dataesr/scientific-paragraphs-categorization huggingface dataset:
Next, competitive adsorption experiments using mixtures of CS and AavLEA1 were conducted at a single detector position over 30 s intervals. This allowed data to be collected at short time points showing how the protein density profiles evolve with time (Figure  6 ). All contrasts were fit simultaneously and, in all profiles, the surface was clearly dominated by AavLEA1. Consistent with the dynamic surface tension measurements, a significant amount of AavLEA1 had adsorbed to the surface after only 30 s, forming two distinct layers (Figure  6a ). No contribution from CS was apparent until the 30-min time point (Figure  6d ), when a small amount of CS was detected ∼5 nm below the interface. By 60 min, some CS started to appear in a bilayer conformation (Figure  6e ). By 2 h, a measurable bilayer of CS was formed at the interface (Figure  6f  ); however, the layer of protein closest to the interface was still predominantly composed of AavLEA1.
In  [18] , the radius of each class was chosen to equalize the misclassified and unclassified errors (Fig. 3 ). Finally, the spike sorting procedure is applied to the data stream  [15]. The distances between a newly detected event to all templates are evaluated. In the case the shortest distance is smaller than the specific radius, the event is assigned to that template. For unbalanced classes the class probability may be taking into account using decision function   () =   (|  ), where   is the class   probability  [23]. In the studies mentioned above, microelectrode distance was large enough to exclude multisite neuronal action potentials observation. Scalar model was consequently applied to each electrode recording.
The traditional monthly publication, containing results of all the laboratories contributing data to the BIPM for the computation of UTC was complemented after the establishment of the Mutual Recognition Arrangement of the International Committee on Weights and Measures (CIPM MRA). This time comparison, which has been the responsibility of the BIPM since 1988, added as a complement the key comparison on time defined by the Consultative Committee for Time and Frequency (CCTF) in 2006 as CCTF-K001. UTC, where the results published are those of national metrology institutes (NMIs) signatories of the CIPM MRA, or designated institutes (DIs). The traceability issues are formalized in the framework of the CIPM MRA. The development of time metrology activities in the different metrology regions, supports the actions of the BIPM time department to improve the accuracy of [UTC-UTC(k)], where the coordination with the Regional Metrology Organizations (RMOs) has a key role.
An important aspect of UTC concerns its role in the framework of the CIPM MRA. After the implementation of CIPM MRA in 1999, the time came in 2006 for the CCTF to decide that the unique key comparison (KC) in the time and frequency field is that corresponding to the computation of UTC by the BIPM, and called it 'CCTF-K001. UTC'. UTC is the key comparison reference value, and the degrees of equivalence of participants 'k', namely [UTC-UTC(k)] are available at the key comparison data base (KCDB) every month, after the publication of BIPM Circular T. In figure 1 a scheme of the different time scales and their relations is reported to help the understanding of the paper.
At the time of its definition, UTC was the unique means of having real time access to UT1, as needed for some specific applications including astronomical navigation, geodesy, telescope settings, space navigation, satellite tracking, etc. The definition of UTC is based on the atomic second, but the time scale is synchronized to UT1 to maintain |UT1-UTC| < 0. 9 s. Since 1972, UTC differs from TAI by an integral number of seconds, value that changes at the insertion of a leap second, which occurs as predicted and announced by the International Earth Rotation and Reference Systems Service (IERS). Since January 2017 and until further notice, the offset between TAI and UTC is 37 s. Since 1988 the BIPM is responsible for the computation of TAI and UTC. An algorithm developed by the BIPM time department treats clock data submitted by institutes worldwide spread to give traceability to the SI second to the local realizations of UTC, named UTC  (k)  where k refers to a laboratory.
The input data for our computation was therefore of the form EAL 139 -h i with i = 1,. , 139. From that, we estimated the clock parameters (initial state and noise coefficients) attributing the whole noise signature to the h i. The noise coefficients are calculated by comparing the clocks to TT(BIPM) and by performing the Hadamard variance. Before analysing this comparison there is another aspect on which we need to focus our attention: the clock weights. The x-reduction procedure in the case of measurement noise delivers, together with Kalman's estimates, a weight for each laboratory, based solely on the clock stability. However this kind of weight does not suit our purposes as it does not introduce a maximum for each clock weight (thus affecting the reliability of the scale). In this case the weights are completely dominated by the Rubidium fountains, as their stability is much better then those of the other clocks.
