dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The ELP2 variants identified in the patients have been deposited for public access within ClinVar  95  with accession numbers SCV000262876. 5 (https://www. ncbi. nlm. nih. gov/ clinvar/variation/225008/), SCV000262928. 5 (https://www. ncbi. nlm. nih. gov/clinvar/ variation/225038/), SCV000590102. 3 (https://www. ncbi. nlm. nih. gov/clinvar/variation/ 432384/), SCV000589770. 3 (https://www. ncbi. nlm. nih. gov/clinvar/variation/225008/), SCV001244968. 1 (https://www. ncbi. nlm. nih. gov/clinvar/variation/870396/), SCV001244969. 1 (https://www. ncbi. nlm. nih. gov/clinvar/variation/870397/), SCV001482303. 1 (https://www. ncbi. nlm. nih. gov/clinvar/variation/1012183/), SCV001482306. 1 (https://www. ncbi. nlm. nih. gov/clinvar/variation/1012184/), SCV001482307. 1 (https://www.
ncbi. nlm. nih. gov/clinvar/variation/870396/), SCV001482309. 1 (https://www. ncbi. nlm. nih. gov/clinvar/variation/1012185/) and SCV001482310. 1 (https://www. ncbi. nlm. nih. gov/clinvar/variation/1012186/). Transcriptomic data that support the findings of this study are contained in Supplementary Data 1 (DE genes) and Supplementary Data 2 (TRIAGE matrix), and proteomic data in Supplementary Data 3 (DE peptides). The raw RNA-sequencing data are available in the NCBI Sequence Read Archive with accession number PRJNA692814. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE 96 partner repository with the dataset identifier PXD023653. All other data generated in this study are available from corresponding authors on reasonable request. Source data are provided with this paper.
Beforehand, culture supports were coated by PBS containing poly-L-ornithine (3,3 µg/cm 2 ; Sigma) then by PBS containing mouse laminin (1 µg/cm 2 ; Roche), each step followed by extensive washes. Stem character of NSCs was systematically assessed by immunofluorescence against Nestin and SOX2 proteins (data not shown). Cell cultures were checked for the absence of mycoplasma (Plasmotest, Invivogen, Toulouse, France).
Nanoparticle tracking analysis (NTA) sEV preparations were diluted 1:100 in filtered PBS (0. 22 µm) and tracked using a NanoSight LM10 (Malvern Panalytical) equipped with a 405 nm laser. Videos were recorded three times for each sample at constant temperature (22 °C) during 60 s and analyzed with NTA Software 2. 0 (Malvern instruments Ltd). Data were analyzed with Excel and GraphPad Prism (v8) softwares.
Bioinformatics data analysis of mass spectrometry raw files Raw MS files were processed with the Mascot software for database search and with Proline  [48]  for label-free quantitative analysis. Data were searched against Human herpesvirus 5 and Human entries of the UniProtKB protein database (Human betaherpesvirus 5 clone VHL-E-BAC19 and release Uniprot Swiss-Prot February 2018). Carbamidomethylation of cysteines was set as a fixed modification, whereas oxidation of methionine was set as variable modification. Specificity of trypsin/P digestion was set for cleavage after K or R, and two missed trypsin cleavage sites were allowed. The mass tolerance was set to 10 ppm for the precursor and to 20 mmu in tandem MS mode. Minimum peptide length was set to 7 amino acids, and identification results were further validated in Proline by the target decoy approach using a reverse database at both a PSM and protein false-discovery rate of 1%.
After mean of replicate injections, the abundance values were log2 transformed and missing values were replaced by random numbers drawn from a normal distribution with a width of 0. 3 and down shift of 1. 8 using the Perseus toolbox (version 1. 6. 7. 0). For statistical analysis, a Student t-test (two-tailed t-test, equal variances) was then performed on log2 transformed values to analyse differences in protein abundances in all biologic group comparisons. Significance level was set at pY= 0. 05, and log2 ratios were considered relevant if higher than 1 or lower than -1. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [49]  partner repository with the dataset identifier PXD029146.
Flow cytometry analysis After incubation of cells with PKH67-stained sEVs, cells were washed twice with PBS and trypsinized, before proceeding to flow cytometry analysis. PKH67 positive cells were analyzed on a Macsquant VYB Flow Cytometer (Miltenyi Biotec), by using FCS and FITC fluorescence parameters, and by subtracting cell autofluorescence background. Data were analyzed with FlowJo (BD) and GraphPad Prism (v8) software.
