dataesr/scientific-paragraphs-categorization huggingface dataset:
The 76 96-well PCR plates were then pooled into four pools of 96 samples. Pooled amplicons were purified by using Clean PCR beads (CleanNA, Waddinxveen, Netherlands) and following the manufacturer's instructions. Purified amplicons were quantified with a Nanodrop and appropriately diluted to obtain an equimolar concentration. The second PCR was prepared in 50 µl and contained 2 µl of equimolar PCR-purified products, 5 µl of 10 × MTP Taq buffer (Sigma-Aldrich, Saint-Louis, MO, USA), 1 µl of 10 mM dNTP, 1. 25 µl of 20 µM forward P5 primer (Supplementary Data Set 2), 1. 25 µl of 20 µM barcoded reverse P7 primer (Supplementary Data Set 2), 0. 5 µl MTP Taq DNA polymerase (Sigma-Aldrich), and 39 µl of PCR-grade water. The list and sequences of the primers are given in the Supplementary Data Set 2.
2. 11)  with the following parameters "-max-overlap 120 -min-overlap 50. " Assembled amplicons were then scanned to identify internal tags on both ends using exact match regular expressions (Supplementary Data Set 3, 4). A total of 14,956,927 reads were obtained for 7,066 out of the 7,259 sequenced colonies, with a mean number of reads per sample of 2,117 (confidence intervals 95%: 177-6889). To taxonomically classify these 7,066 samples, we used the DADA2  (Callahan et al. , 2016)  pipeline (version 1. 6. 0) to identify 6,362 amplicon sequence variants (ASVs) that we assigned to the closest taxon using a manually curated gyrB database composed of 38,929 sequences (version 2) (Supplementary Data Set 5;  Barret et al. , 2015). Among the 7,066 samples, we identified 5 samples with at least 10% of the reads belonging to an ASV matching to OTU2 with an identity ≥98%.
, 2018). Serial dilutions from glycerol stocks were performed from 10 -2 to 10 -7 using sterile distillated water. For each dilution, 100 µl were plated on the corresponding selective medium in 10 cm × 10 cm square Petri dishes. Colonies were grown at 28 • C for at least 48 h. Colonies selected based on morphological aspects and for amplifying with the specific primers, were further amplified and sequenced with the Sanger technology for the gyrB gene with universal primers  (Barret et al. , 2015). The resulting gyrB sequences were blasted against both (i) a database that contains the sequences of the 6,627 most abundant OTUs identified across our 163 natural populations of A. thaliana  (Bartoli et al. , 2018) , and (ii) the NCBI database. All the bacterial strains with a gyrB sequence matching one of the gyrB OTU sequences of interest with an identity ≥98% and over ≥200 bp were stored in a 20% glycerol solution at -80 • C.
Assembly was performed with FLYE assembler (version 2. 9)  (Kolmogorov et al. , 2020)  with the specific parameters "-pacbiohifi" and an genome assembly size was estimated for each strain (from 4 to 9 Mb). We used DnaA gene sequence to determine chromosome start for each assembled circular chromosome and annotate genomes using bacannot (version 3. 1) 2 , InterProScan (version 5. 54-87. 0) (Jones et al. , 2014) , and Blast2GO (version 1. 5. 1) (Götz et al. , 2008)  to infer putative functions. The whole genome sequences of the bacterial strains have been deposited at DDBJ/ENA/GenBank, with the BioProject accession number is PRJNA848627.
Average Nucleotide Identity taxonomic affiliation In order to accurately assign the taxonomy of the sequenced strains, we used FastANI, an algorithm representing the Average Nucleotide Identity (ANI) of all orthologous genes shared between two genomes  (Jain et al. , 2018). To do so, for each OTU, the 10 closest genomes were retrieved from each of three qualitycontrolled and curated databases, i. e. , JSpecies  (Richter et al. , 2016) , GTDB-Tk  (Chaumeil et al. , 2019) , and TYGS (Meier-Kolthoff and Göker, 2019). Identical genomes among the three 2 https://zenodo. org/record/6780410#. YyCAYHZBxPY Lines highlighted in light grey correspond to OTUs for which representative strains have been isolated. databases were considered only once. Subsequently, FastANI was used to compute pairwise ANI between each strain and the JSpecies-GTDB-Tk-TYGS list of closest genomes.