Then advances in obtaining precise satellite orbits and clock parameters allowed the introduction of another technique, named All in View (AV)  [41]  that eliminates the constraint of having simultaneous observations, thus becoming independent of the length of the baseline for having suitable observed satellites. In AV, data from all satellites in view at a station are used. The International GNSS Service (IGS) has provided since 2004 high precision GPS satellite clock products referred to the timescale of the IGS (IGST)  [37] , which relative frequency instability is of order 10 -15 for a one-day averaging time, two orders of magnitude better than that of the GPS time. Since this minimizes the impact of the error coming from satellite orbits and clocks, it has been possible to use the AV method instead of the GPS CV with the benefit of adding data from satellites at high elevations and thus improving the statistical uncertainty of the time links, particularly the very long ones  [41].
The protocol for TWSTFT data exchange has been developed at the International Telecommunication Union (ITU) for use in multiple applications  [53]. This protocol is used for the regular submission of TWSTFT data to the BIPM. The number of institutes operating two-way stations for time comparisons remains small for a number of reasons. First, the high cost of the TWSTFT equipment, several times that of a GNSS receiver; second the complexity of its operation, and finally the fact that a satellite service provider is necessary for the transmission of the signals, involving additional cost and coordination with the other participating institutes. It has, however, the advantage of the easy treatment of the observations, since time comparisons are already computed in the ITU protocol, and the final solution is purely geometrical, and safe from corrections.
Time links in UTC, their comparison and combina- tion. For two decades, GPS C/A-code observations have provided a unique tool for clock comparisons in UTC, rendering impossible any test of its performance with respect to other independent methods. The present situation is quite different; the introduction of the TWSTFT technique and the GLONASS observations have resulted in the opportunity of comparing the time links obtained with GPS techniques with those coming from the independent GLONASS and TWSTFT techniques, and making the system more reliable. In parallel, new GNSS receivers had been developed, and refined methods for treating their observations had been implemented. With the dual frequency receiver data and the provision of RINEX files, iono-free P3 links and code/phase measures solutions (PPP) are computed whenever possible  [44].
This gives the BIPM the possibility of computing and comparing a redundant number of time links for some baselines, the best being used in the calculation of UTC and the others kept as backup. The time links and the results of time link comparisons are available on the BIPM ftp server  [54]. Redundant links over a baseline can be combined to enhance the comparison of the clocks involved  [55]. This approach has been in use at the BIPM since January 2011, with the introduction of combined GPS/GLONASS and TWSTFT/GPSPPP time links. The combination of TWSTFT and GPSPPP results in a link characterized by the accuracy of TWSTFT (u B 1 ns) and the short-term stability of GPS PPP, at the sub-ns level  [56]. Data from single channel receivers stoped in mid-2015. As of January 2018, 22% of the links in UTC are obtained using GPS single-frequency data, 70% of time transfer data is from dual frequency GPS receivers, from which 58% is used in GPS PPP solutions.
Fibre links. The optical fibre link (OFL) techniques have grown fast. Over thousands of kilometres, a coherent fibre link can compare clocks with an uncertainty of few parts in 1 × 10 18 in 1000-10 000 s. Also time transfer over fibre reports large advances, and today it can offer a sub-ns inacc uracy. Nonetheless, only two time links in Europe (AOS-GUM and BEV-TP) regularly report data to the BIPM connecting remote atomic clocks. The implementation of fibre links reporting data to the BIPM shall be encouraged and pursued to take advantage of a widespread optical fibre network. Fibre links comparisons demonstrated to be the currently most suitable means to compare remote optical frequency standards. More comparisons shall now be realized in order to achieve the target identified in the roadmap for the possible redefinition of the SI second, collecting more and more comparisons of optical frequency standards with an uncertainty of parts in 10 18.
SDR. The software-defined radio (SDR) receiver  [74] [75] [76]  has been developed at the Telecommunications Laboratory (TL, Chinese Taipei) for implementation in TW Earth stations. It is aimed at improving the stability of TW time transfer, with particular impact on the diurnal signature present in almost all the links, which is the major uncertainty source in TW time links. The BIPM and the Consultative Committee for Time and Frequency (CCTF) Working Group on TWSTFT launched a pilot study in February 2016 for validating the SDR receiver in view of its implementation for use in UTC time links. Participants to this pilot study were the laboratories contributing to UTC which operate the SDR receiver and the BIPM time department. Goals of the pilot study were first, to validate the efficacy of the SDR receiver for improving the TWSTFT uncertainty and significantly reduce the diurnal effects; second to implement routine TW code measurement data through SDR receiver to improve the UTC time compariso ns.
