dataesr/scientific-paragraphs-categorization huggingface dataset:
This study is registered at NCT02803346. Septic shock patients were identified according to the diagnostic criteria of the 1992 consensus definitions of the American College of Chest Physicians/Society of Critical Care Medicine  31. Patients were excluded if under 18 years of age or presented with aplasia or pre-existent immunosuppression. Septic shock diagnosis was based on the combination of an identifiable site of infection with persisting hypotension -despite fluid resuscitation -and evidence of a systemic inflammatory response manifested by at least two of the following criteria: a) temperature >38 °C or <36 °C; (b) heart rate >90 beats/min; (c) respiratory rate >20 breaths/min; (d) white blood cell count >12,000/mm3 or <4,000/mm3. The onset of septic shock was defined by the beginning of vasopressor therapy.
Peripheral blood was collected in patients 3 days after the onset of septic shock during routine blood sampling procedure, since previous studies showed that this time-point corresponds to the nadir of sepsis-induced immunosuppression  5. Clinical and biological parameters were collected, including demographic characteristics, date and cause of ICU admission, status at day 28 after inclusion, type of infection, comorbidities allowing to calculate Charlson comorbidity index  32. Severity scores were recorded: the Simplified Acute Physiology Score II (SAPSII; range 0-194)  33  and the Sepsis-related Organ Failure Assessment (SOFA; range 0-24)  34. Concomitantly, healthy volunteers were obtained from the blood bank Etablissement Français du Sang (EFS) in Lyon, France. According to EFS standardized procedures for blood donation, informed consent was obtained from healthy donors and personal data were anonymized at the time of blood donation and before the transfer of blood to the research lab.
Cells were resus- pended in 1×EQ Four Element Calibration Beads (Fluidigm Sciences) according to the manufacturer's instructions. Samples were collected on a Helios mass cytometer (Fluidigm). Events were normalized prior to analysis as described elsewhere  35. Normalized data were imported into Cytobank (Santa Clara, CA) and manually gated: first, singulets were identified as 191 Ir + 193 Ir + events and then viable cells as 195 Pt negative events. Eventually, normalization beads were excluded by gating on 140Ce negative events (see Supplementary Fig. S2A ). To analyse proportions of main PBMCs' populations, cells were first gated according to their expression of CD3. CD4 and CD8 T cells were identified within CD3 + cells, while CD19 + , CD14 + and CD16 + /CD56 + non-T cells were used to classify B cells, monocytes and NK cells respectively (see Supplementary Fig.
Data processing and analysis pipeline. The whole analysis pipeline is illustrated in Fig. 2. X-shift. The 06/29/2017 version of VorteX was downloaded and manually gated singulet viable events from the 5 healthy donors and 5 septic shock samples were uploaded into the VorteX clustering environment  36. The imported settings described previously were used  12  : minimal Euclidean length of the profile: 1. 0, import maximum: 10,000 events, and merge all files into one dataset. All 24 parameters were selected for clustering. After the data were imported, the following clustering settings were used: numerical transformation: arcsinh (x/f), f = 5. 0, noise threshold: 1. 0, feature rescaling: none, normalization: none, distance measure: angular distance, clustering algorithm: X-shift (gradient assignment), density estimate: N nearest neighbors (fast), number of neighbors for density estimate (K): from 150 to 5, with 30 steps, and number of neighbors for mode finding (N): determine automatically.
Following clustering, the elbow point for cluster number was calculated as K = 35, which corresponded to 78X-shift-defined clusters within the dataset. Spanning-tree Progression Analysis of Density-normalized Events. SPADE analysis was performed on manually gated singulet viable events from the 5 healthy volunteers and 5 septic shock samples on Cytobank. Raw median intensity values were transformed to a hyperbolic arcsine (arcsinh) scale with a cofactor of 5. The target number of nodes was adjusted to 78 as defined with the X-shift clustering tool. Clustering channels included all 25 parameters except for CD138, because of elemental contamination in 2 samples. Percentage downsampling was 10%. SPADE trees for each individual across all 24 markers were downloaded from Cytobank. Intensity and cellular abundance of each node from each individual were exported for further analysis.