Orthogroups In order to extract core-genes, we looked for orthologous and paralogous genes using OrthoFinder  (Emms and Kelly, 2015)  software (version 2. 5. 4, default parameters with diamond software as aligner). OrthoFinder results are available and queryable through Family Companion  (Cottret et al. , 2018)  software at this url: https://doi. org/10. 25794/e29x-aq46. Based on Orthofinder results, a list of orthogroups was extracted for each of the six strains belonging to the OTU6. To estimate and illustrate the number of common and specific orthogroups between these six strains, a Venn diagram was generated using jvenn  (Bardou et al. , 2014).
Statistical analysis For both experiments, we explored the genetic variation among the eight accessions of A. thaliana in response to each of the 22 bacterial strains, using the following mixed model (PROC MIXED procedure in SAS v. 9. 4, SAS Institute Inc. , Cary, NC, USA): Y ijktlm = µ trait + Block i + Treatment j + Block i * Treatment j +Accession k + Time t + Accession k * Treament j +Accession k * Time t + Treatment j * Time t +Accession k * Treament j * Time t + Line l Treatment j * Time t + Column m Treatment j * Time t + ε ijklm Model 1 where Y corresponds to the score of plant development, "µ" is the overall mean of the phenotypic data, "Block" accounts for differences in micro-environmental conditions between the blocks, "Line (Treatment)" and "Column (Treatment)" accounts for difference in micro-environmental conditions within 48well or 24-well plates, "Treatment" corresponds to the effect of each bacterial strain in comparison with the mock treatment, "Accession" corresponds to the genetic differences among the eight accessions, "Time" expressed in dbi/dai accounts for the evolution of developmental stage along the course of the experiment, the interaction terms test whether pairwise genetic differences among the eight accessions differ between the mock treatment and each treatment with a bacterial strain and/or with time, and "ε" is the residual term.
All factors were treated as fixed effects. For calculating F-values, terms were tested over their appropriate denominators. Given the split-plot design used in the two experiments, the variance associated with the "block × treatment" term was for example used as the error term for testing the "block" and "treatment" effects. A correction for the number of tests was performed to control for the False Discover Rate (FDR) at a nominal level of 5%. For both in vitro experiments, least-square means (LSmeans) of the eight accessions were estimated for each "treatment × scoring time" combination using the following model (PROC MIXED procedure in SAS v. 9. 4, SAS Institute Inc. , Cary, NC, USA): Y ikl = µ trait + Block i +Accession k + Line l + Column m + ε iklm Model 2 Because A. thaliana is a highly selfing species  (Platt et al.
BACKGROUND Pharmacokinetic in silico models are essential for pharmacological studies and drug development. During the drug discovery and development process, potential clinical candidates are screened for their absorption, distribution, metabolism, and excretion (ADME) properties to avoid clinic failures related to inappropriate ADME properties. Until recently, most pharmacokinetic models were aimed at predicting the properties of small (<1 kD) molecules after oral or intravenous (i. v. ) administration. Recently, biologics have become a significant portion of therapeutic agents, and the old small molecule software strategies had to be reformulated to adapt to large (>10kD) molecular weight proteic therapeutics. While ad hoc software has been developed by Industry, academic efforts have used available platforms (e.
METHODS We used MATLAB 2019-2021b (MathWorks, Natick, MA) to design, test and simulate the model. The toolbox used was MATLAB's Simbiology app (versions 5. 0 to 6. 2), aided by packages for partial differential equations, statistics, global sensitivity analysis and parallel computing. Data were plotted in MATLAB and exported to CorelDraw (Corel Co. ) as extended metafiles. We developed two separate and independent models to mimic the behavior of circulating brain-derived small molecular weight proteic biomarkers. Model 1 was used primarily to assess the relevance of various peripheral organs to the signal measured in blood (Figure  1 ). Model 2 was developed after ruling out the contributions by heart, bone, and skin (Figures  4  & 5 ): these organs are not included in Model 2, which uses a different set of equations to focus on the contributions of adipose, muscle, and gut tissues to measured levels of the biomarker in blood.
