dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometry Flow cytometry data were acquired using a BD LSRFortessa X-20 (BD Biosciences) and analyzed using FlowJow software. C3 deposits. Normal human serum (Etablissement Français du Sang) diluted to 33% in PBS was added to A498, Caki-1, A549 and SK-MES cells for 30 minutes at 37°C with or without a blocking anti-FH (Ox-24). Staining was performed with mouse monoclonal anti-C3c (Quidel, cat. #A205) or isotype control IgG1 (Dako, cat. #X0931) followed by a secondary goat anti-mouse AF700 (Invitrogen, cat. #A-21036). Proliferation and viability. CFSE reagent 1:1000 (Invitrogen, cat. #C34554) was added to A498, Caki-1, A549 and SK-MES cells for 20 minutes at 37°C. Standard culture medium of each cells was added to stop CFSE staining. Cells were seeded in a 6-well plate and cultured for 72 h in the presence or absence of complete medium supplemented with purified FH at 2 µg/mL or 20 µg/mL.
Statistical analysis The package "survival" in R software was used to generate Kaplan-Meier curves, with censoring at 2500 days. A log-rank test was applied to examine the difference between curves. Association between the distributions of two semi-quantitative variables was assessed by the Chi2 test. The difference between siFH and siC cells with regard to proliferation and survival was assessed by the paired T-test; the unpaired T-test is used for cell migration. * p<0,05, ** p<0,01, *** p<0,001, **** p<0,0001.
Int-FH but not mb-FH is associated with a negative impact on ccRCC patient survival In the ccRCC cohort in the TCGA database (KIRC cohort), gene expression of CFH above the median was significantly associated with decreased DFS (p=0. 03) and OS (p=0. 017), suggesting a role of locally produced FH in this type of cancer. Staining of human ccRCC tumor sections for FH revealed heterogeneous staining patterns by immunohistochemistry (IHC) (Figure  1 ). There were deposits, defined as membranous FH (mb-FH), surrounding the tumor cells and intracellular staining of the tumor cells (int-FH). The mb-FH colocalized with IgG and the NaK ATPase pump by IF (Supplementary Figure  S1C, D ), suggesting an extracellular location of the mb-FH, corresponding to deposits, which is associated with complement activation  [7].
Silencing of FH in ccRCC and lung ADC cell lines induced a strong modulation of the tumor cell phenotype, including proliferation, cell cycle, morphology, viability and migration. These results can be explained by the profound modification of the transcriptional program of FHsilenced tumor cell lines, which was marked by alteration in the expression of genes governing the biological processes of adhesion and locomotion. Moreover, within the tumors, int-FH + cells were frequently also positive for mesenchymal markers, further suggesting a motile phenotype. We found that FH was localized mainly within lysosomes. This localization is consistent with the observation that C3 in T cells is in lysosomes, allowing C3a-C3aR interactions and promoting cell survival  [21]. FH is stable at low pH (pH 5)  [46] , suggesting that it can function within this organelle.
The volcano plot was generated with R software and the EnhancedVolcano package. Red points represent genes with a log2-fold change >1 and Padj< 0. 05. I) Heatmap of log2 counts for the top 50 differentially expressed genes after silencing A498 cells with siFH vs siC. The most differentially expressed genes were selected on the adjusted pvalue. Expression across each gene was row scaled, with relatively high expression of a given gene marked in red and samples with relatively low expression marked in blue. Lighter shades and white represent genes with intermediate expression levels. J) GO analysis of functional annotations (biological process) up-or downregulated by FH silencing vs. A498 siC    staining by IF on paraffin-embedded ccRCC sections. D) calnexin (green) for endoplasmic reticulum and FH (red).
Acknowledgments The cytometric and microscopy analyses were performed at the  Centre d'Histologie, d'Imagerie et de Cytométrie (CHIC ) and the  Centre de Recherche des Cordeliers UMRS1138 (Paris, France). We are grateful to the  CHIC team  for their excellent technical assistance. CHIC is a member of the  Université Pierre et Marie Curie Flow Cytometry network (RECYF). We thank the  Centre d'Expérimentations.
This work was supported by grants from the  Cancer Research for Personalized Medicine (CARPEM)  and  ARC  contre le cancer and  La Ligue  contre le cancer to LTR and équipe on May 27, 2021. labellisée Ligue contre le cancer (WHF and CSF). This work was also supported by  INSERM, University of Paris ,  Sorbonne University , and the  Labex Immuno-Oncology Excellence Program. MVD received a Ph. D. fellowship from  ARC.
