dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Most proteins involved in cytokinetic abscission strongly accumulate at the midbody  3, 9. Here, we identified 489 proteins enriched in purified MBR (Enriched Flemmingsome) (Fig. 1 , Supplementary Data 1). To isolate MBRs, we developed an original flow cytometry-based protocol that yielded preparations that display three important features. First, the MBR+ fractions were highly pure. Second, the MBRs were obtained from unperturbed cells (no drugs for cell synchronization and no treatment for stabilizing actin or microtubules). Most importantly this purification did not involve any detergents. This allowed us to identify both transmembrane (29 proteins, Supplementary Data 1, TAB2, in blue) and membrane associated proteins in this organelle. This includes the 4 Rab proteins already involved in cytokinesis (Rab8, Rab11, Rab14, and Rab35)  3  , as well as transmembrane proteins such as chloride channels (CLIC1 and CLIC4, previously localized at the midbody  52  ), adhesion/tethering/signaling molecules (ITGA3, PlexinB2, ICAM1, BST2, and CD44) and tetraspanins (CD9, CD9-P1, and TM4SF1) whose potential functions during cytokinesis will be studied in the future.
These three points are key improvements, when comparing with previous proteomes of intercellular bridges, which already proved to be seminal in identifying essential proteins in cytokinesis  44, 53. Of note, 68 and 29% of the final list of Skop et al. (160 proteins from CHO cells) were respectively present in our Total Flemmingsome (1732 proteins) and Enriched Flemmingsome (489 proteins) (Supplementary Data 1, TAB6). The difference in extent of protein recovery between both studies could be explained by (1) differences in cell origins (hamster CHO vs. human HeLa), (2) differences in actual organelles (intercellular bridges before abscission vs. post-abscission, free midbodies as generated at the time of abscission), (3) the membrane integrity of the organelles (use of detergents vs. detergent-free, thus preserving cytosolic components), (4) reduced contaminations and finally the relative quantification of protein abundance (the Enriched Flemmingsome is based on significant enrichment compared to control fractions, including total cell lysates).
In addition, proteasome inhibition by MG132 has been previously found to delay abscission  57  and here we revealed the presence of major proteasome components in the Enriched Flemmingsome (Fig. 1 , GOenrichment and Supplementary Data 1). Importantly, 31% of the proteins present in the Enriched Flemmingsome (150 proteins) were already demonstrated to be localized to the furrow, bridge or midbody during cytokinesis and/or to be involved in cytokinesis (Supplementary Data 1, TAB2). This asserts the strength of our proteomic study, as well as its potential for identifying additional candidates (339) important for cytokinesis. To define the Enriched Flemmingsome, we decided to arbitrarily put the threshold of enrichment to 1. 3-fold and a 5% FDR. This allows to include proteins that are significantly enriched in MBRs and that are also present in the total cell lysate, as many proteins involved in cytokinesis (e.
g. actin-related and traffic-related) have also functions in interphase. The Flemmingsome thus represents a useful resource for the cytokinesis community and we created a website to share this data (https://flemmingsome. pasteur. cloud/). Each hit in the Enriched Flemmingsome, in particular, was browsed in the literature for an eventual function/localization linked to cytokinesis and this reference database will be regularly updated. Following up on the Enriched Flemmingsome, we found that ALIX, syntenin and syndecan-4 are indeed highly enriched at the midbody, then at the abscission site (Fig. 2 ). Functional analysis demonstrated that the three proteins are required for proper abscission (Fig. 4 ), and that they function together for ESCRT-III localization, specifically at the abscission site (Fig.
Cells were then permeabilized with 0. 1% Triton-X100, blocked with PBS containing 0. 2% bovine serum albumin (BSA) and successively incubated for 1 h at room temperature with primary (Supplementary Table  1 ) and secondary antibodies diluted in PBS containing 0. 2% BSA with DAPI staining (0. 5 mg/ml, Serva). Cells were mounted in Mowiol (Calbiochem). Images were acquired with an inverted TiE Nikon microscope, using a ×100 1. 4 NA PL-APO objective lens or a ×60 1. 4 NA PL-APO VC objective lens and MetaMorph software (MDS) 7. 8. 0. 0 driving a CCD camera (Photometrics Coolsnap HQ). Images were then converted into 8-bit images using ImageJ software (NIH). Purified MBR+ from flow cytometry were concentrated at 1200g and a 5 μl-drop was incubated overnight on a glass coverslip. The MBR+ particles were processed for immunofluorescence as described above for cells.
