dataesr/scientific-paragraphs-categorization huggingface dataset:
123 versus 0. 151, F = 730. 0, p < 0. 001***). Spatial functional beta diversity was significantly higher for UDB compared to DBB for the three datasets (Table  3 , Fig. 3 ). Spatial functional replacement was higher in UDB than DDB when considering all taxa and aerial dispersers but not aquatic dispersers (Table  3 , Fig. 3 ). Spatial functional richness difference was higher for UDB than DDB for all datasets (Table 3, Fig. 3 ). On average, across all basins, functional replacement was higher than functional richness difference when considering all invertebrates (respectively 0. 225 versus 0. 186, F = 13. 3, p < 0. 001***). For aerial and aquatic dispersers separately, however, functional replacement was lower than richness difference (respectively 0. 173 versus 0. 463, F = 341. 8, p < 0.
001*** and 0. 128 versus 0. 659, F = 605. 1, p < 0. 001***). Tests on subsets of the dataset to account for a possible confounding effect of differences of environmental heterogeneity showed that differences of spatial beta diversity between UDB and DDB were robust (Supplementary material Appendix 2C). Table  2. Mean values, standard deviation, maximum and minimum values of number (TotNum) and duration (TotDur) of drying events in the selected river basins (all, downstream and upstream-drying basins). DDB = downstream-drying basin, UDB = upstream-drying basin. Degree of freedom is equal to one for each likelihood test presented in this.
H2: effect of the temporal dynamics of drying events on temporal beta diversity Temporal taxonomic beta diversity increased with TotNum across all datasets (Table  4 ). When considering all invertebrates and aerial dispersers, temporal taxonomic replacement increased with increasing TotNum, but it did not for aquatic dispersers (Table  4 , Fig. 4 ). Across all datasets, taxonomic richness difference did not vary with increasing TotNum (Table  4 ). As with taxonomic beta diversity, temporal functional beta diversity increased with increasing TotNum for all datasets (Table  4 , Fig. 4 ). Temporal functional replacement increased with increasing TotNum for the entire dataset and aerial dispersers, but not for aquatic dispersers (Table  4 , Fig. 4 ). Temporal functional richness difference increased with increasing TotNum for all datasets (Table  4 , Fig.
4 ). Temporal taxonomic beta diversity increased with increasing TotDur when considering all taxa and aquatic dispersers, but not aerial dispersers (Table  4 , Fig. 4 ). For all datasets, taxonomic replacement increased with increasing TotDur but taxa richness difference did not vary (Table  4 , Fig. 4 ). Temporal functional beta diversity increased with increasing TotDur when considering all invertebrates and aquatic dispersers but not aerial dispersers (Table  4 , Fig. 4 ). For all datasets, temporal functional replacement did not vary with increasing TotDur (Table  4 ). Temporal functional richness difference increased with increasing TotDur only for aquatic dispersers (Table  4 , Fig. 4 ).
H3: effect of the spatial drying pattern on the temporal patterns of beta diversity Temporal taxonomic beta diversity increased with increasing TotNum in DDB, whereas it decreased in UDB when considering all invertebrates but not considering aerial and aquatic dispersers separately (Table  5 , Fig. 5 ). For all datasets, there was no interaction effect of TotNum and the spatial drying pattern on taxonomic replacement nor on taxa richness difference (Table  5 , Fig. 5 ). For all datasets, there was no interaction effect between TotNum and the spatial drying pattern on temporal functional beta diversity, temporal functional replacement and temporal functional richness difference (Table  5 ). Temporal taxonomic beta diversity increased with increasing TotDur on DDB, whereas it slightly decreased in UDB when considering all invertebrates and aerial dispersers     5 , Fig.
5 , Supplementary material Appendix 7A). For aquatic dispersers, temporal beta diversity increased faster on DDB than in UDB with increasing TotDur (Table  5 , Supplementary material Appendix 7B). For all datasets, there was no interaction effect of TotDur and the spatial drying pattern on temporal taxonomic replacement (Table  5 ). When considering all invertebrates, taxonomic richness difference decreased with TotDur for UDB, whereas it remained stable for DDB but, for aerial dispersers and aquatic dispersers, there was no interaction effect of the spatial arrangement (Table  5 , Fig. 5 ). Temporal functional beta diversity increased for DDB, whereas it decreased for UDB when considering all invertebrates (Table  5 , Fig. 5 ). This was also true for aerial dispersers and aquatic dispersers (Table  5 , Supplementary material Appendix 7A, 7B).
