dataesr/scientific-paragraphs-categorization huggingface dataset:
(54) , estimated maturity of sleep state was also correlated with PMA both on conventional electroencephalography, cEEG (r 2 = 0,86) and amplitude-integrated electroencephalograph, aEEG (r 2 = 0. 72) in 51 3-4 h recordings. The definition of QS is less and less easy to define as term decreased under 30 weeks gestation, especially in terms of duration and transition between the sleep stages. Furthermore, the way to evaluate QS during these periods could be challenged. It is one of the reasons why we have chosen to analyze sleep by periods of 20 s with very detailed observational criteria. Despite this, we observed that PMA estimation is less accurate before 30 weeks of observed PMA and that evaluation of QS is probably more accurate and usable after 30 weeks. We think therefore that the annotated database built in this study can be used as a learning base to set an automated multiparametric detection of QS phases based on video, heart rate and respiratory rate analyses.
ViSiAnnot was designed so that the user can easily configure the number of videos and signals to display, as well as the number of labels to annotate. Thus, it can adapt to other studies where it is needed to annotate temporal events. Multimodal visualization was necessary to get precise behavioral evaluation of sleep and to get a database that can be worked offline by different experts which gives strength to the annotations. To our knowledge there are only a very few software tools that allow multimodal visualization together with an annotation tool and none of them allow simultaneous visualization of video and physiological signals. ViSiAnnot will be available as an open-source software. The advantage is that the software will be available to everyone, not depending on a thirdparty software requiring a license and allowing collaborative work for improving the software and adding features.
Motility of recruited innate immune cells is different after injection of planktonic or biofilm forms of Staphylococcus aureus in the mouse ear pinna Using Imaris software, we created an analysis protocol to track the motility properties (average speed and straightness) of EGFP+ cells recruited at the injection zone from the previously acquired time-lapse videos (Fig. 4 ). Using the "Spots" tool we attributed a sphere to a number of immune cells observed in the acquisition field (Fig. 4a-b , white spheres). This enabled us to establish a trajectory (Fig. 4a-b , multicoloured lines) for each sphere corresponding to the path taken by each cell over time in the ear tissue. We then compared the average speed and straightness of the trajectories of phagocytic cells in response to the two bacterial forms.
In vivo confocal imaging: analysis Time-lapse video analysis Videos acquired with the 10X objective were first stitched together using ZEN software. Each image extracted from time-lapse videos corresponds to the Zprojected average intensity signal for each channel at the corresponding time point. Time-lapse videos at 20X and 10X were then analysed with Imaris software using the "Spots" tool. For each cell, a track was generated by the software and manually corrected according to specific criteria: number of frames superior to three and elimination of converging tracks between two different cells. Two different parameters (average speed and straightness) of immune cell dynamics were then extracted. For each time point, both parameters were analysed in different zones of the cutaneous injection site, where cells were in contact or not with the bacterial inoculum.
Mosaic analysis Images acquired on the ZEISS LSM 800 confocal were stitched together using ZEN software to reconstitute an entire image of the ear tissue at early and late time points. A maximum intensity projection image was created from image Z-stacks. A ROI was then drawn manually around the EGFP fluorescent zone of the 24 h image to obtain the sum of EGFP fluorescence intensities of each pixel in the ROI. The shape of the ROI was conserved and applied to the early time point image. The ratio of the sum of intensities of EGFP fluorescence to the area of the ROI was then calculated for both time points. The images shown represent the Z-projected maximal intensity signal of a reconstituted image of the ear tissue for the EGFP channel.
Acknowledgements We wish to thank  Alexander R. Horswill  ( Department of Immunology and Microbiology, University of Colorado, Denver, USA ) for pAH9 plasmid,  Ivo Boneca  ( Bacteria-Cell interactions Unit, Pasteur Institute, Paris, France ) for the LysM-EGFP transgenic mouse line,  Michael Givskov  and  Youg-Tae Chang  for the CDy11b probe,  Caroline Vachias  and  Pierre Pouchin  ( Confocal Microscopy Facility ICCF, University Clermont Auvergne ) for their help with setting up image acquisition settings and image analysis and processing,  Christelle Blavignac  for treating SEM samples and images ( Centre d'Imagerie Cellulaire Santé Facility CICS, University Clermont Auvergne ),  Elisabeth Billard ,  Alan Diot ,  Jérome Josse  and  Geneviève Milon  for critical reading of the manuscript and helpful discussions, and  Karim Alloui  for its care of the animal housing facility.