DAPI staining (1 µg/ml; Sigma) was performed for 10 min at room temperature. ProLong Gold without DAPI (Thermo Fischer Scientific) was used for coverslip mounting. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted November 19, 2021. ; https://doi. org/10. 1101/2021. 11. 18. 468660 doi: bioRxiv preprint 11 Widefield acquisitions were realized using Apotome microscope (Zeiss) and confocal acquisitions were made on SP8-STED microscope (Leica). Image processing was performed using ImageJ. GraphPad Prism (v8) software was used to perform data statistical analysis.
Cytotrophoblast infection by hCMV leads to an increase of sEV secretion To study the consequences of hCMV infection on placental sEV secretion and composition in early pregnancy, we used an immortalized cell line derived from first (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted November 19, 2021. ; https://doi. org/10. 1101/2021. 11. 18. 468660 doi: bioRxiv preprint 12 trimester extravillous cytotrophoblasts (HIPEC  [37, 52] ), for which the expression of the cytokeratin 7 specific cytotrophoblastic marker was verified (data not shown). Cell supernatants were collected and pooled upon 48-72 h of infection before sEV preparation. To exclude any possible effect of viral contamination of sEV preparations in further experiments, an infectivity test was systematically carried out at the end of the sEV preparations (Supplementary Figure  1A ).
sEVs from hCMV-infected cytotrophoblasts potentialize further infection of recipient MRC5 cells Since proteomic data suggested a proviral activity for sEVs issued from infected cytotrophoblasts, we assessed the ability of sEVs to modulate hCMV infection. As sEV cargo was composed of proteins prone to act on early steps of infection -entry and immediate early transcriptions -we reasoned that any putative action of sEVs should take place immediately after delivery of their content into recipient cells. In this regard, different amounts of sEVs from non-infected or hCMV-infected cytotrophoblasts were deposited on MRC5 cells either concomitantly or 2 h before addition of hCMV (Figure  5A ). 24 h later, cells were subjected to an anti-IE immunofluorescence (Figure  5B ). When added alone, sEVs prepared from hCMV-infected cells did not lead to any detectable expression of IE in MRC5 cells (Figure  5Ba ), indicating that they were not contaminated by residual infectious viral particle, as already assessed previously (Figure  1A  and Supplementary Figure  2 ).
Finally, the impact of sEVs prepared from amniotic fluid (AF) obtained from naturally infected pregnant women was assessed (Figure  7 ). Even if AF sEVs originate from various tissues, an important proportion of them have been described to express placental alkaline phosphatase, testifying of their placental origin  [69]. Thus, we reasoned that AF sEVs may also be endowed with a proviral activity and facilitate the infection of NSCs by hCMV. AF was collected at third trimester of pregnancy from 3 women, whose clinical data are summarized in Supplementary imaging. The third one (NP12) also seroconverted during first trimester of pregnancy but her AF was positive for hCMV by qPCR and the fetus presented major brain lesions. From the AF of these patients, sEVs were prepared and incubated on NCSs during various times before infection by hCMV.
NSCs were subjected to an anti-IE immunofluorescence 24 h later to assess their level of infection. A control experiment verifying that no infectious particle was present in sEVs preparations was systematically performed in parallel (data not shown). Compared to the other sources of sEVs used previously (Figure  6 ), a significant effect of sEVs on viral infection was observed at earlier times of incubation with NSCs but not at 24 h, certainly due to a difference in sEVs uptake efficiency (Figure  7 ). When incubation was realized simultaneously or 2 h before hCMV infection, level of infection of NSCs incubated with sEVs prepared from the non-infected patient NP19 or NP6 (infected patient but with AF negative for hCMV) remained similar. Remarkably, when sEVs were prepared from NP12, a case of hCMV positive pregnancy with severe impairment of fetal development, a significant increase of infection of around 20 % was observed in comparison to incubation with sEVs from NP6 or NP19, indicating that NP12 sEVs allowed the enhancement of hCMV infection in recipient NSCs.