Images were acquired with a ×60 1. 4 NA PL-APO VC and MetaMorph software (MDS). Statistics and reproducibility data. All values are displayed as mean ± SD (standard deviation) for at least three independent experiments (as indicated in the figure legends). Significance was calculated using unpaired, one-sided t tests or onesided exact Fisher's tests, as indicated. For comparing distribution of abscission times, a nonparametric Kolmogorov-Smirnov test was used. In all statistical tests p > 0. 05 was considered as non significant. p values are indicated in the Figures. Provided representative images have been observed reproducibly as indicated below: Fig. 1a  (n > 20 cells), Fig. 1b  (n > 30 FACS sortings), Fig. 1c  (n = 2 blots), Fig. 1d  (n > 20 midbodies), Fig. 2b  (n > 30 ICBs), Fig. 2c  (n > 3 ICBs), Fig.
Proteolysis was carried out at 37 °C overnight. Three rounds of 50 μL of recovery buffer (50 mM ammonium bicarbonate pH 8) were used to elute the peptide-rich solution. Then peptides were processed as described in ref. 48 and resuspended in 2% acetonitrile, 0. 1% formic acid prior to LC-MS/MS analysis. For eFASP, three independent replicates were made for Tot and MBRE and two independent replicates for MBR+ and matched MBR-flow cytometry-sorted samples (Supplementary Data 1, TAB3). MS analysis. Tryptic peptides from in-gel digestion were analyzed on a Q Exactive HF instrument (Thermo Fisher Scientific, Bremen) coupled with an EASY nLC 1000 chromatography system (Thermo Fisher Scientific). Sample was loaded on an in-house packed 50 cm nano-HPLC column (75 μm inner diameter) with C18 resin (1.
Dynamic exclusion was enabled for 45 s. Tryptic peptides from eFASP digestion were analyzed on a Q Exactive plus instrument (Thermo Fisher Scientific, Bremen) coupled with an EASY nLC 1000 chromatography system (Thermo Fisher Scientific) and processed as described above. Peptides were first eluted using a 2-5 % gradient of solvent B (ACN, 0. 1% FA) during 5 min, then a 5-22% gradient of solvent B during 150 min, a 22-45% gradient of solvent B during 60 min and finally a 45-80% gradient of solvent B during 10 min all at 250 nL min -1 flow rate. The instrument method for the Q Exactive Plus was set up in the data dependent acquisition mode. After a survey scan in the Orbitrap (resolution 70,000), the 10 most intense precursor ions were selected for HCD fragmentation with a normalized collision energy set up to 28.
Charge state screening was enabled, and precursors with unknown charge state or a charge state of 1, 7, 8, and >8 were excluded. Dynamic exclusion was enabled for 45 s. Protein identification and quantification. All data were searched using Andromeda  65  against a Human Uniprot database (downloaded in 2015 08 18, 20,204 entries), usual known MS contaminants and reversed sequences of all entries. Andromeda searches were performed choosing trypsin as specific enzyme with a maximum number of two missed cleavages. Possible modifications included carbamidomethylation (Cys, fixed), oxidation (Met, variable), and Nter acetylation (variable). The mass tolerance in MS was set to 20 ppm for the first search then 4. 5 ppm for the main search and 20 ppm for the MS/MS. Maximum peptide charge was set to seven and five amino acids were required as minimum peptide length.
The "match between runs" feature was applied for samples having the same experimental condition with a maximal retention time window of 1 min. One unique peptide to the protein group was required for the protein identification. A FDR cutoff of 1% was applied at the peptide and protein levels. Quantification was performed using the XIC-based LFQ algorithm with the Fast LFQ mode as described in ref. 66. Unique and razor peptides, included modified peptides, with at least two ratio count were used for quantification. Statistical analysis for proteomic studies. For the differential analyses, proteins identified in the reverse and contaminant databases and proteins "only identified by site" were first discarded from the list of identified proteins. Then, proteins exhibiting fewer than two quantified values in at least one condition were discarded from the list.