Statistical analysis. Statistical analyses, heatmaps and graphics exploring cellular abundance and expression were performed on RStudio software. Non-parametric Mann-Whitney tests were used to compare data between septic shock patients and controls. Non-parametric Wilcoxon paired test was used when comparison within the same sample was performed. Values from continuous variables are presented as medians with Q1-Q3 interquartile ranges [IQR] between brackets. p value < 0. 05 was considered as significant.
We developed a novel method based on arrays of fluorescently tagged antisense oligonucleotides  19  for in vivo imaging of transcription and transcript accumulation dynamics, which we called MOrpholino VIsualisation of Expression (MOVIE). Using MOVIE we demonstrate that the earliest gene expression in zebrafish embryos is confined to a unique transcription compartment, which forms during the Sphase of extremely short cleavage stage cell cycles without gap phases. Nuclear organisation of transcription suggests a previously unappreciated nuclear body formation, which is seeded by transcribed loci on pairs of homolog chromosomes and is dependent on cell cycle lengthening. MOVIE enabled investigation of the spatio-temporal dynamics of this transcription compartment, which is a characteristic feature of pre-MBT embryos.
MOVIE detects native gene transcription in living zebrafish embryos. We designed a morpholino oligonucleotide (MO) array-based approach to detect 5' ends of transcripts and their accumulation at endogenous genes in living cells (Fig. 1a ). We chose the primary transcripts of miR430 microRNA genes as test for our approach. MiR430 genes are the earliest and highest expressed genes during the first wave of zygotic gene activation  11  and play a key role in the clearance of maternally deposited mRNA during MBT. We have used cap analysis of gene expression (CAGE) sequencing datasets  20  to map the transcription start sites of the miR430 gene cluster. CAGE data revealed at least 8 transcriptional start sites on the reference zebrafish genome (GRCz10), with 6-9 miR430 genes predicted to be transcribed from each of them (Suppl.
Transcription of miR430 is metasynchronous and follows the cleavage stage cell cycle periodicity Zebrafish cleavage stages are characterised by metasynchronous cell cycles in which synthesis and mitosis phases are cycling without gap phase  3, [21] [22] [23]. To understand the temporal relationship between miR430 transcription and the fast cell cycles of cleavage stages we monitored miR430 expression by 4D time lapse imaging with MOVIE and simultaneously monitored cell cycles using the nuclear marker mRuby3:H2B (Fig. 2a , Suppl. Movie 1). Quantification of the miR430 transcript detection led to three main observations. Firstly, miR430 expression was first detected at 64-cell stage matching previous transcriptome analyses  11, 20, 24  , (Suppl. Fig 1a ) and was present until late epiboly stages in diminishing numbers of cells.
Movie 2). There was no obvious allelic (parental) bias observed with most cells showing two foci of activity (Fig. 5g ). Thus transcription of miR430 genes first detected at the 64-cell stage, does not appear to respond to a precise developmental stage timer co-ordinately. Instead, miR430 expression appears in sequentially increasing number of nuclei and appears to be coordinated with the overall timing of metasynchronous cell cycles. Transcription and transcript accumulation of miR430 occurs in the S-phase of permissive, elongating cell cycles. The single-cell resolution of MOVIE also allowed us to address the temporal dynamics of each cell's transcriptional activity during metasynchronous cell cycles. We used Tg(Xla. Eef1a1:h2b-mRFP1) transgenic embryos  26  or mRuby3:H2B microinjected protein to analyse cell cycle length.
Movie 3). Next we asked about the relationship of the observed transcription compartment with other compartment forming nuclear transcriptional activities. Four out of five loci of 45S rDNA, which encode Pol I-transcribed ribosomal RNAs (rRNA) reside on chromosome 4  38. We asked whether nucleoli forming rRNAs can also be visualised similarly to Pol II transcribed genes on the same chromosome. Using MOVIE we found that rRNA transcripts can be detected, however only after the main wave of genome activation at MBT, with low signals at 30% epiboly and frequent activity at shield stages (Fig. 4g ) which is in line with previously described rRNA expression patterns  36, 38  ). At post-MBT stages miR430 transcripts were still detected in a mosaic fashion (Fig. 4g ) and allowed subnuclear localisation analysis of both RNAs when both gene products were detected in the same cells.