For all datasets, there was no interaction effect of TotDur and the spatial drying pattern on temporal functional replacement (Table  5 ). For all invertebrates, functional richness difference decreased for UDB, whereas it increased for DDB (Table  5 , Fig. 5 ). For aquatic dispersers, functional richness difference increased faster for DDB than for UDB (Table  5 , Supplementary material Appendix 7B), whereas there was no trend for aerial dispersers (Table  5 ).
2018)  or averaged patterns over time  (Leigh et al. 2016)   Figure  4. Relationships of taxonomic and functional temporal beta diversity (blue circles and lines) and their relative components replacement (red crosses and lines) and richness difference (orange triangles and lines) with the number of drying events (left) and duration of drying events in days (right) for all invertebrates, for aerial dispersers only and for strict aquatic dispersers. When a significant effect was found for the associated mixed model, a line was plotted with the intercept and slope estimated in the model. Table  5. Results of mixed models of H3 for each dataset: all invertebrates, aerial and strict aquatic dispersers; results are displayed as in Table  4. Likelihood ratio tests represent the significance of the model compared to the simpler version of the model.
Taxa and traits responding the same way to the spatial drying pattern could also imply that observed dissimilarities between UDB and DDB are due to environmental selection of taxa with particular biological traits rather than stochastic extinction processes. Moderated functional redundancy was found across the dataset and the slightly higher redundancy in UDB compared to DDB might indicate a stronger environmental selection of adapted taxa on these basins, as functional redundancy has previously been attributed to traits selection following environmental harshness  (Boersma et al. 2014 , Vander Vorste et al. 2016). There was more spatial taxonomic replacement than spatial taxonomic richness difference for aerial dispersers, but the opposite was found for aquatic dispersers, with such differences being stronger in UDB.
Results of mixed models of H2 for each dataset: all invertebrates, aerial and strict aquatic dispersers. We modelled taxonomic and functional beta diversity and contribution of their replacement component as a function of TotNum (upper part of the table) or TotDur in days (lower part). Likelihood ratio tests represent the significance of each model compared to the associated null model. River as random effect was significant to very significant for 95% of all models and is not displayed in the table. For clarity, only the sign of the estimate was indicated: it indicates if the number of drying events and their durations had a positive or a negative effect on beta diversity and the contribution of replacement to beta diversity. Degree of freedom is equal to one for each likelihood test presented in this table.
Acknowledgements -Authors thank  R. Vander Vorste ,  G. Le Goff  and  R. Mons  for help in sample collection,  M. -C. Roger  and  G. Le Goff  for invertebrate processing. This manuscript benefited from the meaningful comments from  R. Sarremejane  and two anonymous reviewers. Funding -Funding for the sampling collection was provided by the  Water Agency RM&C, Temporary River Ecology Programme.
8%, giving an unclosed molecule. Automatic annotation was performed with Prokka version 1. 12 (7). The genome (one chromosome but no plasmid) presents one repeat region and is predicted to carry 1,951 genes, including 1,902 protein-coding genes and 49 tRNA genes. Strain Marseille-P9525 T exhibits 98. 3% 16S rRNA gene sequence identity with E. phocaeensis strain Marseille-P3242 (GenBank accession number NR_147403), 96. 76% identity with E. timonensis (NR_144707), 95. 30% with E.
ACKNOWLEDGMENTS J. S. and M. F. each obtained a PhD grant from the  Fondation Méditerranée Infection, Marseille, France. This work was supported by the  French Government  under the  Investissements d'Avenir (Investments for the Future) program  managed by the  ANR  (reference, Méditerranée Infection  10-IAHU-03 ), as well as the  Région Provence Alpes Côte d'Azur  and European funding from  FEDER PRIMMI. We thank  Claudia Andrieu  for genome sequencing,  Maxime Descartes Mbogning Fonkou  and  Sabrina Naud  for technical help, and  Olga Cusack  for participation in editing the manuscript.
1; 594, bandpass 570-620 and emission 561. 4; and 680, bandpass 663-738 and emission 640. 0. Images were captured using a 60x oil immersion, N. A 1. 40 objective. Images and 3D renderings were acquired with NIS Elements AR 4. 00. 08 software (Nikon). For the supplementary 3D reconstructions, Imaris 3. 0 (Bitplane) was used. Surfaces segmentation for the red, green and blue channels was performed.