A. Acquisition of empirical human structural brain data Human anatomical brain networks were reconstructed by applying deterministic tractography algorithms to diffusion-weighted MRI. In this study, we used a single, group-representative composite network assembled from 30 subject-level networks  [54] [55] [56]. The mean age of participants was 26. 2 years, the standard deviation was 5. 7 years, and 14 of the subjects were female. In order to map anatomical networks, diffusion spectrum and T1weighted anatomical images were acquired for each individual. For the DSI scans, 257 directions were sampled using a Q5 half-shell acquisition scheme with a maximum b-value of 5000 and an isotropic voxel size of 2. 4 mm. We used an axial acquisition with repetition time TR = 5 seconds, echo time TE = 138 ms, 52 slices, and field of view of  [231, 231, 125] mm.
For frequencies f > 0, we thus have that XH (f ) = -i X(f ), from which it becomes clear that the Hilbert transform just induces a phase shift of π/2 to each frequency component in the signal. In this study, we computed Hilbert transforms of the simulated neural activity using the 'hilbert' function in MATLAB. As described in Sec. IIF1, the Hilbert Transform was applied after first filtering the raw time-series in a specified frequency band, in order to ensure that the corresponding phase variable is well-defined  [5].
Measurement of the fission fragments in coincidence with the emitted prompt neutrons was undertaken recently, at JRC-Geel institute, for the 235 U(n,f) reaction in the resolved resonance energy region, up to 160 eV incident neutron energy. From this experimental work, fluctuations of several fission observables (mass yields, average total kinetic energy TKE, average prompt neutron multiplicity ν P ) were clearly observed. In the present work, these experimental pre-neutron fission fragment mass and kinetic energy distributions were used as input data for the FIFRELIN Monte Carlo code. By adopting the Hauser-Feshbach statistical model, the code simulates the de-excitation of the fission fragments. Four free parameters are available in the code: two of them (called R min T and R max T ) govern at the scission point the sharing of the total available excitation energy between the two nascent fission fragments, while the two others (called σ L and σ H ) assign the initial fission fragment spins.
Indeed, for the first time, they were able to measure (for 88 resonances) correlations between fission fragments properties (mass and kinetic energy) and prompt neutron multiplicity. By using their fission fragments pre-neutron mass and kinetic energy distributions, we have performed calculations with the Monte Carlo code FIFRELIN (FIssion FRagment Evaporation Leading to an INvestigation of nuclear data) in order to deduce characteristics of the emitted prompt particles (neutrons and γ). These calculations are presented and discussed in Sect. 3.
Calculations performed with the FIFRELIN Monte Carlo code The FIFRELIN Monte Carlo code, developped at CEA-Cadarache, aims at calculating post-fission observables (spectra and multiplicities of the prompt neutron and gamma particles; fission energies released). In particular, this code is a nice tool to investigate correlations between fission observables, which can be very useful for improving our understanding of the fission process. All the details related to this code can be found in Refs. [18] [19] [20].
Using bioinformatic tools we highlighted in cGEPan upregulation of E2F, MYC and mTORC1. Next, using Connectivity Map (CMAP) and CMAPViz a visualization procedure for CMAP data that we developed, we selected in silico three FDAapproved, bioactive molecule candidates: a-estradiol (a-E), nordihydroguaiaretic acid (NDGA) and prochlorperazine dimaleate (PCZ). At a biological level, we showed that the three drugs triggered an apoptotic cell death in a panel of T-ALL cell lines, activated a DNA damage response and interfered with constitutive mTORC1 activation and c-MYC expression. This analysis shows that the investigation of conserved leukemogenesis pathways could be a strategy to reveal new avenues for pharmacological intervention.
Nevertheless, most processes that define cell states such as cell division, apoptosis or senescence have been conserved in evolved organisms and this could be the same for cancer. We thought to determine the cancerassociated processes that are conserved across human T-ALL and a murine T-ALL model to untangle cancer complexity at its core. We thus compared the gene expression profiles of human T-ALL samples present in public databases to that of a in-house murine T-ALL model generated by the thymocyte-specific deletion of the PTEN tumor suppressor  (15). It allowed us to highlight the conserved differentially regulated genes and to determine through bioinformatic enrichment tools the cellular functions that are associated with leukemia and conserved between the two models. Then, we took advantage of the Connectivity Map (CMAP) computational pipeline from The Broad Institute (https://www.