Characterisation of miR430 locus Identification of predicted multiple promoter sites at the miR430 locus were determined from CAGE-seq data  20  mapped to zebrafish assembly GRCz10 by Tophat  55  allowing up to 80 multi-mapped reads. Start sites of transcription were determined by usage of CAGEr software  56. Annotation of promoter regions (Supplementary Figure  1 ) were defined as 400 bases upstream and 100 bases downstream of the detected TSS with pre-miR430 gene annotations determined from Ensembl database with addition of 2 pre-miR430 c-subtype sequences found by BLAT search, completing the a-c-b triplet structure  57. Detection of unique SNPs in miR430 promoter sequences was achieved by multiple sequence alignment with Clustal Omega  58. Thus allowing detection of nascent RNA  11  reads aligning to two individual promoters in the miR430 cluster by unique mapping with STAR aligner  59.
Morpholino oligonucleotides design for detecting transcription of zinc finger genes on chromosome 4. Targeting morpholino oligonucleotides were designed specifically to visualise transcription of the early zygotic, most highly, expressed zinc finger genes on chromosome 4 as determined from CAGE-seq dataset  20. Twenty three such genes with a fully annotated transcript were identified. The 5' UTRs of these genes with 50 bp up-and downstream flanking sequence were used for conservation assessment via multiple sequence alignment using MUSCLE alignment algorithm  60  and subsequent visualizations of inherent similarity were achieved with JalView 61. Consensus sequence was generated and used for morpholino design, targeting conserved regions in the 5'UTRs. Altogether 4 morpholino were designed (Supplementary Figure  5b ).
To identify all potential znf genes on chromosome 4 targeted by the designed morpholino oligonucleotides, a BLAST search against Ensembl version 92 cDNA database was performed. A transcript was considered as potential target by a morpholino if at least a 20 bp match with 84% identity was found. Intersections were calculated from the identified potential target genes list for each morpholino by VennMaster  62  and quad venn diagram graphic (Supplementary Figure  5c ) was generated with VennDiagram CRAN R package  63.
Analysis of spatio-temporal dynamics of MO signal For global temporal analyses, lightsheet microscopy (morpholino/transgenic mRuby3:H2B) datasets were transformed into maximum intensity projections (MIPs), using Zeiss Zen Black software. Each MIP was imported into Icy Bioimage (Quantitative Image Analysis Unit, Pasteur Institute, France) where the spot detection plugin was used to detect transcription foci and nuclei, as these features are represented as regions of locally elevated intensity, in their respective channels. The plugin was guided to detect features as bright features against a darker background. Transcription foci were typically detected using a pixel size parameter of 3px, with a sensitivity of between 80 and 150% depending on noise (with higher sensitivity for higher noise).
Feature size limits for detecting transcription foci were between 3 and 75 pixels in area. Nuclei were detected using a pixel size of between 13 and 25px, with similar sensitivity settings. Feature size limits for detecting nuclei were between 200 and 3000 pixels in area. Spot detection would be run on each channel of the image separately to detect the features being represented in that channel. Spot detection produced 2D regions of interest (ROIs) around defined features. Transcription foci were given a label which associated with their corresponding nucleus, and this was applied on a frame by frame basis to allow counting of nuclei expressing transcription foci. ROI metadata was exported to Microsoft Excel were transcription foci could be quantified.
3D analysis 3D datasets (Airyscan morpholino/injected mRuby3:H2B protein) collected from the Zeiss 880 confocal microscope with Fast Airyscan Module were imported into Icy Bioimage, where the spot detection plugin was used to identify nuclei and transcription foci, using the same sensitivity settings as described previously. Size features were scaled up to account for volumes: feature size settings for transcription foci were between 5-150 pixels, and size settings for nuclei were between 750 and 20,000 pixels. This would produce a 3D ROI around the detected feature. In the case of nuclei this ROI could be viewed in the 3D VTK viewer on Icy Bioimage, where a video of the rendered ROI could be recorded using the built-in recording function (Supplementary Figure  6 ). ROI data were exported to Microsoft Excel where transcription foci volumes were extracted per frame and timeseries could be plotted (Figure  5c ).
