dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of CD4 and CD8 data set caught approximatively 92 % and 75% of the variation of the initial information, thus allowing for conserving most information. We then added to the PCA analysis the information on NCspecific CD8 T cells (92 % data set used) to distinguishing recovered from deceased patients. Using PCA, we demonstrated that balance in T cell compartment during SARS-CoV-2 infection allowed to differentially cluster patients who recovered from the one with fatal outcome. Among all sub-populations, the most significant changes were observed in Teff PD- cells were of 14. 9 and 12. 2, respectively. The hazard ratio calculated for confounding factors such as age, smoking, obesity and HT, were not statistically significant using this model. In conclusion, these two parameters correctly foreseen survival or death prognostics of patients following critical SARS-CoV-2 infection reinforcing the important role of T cells during COVID-19 infection.
Mass cytometry analysis After sample acquisition, data from each sample were normalized (Mass cytometer. software version 6. 7. 1014 (Fuidigm). A quality control step consisting in checking number of cell events and marker signal in comparison with an internal control was then performed. Data were then cleaned based on beads, Barium/Cesium contamination, doublet and dead cell removal to keep only CD45+ cells. CD3+CD4+ T cells and CD3+CD8+ T cells (Supplementary Figure  S1A ) were selected prior analysis using OMIQ (https://www. omiq. ai/). FlowSOM algorithm allowed automatically split CD4+ T cell and CD8+ T cells population into major clusters identified in regard to CCR7 and CD45RA (Supplementary Figure  1B ). Cell cluster were labeled based on CD45RA/CCR7 expression. OMIQ platform was used to display Opt-SNE, FlowSOM analyses and heatmap representations.
Funding The study was supported by  Fondation de France ,  « Tous  unis contre le virus » framework  Alliance  ( Fondation de France ,  AP-HP, Institut Pasteur)  in collaboration with  Agence Nationale de la Recherche  ( ANR Flash COVID19 program ), and by the  SARS-CoV-2 Program  of the  Faculty of Medicine from Sorbonne University  ( I-COVID  programs). BC laboratory is supported by grants from  AG2R LA MONDIALE (Region IDF, France) ,  Fondation pour la Recherche Médicale (FRM)  team award. LA and PR are recipient of postdoctoral fellowships from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No. 681137  and  ANR   Flash COVID19 program.
To achieve this, the Data Analytics University (DAU) was created. An in-house training program composed by two pathways that provided a framework for clinical quality staff to develop their data analytics capabilities. The rst pathway covers the basics of statistics, probability and data-related terminology, while the second deepens further into the topics covered in the former followed by hands-on activities to put the knowledge to test. After successful completion of 15 DAU sessions, over 310 trained staff were able to apply their learning on data analytics and solve potential issues that might arise with a given dataset. In the near future, the DAU will be made available externally as an e-learning training program.
In Phase I, drugs are tested on a small sample of healthy volunteers to determine safety and dosage. In Phase II, drugs are tested on a small number of patients to have an initial reading on e cacy. Subsequent Phase III trials are larger pivotal trials on a su ciently large number of patients to adequately prove drug safety and e cacy. A successful Phase III study will usually lead to drug approval by the competent Health Authorities (HA), but sometimes additional postapproval Phase IV studies could be requested. To assess drug e cacy and safety, appropriate data need to be collected in each phase of the clinical trials, by often hundreds of clinical trial sites that can be located all over the globe. This requires well-coordinated data management and analysis plans. Therefore clinical trial sponsors are required by the International Conference on Harmonization (ICH) guidelines to implement and maintain Quality Assurance (QA) and quality control systems to ensure the rights, safety and well-being of research subjects and the integrity of clinical research data  [3].
Methods And Outline We designed a program consisting of two consecutive pathways titled Freshman and Graduate. Targeted to quality professionals from all ages and backgrounds (see Fig. 1 ), the aim was to empower them to make their own self-service data requests and to perform a self-guided descriptive analysis using commonly available spreadsheet software such as Microsoft (MS) Excel. To accomplish this, the program would enable them to develop an understanding of basic data vocabulary in conjunction with a sense for statistical thinking to effectively use data products -such as dashboards -developed by quality data analysts and scientists. Among the audience on the rst pathway we wanted to identify a group of quality data champions that would assist as Subject Matter Experts (SMEs) in the development of such data products.