Input data and algorithm The elaboration of UTCr can be split into four steps: 1. Data reporting and checking. Due to the short delay in publication, procedures have been developed to allow the automatic treatment of data and calculation of the solution. In operational use, interaction with laboratories for data checking is significantly reduced. The automatic processing is based on the name of the files and the structure of the ftp directories. Standard file naming conventions must be respected, see  [54]  for guidelines. Manual handling is required only to allow the inclusion of new data in the data set. With this exception, a number of tasks are automatically carried out in the following steps: continuous detection of incoming files, automatic report of unknown file names, automatic checking of the data format for the known file names, automatic report on recognized data files and automatic data reminders sent to laboratories on Tuesday 12:00 UTC.
Patients and samples Senegalese patients with clinical malaria were enrolled at the Principal Hospital of Dakar, the National Hospital Centre of Pikine, and the Regional Hospital of Tambacounda. Prior to enrolment, written or verbal information on the study was given in their native language, and informed consent was obtained from the participants or their relatives. At the day of admission, biological data including P. falciparum parasitaemia, haematological and biochemical characteristics were determined by hospitals' medical laboratories and recorded, as previously described  [16]. For all patients, the presence of P. falciparum infection was determined by an immunoassay detecting PfHRP2 (Standard diagnostics-Abott-Inc, Chicago, Illinois, USA). For 12 out of 16 patients P. falciparum parasitaemia was determined by microscopic examination of thin and thick blood smears, prior to anti-malarial treatment.
Microarray analysis The quantification of signals was performed with Agilent's Feature Extraction Software. From the raw data, data filtering, normalization and data backup were performed using the R statistical software package. The quantile method was used to normalize the data. A filter was applied on the raw data to delete controls, and then a second filter was applied to delete probes that showed a signal under the background; finally, 26,372 probes were further analyzed. Data were deposited in the GEO database under GSE116306. Statistical analysis was performed using the TIGR MeV (MultiExperiment Viewer) v4. 1 software (http://mev. tm4. org/#/welcome) and the GeneANOVA program  [18, 19]. Figure  1  shows the schematic outline of statistical analyses. Welch t-test was used to compare CM patient gene expression levels with those of MM patients.
The list of those genes is shown in Additional file 1: Table  S1  Functional enrichment analysis The gene annotation of the selected genes was performed using Genomic Regions Enrichment of Annotation Tool (GREAT), Enrichr, and NetworkAnalyst  [20] [21] [22]. Those programs, which allow a biological interpretation of gene clusters on the basis of Molecular Signatures Database, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and protein-protein interactions, were used to assess the functional enrichment for differentially expressed genes. Fisher exact and hypergeometric tests were used, whereas an FDR of 10% was applied to correct for multiple tests. The selected genes were used to construct and visualize the network using NetworkAnalyst  [21]. The underlying protein-protein interactions were obtained from InnateDB  [23].
Functional enrichment When analysing the genes showing a differential expressions in CM, NCM, and MM patients, there was no significant functional enrichment on the basis of the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways. Nevertheless, the analysis of molecular immune signatures from the Molecular Signatures Database (MSigDB) showed an over-representation of genes regulated in activated CD4 lymphocytes, CD8 lymphocytes, B lymphocytes, NK lymphocytes, macrophages, monocytes, and dendritic cells (Table  4  and Additional file 3: Table  S2 ). Using the differentially expressed genes in the CM and MM groups, we found very similar results (Table  5  and Additional file 3: Table  S2 ). The products of those genes, whose expressions differed in the MM and SM groups based on the multiway ANOVA, were further mapped onto a human protein-protein network, leading to subnetworks.
up-regulated in comparison of peripheral blood mononuclear cells (PBMC) from TIV influenza vaccine pre-vaccination versus those at day 3 post-vaccination. 5,16E-07 9,85E-04 GSE17721 Genes up-regulated in comparison of dendritic cells (DC) stimulated with poly(I:C) (TLR3 agonist) at 1 h versus DC cells stimulated with Pam3Csk4 (TLR1/2 agonist) at 1 h. 9,56E-06 4,56E-03 GSE3337 Genes down-regulated in comparison of untreated CD8+ dendritic cells (DC) at 16 h versus those treated with IFNG [GeneID = 31,658] at 16 h. 2,75E-05 8,76E-03 GSE17721 Genes up-regulated in comparison of control dendritic cells (DC) at 4 h versus those stimulated with CpG DNA (TLR9 agonist) at 4 h. 5,22E-05 1,11E-02 GSE17721 Genes up-regulated in comparison of control dendritic cells (DC) at 2 h versus those stimulated with LPS (TLR4 agonist) at 2 h.