After log2 transformation of the leftover proteins, LFQ values were normalized by median centering within conditions (normalized function of the R package DAPAR 67 ). Remaining proteins without any LFQ value in one of both conditions have been considered as proteins quantitatively present in a condition and absent in another (Supplementary Data 1, TAB4). They have therefore been set aside and considered as differentially abundant proteins. Next, missing values were imputed using the imp. norm function of the R package norm  68. Proteins with a fold-change under 1. 3 have been considered not significantly differentially abundant. Statistical testing of the remaining proteins (having a fold-change over 1. 3) was conducted using a limma t test  69  thanks to the R package limma  70. An adaptive Benjamini-Hochberg procedure was applied on the resulting p values thanks to the function adjust.
The "merged p value" (y-axis) has been obtained using the Fisher's method from the different p values that have been measured in the comparisons. Note that these two quantities have been computed only when data were available. UpsetR graph and STRING functional association network. The Upset graph (Supplementary Fig. 1d ) represents the distribution of the significant proteins coming from the different statistical analyzes. The Venn diagram represents the numbers of differentially abundant proteins in function of the kind of protein sample preparation (In-Gel or eFASP). Functional association network was determined by STRING  73  and displayed using Cytoscape  74. GO cluster diagrams. The over-representation analyses of GO-term clusters in Fig. 1g  and Supplementary Fig. 1e  have been performed using hypergeometric tests.
Acknowledgements We thank  R. Basto ,  G. Hickson ,  J. Mathieu ,  J. -R. Huyhn ,  R. Shaughnessy ,  M. Serres , and  T. Wai  for critical reading of the paper; the Echard Lab members for helpful discussions; the Recombinant antibodies platform ( TAb-IP, Institut Curie, Paris ) and the  DSHB (University of Iowa ) for antibodies. GFP-MKLP2 cells were from the  Hyman Lab MPI-MCBG Dresden 45. UTechS PBI  is part of the  France-BioImaging infrastructure network (FBI)  supported by the  French National Research Agency  ( ANR-10-INSB-04 ; Investments for the Future), and acknowledges support from  ANR/FBI  and the  Région Ile-de-France  (program " Domaine d'Intérêt Majeur-Malinf ") for the use of the Zeiss LSM 780 Elyra PS1 microscope. We thank  P. H. Commere  from the  Utechs CB, Institut Pasteur  for FACS sorting.
As habitat type may influence responses by waterbirds, we used vegetation characteristics and waterpan size data collected in July 2014 and January 2015 as part of a broader survey of waterpans in our study area. We conducted assess-Table  1. Description of behaviours used to categorise the original activity and responses of waterbirds to disturbances. In the original activity category, F. V = Feeding allowing vigilance, F. nV = feeding not allowing vigilance, nF. V = non-feeding allowing vigilance, nF. nV = non-feeding not allowing vigilance; and in the response categories, Vig = vigilance, noRes = no response and Esc = escape. ments of the surrounding vegetation at each waterpan by establishing 30 m long transects along the cardinal and intercardinal directions from the water edge. On each transect we placed a 1-m 2 quadrat about 10 cm from the water's edge and recorded the grass cover (visual estimate) and representative grass height (using a standard calibrated wooden ruler).
Analysis We excluded from our analysis observations where waterbirds responded to anything that we did not see and those when our monitoring team had initiated the response (these comprised 2. 2% of the original dataset). We wanted to specifically test for a group vigilance effect (H2) but also tested for a possible confounding effect with the waterbird guilds, as group size may vary widely among waterbird species and guilds. Although we noted that group sizes were significantly different across waterbird guild (using one way ANOVA), the average group sizes fell within the same order of magnitude (between 12 and 21), and thus any effect of group size was unlikely to be explained uniquely by our waterbird guild. We therefore used a set of independent variables (land use, season, waterbird guild, group size, original activity, disturbance type, waterpan cluster group, waterpan identity and time of day) that we thought could influence the probability of reacting to disturbance and length of time spent flying after a disturbance.