Data Processing and Differential Expression Analysis Normalized gene expression datasets were retrieved from Gene Expression Omnibus (GEO: https://www. ncbi. nlm. nih. gov/gds/). Gene expression profiles (GEP) of 13 T-ALL patients samples and 17 control healthy samples (T cells) were studied from GSE48558. GEP from the tPTEN-/-mouse genetic T-ALL model was obtained from GSE39591  (16). A GEP Linear Models for Microarray Data (LIMMA) method was used to reveal differentially expressed genes (pValue<0. 05) using the web interface Phantasus  (17)  (Supplementary Table  1 ). Gene lists were processed using R and biomaRt library to match mice gene symbols to human gene symbols after conversion and to identify the conserved differentially expressed genes (cDEG) between the two species. Symbols of conserved genes that vary in the same direction in the two models are available in Supplementary Table  2.
Enrichment Analysis and In Silico Drug Screening To extrapolate from genes to cellular mechanisms we used the R package fGSEA  (19)  to calculate the enrichment of the dysregulated genes in GSEA (Gene Set Enrichment Analysis) hallmarks and reactome pathways. For the fGSEA on reactome database, we selected the first 10 most enriched processes. Also we queried the Gene Ontology database through the R package GOplot  (20). We applied a pValue and qValue cutoff at p<0. 001 and queried all ontologies. All enrichment results are available in Supplementary Table  3. For the in silico identification of bioactive compounds we used CMAP with the top 1000 HG-U133a probes for both up and down regulated genes in our signatures. We selected only the results with negative enrichment scores with at least 3 molecules in a batch on the HL60 Acute Myeloïd Leukemia (AML) cell line which was chosen as a representative of hematologic malignancies in CMAP.
Bibliometric Score and Graphical Representation (CMAPViz) To help the selection of molecules to be tested within the CMAP hit molecule list we implemented a bibliometric score based on three criteria, 1: the number of clinical trials (c) filed at https:// clinicaltrials. gov/ and related to acute lymphoblastic leukemia, 2: the number of publications (p) referring to the hit molecule (m) in association with leukemia on PubMed and finally 3: a score ranging from 1 to 3 proportional to the best impact factors (i) for the previously mentioned association. Additionally a weight (w) of 2 was given to the final score to enhance the visualization of the score. Details of scores can be found in Supplementary Table  4. Bibliometric score m = log (c m * p m * i m * w) In order to visualize the results in a more user-friendly manner we developed a tool called CMAPViz available as a R package.
Analysis of Cell Viability In a 96-well plate, 40,000 cells per well were incubated with effectors for 48 h at 37°C (n=3 per experiment). Cell death was assessed after staining with 1μg/ml of DAPI (4,6 diamidino-2-phenylindole) followed by FACS analyses on MACSQuant10 ® analyzer (Miltenyi Biotec, Bergisch Gladbach, Germany). Living cells were quantified as the percentage of DAPI negative cells. All statistical data from functional assays are presented as the mean ± s. d.
Western Blotting After treatment, cells were solubilized in lysis buffer (50 mM HEPES, pH 7. 4, 150 mM NaCl, 20 mM EDTA, 100 mM NaF, 10 mM Na3VO4, 1 mM PMSF, 1mM aprotinin and 0. 5% NP40) and incubated for 30 min at 4°C. Lysates were centrifuged at 12,000 g for 15 min at 4°C and supernatants were adjusted with Laemmli SDS sample buffer. Proteins were separated by SDS/ PAGE and transferred to ECL membranes (GE Healthcare, Velzy-Villacoublay, France) in a Tris (20 mM), glycine (150 mM), and ethanol (20%) buffer at 100 V for 1 h at 4°C. Antibodies were incubated in saturation buffer (50 mM Tris pH 7. 5, 50 mM NaCl, 0. 2% Tween, 5% BSA) and revealed with a secondary-peroxidase-conjugated antibody followed by ECL detection (GE Healthcare). Protein bands were quantified using the ImageJ software.
