dataesr/scientific-paragraphs-categorization huggingface dataset:
SPIED analysis For SPIED identification of small molecules, the "dorsal NSCs/TAPs," "lateral NSCs/TAPs," "postnatal NSCS/TAPs," "oligodendroglial lineage," and the "rejuvenating" transcriptional signatures were defined using Partek (1. 8-fold change, FDR < 5%), as follows. Probes significant across multiple normalized datasets in the background (representing ~40K probes) were processed. For identification of "dorsalizing" small molecules, dNSCs/dTAPs datasets (positive range) were compared with probes significantly different in the background. This list was then further refined and compared with other postnatal datasets (negative range), using the advanced tab in list manager followed by criteria configuration in generating lists with merged expression profiles. For identification of "ventralizing" small molecules, lNSCs/lTAPs datasets (positive range) were compared as above against with probes significantly different in the background.
This list was further refined and compared with other postnatal datasets from the same study (negative range)  [1]. For identification of "pro-oligodendrogenic" small molecules, publicly available datasets of forebrain-derived OL lineage cells (positive range; GSE9566) were compared to dNSCs and dTAPs (negative range) from which they emerge. Finally, for identification of "rejuvenating" small molecules, publicly available datasets of adult NSCs  [29]  (positive range) were compared with postnatal dNSCs/lNSCs (negative range)  [1]. These expression profiles, consisting of gene symbols of "enriched" genes, were next uploaded onto SPIED (http://www. spied. org. uk/cgi-bin/HGNC-SPIED3. 1. cgi) to interrogate the CMAP initiative in an unbiased way and identify small molecules predicted to promote the positive ranges of gene signatures using default parameters.
The Broad CMAP 2. 0 (CMAP2. 0) database consists of the transcriptional profiles corresponding to the effects of small molecules at various concentrations and treatment times using panels of human cell lines. The data are available for download in the form of ranked probe sets for each microarray sample on the [HG-U133A] Affymetrix Human Genome U133A Array platform. Identified small molecules cellular targets were exhaustively characterized using publicly available drug repositories (www. DrugBank. ca/; www. genome. jp/kegg/drug/; http://insilico. charite. de/supertarget/; www. pharmgkb. org; http://stitch. embl. de/). Small molecules protein targets identified were cross-checked in http://www. genecards. org/ for classifying them under general GO terms. All analyses presented in Small molecule target gene analysis "Target genes" are defined as the genes from the queried expression profiles that are also induced by a given small molecule.
Target genes for each analysis were generated as follows. To generate lists of genes perturbed by the small molecules, gene replicates were pooled and the relative expression levels calculated. Changes passing the Student's t test p-value of 0. 05 were processed, and when there were multiple probes for a given gene, the probe with the biggest fold change was assigned to the gene. These were aligned for matching signatures with the transcriptional profiles corresponding to the small molecules repurposed in the CMAP using pattern-matching algorithms that enable identification of functional connections between drugs, genes, and diseases through the transitory feature of common gene-expression changes  [4]. The entire database is available for download (http://www. broadinstitute. org/ gsea/msigdb/index.
jsp) in the form of ranked probe sets for each microarray sample on the [HG-U133A] Affymetrix Human Genome U133A Array platform. For analysis of "target genes" of select small molecules, we first performed hierarchical clustering of their expression profile in the various cell types and lineage that compose the SVZ using the following datasets: purified postnatal NSCs and TAPs (GSE60905  [1] ), for purified glial cells (GSE9566  [25] ), and adult NSCs/NPs/ependymal cells (GSE18765  [29] ). Target genes were then classified by "Process network and pathway maps" GO categories. Briefly, target gene lists containing the contrasts and fold changes were analyzed via the web platform http://www. broadinstitute. org/cmap/index. jsp and functionally classified using Genego Metacore (https://portal.
genego. com/) for Process Networks and Pathway Maps. Last, target genes were studied further for obtaining the shortest path between genes associated with highly ranked process networks and pathway maps using standard Dijkstra's shortest paths algorithm and applying default parameters  [7, 56]. Background RMA normalized data for all probe sets relevant for the postnatal and adult SVZ derived from postnatal NSCs and TAPs ((GSE60905)  [1] ), purified glial cells ((GSE9566  [25] ), and adult NSCs/NPs/ependymal cells ((GSE18765  [29] ) were uploaded onto Genego Metacore (raw data are provided with the manuscript). This allows obtaining, visualizing, aligning, and clustering the most relevant target genes based on small molecule target genes with reference to basally expressed genes in the SVZ.