In PCNA imaging data obtained on Zeiss 880 confocal with Airyscan, PCNA deficient regions were quantified using the spot detection plugin, with the setting to search for dark features against a bright background. Pixel sensitivity and 3d size settings were the same as for transcription foci detection, described previously. Relevant ROI's were confirmed by visually inspecting that they correlated with signal deficient regions, and labelling the same feature with the same name per frame. The volume for each feature was extracted and plotted over time using Microsoft Excel.
Cell cycle segmentation reference Cell cycle phase segmentation was based on a visual library of cell cycle phase features generated from previously published confocal imaging of chromatin  [64] [65] [66]. This reference library was used as a base for segmenting nuclei from MIP Zeiss Lightsheet data into cell cycle phases. Once phases were assigned to an initial batch of nuclei, the imaging parameters were extracted. These parameters were used to generate a quantitative measure of chromatin consistency throughout the cell cycle and assign a measure of cell cycle phase to each nucleus. Imaging parameters included 2D area covered by a nucleus ROI and standard deviation in pixel intensities across the nucleus (supplementary figure  3c ).
The aim of this article is to introduce an open-source software-Turtle Sport-that is capable of automatically importing the GPS traces of several types of GPS sport watches (Garmin, Polar, Suunto, Timex, TomTom, etc. ) or of importing a number of GPS files. The GPS data are also uploaded locally to the researcher's computer workstation, and not to Cloud, which may raise important ethical issues. Turtle Sport also allows users to: manage a number of users; visualize the traces and statistics for the races; and export the traces to external files (GPX, KML). Developed in Java, Turtle Sport is a stand-alone, multiplatform (Windows, Mac and Linux) and multi-language (11 languages supported) application.
Why Use a Free Software to Communicate With GPS Watches? Aside from respecting the confidentiality of the data of the research project participants, there are three other reasons for using a free and independent application that makes it possible to easily communicate with GPS watches: the interoperability of the data of the GPS tracks, having a multiplatform software, and having a multi-user application. As mentioned above, there are many GPS watches that use different data formats for GPS tracks: Garmin's Flexible and Interoperable Data Transfer (FIT), GPS eXchange Format (GPX), Training Center XML (TCX), or XML. Being able to import all of these files automatically in a single software thus ensures that one can use a wide range of GPS watches.
Implementation and architecture From a technical point of view, Turtle Sport is an opensource software (GNU Lesser General Public License, version 2. 1) written in Java. In order to broaden access as widely as possible, Turtle Sport is a stand-alone, multilanguage and customizable application. Since it was developed in Java, the application works in Windows, Mac OS X and Linux operating systems (Linux Debian, Ubuntu, RPM and other Linux distributions). The user interface currently supports eleven languages (English, French, Spanish, Chinese, German, Dutch, Hungarian, Italian, Portuguese, Swedish and Catalan). As for the application's features, it allows users to: 1) automatically import the GPS traces of several types of watches (Garmin, Polar, Suunto, Timex, TomTom, etc. ) or to import several GPS files (GPX, HST, FIT, XML, TCX); 2) manage a number of users; 3) visualize the traces and statistics for the races; and 4) export the traces to external files (GPX, KML).
All of these reported functions are described in detail in the following section. The application and its Java source code are available free of charge and can be downloaded from the website http://turtlesport. sourceforge. net, which is in English and French. The first version of Turtle Sport was released on  March 19, 2008. Ten years later, the number of downloads reached 51,710 in 143 countries, distributed across Linux, Windows, Mac and other operating systems (respectively 44%, 35% and 10% of downloads; 10% unknown). It is worth mentioning that the latest version (2. 0) released in June 2017 has been downloaded close to 5,000 times at the time of submission of this paper. This is an indication of the widespread interest in the application, and the variety of its users. The source code is split into several modules (Figure  1 ).