Identification and Enrichment Analysis of Conserved Genes in Murine and Human T-ALL Our whole gene analysis pipeline is reported on Figure  1A. We confronted transcriptomic data obtained from human T-ALL gene signatures present in public datasets to the one we established from a tPTEN-/-T-ALL mouse model, to highlight the gene dysregulations that could be shared between the two models. We first extracted the differentially expressed genes (DEG) between leukemic cells and their normal counterparts, both for the murine T-ALL model (GSE38597) and the human T-ALL samples (GSE48558), to identify 3,554 and 6,341 DEG (p<0. 05) from mouse and human datasets respectively. The similarity of the two DEG from the two species was found statistically significant (overlap: 1,355 genes, Fisher exact test p=2e-31).
Then, by comparing the murine and human lists, we identified among the 1,355 overlapping DEG, 844 common differentially expressed genes (cDEG) (Figure  1B ) that were similarly differentially expressed (in the same direction: up/up, down/down, between the human and murine DEGs, Figure  1C ). Using data from tPTEN-/-cells could be a bias as PTEN mutations are observed in only 15%-20% of the cases, although it has to be kept in mind that the PI3K/Akt/mTOR pathway negatively controlled by PTEN is abnormally active in almost 90% of T-ALL cases. We used a data set (GSE117165) corresponding to a transgenic mouse model expressing an oncogenic form of NOTCH1, an event observed in more than 60% of T-ALL. We extracted 491 cDEG with the human T-ALL dataset (compared to 844 when using tPTEN-/-cells) available in Supplementary Table  6.
Drug Query Using CMAP and Identification of Drug Repositioning Candidates Through CMAPViz We applied the CMAP gene pattern matching algorithm (  21 ) to our T-ALL cDEG signature in order to search in silico for molecules that could target the conserved mechanisms of T-ALL leukemogenesis. CMAP is a database of gene expression signatures produced by molecules/drugs/perturbagens acting on a panel of cell lines that are representative of human cancers such as MCF7 for breast cancer, PC3 for prostate cancer, HL60 for leukemia. We searched in CMAP for molecules with a negative enrichment score (ES), meaning that they generated gene signatures on HL60 that negatively correlated with our T-ALL cDEG query signature. By reversing gene expression associated with leukemogenesis, we made the assumption that these molecules would trigger T-ALL cell death (Figure  4 ).
Then, because most of the molecules listed in CMAP database have been thoroughly studied, even in T-ALL, we decided to implement a bibliographic score that takes into account, and for each molecule: i) the number of publications referring to the hit molecule, ii) its relationship to acute lymphoblastic leukemia, iii) the number of leukemia-related clinical trials it is involved in, and iv): the impact factor of the publications. Finally, we selected 3 molecules of interest: a-Estradiol (a-E) (n= 3, ES= -0. 477), NDGA (n= 3, ES= -0. 291), PCZ (n= 4, ES= -0. 554) that present the lowest bibliometric score, underlying that they were scarcely described on leukemia. Thanks to the CMAPViz visualization tool we developed, it was possible to easily identify candidate drugs that immediately stood out from other drugs.
While a-Estradiol had a negative ES, estradiol (17ßestradiol, E2) showed a positive ES: 0. 576 (HL-60, n=8), suggesting that the ß estrogen isomer has an opposite response to a-E by mimicking the cDEG response. To further ensure that our results are not driven by PTEN deletion but reflects more globally the core processes at play in T-ALL, we queried the CMAP database using the cDEG derived from a NOTCH1 oncogenic mouse model (GSE117165) compared to T-ALL (GSE48558). We obtained similar results (i. e: negative enrichment score) for a-E and PCZ. The results are shown in Supplementary Table  7.
Tanespimycin/17-AAG has a negative enrichment score (ES= -0. 799, n=12) on our T-ALL cDEG signature. 17-AAG had already been shown to have anti-leukemic properties  (22). We observed that 17-AAG induced a dose dependent cell death in the three cell lines (Supplementary Figure  1 ). Netilmicin was selected because of its positive ES (1, n=1) corresponding to drugs that rather mimics the query signature in CMAP. We found that netilmicin was totally neutral on the three T-ALL cell lines (Supplementary Figure  1 ). We also observed in the CMAP database that a-E down-regulated all the cDEG genes that were found positively enriched in early and late estrogen   response datasets (Figure  2B ), except FASN (Supplementary Figure  2  and Supplementary Table  8 ).