Graduate pathway After undertaking the Freshman pathway, participants could apply to enroll on the Graduate pathway with the prerequisite to demonstrate their learning by submitting examples of how they have used data-driven approaches and solutions to help them in their daily work and how they plan to use analytics in a future project as well as taking a supplementary class on how analytics can help on the decision making. For this pathway's design, we kept the same principles as in the Freshman, however these classes were focussed on action-oriented problem solving skills with MS Excel (more than 50%), and the in-depth understanding of descriptive and visual analytics. The pathway concluded with the completion of a nal work-related certi cation project. In order to properly recognize the achievement of each participant individually and to ensure that the training met industry quality standards the program was certi ed by the CPD Certi cation Service  [13] , which issues online certi cates that can be included in digital CVs.
Graduate pathway This pathway started with a prerequisite module that had to be completed o ine, followed by 2 full days divided in 3 modules of about 2. 5 hours each and with breaks in-between, ending with a nal Certi cation Project. a. Class 1 -Advanced Strategies for Data Analysis: Addressed business problems using advanced MS Excel functions, namely -Pivot Tables, advanced formulas and Data Joins. b. Class 2 -Data Visualisations and More: Basic Understanding of Random Variables and Data Distributions followed by the principles of data visualisation and how to implement them in MS Excel. c. Class 3 -GxP Problem Solving (with data!!): Students were asked to put their learning into test by individually solving an analytical problem statement. This was part of the requirements needed to progress onto their Certi cation Project.
Challenges Some of the challenges faced while conducting DAU trainings were mainly around different devices being used for example Windows vs. Mac and different versions of MS Excel. The DAU training consists of several case studies in each module. These case studies are instructor led, where the trainer goes through certain steps within Excel and the trainee must follow along. Because of different devices and different versions of software, we observed that these case studies usually took a little longer to get through as the trainees required additional assistance in following along. A common challenge for learning programs is the forgetting curve, which was rst theorized in 1880 by Hermann Ebbinghaus  [16]. As some of the modules of the DAU were spaced in time, and as some of the trainees did not always have an immediate opportunity to apply their learning, we needed to establish a mechanism to defy the forgetting curve.
Subjects Patients from Iceland, Sweden and Norway with complementmediated renal diseases are referred to the laboratory at the Dept of Pediatrics in Lund for genetic diagnostics. Three patients were found to have CFB mutations. The patients and their laboratory data are presented in Table  1. Samples were obtained from apparently healthy adult controls (n=12, 6 female) who were not using any medications. The study of patients and healthy controls was performed with the approval of the Ethics Review Board at Lund University. Approval included genetic analysis of Nordic patients and phenotypic studies of complement mutations. The study was also approved by the National Bioethics Committee of Iceland and the Data Protection Officer at Oslo University Hospital, Oslo Norway. Informed written consent was obtained from the patients or the parents of Patient 3 and the healthy controls.
Alignment and variant calling were performed according to GATK "Best Practices for Germline SNP & Indel Discovery in Whole Genome and Exome Sequence". Sequence data was mapped to the hg19 genome build using BWA 0. 7. 15  (23)  and variants were called using HaplotypeCaller from GATK 3. 7 and processed according to the best practice recommendations  (24, 25). Variants were annotated using Ensembl Variant Effect Predictor release 87. Reported variants with frequencies <2% according to Genome Aggregation Database (gnomAD) were included in the genetic evaluation.
C3c deposition on the cells was detected using rabbit anti-human C3c antibody:FITC (DAKO, Glostrup, Denmark) diluted 1:50 in 1% BSA for 1 h. Cells were stained with 4´,6-diamidino-2-phenylindole (DAPI, Thermo Fisher, Eugene, OR). Fluorescence was detected using a Ti-E inverted fluorescence microscope equipped with a Nikon structured illumination microscopy module (Nikon Instruments, Tokyo, Japan). Image stacks at 10 x magnification were converted to maximal intensity images. Stained cells were outlined with a threshold above the background to select the area occupied by cells (DAPI-positive). Quantification was performed using ImageJ Fiji Software (Version 1. 53h, NIH, Bethesda, MD). In certain experiments the cells were incubated with FB constructs (50 mg/mL) in FB-depleted serum diluted 1:4 in serum-free EGM-2.