Unbiased quantification of α-syn cell-to-cell transfer Using MATLAB (Mathworks) we determined the proportion of grafted TH-immunoreactive neurons and Iba-1-positive microglia that contain huα-syn. The red and green channels were collected as 12-bit monochrome (gray) images for each channel. The green channel was used to create a mask aimed at identification of the neuronal cell body. This was accomplished using an averaging filter with large dimensions and an appropriate threshold. Additional masks were created for the green and red channels using individual thresholds. The two masks were combined. and the resulting composite image displayed white pixels where the green and red exceeded their thresholds and were in the cell body of the neuron. The data are presented as the percentage of TH-immunoreactive neurons and Iba-1-positive microglia cells containing huα-syn.
Quantification of Iba-1 cell density The density of Iba-1-positive cells was quantified using NIS Elements AR 4. 00. 08 software (Nikon) in a blinded manner. Briefly, graft pictures were obtained on a Nikon Eclipse Ni-U microscope (Nikon); images were captured with a color Retiga Exi digital CCD camera (QImaging) using NIS Elements AR 4. 00. 08 software (Nikon) and 20x magnification (air immersion, 0. 75 N. A. , n = 3/animal/hemisphere), and a border of the graft and striatum was outlined. All the Iba-1-positive cells in the field of view that were within the border were counted using the NIS Elements AR 4. 00. 08 software (Nikon) using the count feature.
Assessment of microglial morphology Color (RGB) images of Iba-1-stained grafts and striatum were acquired at 60x magnification (oil immersion 1. 40 N. A. ) using a Nikon Eclipse Ni-U microscope (Nikon). A total of nine images/animal were analyzed; three images/from three sequential brain sections through the striatum/graft. Images from within the graft were taken to sample the dorsal, mid, and posterior parts of the graft within each section. In order to assess the morphology of microglia in the samples, RGB color images were processed by a custom MATLAB (Mathworks) script. Cell bodies and other large regions were segmented first. Dynamic thresholds were determined for both the blue intensity and saturation channels of the image. Upper and lower bounds of the thresholds were set to match the full width at half maximum of curves fit to the histograms of each channel.
Three contrasts were made comparing each pairwise combination of LPS, IL-4, and PBS treatment groups for differential gene expression. A significance threshold of FDR = 0. 05 was used with a secondary abs (log2FC) > 1 filter used to define the highest magnitude changes. After the PBS group was excluded from further analysis an additional minimum average expression filter of log2CPM > 0 in either the LPS or IL-4 groups was imposed. Expression values (log2CPM) for each sample are included as supplemental data. Some expression data was later converted to FPKM or z-scores for later visualization and comparison. Z-scores were calculated based on the mean and standard deviation of the IL-4 treatment group only.
Statistics The Plexxikon study was performed on 8-18 control and 8-10 PLX-treated animals for histological analysis and was repeated for biochemical assessment (3-6 control and 4-8 PLX). Microglial density and morphology were analyzed using two-factor ANOVA, with treatment and hemisphere as factors. The cell-to-cell transfer counts were analyzed using negative binomial mixed-effects regression and a false discovery rate adjustment on linear contrasts via the R package 'lme4' (v3. 3. 0) (http:// lme4. r-forge. r-project. org/ and https://www. r-project. org/). The experiments involving changing the activation state of microglia were conducted in three separate sessions with a total of 20 controls, 20 LPS-injected, and 23 IL-4-injected mice used for immunohistochemistry and cell-to-cell transfer analysis.
The cellto-cell transfer counts were analyzed using negative binomial mixed-effects regression and a Benjamini-Hochberg false discovery rate adjustment on linear contrasts for all the analysis via the R package 'lme4' (v3. 3. 0). We report means plus or minus standard error of the mean. A p-value of < 0. 05 was taken as significant for all statistical tests. All tests were two-sided. Graphs were prepared using Prism 6. 0, GraphPad.