DISCUSSION In this article, we compared datasets of gene expression profiles from human and murine leukemic samples with the aim to identify common pathways associated with leukemogenesis. There are many possible sources of heterogeneity in data production because of differences in sample handling and processing, in the sequencing technology used, and in this case in the species studied. Despite this, we showed that the differentially expressed genes (DEG) we identified in either human T-ALL patient samples and in a tPTEN-/-deficient T-ALL murine model (compared to their normal counterparts) were highly statistically similar, suggesting the existence of conserved core leukemogenesis mechanisms. Within 1,355 DEG that overlap on the two types of data, we found 844 common differentially expressed genes (cDEG) which had their expression similarly affected by leukemia development in both human and murine situations.
By reversing gene expression associated with leukemogenesis, it is envisioned that these molecules could trigger T-ALL cell death. Moreover, to search from the outcome of CMAP for original compounds not yet described for leukemia, we implemented a bibliographic score to introduce a selection allowing us to limit the number of potential molecules to be tested. So the three molecules we selected: a-Estradiol (a-E), NDGA, and PCZ had rarely been associated with leukemia, in neither publications nor clinical trials. Their enrichment values were relatively low, because only few concentration values have been reported for them (a-E: n=3; NDGA: n= 3; PCZ: n=4) in the CMAP database on the HL60 cell line, opposite to more widely studied drugs such as the histone deacetylase inhibitors trichostatin A (n=34) and valproic acid (n=14).
Although counter-intuitive at first glance, this study could suggest that the anti-leukemic effects of estradiol and tamoxifen could be mediated through the G protein-coupled Estradiol Receptor (GPER) and not via classical ER, that are not bound by TAM and not expressed in several T-ALL cell lines  (29). While TAM is an antagonist of ERaband ERb receptors it is an agonist of GPER. Along the same line, it was also reported that a-E through GPER1 could inhibit the spreading of granulosa cell tumors  (30). Our GSEA analysis of the T-ALL cDEG revealed an upregulation of genes associated with early and late responses to estrogen, signatures that were defined as genes differentially modulated by 17ß-estradiol/E2 in cellular models (genesets refinements datasets: GSE15717,GSE36683, GSE5840,GSE9757, GSE2251, GSE2292, GSE26834, GSE8597).
While PTEN abnormalities are clearly associated with a poor outcome for childhood T-ALL  (38) , this bad prognosis is mitigated by concomitant NOTCH1 mutations  (39). T-ALL also frequently express a constitutive activation of the IL7-JAK-STAT5 pathway, through activating mutations in the genes for IL7 receptor  (40) , JAK3  (41)  or STAT5  (42). It would thus be interesting to analyze on a panel of T-ALL primary samples or PDX, the sensitivity of the different mutational patterns to the three drugs we identified here. Altogether our study reveals the existence of conserved genes potentially composing the core of T-ALL pathogenesis and that the bioinformatic mining of genetic signatures from public datasets and experimental models could be exploited to set up a strategy to search for new treatment avenues in T-ALL.
Acknowledgements CJ, PB, FL, ROT, and PD drafted the protocol, JP put the two surveys online, CL performed the statistical analysis, CJ interpreted the findings and drafted the article, and FL, ROT, and PD reread and reviewed the draft. The surveys were organized, coordinated, and delivered on-site with help from the following physicians and CRTs:  C. Faudon , T. Allegre Aix-en-Provence; M. Lombard, JL. Schmit Amiens;  S. Rehaiem ,  V. Rabier Angers ;  A. Valran ,  E. Piet Annecy ;  J. Gerbe , F. Caby Argenteuil;  E. Chevalier ,  A. Foltzer Besancon ;  L. Traore ,  O. Bouchaud Bobigny ;  JC. Duthe ,  S. Ansart ,  L. de Saint Martin Brest ;  P. Goubin ,  R. Verdon Caen ;  J. Prouteau ,  C. Jacomet Clermont-Ferrand ;  C. Chesnel ,  L. Richier ,  S. Gallien ,  B. Elharrar Créteil ;  I. Calmont ,  A.
Mediating Objects: Sound, Music, Contact, and Vibration Media The sound, music, and vibrotactile mediation framework we designed comprised 16 audiovibrotactile stimuli as well as several devices-four audiovibrotactile mediating objects, two microphones, a gesture control MIDI interface, a computer, a software tool, a mixing console, and an amplifier. We describe the features and functions of both the stimuli and the devices in the following subsections.