ACKNOWLEDGMENTS We greatly thank  C. Chouquet ,  B. Rauwel ,  M. Mouysset ,  L. Battut ,  C. Mengelle ,  F. Chauvrier ,  P. Verdy ,  N. Kopf , as well as the whole ViNeDys team, for their technical assistance and their numerous advice and discussions which allowed the progress of this work. We thank also  AL. Iscache ,  V. Duplan-Eche  and  F. L'Faqihi-Olive , from the cytometry facility of Infinity, as well as  S. Allart  and  D. Daviaud , from the imaging facility of Infinity, and finally the  GeT technical service of Infinity. We thank the medical and paramedical staff of the gynecology unit at  Paule de Viguier Hospital , the patients who agreed to participate in the study, as well as  L. Bujan  and  M. Aubry , from the  Germethèque. We finally warmly thank  E. Haanappel  from the  Institute of Pharmacology and Structural Biology , who granting us access to the Nanosight device, and  Audrey Esclatine  and  Daniel Gonzalez-Dunia  for critical reading of the manuscript and insightful comments.
FUNDING This project has received financial support from the  French Biomedicine Agency , institutional grants from  Inserm ,  CNRS  and  Toulouse 3 University. This project is part of the doctorate thesis of  Mathilde Bergamelli , who was funded by the  Ministry of Education and Research (MESR). The TEM experiments were performed on  PICT-IBiSA, Institut Curie, Paris , member of the France-BioImaging national research infrastructure, and were supported by the  French National Research Agency  through the  "Investments for the Future" program  ( France-BioImaging ,  ANR-11-INSB-04 ), supported by the  CelTisPhyBio Labex  ( N° ANR-11-LB0038 ) part of the  IDEX PSL  ( N°ANR-10-IDEX-0001-02 PSL ). The proteomic part of this project was supported in part by the  Région Occitanie, European funds (Fonds Européens de Développement Régional, FEDER) ,  Toulouse Métropole , and by the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 ).
087859e- 259 , R package gProfiler2 [57]), with 962 of them (32. 8 %) associated with the "extracellular exosome" GO term. Conversely, these 962 proteins constituted 44. 1 % of the proteins which define the GO term, and the 94 of the top 100 most frequently identified exosomal proteins, as defined by the Exocarta database demonstrated to play an important role to facilitate hCMV entry into cells infected cytotrophoblasts were analyzed by mass spectrometry-based quantitative 14 To this end, equivalent amounts of sEVs prepared from non-infected or hCMV-((http://exocarta. org/exosome_markers_new), were detected in the sEV preparations. Mass spectrometry-based quantitative proteomics data indicated that 37 proteins, including 25 viral proteins, were significantly over-represented and 15 human proteins under-represented in sEVs secreted by cytotrophoblasts upon hCMV infection, as illustrated in the Volcano plot (Figure3A).
This criterion, that had to be specified by the user in  [18] , is derived from the statistics of the group selected after stage (i) in  [9]. This strategy offers a high degree of automation, however some limitations have been experienced. The k-means algorithm used in stage (i) is not necessarily the best option for building the first partition, as it was also noticed in  [25]. For most indicators computed in stage (i), the cluster of possibly physical modes resembles an exponential distribution whereas the cluster of spurious modes tends to be normally distributed. The k-means algorithm is always dominated by the variables with highest variance and, when applied to normally distributed data, the algorithm tends to split datasets in approximately equally sized clusters  [26]. For an optimal partitioning, one would thus have to choose the maximum order n from the number of expected observable modes (that is not known in advance) or at least specify a sufficiently large maximum n in order to guarantee that no possible physical modes will be discarded after stage (i).
The evolution of the modal signature will be correlated to the damage actually observed on the specimen and a comparison between Output-Only (OO) and Input/Output (IO) results will be made in order to estimate the interaction between the specimen and the shaking table device. The paper is organized as follows. Section 2 gives a brief recap of the SMART2013 test campaign, describes the processed dataset and details how subspace-based algorithms are tuned. The single mode validation criteria used in this paper are also defined, paying particular attention to a newly derived indicator, called normalized Modal Transfer Norm (nMTN) difference, that is developed for promoting the detection of weakly excited modes. Section 3 describes the fuzzy-enhanced three stages procedure that is central to this contribution.
In addition to that, the accelerations of the eight hydraulic actuators rods have been recorded during the whole test campaign. Further information regarding data acquisition strategy and experimental results can be found in  [20]. Table  1  gives a brief recap of the SMART2013 test campaign. This one consists of an alternation of bi-axial seismic tests (gray lines of the table) of increasing level, which one wishes to be gradually damaging for the specimen, and of random tests (white lines) with an acceleration level chosen such that the first eigenmodes of the experimental system are excited but without adding further damage to the RC medium. For this last type of test, the reference ground motion is defined as a bi-axial random white noise; more precisely, the reference acceleration € u c sent to the MTS controller (see Fig.