ACKNOWLEDGMENTS The authors wish to thank  Dr Marina Noris  and  Dr Roberta Donadelli ,  Istituto di Ricerche Farmacologiche Mario Negri ,  Bergamo Italy  for excellent technical advice for the assay of factor B cleavage by the C3 convertase. Dr Ravi Bhongir  and  Dr Sandra Jovic ,  Infection Medicine, Clinical Sciences Lund  are acknowledged for their help with the surface plasmon resonance assays. The authors thank  Drs Markus Heidenblad ,  Sofia Saal and Björn Hallström of the Center for Molecular Diagnostics, Region Skåne and Clinical Genomics Lund, SciLifeLab, Lund University  for next-generation sequencing. Dr Henning Gong  carried out part of the mutagenesis study as part of his master's thesis. The kidney biopsies of Patient 3 were assessed by  Dr. Melinda Raki ,  Department of Pathology, Oslo University Hospital ,  Oslo Norway ,  Dr.
BFIGURE 1 | 1 FIGURE 1 | The molecular structure of factor B and the location of mutations described in this study. (A) Location of the four mutated residues (D371G, E601K, I242L and D279G), visualized on the structure of the C3 pro-convertase C3bB depicted in grey (PDB ID 2XWJ) (18) using PyMol. The Metal Ion Dependent Adhesion Site (MIDAS) on C3b is depicted by a star. The colors of the domains correspond to the domains depicted in (B). (B) The linear structure of factor B divided into the signal peptide/leader, Ba and Bb fragments with a linker sequence in between. The amino acid numbers are given in parentheses. The protein is composed of a complement control protein (CCP) domain with three CCPs, followed by the linker, the von Willebrand factor type A domain (VWA) and the serine protease (SP) domain.
12 ased on these observations, we set up a basket clinical trial combining pembrolizumab and vorinostat [a histone deacetylase inhibitor (HDACi)] in patients with recurrent and/or metastatic SCC of several primary tumor locations. We will review here the preclinical and clinical evidence supporting the combination of anti-PD-1 monoclonal antibodies with HDACi and present the PEVO data project including the PEVOsq trial (NCT04357873). An emphasis will be given to the integration of clinical and biological data obtained from sequential tumor and blood sampling in order to identify predictive and pharmacodynamics biomarkers of drug efficacy and resistance.
MOLECULAR FEATURES OF SCCS SCC can derive from most squamous epitheliums. Integrated analysis of genomic, epigenetic and gene expression alterations in SCC all point towards significant similarities unrelated to their original location. For instance, almost all lung SCCs, and HPV-negative HNSCCs display somatic mutations of TP53 (60%-80%). Common alterations of cell cycle control (i. e. TP53, CDKN2A/RB1 mutations), oxidative stress (i. e. NFE2L2/KEAP1/CUL3), cell survival (i. e. genes in the Pi3K/Akt pathway), cell differentiation (i. e. SOX2/TP63/ NOTCH1) and epigenetic (i. e. 4] [15] [16] [17]  Moreover, overlapping molecular features collectively setting SCC apart from other cancers, independently of their organ of origin, were reported in a study comparing the pan-SCC and panCan-33 datasets.
The centralized processing step includes DNA and RNA extraction from all frozen and/or formalin fixed paraffin embedded (FFPE) samples as well as circulating tumoral DNA extraction from plasma. Standard operating procedures will ensure standardization of procedures for all samples and thus ensure fitness-for-purpose for downstream analytical steps (Figure  3 ). Each partner within the consortium will handle and generate specific sets of data: (i) HLA sequencing will be done on site at IBBL; (ii) molecular analyses will be carried out by the Instituto Europeo di Oncolegia (IEO, Milan, Italy) and Institut Curie (IC, Paris, France); (iii) bioinformatics analyses will be centralized by University of Leipzig (Leipzig, Germany) and IC; (iv) the clinical database will be set up and data managed by Unicancer, the sponsor of the trial; (v) biostatistical analyses will be carried out by Institut Claudius Regaud (Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France) in collaboration with all partners and (vi) computer-aided interpretation of molecular data will be carried out by Oncompass Medicine.