2c ). We further confirmed the reduction in microglia using an antibody directed towards the specific microglia marker TMEM119  [54]  (Additional file 1: Figure  S1c ). PLX reduced the number of TMEM119-positive microglia in both the cortex and striatum compared to control mice. It has been reported that PLX compounds do not affect the morphology of the remaining microglia  [55] , and to confirm this in our model, we analyzed images of microglia from within the graft and striatum using a MATLAB script and calculated the hydraulic radius (area:perimeter ratio) of each cell. Activated microglia are amoeboidshaped and therefore have a high index, i. e. , a large area and a small perimeter. Our analysis revealed no significant difference in the hydraulic radius (area/perimeter) of Iba-1-positive cells between PLX-treated and control mice confirming that PLX did not significantly alter the morphology of remaining microglia (Fig.
2d ). There were significant differences between the morphology of microglia in the contralateral striatum versus that of microglia located in and around the graft, most likely due to the stereotactic injections and grafting procedure. Collectively, our data show that the number of microglia can be significantly reduced using a CSF1R inhibitor (PLX 5622) in our mouse model of α-syn cell-to-cell transfer and that under the conditions used, the morphology of microglia did not differ between mice on control chow versus PLX chow. Because autophagy is a pathway implicated in PD pathogenesis  [56] , we undertook further biochemical analysis of PLX-treated vs. control mice to explore whether PLX treatment altered autophagy markers or altered phosphorylated α-syn levels in these animals. Autophagy markers LC3B-1 and p62, as well as lysosomal marker LAMP1, were assessed using western blotting in control and PLX-treated mice.
Microglia depletion increased accumulation of huα-syn After establishing that PLX significantly reduced the number of Iba-1-positive cells within the striatum in our model, tissue sections were co-stained for Iba-1, TH, and huα-syn to quantify cell-to-cell transfer using confocal imaging. The α-syn 211 antibody was selected for this study due to its detection of huα-syn. Each confocal stack was analyzed using MATLAB to localize the huαsyn signal within the cell body. Because this antibody detects huα-syn and because there is no human protein in the grafted neurons that originates from mice, the source of the human protein within neurons and microglia is from the striatal terminals. Huα-syn was detected within grafted TH positive neurons and Iba-1 positive microglia (Fig. 3  and Additional file 4: Figure  S4 ).
Acknowledgements We thank  Drs. Dave Cherba  and  Mary Winn  of the  Bioinformatics & Biostatistics Core of the Van Andel Research Institute, Anderson Peck of the Small-Animal Imaging Facility , the staff of the  Vivarium of Van Andel Research Institute  for animal care. We also acknowledge and the  Confocal Microscopy and Quantitative Imaging Core of the Van Andel Research Institute  and we thank the  Van Andel Genomics Core  for performing the RNA-seq. We also thank  David Nadziejka  for editing the manuscript. We acknowledge the  Van Andel Research Institute  and the many individuals and corporations that supported financially the neurodegenerative research at  Van Andel Research Institute. We thank  Plexxikon  for freely and openly providing PLX5622-containing and control diets. S. G. is supported by the  Peter C.
5% at low momentum to 1. 0% at 200 GeV/c. A sketch of the various track types relevant in LHCb is shown in Fig. 2. The minimum distance of a track to a PV, the impact parameter, is measured with a resolution of (15 + [29 GeV/c]/p T ) µm. Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors  [9]. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad (SPD) and preshower detectors (PS), an electromagnetic calorimeter (ECAL) and a hadronic calorimeter (HCAL). Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers (MUON)  [10]. The LHCb detector data taking is divided into fills and runs. A fill is a single period of collisions delimited by the announcement of stable beam conditions and the dumping of the beam by the LHC, and typically lasts around twelve hours.
At an average event size of 55 kB passing HLT1, this buffer allows for up to two weeks of consecutive HLT1 data taking before HLT2 has to be executed. Therefore, it is large enough to accommodate both regular running (where, as we will see, the alignment and calibration is completed in a matter of minutes) and to serve as a safety mechanism to delay HLT2 processing in case of problems with the detector or calibration. Around 40% of the trigger output rate is dedicated to inclusive topological trigger lines, another 40% is dedicated to exclusive c-hadron trigger lines, with the rest divided among dimuon lines, trigger lines for electroweak physics, searches for exotic new particles, and other exclusive trigger lines for specific analyses. There are in total around 20 HLT1 and 500 HLT2 trigger lines.