8,88E-05 1,54E-02 GSE19825 Genes down-regulated in comparison of naive CD8 T cells versus effector CD8 T cells. 1,34E-04 1,82E-02 GSE14308 Genes up-regulated in comparison of Th2 cells versus naive CD4 [GeneID = 620] T cells. 1,32E-04 1,94E-02 GSE17721 Genes up-regulated in comparison of dendritic cells (DC) stimulated with Gardiquimod (TLR7 agonist) at 0. 5 h versus those stimulated with Gardiquimod (TLR7 agonist) at 8 h. 1,62E-04 2,07E-02 GSE9988 Genes down-regulated in comparison of monocytes treated with 5000 ng/ml LPS (TLR4 agonist) versus monocytes treated with control IgG. 2,38E-04 2,39E-02 GSE29617 Genes up-regulated in comparison of peripheral blood mononuclear cells (PBMC) from TIV influenza vaccinee before vaccination versus that after the vaccination. 2,70E-04 2,57E-02 GSE36476 Genes down-regulated in comparison of memory CD4 [GeneID = 920] T cells from young donors treated with TSST at 40 h versus those from old donors treated with TSST at 40 h.
4,19E-04 3,48E-02 GSE17721 Genes up-regulated in comparison of control dendritic cells (DC) at 24 h versus those stimulated with Pam3Csk4 (TLR1/2 agonist) at 24 h. 5,45E-04 4,00E-02 GSE9988 Genes down-regulated in comparison of monocytes treated with anti-TREM1 [GeneID = 54,210] and 5000 ng/ml LPS (TLR4 agonist) versus monocytes treated with control IgG. 5,41E-04 4,14E-02 GSE17721 Genes up-regulated in comparison of control dendritic cells (DC) at 12 h versus those stimulated with LPS (TLR4 agonist) at 12 h. 6,79E-04 4,32E-02 GSE25087 Genes down-regulated in comparison of fetal regulatory T cell (Treg) versus fetal conventional T cells. 2,69E-03 8,85E-02 GSE24634 Genes up-regulated in comparison of naive T cells at day 0 versus CD25+ regulatory T cell (Treg) treated with IL4 [GeneID = 3565] at day 7.
84E-05 DAP12 signaling 164 10,2 27 2,47E-06 4,23E-05 TRAF6 mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation 76 4,72 17 2,82E-06 4,76E-05 Toll Like Receptor 7/8 (TLR7/8) Cascade 77 4,79 17 3,42E-06 5,51E-05 TRIF-mediated TLR3/TLR4 signaling 87 5,41 18 4,73E-06 7,21E-05 Toll Like Receptor 9 (TLR9) Cascade 79 4,91 17 4,96E-06 7,30E-05 MyD88-independent cascade 88 5,47 18 5,61E-06 8,03E-05 Toll Like Receptor 10 (TLR10) Cascade 74 4,6 16 8,84E-06 1,15E-04 Toll Like Receptor 5 (TLR5) Cascade 74 4,6 16 8,84E-06 1,15E-04 Pathways from KEGG database a Total Expected Hits P value FDR Neurotrophin signaling pathway 123 8,75 31 1,98E-10 7,16E-09 Bacterial invasion of epithelial cells 56 3,98 20 4,68E-10 1,45E-08 Notch signaling pathway 47 3,34 18 9,49E-10 2,06E-08 T cell receptor signaling pathway 98 6,97 25 9,42E-09 1,57E-07 Endocytosis 101 7,19 23 3,59E-07 3,71E-06 B cell receptor signaling pathway 75 5,34 19 6,75E-07 6,1E-06 RIG-I-like receptor signaling pathway 49 3,49 12 0,000115 0,000653 Leukocyte transendothelial migration 108 7,68 19 1,75E-04 8,61E-04 Epithelial cell signaling in Helicobacter pylori infection 37 2,63 10 1,79E-04 8,61E-04 Toll-like receptor signaling pathway 97 6,9 16 1,18E-03 4,64E-03 Fc epsilon RI signaling pathway 75 5,34 12 6,10E-03 2,1E-02 Alzheimer's disease 49 3,49 8 2,08E-02 6,28E-02 a.