Effects of parenchymal trauma on biomarker's levels Brain damage and BBB disruption contribute to the overall levels of S100B in blood  35, 36. However, in clinical practice is impossible to dissect out the contributions of these two factors independently. We have developed a subroutine in our software model that allows us to quantify and describe these two sources of blood S100B (Figure  11 ). We ran a simulation where levels of blood S100B in response to two BBB disruption events were monitored. When the Trauma_index was set to zero (no contribution of cellular release of S100B on peripheral or brain interstitial levels), we noted that the second BBB disruption episode did not cause an increase of S100B in venous blood unless a minimal level of disruption (BBB_Index = 0. 1) was used for the first event.
Future directions The open-source format of the software developed herein (available at https://www. mathworks. com/matlabcentral/fileexchange/106145-diagnostic-pbpk-modelfor-s100b?s_tid=srchtitle_Damir_2) will enable other researchers to adapt the core model to other situations and answer questions perhaps related to other biomarkers. Future developments will add the effect of molecular size (molecular weight and radius; see) on the movement across different compartments. This was already done in an older version of this model  3. The main changes due to molecular size are likely to affect kidney filtration, passage across the BBB, and the overall kinetic properties of the marker. As new markers of CNS function are unveiled, we will focus on the physical and chemical properties of these proteins related to the voyage across organs and biofluids.
RT-qPCR Total RNA from transfected HeLa cells was extracted using a Nucleospin RNA kit (Macherey Nagel 740955. 50, Duren, Germany) according to the manufacturer's protocol. A SYBR Green-based real-time PCR assay (Luna Universal One-Step RT-qPCR Kit, New England Biolabs E3005S, USA) was performed in a final volume of 5 µL per reaction in white ultraAmp 384-well PCR plates using a QuantStudio 6 Flex real-time PC system (Applied Biosystems 4485689, Waltham, MA, USA). Briefly, 2 µL of RNA was added to 2. 5 µL of 2X Luna Universal One-Step Reaction Mix and primers (Eurofins Genomics, Nantes, France) at a final concentration of 0. 4 µM (sequences available on request). The PCR program included a reverse transcription step at 55 • C for 10 min, followed by 40 cycles of denaturation at 95 • C for 10 s and extension at 60 • C for 1 min.
SYBR Green fluorescent emission was measured at the end of the elongation step. Subsequently, a melting curve program was applied with a continuous fluorescent measurement starting at 60 • C and ending at 95 • C (ramping rate of 0. 05 • C. s -1 ). Crossing point values (Cp) were determined by the second derivative maximum method in QuantStudio software. For result normalisation, the following control genes were tested with the Normfinder program  [49] : Actin B (beta actin), B2M (beta-2 microglobulin), GAPDH (glyceraldehyde-3-phosphate dehydrogenase), PPIA (peptidylprolyl isomerase A), HPRT (hypoxanthine-guanine phosphoribosyltransferase). PPIA was selected as the most stable reference gene for the Hela cells. The relative RNA quantities were calculated as follows: (E target ) ∆Cp(target) /(E ppia ) ∆Cp (PPIA) , with E the PCR efficiency of each primer pair and ∆Cp the Cp difference between control and sample for each gene  [50].
Software Development Our tool to automatically detect intercellular bridges was written in Java as a plugin within the Icy image analysis software  [51]. The source code is available in GitLab and Zenodo. The plugin can be downloaded from the Icy website, as described in the data availability section. Identification of the midbody, the nucleus and the cytoplasm by detection of the respective signal is required to classify a midbody as part of an intercellular bridge or as remnant. The logic behind this plugin is explained in the results and the discussion.
Image Analysis and Quantification All the analyses were executed with the Icy image analysis software, with which DAPI-stained nuclei and MKLP1 spots can be automatically detected by the HK-means method. During the development of the image analysis plugin, intercellular bridges were first detected by eye, then the results were compared to those obtained with different versions of the program. An average of one thousand cells was analysed per condition during this initial step. The optimal plugin, whose code is available, was applied for screening, where an average of two hundred and fifty cells were analysed per condition. During the screen, the frequency F of intercellular bridges was estimated by F = I/(N -I) × 100, where the number of cells is calculated by subtracting the number of cytokinetic bridges I to the number of nuclei N in the condition.