61 To that effect, an ongoing trial evaluates a 2 weeks priming approach with either azacitidine, romidepsin or both, and then a combination of the epidrug and pembrolizumab in advanced colorectal cancer (NCT01252172). Lastly, PEVOsq is testing an innovative approach in big data management and international collaborations, in order to better apprehend and exploit the overwhelming load of data produced and the drastic underuse of information. Developing technologies and methods to allow both analysis and sharing of international data is paramount in the era of big data and precision medicine. Those trials generate prospective data with a wide spectrum of molecular characterization and a temporally dynamic analysis, which aim to facilitate our constant research for efficient biomarkers. But without supervised analysis and a comprehensive integration with clinical data and treatment outcomes, it can result in the search for a needle in a haystack.
FUNDING This work was supported by:  EraPerMed  [grant number  ERAPERMED2018-078-PEVOdata] (ANR-18-PERM-0010 ); The  Luxembourg National Research Fund  [grant number INTER/ ERAPerMed/18/13061865];  Association pour la Recherche contre le Cancer  [grant number  PGA1RC20190208493 ];  Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Hivatal) (National Research, Development and Innovation Office, NRDI Office)  [grant number  ED_18-2-2019-0001 ];  Hungarian Innovation Agency  [grant numbers  ED_18-2-2019-0001 ,  NVKP_16-1-2016-0005 ,  2019-1. 1. 1-PIACI-KFI-2019-00367 ]; Diese Massnahme  wird mitfinanziert mit  Steuermitteln auf Grundlage des von den Abgeordneten des Sächsischen Landtags beschlossenen Haushalts, Germany. Free State of Saxony, Sächsische Aufbaubank (SAB) , application no.
It makes the link between Secs. II and III. Modal Stokes parameters are derived and illustrated on a simulated scenario that mimics SBCEX17. Section V introduces the concept of polarization spectrograms, which extend the Stokes parameter to the TF domain and enable the visualization of modal Stokes parameters using a single vector sensor and an impulsive source. Polarization spectrograms for modal propagation are illustrated on simulated data as well as on SBCEX17 experimental data in Sec. VI. The article is concluded in Sec. VII, which provides a summary and discusses future opportunities. Two appendixes supplement the article. Appendix A presents a comparison between the proposed Stokes framework and vector metrics recently defined in  Dahl and Dall'Osto (2020a). Appendix B provides an experimental observation of the degree of polarization (i.
The only difference from the previous simulation is that c TOP mud ¼ c BOT mud ¼ 1600 m/s. The resulting data are also shown in Fig. 4  (dotted lines), and these differ markedly from the previous dataset. This suggests that the Stokes parameters have a high sensitivity to the environment and may be good inputs for geoacoustic inversion. This is confirmed by looking at the group speed associated with the two different environments, also plotted in Fig. 4. Group speeds are usually derived from pressure data and are known to be good FIG. 4. (Color online) Stokes parameters and group velocities simulated in an environment with a mud layer (continuous lines) or sand (dotted lines) layer. Note that the vertical scale goes from À1 to 1 for s 1 and s 3 , while it goes from À10 À3 to 10 À3 for s 2.
Note that a PYTHON toolbox dedicated to the TF analysis of bivariate signals is available with  Flamant et al. (2019). An important property of polarization spectrograms is that, for a single-component noise-free signal and no interference, the polarization spectrogram values at the signal's TF location (i. e. , the ridge) exactly give the values of the underlying Stokes parameter  (Flamant et al. , 2019). In more complex scenarios, if the polarization spectrograms are not overly contaminated by interference and/or noise, this property is still approximately true. Note that this behavior is fully similar to that of traditional spectrograms, which give the signal's energy along the TF ridge (up to a constant multiplicative factor that depends on spectrogram parameters). As a reminder, in our modal propagation context, the theoretical TF location of mode m with frequency f is given by  (Bonnel et al.
The method description and its application to SBCEX17 CSS data are presented in depth in  Bonnel et al. (2020) , along with data examples and companion MATLAB code. The preprocessing chain (horizontal projection, filtering, and source deconvolution) leads to an experimental bivariate signal vðtÞ ¼ ½v r ðtÞ; v z ðtÞ T that represents the waveguide impulse response [i. e. , as if Xðf Þ ¼ 1]. This signal is presented in Fig. 6. Since the signal contains several modes, its time-domain representation is not legible. Still, one notes that the peak value of v z ðtÞ is roughly an order of magnitude smaller than the peak value of v r ðtÞ. This leads to a 2D trace in the r-z domain that looks like a horizontal elongated ellipse. This will be further quantified, on an individual mode basis, using polarization spectrograms.