The maximum order n max of the state-space model to be interpolated to the dataset is generally chosen as large as possible (at least twice the number n ph of observable modes expected on the frequency range of interest). In this study, the value n max ¼ 300 is chosen. One ends up 1 Canonical Variate Analysis One can note that, in this paper, the order n is the only tuning parameter that is investigated; no variations of i (as done for example in  [19] ) is taken into account, but the presented methodology could perfectly enable the partition of the broader mode set k | : ; / | : À Á | : that would have been generated if multiple values of i were to be considered. Regarding the SMART2013 dataset, the too low values of i that did not meet the criterion (4) led to irrelevant results (even failing to catch low frequency modes) and too high values led to CPU limitations.
5). Hard clustering k-means algorithm is used in  [9, 25]  for stage (i). The centers C 1 (of E ph ) and C 2 (of E sp ) are sought as: C 1 ; C 2 f g¼ arg min C X 2 k¼1 X J j¼1 kX | : À C k k 2 2 ð16Þ However, this algorithm is known for having two strong limitations  [26] : (a) Due to its euclidean metrics, the k-means algorithm is suitable for normally distributed clusters of similar variance which is not the case for modal indicators; (b) Even when applied to normally distributed data, k-means tends to split the datasets in approximately equally-sized clusters. Fig. 3  shows a scatter plot of the modal indicators nMTN and MPD. One can observe two not normally distributed clusters of in-homogeneous size that furthermore do not exhibit good convex properties. On Fig. 3 , the marginal probability density functions (pdf) f have been estimated in the maximum likelyhood sense using a combination of two direct-and reversedlog-normal distributions: f t; a; s 1 ð Þ ; l 1 ð Þ ; r 1 ð Þ ; s 2 ð Þ ; l 2 ð Þ ; r 2 ð Þ À Á ¼ a log N t þ ; l 1 ð Þ ; r 1 ð Þ À Á þ 1 À a ð Þlog N t À ; l 2 ð Þ ; r 2 ð Þ À Á ð17Þ with t þ ¼ t À s 1 ð Þ and t À ¼ À t À s 2 ð Þ À Á and: log N t; l; r ð Þ¼ 1 r ffiffiffiffiffiffi ffi 2p p t exp À log t À l ð Þ 2 2r 2 ! ð18Þ The limitations of the k-means algorithm and the disadvantages of using it at stage (i) have also been noticed in  [25].
Now, writing the stationarity of J with respect to every solution field in C; l; k; A; b; q; c ð Þ gives rise to a set of equations, that, after elimination of the Lagrange multipliers k; b; c ð Þ , leads to: 8k; C k ¼ P J j¼1 l m jk X | : P N j¼1 l m jk ð25Þ 8j; k; l jk ¼ 1 P c p¼1 kX | : ÀC k k 2 A k kX | : ÀCpk 2 Ap 1 mÀ1 ð26Þ 8k; q k ¼ cjD k j 1 dÀ1 P c p¼1 jD p j 1 dÀ1 ð27Þ 8k; A À1 k ¼ 1 q k jR k j 1 d R k ð28Þ with R k ¼ P J j¼1 l m jk X | : À C k À Á X | : À C k À Á T = P J j¼1 l m jk and D k ¼ P J j¼1 l m jk d =jR k j. This set of equations is solved using a classical fixed-point algorithm with alternate search directions (see Algorithm 2) that proved quick and effective convergence for all tested datasets. (Further clues indicating why such algorithms should converge, esp. their link to maximum likelyhood estimation can be found in  [34] ).
Modal analysis results Algorithm 3 is carried out for separating E ph from E sp in R 8 after computation of the X 1:8 ½ modal indicators of Table  2  for the SMART2013 dataset. In the rest of current Section 3, derivations will be illustrated using test results recorded for RUN6 in the OO context. Fig. 8  shows a scatter plot of four out of the eight validation criteria used for dissociating the two families. The theoretical center C 1 of E ph is not updated during the FCM algorithm (diamond and square are superposed) so that the membership function l j1 can be interpreted as a performance indicator for each mode | :. Indeed, modes with the higher membership function value are closer to the theoretical ideal center C 1 and have thus more chances to be good physical mode candidates. This membership function l 1 is preciously kept as performance indicator and will be reused at stage (iii) (see Section 3.