Development of Automated Detection of Cytokinetic Bridges Taking benefits from the environment of the Icy image analysis software, we developed a plugin written in Java to automatically detect intercellular bridges. To this aim, we first had to detect the different types of biological object before interrogating their relationships. The preliminary step was to determine optimal and robust immunofluorescence staining conditions for each relevant type of object. DAPI and β-tubulin are frequently used to stain nuclei and cytoplasms, respectively. As part of the Centralspindlin complex, MKLP1 is one of the earliest elements of the midbody and mediates the recruitment of CEP55, which highlights its relevance as a marker for the midbody. Staining and imaging conditions were easily calibrated and are summarised in the methods (Figure  1 ).
Discussion Since previous cytokinetic screens relied on the detection of multinucleated cells, our first technical aim was to enable the endpoint detection of intercellular cytokinetic bridges. The constant improvement of mathematical functions to detect standard biological objects in microscopic images has allowed us to write a plugin within the environment of the Icy image analysis software. This plugin first recognises the stained nuclei, cytoplasms and midbodies, before classifying the latter as remnants or as part of cytokinetic bridges. The performance of the automated detection in HeLa and U2OS cells gives us confidence in our ability to detect and classify the same structures in other fixed cell lines or even in primary cells. This tool will become very useful to identify active compounds or any biochemical modifier that blocks the late cytokinetic events leading to abscission.
and F. A. [ANR-11-CNRT-04]. K. N. S. H. was supported by a Ligue Nationale Contre le Cancer fellowship. A PhD fellowship from the Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation was given to M. B. The APC was funded by the Institut Pasteur. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Scope of cookies: host and owner In this paper, we make a distinction between the notion of cookie host and cookie owner. When a cookie is stored in the browser, it is identified by a tuple (host, key, value). If the cookie is set via an HTTP(S) response header, then the host of the cookie represents a domain that sets the cookie. However, when the cookie is set programmatically via a JavaScript script included in the website, the script gets executed in the context, or "origin" where it is included. Due to the Same Origin Policy (SOP)  [57] , the host of a cookie set by the script is the origin of the execution context of the script, and not the domain that contains the script. Given a cookie stored in the browser with its (host, key, value), when a browser sends a request to a domain, it attaches a cookie to the request if the cookie host matches the domain or the subdomain of the request  [44].
A cookie owner is responsible for setting the cookie. It is either a domain that sets a cookie via HTTP(S) response header (and in this case, matches with the cookie host), or the domain that hosts a script that sets the cookie programmatically (generally speaking, here the owner is different from host). For example, site. com includes a third party script from tracker. com. After loading, the script sets a cookie in the context of the visited website site. com. In this case, the cookie owner is tracker. com, but the cookie host is site. com.
First-party cookies set by first-party domains are capable to track users within the same website. Third party cookies set by third-party domains allow third parties to track users cross-websites  [55]. Browser fingerprinting. Browser fingerprinting is a stateless tracking technique that provides the ability to identify and track users without using their browser storage  [3, 5, 14, 22] , unlike cookie-based tracking. When a user visits a web page that includes a fingerprinting script, this script will return to a fingerprinter server a list of features composed of user's browser and machine characteristics, such as user agent or time zone. The trackers use these collected features to build a unique identifier. Cookie respawning. Cookie respawning is the process of automatically recreating a cookie deleted by the user (usually by cleaning the cookie storage).
Methodology When a user visits a web page with some content located on a tracker's server, the user's browser sends an HTTP(s) request to the server to fetch this content. This request contains several HTTP headers, such as user agent, and an IP address that tracker's server receives passively. We refer to such information as passive features. To collect additional information, the tracker can include in the visited web page a script that gets executed on the user's browser. The script retrieves multiple browser and machine information, such as the time zone, and sends them to a server of the remote tracker. We refer to such information as active features. In the following, we define a browser fingerprint as the set of active and passive features accessed by the tracker. We say that a tracker respawns a cookie when it recreates the exact same cookie after the user revisits the website in a clean browser.