Spinning Enhanced Visible and Infrared Imager (SEVIRI) Data and the Satellite Application Facility for Now-Casting (SAFNWC) Cloud Algorithm The SEVIRI (Spinning Enhanced Visible and Infrared Imager), onboard the geostationary satellite MSG (METEOSAT second Generation, http://www. eumetsat. int/website/home/Satellites/index. htm (accessed on 23 March 2021)), is a visible and infrared multi-channel imager which observes the Earth's atmosphere and surface at 13 different wavelengths: 3 visible channels, 2 near-infrared channels and 8 infrared channels. A very important feature of SEVIRI is its continuous imaging of the Earth, with a baseline repeat cycle of 15 min. The imaging sampling distance is 3 km × 3 km at nadir for standard channels, and down to 1 km for the high-resolution visible (HRV) channel.
Cloud types and cloud top pressure used in our studies are produced with the SAFNWC software (www. nwcsaf. org (accessed on 23 March 2021)) applied to SEVIRI data. The algorithm used in this software was developed within the framework of the Eumetsat Satellite Application Facility in support for Now Casting and very short range forecasting (NWC SAF). It is described in the Algorithm Theoretical Basis Document written by Derrien and Legleau  [26]. Here is a summary of the main features of this algorithm which is applied to each pixel and performed in three steps: (1) cloud detection, (2) cloud type classification, (3) cloud to pressure determination. For the first step, one can find more concise information on the algorithm in Derrien and Le Gleau  [27, 28]. In step 1, to separate clear pixels from cloudy pixels, a series of threshold tests are applied sequentially  [26] [27] [28] : tests on window channel brightness temperatures (BT at 10.
These values are computed from simulated clear sky BT and bi-directional reflectance simulated by the RTTOV (Radiative Transfer for TOVS) radiative transfer model  [29]  for thermal bands and the 5S radiative transfer model for the solar spectrum  [30]  for the atmospheric and surface conditions given by the ancillary data set. In step 2, the method for assigning a cloud type to cloud pixels is based on the same approach as in step 1 (i. e. , a set of threshold test on BT, differences on BT, and spatial variability of BT and reflectance). To the ancillary data set used in step 1 is added a coarse description of the temperature and humidity profiles using numerical weather prediction model profiles. This classification process, at the end of which 10 classes of clouds are defined, makes it possible to differentiate thick cloud from thin cloud or partial cloud cover.
350 hPa and 500 hPa were retained as thresholds (see Appendix A). For partial cloud cover for which no cloud top pressure is available, to assign a level to these SEVIRI partially cloudy pixels, the same approach as in Dommo et al. [36]  is used: each pixel of the partial coverage class is reclassified as high-top, mid-top or lowtop cloud depending on the distribution of these three cloud types in the vicinity of the pixel (see Appendix B). Both the choice of the cloud top pressure thresholds and the results of the reclassification process of the SEVIRI partial cloud cover have been evaluated in a comparison with the CALIOP lidar data carried out using statistics and pixel-by-pixel comparisons (see Appendix C).
European Center for Medium-Range Weather Forecasting (ECMWF) Reanalysis (ERA) Interim Data Cloud cover occurrence is characterized in terms of large scale circulation patterns and moisture budget in the region by using atmospheric variables extracted from the ERA-Interim reanalysis (ERAI) dataset (  [37] , https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era-interim (accessed on 23 March 2021)). For the 2008-2014 period, JJAS water vapor path, horizontal and vertical winds, temperature, specific humidity and geopotential height 6-hourly data, at 0. 75° latitude-longitude resolution (i. e. , 80 km at the equator), on 37 vertical levels, in the domain 25° W-35° E and 0° N-45° N, are used. The SHL location is defined following Lavaysse et al. [4]. At each time step, the cumulative distribution of potential temperature at 850 hPa is computed for the grid points within the 13° W-15° E and 15° N-35° N domain.
These observations are confirmed by the results of CALIOP/CLOUDSAT analysis of Stein et al. [19]. But the low-top COFs observed in Figure  4 , although low, are higher than those given by the CALIOP climatology. Over the Sahara, the SEVIRI-derived low clouds are in fact most often detected as broken edges of mid-top clouds or as clear air in the CALIOP data set (seeAppendix C ). It can be noted that the northward progression of the ITD from June to August, as well as its withdrawal in September, coincides well with that of the high-top clouds (Figure  2b ) over the same period.