For western blot (S1A Fig), pups aged at P10 were treated with LY-294002 (see below), and 45 min following injection, pups were systematically killed by cervical dislocation and tissue microdissected and flash frozen in lysis buffer in liquid nitrogen for storage at -80˚C  [10]. One litter of pups was pooled to yield 1 "n" number. For qPCR experiments, relative gene expression was determined using the 2 ΔΔ-CT method versus the housekeeping gene GAPDH (Glyceraldehyde-3-phosphate dehydrogenase). See S7 Table for a list of primers used in the study. Primers were designed by Primer Express 1. 5 software and synthesized by Eurofins (Ebersberg, Germany). Unstated primers in main text were custom designed and obtained from (Qiagen). Protein was extracted with lysis buffer and standard procedures as previous  [57].
5. 2. 0. 121) or LAS-AF software (V. 2. 7), maintaining the acquisition parameters constant to allow comparison between samples. The number of cells expressing the markers Ki67, EdU, Olig2, Tbr2, Mcm2, Dcx, Ascl1, and GFAP were quantified with at least three fields of view per section on series of equally spaced sections of 40-μm thickness encompassing the entire rostral lateral ventricle as previously described  [10, 59]. The dSVZ in both ages was defined based on DAPI counterstaining (Invitrogen). Quantifications were performed on confocal z-stacks of 230 μm 2 × 230 μm 2 in the x-y-plane and 30 μm in the z-plane, with a volume of 1. 6 × 10 6 μm 3. For the case of GFAP + cells, i. e. , NSCs, only cells directly adjacent to the ependymal layer were analyzed as previous for similar postnatal ages  [10].
e. , dorsalization); (C) lNSCs/lTAPs compared to other early postnatal datasets (i. e. , ventralization); (D) oligodendrocyte (OL) lineage cells compared to dNSCs and dTAPs; (E) Postnatal NSCs compared to adult NSCs. dNSCs, dorsal NSCs; dTAPs, dorsal TAPs; lNSCs, lateral NSCs; lTAPs, lateral TAPs. https://doi. org/10. 1371/journal. pbio. 2000698. g002.
Funding:  Biotechnology and Biological Sciences Research Council  (BBSRC) http://www. bbsrc. ac. uk/ (grant number  BB/M029379/1 ). Funding to AB , or preparation of the manuscript. Multiple Sclerosis research/for-researchers (grant number  40 ). Funding to AB lab. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Investissements d'Avenir/ French National Research Agency (ANR)  http://www. agence-nationalerecherche. fr/(grant number  ANR-11-IDEX-0007 ). Program " Investissements d'Avenir " to OR lab. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Swiss National Science Foundation  (SNF) http://www. nrp63. ch/en(grant number  406340_128291 ).
REJUVENATION Small molecules Number of target genes Small molecules Numbers of target genes trichostatin_A 3292 adiphenine 1227 tanespimycin 2732 helveticoside 1157 LY-294002 2296 thapsigargin 1070 vorinostat 2247 AR-A014418 998 GW-8510 2093 anisomycin 989 camptothecin 1927 lanatoside_C 865 phenoxybenzamine 1871 viomycin 776 H-7 1803 monensin 762 sirolimus 1783 nadolol 761 irinotecan 1752 podophyllotoxin 757 CMAP, connectivity map; SPIED, searchable platform-independent expression database; SVZ, subventricular zone. https://doi. org/10. 1371/journal. pbio. 2000698. t001.
The Wiener filter  [41]  is one of the first such methods operating on the Fourier transform. Donoho et al. [9]  extended it to the wavelet domain. Space-domain methods traditionally use a local notion of regularity with edge-preserving algorithms such as total variation  [33] , anisotropic diffusion  [26] , or the bilateral filter  [38]. Nowadays however spatial-domain methods achieve remarkable results by exploiting the self-similarities of the image  [2]. Patch-based methods are called non-local because they denoise by averaging similar patches in the image. Patch-based denoising has developed into attempts to model the patch space of an image, or of a set of images. These techniques model each patch as a sparse representation on a learned patch dictionary  [11, 23, 22, 44, 8] , using a Gaussian Scale Mixture model  [45, 31, 32] , or with non-parametric approaches by sampling from a huge patch database  [19, 20, 29, 24].