As a result, an identifier is stored in the browser's cookie database (step 4 ). Figures  1(b)  shows what happens when the user does not have a cookie 123 in the browser, however the fingerprint fp456 remains the same. In this case, the fingerprint fp456 is sent to the server of tracker. com (step 5 ), and it allows the tracker to match the known fingerprint and the cookie previously set for this user (step 6 ). As a result, the tracker is able to set again the same cookie 123, previously deleted by the user (step 7 ). This allows the tracker to respawn deleted user cookies with browser fingerprinting and continue tracking the user via such cookies (step 8 ). Figure  1 (c) presents the consequences of cookie respawning with browser fingerprinting. When the browser fingerprint of the user is updated from fp456 to fp789, the server of tracker.
wifi. uri advanced preference in the browser and point it to Alaska. All our crawls are based on the notion of stateless crawling instances. We define a stateless crawling instance of a website X as follows: (1) we visit the home page of the website X and keep the page open until all content is loaded to capture all cookies stored (we set the timeout for loading the page to 90s), (2) we clear the profile by removing the Firefox profile directory that includes all cookies and browser storages. The rationale behind the stateless crawling instance is to ensure that we do not keep any state in the browser between two crawling instances. This guarantees that respawned cookies do not get restored from other browser storages. We perform stateless crawling instances of the Alexa top 30, 000 websites and for each stateless crawling instance, we extracted HTTP and scripts information automatically collected by OpenWPM (see list of recorded data in Appendix B).
We validated that each feature is properly spoofed on our own testing website with a fingerprinting script and also by using whoer website  [72]  that verifies the information sent by the user's browser and machine to the web. For each website, we perform an Initial crawl from machine A and, a User specific crawl from machine B to detect machine unrelated cookies. After Initial crawl finishes, we start a Reappearance crawl from machine A to detect reappearance of cookies. Using Initial crawl, User specific crawl, and Reappearance crawl we detect user-specific cookies that reappear in Reappearance crawl, but not in User specific crawl. For such cookies, we randomly chose one configuration C i : either spoof one feature at a time or to set all features to initial value. We perform 99 stateless crawls (11 Spoofing crawls per feature and 11 Control crawls where the studied features are unspoofed).
Spoofing using Firefox preferences Firefox allows to change its settings in the browser preferences of about:config page. With this method, we spoofed the following features. User agent. The User-Agent HTTP header allows the servers to identify the operating system and the browser used by the client. The Initial crawl runs in Firefox under Linux (see Table  7  in the Appendix for details). To spoof the user agent, we changed the general. useragent. override preference in the browser to Internet Explorer under Windows: Mozilla/5. 0 (Windows NT 6. 1; WOW64; Trident/7. 0; AS; rv:11. 0) like Gecko. We checked the spoofing efficiency on our testing website with a fingerprinting script. The script returns the user agent value using the navigator. userAgent API. We tested the user agent value returned with the HTTP header using the whoer website  [72].
We found that the user agent value was spoofed both in JavaScript calls and HTTP headers. Geolocation. The geolocation is used to identify the user's physical location. The Initial crawl has as location Cote d'Azur, France. We spoofed the geolocation parameters latitude and longitude by modifying the value of geo. wifi. uri preference in the browser and point it to the Time Square, US ("lat": 40. 7590, "lng": -73. 9845). We validated the spoofing efficiency using a script call to navigator. geolocation API. WebGL. The WebGL API is used to give information on the device GPU. In our study, we focus on the WebGL renderer attribute that precises the name of the model of the GPU. We spoofed the WebGL renderer using the webgl. render-string-override preference in the browser. We changed the value of We-bGL renderer to GeForce GTX 650 Ti/PCIe/SSE2.
Canvas. The HTML canvas element is used to draw graphics on a web page. The difference in font rendering, smoothing, as well as other features cause devices to draw images and texts differently. A fingerprint can exploit this difference to distinguish users. We spoofed the canvas by adding a noise that hides the real canvas fingerprint. To do so, we used the Firefox add-on Canvas Defender  [13]. To test the add-on efficiency, we inject a canvas fingerprinting script on our own website. The script first draws on the user's browser. Next, the script calls the Canvas API ToDataURL method to get the canvas in dataURL format and returns its hashed value. This hashed value can then be used as a fingerprint. To evaluate the add-on efficiency against the canvas fingerprinting, we revisited the customized website and compared the rendered canvas fingerprint.