The variation in the total HLT1 efficiency as a function of the data-taking period is shown in Fig. 26. The b-hadron efficiencies have been stable throughout the Run 2 data taking. The c-hadron efficiencies decreased midway through 2016, when a tighter HLT1 configuration was used to prevent the disk buffer from overflowing due to unexpectedly high LHC efficiency and availability. The improvements seen for some of the c-hadron channels in 2017 with respect to 2016 are caused by changes in the corresponding reference offline selections leading to a different average p T and displacement of the c-hadron, not any intrinsic variation in the HLT1 performance or thresholds.
Low multiplicity event trigger lines At the HLT2 stage there are dedicated selections for each relevant final state with a low track multiplicity. There are 32 lines: two to select exclusive dimuon production, three to select exclusive production of photons or electrons, and the remainder to select various hadronic final states, dominated by lines that select low-p T hadrons. The HLT2 trigger efficiencies have been determined in data and are shown in Fig. 34  for two channels of particular interest: dimuon and dihadron. The dimuon HLT2 trigger efficiency is determined using a sample of independently triggered candidates reconstructed in events containing exactly two muon tracks inside the detector acceptance. The dimuon candidate is required to have satisfied the relevant low-multiplicity L0 trigger.
RHUM-RUM's main objective was seismological imaging of the area from crust to core, in order to strengthen or reject the mantle plume hypothesis  (Barruol & Sigloch 2013). A large array of 57 broadband OBSs was deployed over an area of 2000 × 2000 km 2 during 13 months in 2012-2013, and 37 temporary land stations were installed between 2011 and 2015 on La Réunion, Mauritius, Rodrigues Island, the Îles Éparses, southern Seychelles and in southern Madagascar. Mazzullo et al. (2017)  used earthquake data recorded by the RHUM-RUM stations to retrieve Rayleigh-wave phase velocities for periods of 30-300 s and group velocities for 16-250 s. A 3-D, regional shear-velocity model was obtained by joint inversion of phase and group velocities, but the wave periods were too long to reliably constrain crustal depths, which were essentially filled in a priori by a crustal reference model, CRUST1.
0  (Laske et al. 2013). The scope of our study is to tomographically image the crust and lithosphere beneath the RHUM-RUM deployment, using crosscorrelations of ambient seismic noise recorded by RHUM-RUM OBSs and island stations. We use phase cross-correlations (PCCs, Schimmel 1999) which do not require special pre-processing, unlike classical CCF calculations. Subsurface structure is constrained by group velocity measurements, which are derived from the PCCs. Finally, our group velocity values are inverted jointly with the earthquake-generated Rayleigh-wave data previously measured and inverted by  Mazzullo et al. (2017). We obtain a 3-D, regional shearvelocity model from the surface down to ∼250 km depth, of which we discuss the upper 80 km, where the new ambient noise data appreciably add constraints.
Section 2 describes the seismic stations used in this study. Section 3 describes the calculation of the PCCs and the stacking procedure. The estimation of group velocity curves from the PCC stacks and the results of the group velocity measurements are presented in Section 4. Section 5 focuses on tomography based on these noisederived group velocity data and on the earthquake-derived data of  Mazzullo et al. (2017). The results are discussed in Section 6, followed by Conclusions in Section 7. We used the open-source software toolboxes Python  (Rossum 1995) , ObsPy  (Beyreuther et al. 2010; Megies et al. 2011; Krischer et al. 2015) , and obspyDMT  (Hosseini & Sigloch 2017)  for data download and processing, and for plotting figures. The exceptions are Fig. 1 , which was generated with the open-source mapping toolbox GMT  (Wessel et al.
12b ) cover the period range <30 s, which is not covered by the earthquakederived data of  Mazzullo et al. (2017)  (blue and cyan) and which is sensitive to shallowest structure. The vertical velocity profile inverted jointly from both data sets requires significantly slower S V -wave velocity in the upper 35 km (black in Fig. 12a ) compared to the model of  Mazzullo et al. (2017)  (orange), and moderately faster velocity between 35 and 100 km. This indication of very slow structure shallowly beneath a mid-ocean ridge segment is plausible, especially beneath this plume-influenced CIR segment. (ii) Slower Madagascar Plateau. South of Madagascar at latitudes of ∼27 • S and 30 • -35 • S, two velocity anomalies that were already intensely slow in the inversion of  Mazzullo et al. (2017)  are rendered even slower by the joint inversion at 20 km.