Relying on similar patches yields very good results, especially near edges and geometric structures; however,     the results on textures can be blurry (see Figure  2c ), since the hypothesis of perfect self-similarity is often too strong. Recently, patch-based denoising has developed into attempts to model the patch space of an image, or of a set of images. These techniques model the patch as sparse representations on dictionaries  [11, 23, 22, 44, 8] , using Gaussian Scale Mixtures models  [45, 31, 32] , or with nonparametric approaches by sampling from a huge database of patches  [19, 20, 29, 24]. Other algorithms are frequency based. These algorithms suppose a certain degree of regularity on the image, and therefore they try to represent its patches in a basis that "sparsifies" them  [34, 21, 13].
Experimental Comparison of Algorithms Our implementation of the DA3D algorithm has been tested against the set of images shown in Figure  11. For γ r and γ f , the parameters of DDID  [15]  were kept (γ r = 0. 7, γ f = 0. 8), but since DA3D does not need to process all patches, the size of the patches themselves was chosen as 64 × 64, with σ s = 14. The parameters τ = 2, σ sr = 20, γ rr = 7, and η = 10 were chosen experimentally on    images outside the test database. The DA3D method was applied to the results of several state-of-the-art algorithms. Each method was tested with noises of σ = 5, 10, 25, 40, 80. The results are summarized in Table  1. DA3D improves the PSNR of every algorithm except NLDD and PID (which is to be expected, since they are based on a similar shrinkage strategy). This improvement is more marked with higher noises, which makes sense since the parameters of DDID were optimized for medium to high noise.
Acknowledgment Work partly founded by BPI France and Région Ile de France, in the framework of the FUI 18 Plein Phare project, the European Research Council (advanced grant Twelve Labours n246961), the Office of Naval research (ONR grant N00014-14-1-0023), Centre National d'Etudes Spatiales (CNES, MISS Project), and ANR-DGA project ANR-12-ASTR-0035. The authors would also like to thank Prof. Jean-Michel Morel for his support, suggestions, and many fruitful discussions.
Because multistep gating such as the manual exclusion-based gating strategy described above relies on the selection of specific markers to define immune populations, and the gating itself can be criticized for its subjectivity, we developed an automated algorithm, MegaClust (http://megaclust. vital-it. ch), which identifies cell populations from complex cytometry datasets (see Supplemental Experimental Procedures). An evaluation of the quality of each cell population identified by MegaClust was included in the workflow and performed considering (1) its proximity to the filtration gates (manual selection of viable CD45 + immune cells), (2)  the proportion of samples' events displaying that population, and (3) its uni-modality on every marker expression. We observed that 43% of MegaClust cell populations were flagged by this quality control, whereas 71% of the populations identified by the gold-standard method viSNE and SPADE were pointed out using the same method (Figures  S3A-S3C ).
Big2 KP Tumors Reflect a Population of Poor-Prognosis Lung Cancer Patients We used a tissue microarray (TMA) comprising 150 patient samples with follow-up survival data from the European Thoracic Oncology Platform (ETOP) to analyze B cell (CD20 + ), neutrophil (polymorphonuclear CD15 + ), and CD8 + T cell abundance as well as Snail expression. We discovered that a group of patients (group D) mirroring murine group Big2 tumors, characterized by a low proportion of B and CD8 + T cells and a high infiltration with neutrophils, had strongly reduced overall survival (p = 0. 0004, hazard ratio [HR] = 2. 93). In contrast, a group of patients (group A) resembling group Small1, with a high infiltration of CD8 + T cells and B cells and a low infiltration with neutrophils, showed a trend toward improved survival (Figures  6A-6D ; Table  S3 ).
Chemokine Measurements Tumors were mechanically dissociated in 200 mL DMEM per 50 mg tissue. Supernatant was generated by centrifugation, filtered, and stored at À80 C. Chemokine quantification was done using the LEGENDPlex proinflammatory chemokine multiplex assay (740451; Biolegend) according to the provider's instructions and analyzed using the dedicated software provided by Biolegend. Data acquisition was performed on a LSRII SORP flow cytometer with HTS module. For Cxcl2, we used the mouse Quantikine ELISA kit (MM200).