Acknowledgements -We thank the  Zimbabwe Parks and Wildlife Management Authority  for granting us the permission to conduct this study (under permit number  23  (I) (C) (II)  04/2017 ). This work was conducted within the framework of the  Research Platform 'Production and Conservation in Partnership' (RP-PCP) , and hosted by the  Zone Atelier Hwange -Hwange LTSER. We thank the  Ministère Français des Affaires Etrangères  for financially supporting  T. Tarakini  through the  FSP-RenCaRe project.
Methodology To capture both the characteristics of different contexts from a multiscale perspective and their conditions for innovation, we use official statistics of different territorial typologies based on the Eurostat database. The multiscale approach proposed in this paper results from the logic behind the Open Access Toolkit (the analytical framework envisioned by the TREnD project) based on sub-regional units of data analysis likely to introduce sensitiveness measures of adaptation towards transition. It has been recognized that regional statistics alone cannot reveal the full and sometimes complex picture of what is happening at a finer level within the EU. Moreover, statistical information at a sub-regional level is an important tool for highlighting specific regional and territorial characteristics  [39].
From the literature review, we find how developing indicator frameworks to measure resilience is a complex task due to the complexity of each (territorial) system, making difficult a straightforward measurement of territories resilience capacity  [23]. We argue that a multiscale approach is important to reveal the variance of the context conditions at different territorial scales. The set of indicators available from the Eurostat database are grouped into different categories, according to the territorial units' typology. The territorial units based on regional statistics, metropolitan regions and urban-rural typology are analyzed through six categories that include several indicators consistent with the main phenomenon explained by each category: 1) demography statistics; 2) economic account; 3) intellectual property; 4) business demography; 5) transport statistic; 6) labor market.
Besides, it is equally important to track the longitudinal behavior of territorial scales over time so as to help regions maintain long-term economic growth by facilitating the transition process  [40]. Therefore, the multiscale approach is consistent with a fine databasedeveloped for the TREnD Project Open Access Toolkit -organized in such a way to apply data analytics methods and combine longitudinal and territorial trends of selected indicators. The aim of analysis conducted for this paper is twofold: 1) Providing evidence on the need to approach the issue of structural change, transition-oriented, of regions by highlighting the conditions and their potentials at different territorial units; 2) Coupling the multiscale approach with innovation policy in conceptualizing transition towards place-sensitive strategies.
Territorial Units and Multiscale Approach To provide evidence on the need to investigate regions' context conditions at different territorial scales concerning regional innovation performance we selected two indicators and two different territorial units, according to the NUTS Regulation  [39]. The figures below show the consistency of metropolitan regions (see Fig. 1 ) and cities, towns and suburbs (see Fig. 2 ) by the degree of density. These units represent the geographical base maps to investigate the distribution of the two factors, selected from the TREnD project database for this paper. Figure  1  shows the distribution of metropolitan areas in EU Member States based on the degree of population density, and how they are mainly concentrated in the central-northern Europe. Figure  2  shows the degree of population density according to the urban-rural typology.
3c, 3d ), namely more concentrated in the medium range of values for metropolitan areas whereas more concentrated in the low range of values for urban-rural areas. With the aim to reveal a measurement of disparities, the distribution of GDP per capita is investigated through the GINI coefficient. The panel data is formed by metropolitan areas and urban-rural typology partitioned according to Member State. The GINI coefficient is a gauge of economic inequalities, based on population and income, and has several shortcomings in investigating the source of inequalities. To estimate the level of concentration, the GINI (a) (b) (c) (d) Fig. 3. GDP per capita distribution (2017) per metropolitan areas (a, c) and urban-rural typologies (b, d). Source: elaboration of Authors from Eurostat  [41]  coefficient filters the two-dimensional area, the gap between the Lorenz curve and the equality line ranges between 0 in the case of perfect equality and 1 in the case of perfect inequality.
06 to a maximum of 0,59 (see Table  4 ). The GINI coefficient as measure of disparities offers interesting insights under the lens of the multiscale approach. Table  4  aggregates the results by member states, whilst the figure below (Fig. 4 ) shows how the value of the GINI coefficient increases according to the disparities between metropolitan and urban-rural areas within each state. The second indicator selected for the investigation rests on the European Union Trade Mark (EUTM) applications in 2017. Eurostat provides a set of indicators based on trade marks -a process that allows creators to establish protection for their industrial property -reflecting also the non-technological innovation in every sector of economic life, including services. In this context, indicators based on trade mark data can provide a link between innovation and the market  [42].
