN/AEI/10. 13039/501100011033and  by " ESF Investing in your future ". This work was (partially) funded by the  Spanish   MICIN/AEI/10. 13039/501100011033and  by " ERDF A way of making Europe " by the  "European Union " through grant  RTI2018-095076-B-C21 , and the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu ') through grant  CEX2019-000918 -M.
Human sampling Salmonella isolates were obtained and stored as part of this INRB-ITM microbiological blood culture surveillance network at Kisangani University Hospital as previously described  [24]. Basic clinical and demographic data were recorded. To assess the genetic relatedness between Salmonella isolates from rats and humans, blood culture isolates obtained during the rat trapping period and six months before and 6 months after this period (01 October 2015-30 June 2019) were selected. Serotyping and AST of blood culture Salmonella isolates were done as described previously  [19].
e. if no more than five alleles are different)  [41, 42]. 2. 6. 2 Genetic relatedness between isolates from this study and the literature. We compared some of the isolates of S. Typhimurium and S. Enteritidis identified in this study with previous studies on iNTS in Africa  [7, 43]  using EnteroBase. This database recovers regularly Salmonella genomes from the GenBank Sequence Read Archive (SRA). The "Custom View" utility allows to add metadata for genomes already present on the site. EnteroBase has recovered 564 genomes from the study on S. Typhimurium ST313 by Pulford et al. [7]  and 503 genomes from the study on S. Enteritidis ST11 by Feasey et al. [43]. A "Custom View" was created to add the information on lineages and plasmid repertoire for the S. Typhimurium ST313 from the Pulford et al.
Data management and analysis, definitions Geographic coordinates, morphometric measurements of rats and microbiological data were entered in an Excel database (Microsoft, Redmond, WA, USA). Rats with at least one isolate confirmed as Salmonella were categorized as Salmonella carriers. Several rats carried more than one Salmonella serotype. Only the first isolate per serotype per rat was used for the analysis of the distribution of serotypes and AST. For the display of MLVA typing results, all Salmonella Typhimurium isolates were assessed but only the first isolate per MLVA type per rat was presented. All genomes obtained by WGS and analyzed on EnteroBase are available at the ENA depository under project number PRJEB54047 (S1 Table ).
The present sample size (n = 566) was large compared to other studies  [48, 51, 72]  and human clinical isolates from the same period and area were available for comparison. In addition, we collected physical data of the rats (which may relate to the presence of zoonosis  [74]  and sampled year-round, precluding seasonal variation. The co-located laboratory facilities allowed for expertized staff and short and regular transport. Deep organs were sampled, and they accounted for three quarters of the non-duplicate serotype isolates. If only stool/rectal swab or content would have been sampled (as done in many studies  [32, 45, 54, 75] ), carriage would have been as low as 2. 6% (15 rats) and ST313 would have been missed. Furthermore, as confirmed here, rats may carry more than one Salmonella serotypes  [44, 45] , which is the reason why we processed multiple colonies per sample.
Related works In this section, the relevant techniques proposed in the literature to deal with the fault tolerance management are reviewed. In  [7] , Chang and al. have presented a fault-tolerant load balancing technique for current web services. In this work, each block within a file is replicated at least three times depending on the access frequency. To reduce the likelihood of data loss, each of the replicas is located on a different server. When the control center detects that E3S Web of Conferences 351,  01012 (2022)  https://doi. org/10. 1051/e3sconf/202235101012 ICIES'22 the number of replicas of a block is less than three due to a network problem or a hardware failure, the control server starts immediately a new replication of the block. Fang and al. [8]  combine the load balancing and the fault tolerance mechanisms in the distributed stream processing engine (DSPE) to reduce the overall resource use while preserving the system's interactivity, highthroughput, scalability and high availability.
Per array, 4 μg of protein and 100 μM ATP were loaded onto the appropriate PTK PamChip in kinase buffer. Phosphorylation of peptides was monitored by PamStation 12 following the manufacturer's protocol as described previously  (Arni et al. , 2017). Technical triplicates were analyzed, and sequence-specific peptide tyrosine phosphorylation was detected by the fluorescein-labeled antibody PY20 (Exalpha, Maynard, MA). Capture of peptide phosphorylation signal is via a computer-controlled CCD camera. Kinomic profiling was performed using the Evolve software (PamGene International). Data were analyzed using the BioNavigator software version 6. 3 (PamGene International) for raw data transformation into kinetic and steady-state values. Data were expressed as the average signal intensity (±SD) of the 196 peptide spots based on end levels of the phosphorylation curve.
Prior to statistical analysis, fluorescent signal intensities were log2-transformed to satisfy the normality assumptions. Significant differences between two conditions were determined using two-sided Student's t-test. The differential activity of kinases was predicted using an in-house bioinformatic approach, upstream kinase analysis (UKA)  (Chirumamilla et al. , 2019). Briefly, UKA identifies kinase activity based on a permutation analysis of the peptide substrate phosphorylation, by using known associations of phosphoproteins and kinases from literature and in silico databases. This permutation analysis gave a specificity score (mapping of peptides to kinases) and a significance score (kinase statistic that indicates the difference between treatment groups). Prior to UKA, the raw signal intensities were transformed using variance stabilizing normalization (VSN)  (Huber et al.
For PMAC samples, the nano-LC method consisted of a 120-min multilinear gradient at a flow rate of 300 nl/min (same gradient slopes as for 4G10 samples). Survey full-scan MS spectra (m/z = 400-2,000) were acquired with a resolution of 60,000 after the accumulation of 1 × 10 6 ions (maximum filling time 200 ms). The 20 most intense ions were fragmented by HCD after the accumulation of 1 × 10 5 ions (maximum filling time 50 ms). MS and MS/MS data were acquired using the software Xcalibur (Thermo Scientific).
Mass Spectrometry-Based Proteomic Data Processing The data were processed automatically using Mascot Distiller software (version 2. 7. 1. 0, Matrix Science). The peptides and proteins were identified using Mascot (version 2. 8) through concomitant searches against Uniprot (Homo sapiens taxonomy, September 2021 version) and a database of 250 classical contaminants (homemade) and their corresponding reversed databases. Trypsin/P was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set, respectively, to 10 and 20 ppm. Peptide modifications allowed during the search were cysteine carbamidomethylation (fixed), acetyl (protein N-terminal, variable), methionine oxidation (variable), and serine, threonine, tyrosine phosphorylation (variable).
Proline software (version 2. 1)  (Bouyssie et al. , 2020)  was used to merge DDA results from proteome analysis and phosphopeptide enrichment. After combination, the results were filtered: conservation of rank 1 peptide-spectrum matches (PSMs) with a minimal length of 6 amino acids and a minimal score of 25. PSM score filtering was then optimized to reach a false discovery rate (FDR) of PSM identification below 1% by employing the target decoy approach. A minimum of one specific peptide per identified protein group was required. Proline was then used to perform MS1-based label-free quantification of the peptides and protein groups from the different samples with cross-assignment activated. Protein abundances were computed as a sum of specific peptide abundances, without using phosphopeptides and their counterparts.