On the side of increasing cloud top pressure from the mid-tropospheric peak, the CALIOP cloud top pressure distribution decreases strongly until 550 hPa to 600 hPa which lies just below the expected altitude of the SABL in summer. These main features of the cloud top pressure distributions are fairly constant across the year in the cloud-top distributions extracted from the GEWEX CALIPSO-ST data set. When the 350 hPa threshold is used to separate high-top clouds from mid-top clouds, the frequencies of the high-altitude clouds in the SEVIRI data and CALIOP V3 data are very close (Figure  A2 ). At pixel scale, the agreement frequency is 70% (Figure  A2 ). The main source of disagreement comes from the classification of CALIOP high-top clouds into mi-top clouds in the SEVIRI data set and vice versa.
For the mid-top cloud pixels detected by SEVIRI, the match with CALIOP is high (82% to 85%), but the occurrence frequency of mid-top cloud is larger for CALIOP than for SEVIRI (bias of 8%) (Figure  A2 ). 60% of CALIOP mid-top cloud and more during the daytime are also classified as fragmented cloud by CALIOP (not shown). This high fraction is in agreement with the results obtained by Kealy et al. [18]  with lidar airborne measurements. When the fraction of cloud cover inside the CALIOP segment decreases, the frequency of non detection or detection of a low level cloud by SEVIRI increases. For SEVIRI partial cover included in the mid-top cloud class, CALIOP most frequently also detects a mid-top cloud cover and, less frequently, clear sky (not shown). The fraction of low-top clouds in the CALIOP V3 data reaches 4% over Hoggar (Figure  A2 ) and these clouds are most of the time undetected by SEVIRI.
In the CALIOP-ST monthly average data, the frequency of low clouds is less than 1%. From version V3 to version V4, the separation between small clouds and dust has been improved  [83]. The low clouds of CALIOP V3 observed over Western Sahara and Hoggar could in fact be dust. SEVIRI low-top clouds are, in the CALIOP classification, most frequently broken mid-top clouds or (but-less frequently) clear-sky. This is in agreement with the large fraction of re-allocated partial cloud covered pixels belonging to this SEVIRI class. The fraction of CALIOP clear segment is the largest for the SEVIRI pixels which were added to the low cloud class by the re-attribution scheme. To summarize, this comparison with CALIOP data confirms the choice of a threshold at 350 hPa in order to separate high-top clouds from mid-top clouds.
For the separation between mid-top clouds and low-top clouds, a threshold at 550 hPa or 600 hPa would be more appropriate than the 500 hPa chosen here for SEVIRI. The classification used in these results however is not affected by this choice due to (1) the shift towards a lower cloud top pressure of the peak of distribution of mid-top clouds in the SEVIRI data on the Sahara and (2) the absence of low cloud. High clouds are well detected by SEVIRI but some of them are in fact mid-top clouds. The mid-top clouds are not as well detected by SEVIRI. But the clouds detected and attributed to the mid-top cloud class by SEVIRI are indeed mid-top clouds for CALIOP. The frequency of low clouds is very small in the CALIOP data set and they are not detected by SEVIRI. Otherwise, this comparison confirms the results of Kealy et al.
Author Contributions: Conceptualization, N. S. , G. S. , M. G. , J. -Y. G. , C. F. and J. C. ; Formal analysis, N. S. , G. S. and J. -Y. G. ; Funding acquisition, N. B. and C. F. ; Methodology, N. S. and G. S. ; Supervision, G. S. ; Writing-original draft, N. S. and G. S. ; Writing-review and editing, M. G. , J. -Y. G. , C. F. , J. C. and N. B. All authors have read and agreed to the published version of the manuscript. Funding: Funding was provided for this research by the «Programme National Exceptionnel (PNE) du MESRS "Ministères de l'Enseignement Supérieur et de la Recherche Scientifique d'Algérie"» and by the national programme «Les enveloppes fluides et l'environnement (LEFE) of CNRS INSU» under grant AO2014-862487 and the support of the Centre National d'Etudes Spatiales (CNES) in the frame of the «Expecting Earth-Care, Learning from A-train» project.
An important feature is machine-to-machine (m2m) communication among the nodes to enable adaptive sampling. When specific signals are recorded by one node (e. g. , harmful algal bloom, oil spills, ocean heatwaves, etc. ) other nodes can refine the event's data acquisition strategy (e. g. , extend geographical coverage, provide higher sampling rate, etc. ) using collaborative robotic solutions. The distributed structure will also ensure a better tolerance to individual node failures and enable the flexibility to satisfy specific tasks (e. g. , aquaculture monitoring, support to tourism, etc. ). The complexity of the network structure and functioning is generally hidden to the final users  (Carpenter and Cannady, 2004; Crise et al. , 2018; Montella et al. , 2018)  and this process of abstraction is relevant to deliver a portfolio of products.