TCGA Data Processing The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma (LUAD) dataset was retrieved from http://cancergenome. nih. gov. Samples with clinical information and mRNA (RNASEQV2) expression data were selected (448 samples), and the data were downloaded using the RTCGA package  (Samur, 2014). Normalized RSEM gene counts as provided by TCGA were taken. As a filtering step, only genes with counts per million >1 in at least 50 samples were kept for further analysis, and counts were converted to log-counts per million using the voom function from the limma package  (Ritchie et al. , 2015). Mouse mRNA Sequencing, Differential Expression, and Pathway Analyses Multiplexed libraries for mRNA sequencing (mRNA-seq) were prepared with the TruSeq stranded mRNA kit (Illumina) starting from 500 ng of good-quality total RNAs (RNA quality scores >7 on the Fragment Analyzer).
One family initially had trouble using the two tablets system related to Wi-Fi connecting problems that were easily corrected. Tailoring treatment during the hospital session and data transfer from home was also easily achieved. To assess the magnitude of stress in the parent-child system during the protocol, we used the Parenting Stress Index (PSI). To compare course of stress at 6 months, we used Linear Mixed model with two main effects: group (Experimental vs. Control) and time (Inclusion vs. 6 months). This allows taking into account individual heterogeneity, variable scores at inclusion and change specific to exposure to GOLIAH within the same statistical regression. Results are shown in Tables  3  and 4. There was a significant improvement at 6 months in both groups for PSI parental distress, difficult child, and total stress scores (all p < 0.
Therapists could easily tailor treatment during the hospital session based on data transferred from home. We found a significant improvement in 4 among 6 imitation games on the quality imitation scores and 3 among 4 JA games on the time to complete the task across sessions. This confirms that training participants with ASD using computer based approaches may be helpful (e. g. Serret et al. [27] ) although this does not imply that participants may generalize their improved abilities outside the gaming context  [14]. In terms of feasibility, we need to specifically discuss acceptance. On one hand, acceptance of GOLIAH was good since we had no drop out during the study. On the other hand, regarding intensive exposure to GOLIAH, the overall observance rate of nearly 1 session done for 2 predicted sessions is disappointing.
Clinical inspiration Effective treatment in ASD such as ABA, TEACCH, ESDM are challenging to implement and require intense exposition  [4]. Serious games represent a potential alternative to apply these approaches (easy access; low-cost; possible at home therapy). Also, they provide storable and accessible data on which clinicians can rely to evaluate the children's progress. Unfortunately, very few games are based on these principles  [40] : ComFim is inspired by PECS  [35] , Invasion of the wrong planet relies on TEACCH  [41] , TeachTown incorporates the basic principles of ABA  [39]. GOLIAH is the first to be based on ESDM. In addition, not only are the different games of GOLIAH inspired by ESDM tasks but they also respect some general principles of the program: developmental perspective (levels of difficulty that the child has to achieve to progress through the game); clinicians can assess the progress of the child and review objectives by adapting the choice of games; a wide variety of games (11 games) which guarantees obtaining an intensive, adjustable enrichment therapy tailored for each child.
Acknowledgements Authors thank the families that participate to the study, and  Sue Orsoni  for English editing. The members of the MICHELANGELO Study Group are:  Silvio Bonfiglio  ( FIMI, Italy ),  Fabio Apicella ,  Federico Sicca  ( Fondazione Stella Maris , Italy),  Lucia Billeci ,  Giovanni Pioggia  ( CNR, Italy ),  Federico Cruciani ,  Cristiano Paggetti  ( I+, Italy ),  Angele Giuliano ,  Maryrose Francisa  ( Accros Limit, Malta ),  Saptarshi Das ,  Wasifa Jamal  ( University of Southampton, UK ),  Leo Galway ,  Mark Donnelly  ( University of Ulster, UK ).
This work has been partially supported by the project  ANR-13-IS02-0001  of the  Agence Nationale de la Recherche , by the  H2020-FETOPEN-2016-2017 - CSA  project  SC 2 (712689 ), by an  ÖAW APART Stipendium , by  FWF   W1255-N23 , by  ERC   Start Grants Graph Games  ( 279307 ) and Matryoshka (713999), and by  FFG  project number  845582  (TRUCONF).
In this article, an advanced multiphase modular power drive prototype is developed for More Electric Aircraft (MEA). The proposed drive is designed to supply a multi-phase permanent magnet (PM) motor rating 120 kW with 24 slots and 11 pole pairs. The power converter of the drive system is based on Silicon Carbide Metal Oxide Semiconductor Field-Effect Transistor (SiC MOSFET) technology to operate at high voltage, high frequency and low reverse recovery current. Firstly, an experimental characterization test is performed for the selected SiC power module in harsh conditions to evaluate the switching energy losses. Secondly, a finite element thermal analysis based on Ansys-Icepak is accomplished to validate the selected cooling system for the power converter. Thirdly, a co-simulation model is developed using Matlab-Simulink and LTspice ® to evaluate the SiC power module impact on the performance of a multiphase drive system at different operating conditions.