Turtle Sport Interface The application's interface is easy to use. It is organized into five panels indicated by the labelled numbers in Figure  2. In the first panel, the list of sports activities downloaded is reported in a schedule. In the second panel, one can visualize the GPS trace on a map. Also in this panel, using the dropdown menu at the top right, the user of the application can select various maps: OpenStreetMap (OpenStreetMap Mapnik, OpenCycleMap Cycle, OpenCycleMap Transport, OpenCycleMap Landscape, MapQuest, Mercator, etc. ). In the third panel, a graph is available with curves for heart rate, altitude, speed (km/h), and pace (min. /km). It is also noteworthy that the data on the graph are interactively linked with the map. The fourth panel offers a summary of the activity (total distance and time, average pace and speed, calories, etc.
Turtle Sport also has a statistics module (Figure  2d ) that makes it possible to generate a number of graphs based on distances, times and number of activities performed per week, month or year by the selected participant. Note that these data per week, month or year can be exported to a text file (CSV). The button in Figure  2e  allows users of the application to modify their preferences, especially in terms of the language and theme of the interface, the units of measurement, and the possible adding of a background map as illustrated in Figure  5. To make it easier to analyze the GPS trace, the user can make a trip proceed more quickly or more slowly, or put everything on pause while exploring the data on the graph (Figure  2f ). Finally, one of the very interesting functionalities (Figure  2g ) of the application is the exporting of the activities to Google Maps (Figure  6 ) or to a KML file to visualize them in Google Earth or GPX.
List of contributors Denis Apparicio is a software architect; he created Turtle Sport. https://github. com/denapp/turtlesport/tree/master/test; they could be easily imported to test the application (Menu File/Import). Secondly, it allows the activities of a number of research project participants to be managed and visualized. Thirdly, Turtle Sport has several data exploration tools (visualization of the GPS trace with OpenStreetMap linked with a graph, numerous statistics on the trip or on a data period for each participant). Finally, the outputs of the activities can easily be exported to other GIS software, especially using KML format. The application can consequently be used in many health science and social science applications.
ucs. inrs. ca). The software was also used during three data collections on exposure to air pollution and noise in Mexico City (March 2017), in Ho Chi Minh City (June 2017), in Paris (September), in Auckland and Christchurch (New Zealand, February 2018) with respectively 10, 3, 3 and 3 participants involved in these projects for one or two weeks. Every evening, the data on the trips from the Garmin watches were imported into Turtle Sport in order to verify the validity of the trips and to conduct exploratory analyses.
ACKNOWLEDGMENTS This research work has been co-funded by the  European Union (European Social Fund ) and Greek national resources under the framework of the " Archimedes III: Funding of Research Groups in TEI of Athens " project of the " Education and Lifelong Learning" Operational Program  ( MlS379389 ). Dr. Olga Vizika , IFP Energies nouvelles, is greatly acknowledged for discussions on the subject. Mrs. Evangelia Ponta ,  TEI Athens , is greatly acknowledged for assisting with the simulations. Valuable remarks and constructive comments from anonymous referees are also gratefully acknowledged.
The activity of each of the 96 flies was stored in a computer at 1 Hz by SSI ExpeData data acquisition software  (v. 1. 8. 2). Phenotyping was conducted with randomly chosen lines measured simultaneously in randomly chosen wells (selected at random by a custom computer program with stochastic reseeding of the random number generator for each experiment) on each 96-well array. Activity data were analyzed using SSI ExpeData data analysis software. CTmax was defined using the method proposed by  Lighton and Turner (2004). In brief, activity, as measured by variance in optical transmission, was converted to the absolute difference sum (ADS), that is, the cumulative sum of the absolute difference between all adjacent data points. The inflection point in the ADS is suggested to be an objective method for identifying the point at which short-term variability in the data declines abruptly, thus indicating the temperature at which insect ceases activity (Figure  1 ).
Thermal tolerance assays were performed on 1-day-old flies. Once each assay finished, we determined the sex of each individual. All statistical analyses were performed using R version 3. 3. 1  (R Core Team, 2016). To analyze variation of CTmax across the measured strains, a maximum likelihood approach was applied for fitting a mixed effects model using lmer function in lme4 package  (Bates, Maechler, & Bolker, 2013). The model included sex as a fixed factor and line and its interaction with sex as random factors. The sex by line interaction was excluded from the model as it failed to increase the model goodness of fit. The significance of the random effects was assessed using the rand function in the lmerTest package  (Kuznetsova, Brockhoff, & Christensen, 2016).