Formally, note that estimating U from the dataset used in  Dahl and Dall'Osto (2020a)  is not straightforward and is beyond the scope of this paper. In short, it would require assumptions about the variability of U. If U is assumed to be spatially homogeneous, then it could be estimated by averaging normalized Stokes parameters (further assuming ergodicity). On the other hand, if the environment is spatially variable, then it is likely that U depends on source position. In this case, it must be estimated using Stokes parameters rather than the normalized ones. In such a scenario, normalized Stokes parameters (as well as Dall and Dall'Osto metrics) would depend on source position.
ACKNOWLEDGMENTS This work was supported by the  Direction G en erale de l'Armement (France)  and the  Office of Naval Research. The authors warmly thank the anonymous "Reviewer 2" for excellent comments and suggestions. The reviewer contribution notably includes accounting for waveguide attenuation with notable impact on Eqs. ( 32) and ( 35 ) as well as suggestions that led to the derivation of Eqs. ( A5 ), ( A6 ), ( A8 ), and ( A9 ).
The tags were all deployed following the annual moult in February. The capture, sedation and tagging procedures followed those described in detail elsewhere  (Mellish et al. , 2010; Heerah et al. , 2013; Shero et al. , 2018) , with the tags glued directly on the head of the seal. The tags recorded data on a seal's diving behaviour as well as in situ hydrographic conditions. Summaries of these data were transmitted via the polar-orbiting Argos satellite constellation whenever seals surfaced to breathe  (Harcourt et al. , 2019). Only seals for which the tag transmitted for longer than 50 days were included in the analyses.
Diving Behaviour and Bathymetry The dive data underwent an initial quality control to remove anomalously deep dives. This was done by calculating a lower edge for the dive duration vs. dive depth scatter plot (using a quantile regression set at 0. 05). Dives that fell below this edge were deemed to be too deep for that duration (thereby requiring unrealistic rates of travel) and were flagged for removal. We also removed dives of less than 5 min duration as these were regarded as unlikely to be foraging, and dives deeper than -1000 m as these were deeper than the Antarctic continental shelf  (Padman et al. , 2010). A location was recorded for each remaining dive by using a state-space-model with a 2 h time step and interpolating from the start time of each dive  (Jonsen et al. , 2020). Each dive was then allocated a bathymetric depth using GEBCO19 [GEBCO Compilation Group (GEBCO), 2021].
e. , the overall mean of the individual UDi raster stack) (iii) the product of these two rasters provides a measure of overall habitat utilisation in terms of seal days per cell. This value is a close analogue of kernel density analysis but has the advantage of not imposing a smoother over the data. With sufficient data this smoothing is unnecessary as we have an empirical, quantitatively assessed, measure of the degree of use per cell. To estimate the degree of representativeness of the sample of seals compared to the actual population of seals at each location, we calculated the cumulative area occupied by increasing numbers of seals (Supplementary Figure  3 )  (Hindell et al. , 2003). Finally, we calculated the percent usage contours by sorting the UDs values in each cell, taking the cumulative sum, and using that to identify user defined contours.
These simulated tracks provide an estimate of the geographic space that each animal could have occupied (given its movement characteristics and track length) if it had no habitat preferences  (Raymond et al. , 2014; Hindell et al. , 2020). We compared the ice concentration on July 30 (averaged for all years of the study) as this was the time when ice extent was nearing its maximum, and when most tags were still transmitting. Analyses were conducted in R 4. 0. 2 (R Core Team, 2021). FoieGras was used to filter the seal tracks  (Jonsen et al. , 2020). Descriptive statistics presented as mean (X) and standard deviation (±s. d) unless indicated. All models were Generalised 1 https://github. com/AustralianAntarcticDivision/availability Linear Mixed Effects Models in the R package LME4. The models were assessed by ranking candidate models using AICc, and evidence ratios calculated for each model comparison to provide a2n indication of the importance relative to the top model  (Burnham and Anderson, 2004).