Results In this study, we obtained eight new genome sequences: four for GI. 3 and four for GI. 2. These included four strains previously identified based on their capsid sequences as GI. 3 (JA10/08-10, BO25/08-133, JA34/09-48 and CHA20/09-100; GenBank accession numbers: LT708119, LT708122, LT708127 and LT708128, respectively) and two strains as GI. 2 (10-28 and 10-32; GenBank accession numbers: HE800531 and HE800532, respectively)  9, 11. We obtained the full-length genomic sequences of strains 10-28 and 10-32, the complete coding sequences (CDS) of strains 16-35 and 16-36, and the CDS along with the 3′ untranslated region (UTR) of the strains JA10/08-10, BO25/08-133, JA34/09-48 and CHA20/09-100. The genomes of the earliest GI. 2 strains 10-28 and 10-32 were 7,448 nucleotides (nt) long and shared 94.
6% nucleotide identity. The length of the 5′ UTR sequences is 9 nt and that of the 3′ UTR is 70 nt. The closest strains were GI. 3 strains with an average nucleotide identity of 93. 8% (94. 3% for the non-structural part of the genomes), while the average identity with GI. 1 strains was 85% (86. 4% for the non-structural part of the genomes). The genomes of the GI. 3 strains were 7,427 nt long (excluding the 5′ UTR), except the CHA20/09-100 strain that was 7,424 nt long. The 3′ UTR sequences had the same length (64 nt). When including the GI. 3 06-11 sequence (GenBank accession number MN737115), the average nucleotide identity between the five GI. 3 strains was 97. 1%. The CDS of the four GI. 2 showed between 93. 2% and 94. 5% of nucleotide identity (93. 2% between 16-35 and 16-36 strains), and when compared to GI.
1 CDS presented no deletions or insertions (7,369 nt long), while the GI. 3 CDS presented a deletion of six consecutive nucleotides within the capsid gene (7,363 nt long instead of 7,369 nt). This deletion is present in the benign strains 06-11, Ashington and the Italian RCV (GenBank accession numbers: EF558587 and X96868)  2, 4, 7  and comprises a codon putatively under positive selection in GI. 1 strains  31. The strain CHA20/09-100 had a further three nucleotide deletion in the minor structural protein VP10 (positions 193-195, amino acid 65). The complete dataset (221 sequences; 7,368 nucleotides) was screened for recombination with RDP (Recombination Detection Program)  32. The different methods available in RDP detected as recombinants, with strong statistical support (p-values < 0. 05; Table  1 ), all the strains from the early GI.
2 outbreaks (strains 10-28, 10-32 and N11), all the GI. 2 strains previously identified as non-recombinants (strains CBVal16, Zar11-11, Rij06-12, Tar06-12, Zar06-12 and Seg08-12) and GI. 2 strains more recently detected (16PLM1, NL2016, Canada2016, 16-35, 16-36). A single recombination breakpoint was determined at the non-structural/structural parts boundary (Table  1 ). For most of these strains, the GI. 3 strain 06-11 was identified as the most likely donor for the non-structural part, with the exception of strain 16-36 for which the most likely donor is CHA20/09-100. A GI. 2 strain (7-13_Barrancos_Portugal_2013 or SOS155_Portugal_2015, GenBank accession numbers KF442963 and MG763946, respectively) was the most likely donor for the structural part. Thus, according to RDP all these sequences (GI.
3 group and is currently unclassified  9. Indeed, the genetic distance between MRCV and GI. 3 VP60 gene sequences is > 15% (data not shown) and no other similar strains (from independent outbreaks) had been identified. As for the four GI. 2 sequences obtained in this study, they clustered within a highly supported group (bootstrap value 100) formed by all the strains previously identified as having a GI. 2 capsid, and that is more closely related to GI. 1 and GI. 3 than to GI. 4. Two strains collected in the Netherlands  34  and Canada in 2016 and that were recently made available (GenBank accession numbers MN061492 and KY235675, respectively) were also part of the GI. 2 cluster. Regarding the ML tree of the non-structural part except p16 (Fig. 2ii ), the four GI. 2 strains obtained in this study 10-28, 10-32, 16-35 and 16-36 clustered with the GI.