Sigfox is currently deployed in more than 70 countries in all five continents, able to cover 1. 3 B people 2  , while LoRaWAN is currently deployed in 162 countries 3. When choosing among the different options, the main factors to be considered are: communication range, data rate, spectrum usage, number and size of messages, energy consumption, and cost  (Mekki et al. , 2019). Sigfox is a proprietary long-range lowpower solution widely used for IoT, but it is quite limited in the number of messages per day and the amount of data that can be transferred. LoRa is also based on a proprietary technology, but only at the physical layer, while the data transmission protocol, namely LoRaWAN, is open and free to use and deploy. As for cellular communications, they have been traditionally leveraged for IoT, but their high-power consumption limits their use in battery-operated devices.
, 2015; Scheffrahn et al. , 2018; Buček et al. , 2019). Here, we used mitochondrial genomes and nuclear ribosomal RNA gene data of 173 kalotermitid samples collected worldwide, which includes species of 22 kalotermitid genera to reconstruct a robust estimate of relationships. Using this comprehensive phylogeny, we reconstructed the global historical biogeography of Kalotermitidae, identifying past natural and human-mediated dispersal events, and distribution patterns that bear the signature of potential events of vicariance through continental drift. Finally, we created a list of species of Kalotermitidae with available foraging data and discussed the evolution of such abilities according to patterns implied by the phylogenetic framework.
RESULTS AND DISCUSSION Evolution of Kalotermitidae inferred from taxonomically representative phylogenetic trees We gathered a dataset comprising the mitochondrial genomes and nuclear 18S and 28S rRNA genes of 173 samples of Kalotermitidae collected across all biogeographic realms (Figure  1 ). We reconstructed four phylogenies, two maximum likelihood topologies and two time-calibrated trees, inferred with and without third codon sites of protein-coding genes. For simplicity, the four trees were summarized into a reduced summary evidence tree (RSE-tree), on which the statistics of the four trees were summed (Figure  1 ). The RSE-tree was obtained by pruning 36 tips that diverged less than 1 Mya and were represented by samples collected in the same country. Our RSE-tree was composed of 138 tips representing 22 of the 23 genera of Kalotermitidae and including more than 120 species, which is equivalent to ~27% of described kalotermitid species-level diversity  (Krishna et al.
, 2013)  (Figure  1 ). Our RSE-tree was composed of 137 branches, 119 of which were recovered in all four analyses, while 18 nodes had at least one topological conflict among analyses. The position of genus-level nodes was congruent among all four phylogenetic analyses except for 1) Longicaputermes and Kalotermes + Ceratokalotermes, which were placed as the first and second diverging branches within Kalotermitidae varying in order among analyses, and 2) Neotermes group B and Incisitermes group C whose positions were variable among analyses (Figure  1 ). Time-calibrated phylogenetic trees were inferred using termite fossils as node calibrations. The timeline of termite evolution inferred with both datasets was largely congruent, with a mean node age difference of ~5 million years (Figure  S2-3 ).
The analysis of the dataset with third codon positions included yielded older node age estimates, reflecting the high level of saturation found in third codon positions, which is known to inflate time estimates  (Zheng et al. , 2011). Therefore, we used the time estimates of the analysis of the dataset without third codon positions as RSE-tree (Figure  1 ). Our Bayesian Binary MCMC ancestral range reconstructions run on our four phylogenetic estimates with four biogeographic models yielded congruent ancestral range distributions (see Table  S2 -S5). These results were further corroborated by ancestral range reconstructions performed with the dispersal-extinction-cladogenesis model (Figure  S5 ).
Reconciliation of extant and extinct kalotermitid evidence Molecular phylogenies are reconstructed using extant taxa. The exclusion of extinct species can lead to spurious historical biogeographic reconstructions  (Lieberman, 2002). Although fossil evidence has been integrated into biogeographic reconstructions inferred from molecular phylogenies by previous studies (e. g. Meseguer et al. , 2015) , these methods are hampered for a family with a fragmentary fossil record and containing many species of uncertain taxonomic affiliations like Kalotermitidae. Kalotermitids are represented by 74 fossils belonging to twelve extinct and six extant genera in the paleontological database PaleoDB (Figure  S4 ). Despite the paucity of available fossils, the age and biogeographic origin of kalotermitid fossils are somewhat consistent with our phylogenetic inferences (nodes 7, 12, 13, 14, and 15 in Figure  1 ), although the earliest kalotermitids are from the Oriental rather than the Neotropical region.