The group with the larger variance has been manually discarded while the other one was reported in Table  3  and marked with a star. Processing the accelerometers of the SMART2013 test campaign, a clear trend of the procedure KM-logX 1:4;6 ½ to produce too low, sometimes unusable, threshold values d 0 has been noted for every run, although it showed good overall performance in  [25]. In the reference, the quasianalytical nature of the tested specimen (cantilevered glass fiber-reinforced polymer plate) enables the discrimination of artefacts based solely on stabilization criteria; physical modes are stable and nicely decoupled while artefacts are more ran- domly distributed. In the SMART2013 dataset however, the modal density is much higher, especially above 30 Hz. Those higher frequency modes intend to model more complex mechanisms occurring at the contact of additional masses with floors and exhibit good overall stabilization properties to the extend that they are massively included in E ph when performing k-means on X 1:4;6.
½. Their presence somehow disturbs the definition of the threshold d 0 that becomes too low and inappropriate, finally missing the selection of the 6 linear dominant modes. This trend is here accentuated by the fact that accelerometers are processed: their high frequency range favors the emergence of numerous modes of that kind in the diagrams. Following this time the methodology proposed in  [9] , a k-means partition is realized on the indicators-set X 1. 5 ; X 8. 10 ½. As advised in the reference, indicators were normalized before partitioning. Final modal selection results are collected in Table  3  referring to KM-X 1:5;8:10 ½ and the selected modes are placed in the diagram of Fig. 16b. For this dataset, final selection results are very similar to those obtained with FCM-X 1:8 ½. One has to notice that, contrary to what could be expected using k-means, a large number of artefacts were removed at stage (i).
This stresses the importance of the nMTN in the set of considered single mode validation criteria. Let one finally remark that in some rare cases (found while processing other experimental datasets), the spectral-fcm Algorithm 3 did not achieve proper partition, gathering the family E ph around a unique mode corresponding to anti-aliasing filter frequency. This kind of issue can be easily bypassed specifying a reduced frequency range on which modes are to be selected or using fuzzy Algorithm 2 instead of Algorithm 3. In this last case, the threshold d 0 has more chances to be sub-optimal, but the rest of the methodology remains unchanged and still gives satisfactory results benefiting from the advantages of the fuzzy framework for pole-splitting and final mode selection as described in Section 3.
Input/output framework In the IO context, the biaxial ground motion of the AZALEE shaking table is defined as input of the experimental system. Two different analysis are performed specifying in the input vector u the two independent components contained in the row space of the data associated to accelerometers, located on the hydraulic actuators rods in a first study IO-1, on the the soleplate (floor 0) in a second study IO-2. Comparing the results obtained in the OO and IO frameworks, the objective is to quantify the the coupling of the tested structure with the hydraulics and AZALEE shaking table. Table  5  collects the results obtained after carrying out procedure FCM-X 1:7 ½ for each case and recalls the OO results for commodity. Fig. 17  presents the stabilization diagram obtained for the IO-1 analysis where the modes selected by the FCM-X 1:7 ½ procedure are highlighted with blue lines.
The potential of the approach has been illustrated using data from the SMART2013 test campaign during which a 1=4 reduced scale reinforced concrete specimen was submitted to a sequence of seismic ground motions of increasing level. The new automated modal selection strategy was applied to low-level broadband intermediate tests and enabled the characterization of the modal signature of the specimen for different damage states. Both Output-Only and Input/Output system identification have been carried out for estimating the coupling between the shaking table device and the experimental specimen. One important conclusion to be seen on the first natural frequencies is that the impedance associated to the AZALEE shaking table itself seem negligible compared to that of the hydraulic actuators. Modeling the shaking-table-to-specimen interaction should focus on the interaction with the controlled hydraulic device.
ACKNOWLEDGMENT The authors would like to acknowledge  H2020-WIDESPREAD-05-2020 -Twinning project  Biomass4Synthons  (B4S: grant agreement  951996 ) for financial support, as well as  CNRS  and  Sorbonne Université. Support through the  Marie Skłodowska-Curie Innovative Training Network European Training Networks  ( ITN-ETN) C-H Activation   for Industrial Renewal (CHAIR : grant agreement  860762 ), is also gratefully acknowledged. Centro de Química Estrutural  acknowledges the financial support of  Fundação para a Ciência e Tecnologia  ( UIDB/00100/2020 ). RS thanks  Università dell'Insubria  for the financial support related to the mobility.