Every "data point" acquired during a run corresponds to two stacks of images, one for each wavelength, while an image stack includes five fringe images with a phase shift of π/2 between consecutive images, as accomplished by current-shifting of the laser sources. For each experiment thousands of image stacks are acquired and stored on dedicated flash disks that are exchanged by astronauts when full. Additionally, sample images are downloaded to earth by telemetry capabilities of the ISS. Typical downloaded sequences are in the order of 100 image stacks for each experimental run. In this paper we describe the control analysis performed on some of the sequences downloaded for runs 1-17 plus run 25, as carried out during the period from 14th December 2018 to 25th January 2019, while the experiment was still running.
Most of the analysed runs belong to the time slot between the start of the mission and the first replacement of the flash disk storing the data. This data became available first; furthermore, the experiments at a mean temperature of 25 • C, which could be directly compared with available literature data and ongoing ground tests, fall mainly into this time slot. This preliminary analysis on telemetry data was performed in order to check the quality of the running experiments and, if necessary, take actions to modify the foreseen sequence of experimental runs. In table 2 a summary of the analysed runs is provided. 3 Data analysis.
We analysed the effect of connectivity on plant assemblages in three habitat types that represent 90% of the landscape: cropland (44%), grassland (31%) and woodland (15%) (for more details about changes over time in the study area, see Supplementary Fig. S1 ). We assessed the effect of connectivity provided by cropland, grassland and woodland habitats on plant assemblage similarity for each habitat type. For each habitat type studied (wheat croplands, temporary grasslands and woodlands), we selected 25 patches scattered throughout the landscape with a distance of at least 150 m between any two patches to reduce spatial autocorrelation. Patch selection was based on land-cover maps in the ZAAr database. The maps were constructed using aerial imagery based on ten land-cover categories: woodland, grassland, grassy strip, fallow land, maize, cereal cropland, other cropland, built up area, road, and aquatic habitat.
The hedgerow network was obtained from the 2016 Kermap (geographic raster) database for woodland habitats. We selected patches of similar size (about 2 ha for woodland, 1 ha for temporary grassland and 2. 5 ha for wheat cropland), based on the average patch size of these three habitat types in the study area. We interviewed farmers to determine and standardize patch age, the management regime and previous land cover (i. e. the habitat type on which the patch was established) for woodland, temporary grassland and wheat cropland, and initial species sowing composition for temporary grassland and wheat cropland. Since the initial species composition was not available for planted woodlands, we selected woodland patches with similar dominant tree species composition, assuming they reflect the initial planting composition (Table  3 ).
We sampled plant assemblages in woodlands in 2013 (some of these data are already published in Mony et al. 24  ) and in temporary grasslands and wheat croplands in 2016. The effect of connectivity on similarity among species assemblages was analysed for each primary dispersal mode. We assessed the primary dispersal mode for each species using the Baseflor database  78  with the following typology: animal-dispersed, wind-dispersed, unassisted (i. e. gravity-dispersed and autochorous species) and water-dispersed species (referred as "other" in Fig. 1 , since they were not included in this study). The main animal species in our study area which could be involved in plant dispersal are mostly small species (e. g. insects, small mammals and granivorous birds), following the disappearance of most large wild species due to the low carrying capacities of the remaining natural areas  79.
We focused on the primary dispersal mode for each species, although most species have the ability to be dispersed by more than one vector (2. 15 on average  80  ), owing to the absence of data on these secondary (or other) dispersal modes in the existing databases. Data on the primary mode of dispersal were available for all the species identified. Plant species in a few genera (e. g. Carex, Epilobium, Leotodon, Taxaracum and Oxalis sp. ) could not be determined to species level (5. 3% of plants in woodlands, 7. 8% in temporary grasslands and 6. 4% in wheat croplands). In these particular cases, we assigned the primary dispersal mode of close relatives by assuming phylogenetic conservatism  81. For each habitat type, we assessed similarity in plant composition for each pair of habitat patches as a proxy of dispersal  8, 23.