A double pulse test is implemented to characterize and evaluate the switching energy losses of the utilized SiC MOSFETs. Then, a finite element (FE) thermal analysis based on Ansys-Icepak is carried out in order to design and validate the cooling system for the power converter. Finally, a co-simulation model is developed using Matlab-Simulink (MathWorks, Natick, MA, USA) (containing the model of the electrical machine and its control system) and LTspice ® (Linear Technology Corporation, Milpitas, CA, USA) (containing the model of the SiC H-bridge converter) to evaluate the impact of the SiC module on the performance of a multi-phase drive system under different operating conditions. The results show that the thermal and dynamic performances of the power drive are significantly improved, making the proposed system an excellent candidate for aircraft applications.
Finally, a co-simulation model is developed using Matlab-Simulink (MathWorks, Natick, MA, USA) (containing the model of the electrical machine and its control system) and LTspice ® (Linear Technology Corporation, Milpitas, CA, USA) (containing the model of the SiC H-bridge converter) to evaluate the impact of the SiC module on the performance of a multi-phase drive system under different operating conditions. The results show that the thermal and dynamic performances of the power drive are significantly improved, making the proposed system an excellent candidate for aircraft applications. This paper is organized as follows: Section 2 presents the system description of the electrical aircraft; Section 3 covers the system modeling, which encompasses the converter, PM motor and control models; the results and discussions are presented in Section 4; finally, the major conclusions are highlighted in Section 5.
Electrical Co-Simulation Model The Simulink/LTspice ® co-simulation model shown in Figure  2  is used to evaluate the converter's behavior: losses and efficiency. The motor and its control strategy are built in Simulink, and the converter is built in LTspice ®. The converter model is built based on the power module CAS325M12HM2 manufactured by CREE ® (Durham, NC, USA)  [19]. Three variables Pulse Width Modulation (PWM) signals for each H-bridge, phase current and back electromagnetic force) are sent from Simulink to the converter in LTspice ®. Then, LTspice ® performs the losses and performance analysis. The next sections will explain the converter and the machine models in details.
Power Converter Electrical Model The converter model is developed in LTspice ®. For high performance drive systems, the switching device needs to be selected based on its performance given the targeted operating conditions. SiC MOSFET technology is chosen for its reduced power losses for switching frequencies higher than 12 kHz  [20] [21] [22]. The CAS325M12HM2  [19]  MOSFET from CREE is rated at 1. 2 kV, 444 A, which is convenient for the application considered here. The schematic of the LTspice ® power converter model is shown in Figure  3. The DC voltage is regulated at 540 V and the gate resistances at 5 Ω. The PWM control comes directly from the Matlab/LTspice ® model via the co-simulation setup that was described above. Likewise, the output current and the back-electromotive force (BEMF) are imposed by Simulink.
ψ(i, θ) = ψ M (θ) + l(i, θ) × I (3) By combining (  2 ) and (3), Equations (  4 ) and (  5 ) can be written: K e (i, θ) = dψ M (θ) dθ + dl(i, θ) dθ Ī × I (4) L d (i, θ) = l(i, θ) + dl(i, θ) di θ × I (5) with K e (i, θ) being the back-electromotive force (BEMF) factor and L d (i, θ) the static self-inductance. The relationship between the rotor's position θ and the mechanical speed ω m is given by (  6 ): dθ dt = ω m (6) Therefore, starting from (1), it is possible to write the voltage balance as follows: l(i, θ). di dt = v -Rs × i -K e (i, θ) × ω m (7) Considering  (7)  and the overall voltage across the phase winding, the BEMF e(i, θ), and the differential voltage drop ∆v di (θ, i) are then defined as (8) and (9). e(i, θ) = K e (i, θ) × ω m (8) ∆v di (i, θ) = l(i, θ) di dt = v -Rs × i -e(i, θ) (9) Thus, the six-phase PM motor is modeled in Matlab/Simulink and simulated in an off-line co-simulation with the Ansys Finite-Element (FE) model of the machine, as is shown in Figure  4 :.