In addition, library preparation was downsized to one-fifteenth of the volume indicated in the manufacturer protocol for all reagents. Other steps were performed as suggested by the manufacturer protocol. One library with unique dual index barcodes was prepared for each sample separately. A total of 3 μl of each library was pooled and sequenced on the Illumina HiSeq X platform. We obtained an average of 52 megabases and 2 gigabases of raw sequencing data for samples sequenced with the amplicon-based approach and samples sequenced with the shotgun sequencing approach, respectively. Raw sequencing data was quality-checked with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/), and trimmed and qualityfiltered with bbduk (sourceforge. net/projects/bbmap/) using the following parameters: "ktrim=r k=23 mink=7 hdist=1 tpe tbo maq=10 qtrim=rl trimq=15 minlength=35".
The resulting trimmed mitochondrial genomes were annotated with MitoZ  (Meng et al. , 2019). The genome annotations of previously published mitochondrial genomes were retrieved from GenBank. MitoZ annotations were visually inspected by first aligning mitochondrial genomes with MAFFT as described above, and then importing the alignments and the MitoZ-generated feature annotations into JalView  (Clamp et al. , 2004). The errors in MitoZ-generated annotations, mostly comprising of 1) omitted tRNA-Val and 2) 5-terminally truncated 16S rRNA gene were manually corrected. The final mitochondrial genomes and their annotations are available on GenBank under accession numbers <WILL BE UPDATED PRIOR TO PUBLICATION>. Mitochondrial genomes prior to trimming of non-coding region between 12S rRNA and tRNA-Ile are available at <WILL BE UPDATED PRIOR TO PUBLICATION>.
18S and 28S annotation We used BLASTn  (Altschul et al. , 1990)  to search for the 18S and 28S nuclear rRNA genes in the 57 whole-genome shotgun assemblies generated with MetaSPAdes to assemble mitochondrial genomes. The 28S rRNA sequences of Glossotermes oculatus, Zootermopsis angusticollis, and Incisitermes schwarzi were used as BLASTn search queries (GenBank ids: JN647689. 1, AY859614. 1, GQ337716. 1). For each sample, the longest blast hit was identified and oriented to match the 18S and 28S rRNA genes of Drosophila melanogaster (GenBank ids: NR_133550. 1 and NR_133562. 1, respectively). In order to annotate the 18S and 28S rRNA genes of Kalotermitidae, we aligned the 18S and 28S rRNA genes of Kalotermitidae and D. melanogaster using MAFFT v. 7. 475  (Katoh and Standley, 2013). Kalotermitidae 18S and 28S rRNA sequences were then extracted from the multiple sequence alignments.
Alignment and concatenation of the final sequence dataset The final sequence dataset consisted of 165 mitochondrial genomes and 52 sequences of 18S and 28S rRNAs of Kalotermitidae sequenced in this study. We also used previously published sequences, including the mitochondrial genomes of eight species of Kalotermitidae  (Cameron et al. , 2012; Bourguignon et al. , 2015)  (Table  S1 ), the COII sequence of Ceratokalotermes spoliator  (Thompson et al. , 2000) , the only described species of the genus Ceratokalotermes that was missing from our sampling, and 58 mitochondrial genomes of non-kalotermitid termites that we used as outgroups  (Bourguignon et al. , 2015). The rRNA and tRNA sequences were aligned using MAFFT v. 7. 475  (Katoh and Standley, 2013). For protein-coding genes, we first translated the nucleotide sequences into amino acid sequences with the transeq command of the EMBOSS suite of programs  (Rice et al.
Bayesian time-calibrated phylogeny We estimated time-calibrated phylogenies of Kalotermitidae using the Bayesian phylogenetic software BEAST2 v. 2. 6. 2  (Bouckaert et al. , 2014). We used the uncorrelated lognormal relaxed clock model to account for substitution rate variation among branches  (Drummond et al. , 2006). The alignment dataset was split into six partitions: one partition for every codon position of the combined 13 mitochondrial protein-coding genes, one partition for the combined nuclear 18S and 28S rRNA genes, one partition for the combined mitochondrial 12S and 16S rRNA genes, and one partition for the combined 22 mitochondrial tRNA genes. Time-calibrated phylogenies were reconstructed using two sequence matrices: 1) the full dataset, including the third codon sites of protein-coding genes (WITH3RD), and 2) a partial dataset, from which the third codon sites of protein-coding genes were removed (WITHOUT3RD).