dataesr/scientific-paragraphs-categorization huggingface dataset:
Two criteria were used to consider absence of a pupil as a blink based on its duration: • bottom limit of 83 ms (following  [25, 26] ), in order to filter out brief data losses; • top limit of 1 second (one of the criteria proposed by Stern et al. [27] ) to exclude other irrelevant events. In order to limit the number of subjects and due to significant individual variability of BR in people (see Introduction section and Stern et al. [1] ), a within-group design was chosen. To avoid any possible influence of the order of the tests, half of the participants went through the helmet condition first, the other half were tested with the DS first. Two experimental sessions for both conditions were taken on two separate days to avoid any possible cross influence. To assess if the blinking data from the FOVE and TX300 are in agreement, two measurements with two participants were performed to compare the counted blinks estimated by the eye trackers to those made by a human observer during two minutes playing sessions.
Analyses Each test condition (helmet and desktop conditions) consisted of three phases: pretest (included a questionnairebased interview), test (playing the game) and posttest (included a questionnaire-based interview). In each phase of the experiment, eye blinks were recorded. Therefore, the data included two subsets: blinking activity and questionnaire results. Blinking was analysed in two aspects: BR and blink duration (BD). Blinking during the test phase was analysed in five minutes intervals (thus, four intervals). Average number of blinks per minute was calculated for each five-minute interval. Therefore, for the test, each participant's data were treated as four pairs of BR and BD averaged for each of the four five-minute intervals. Only the blinks recorded during the first two minutes were used in the analysis for the pretest and posttest interviews, as the duration of the interviews varied with participants.
62; VFQ: V = 15. 5, P = 0. 13, see Fig. 3 ). In view of the absence of differences between the two conditions and the importance of individual variations in blinking (see previous sections), individual results were analyzed to see if for some of the participants BR varied more significantly than for others, depending on the test conditions (see Table  3 ). In this analysis, 20 BR values (for each minute) were taken for each participant to obtain more reliable datasets. False discovery rate correction for multiple comparisons was used. The data show that among 12 subjects, who participated in the experiment, four demonstrated significantly more blinks in the helmet condition, seven subjects showed significantly more blinks in the desktop condition, and in one participant, no significant difference was found in BR between the conditions.
Design and procedure Participants played the game ''Flappy bird'' for 20 minutes as described in experiment 1. Two groups of subjects participated in this experiment. One group played the game on a DS, the other group played it wearing the FOVE HMD. LLT (both eyes) was measured by the same trained orthoptist right before (pretest) and right after (posttest) playing the game. Only right eyes data were used in the study. To double check the repeatability of the data provided by the apparatus, a small study was carried out with four subjects (including one author) (25. 5 years old; 95% CI: 18. 4--32. 6 years, 3 female, 1 male) who were tested by the same observer four times during the day between 10 a. m. and 4 p. m. with two hours intervals in same conditions (same room, same lighting, with humidity and temperature controlled by an airconditioner).
Participants The desktop group consisted of 12 subjects (including 3 authors) (31. 3 years; 95% CI: 25. 5--37. 2 years, 6 female and 6 male). The helmet group consisted of 12 subjects (including 3 authors) (32. 8 years; 95% CI: 25. 9--39. 7 years, 6 female and 6 male). Data of two subjects were discarded from the helmet group due to value of LLT exceeding the maximum value measurable with LipiView (100 nm). In order to even out the sizes of the samples in the two groups, data of two randomly chosen participants in desktop group also were not included into the analysis. The required sample size was calculated using G*Power3 software  [15]  for 2 × 2 between-within subject ANOVA. The within-subject factor was represented with the measurement stage (pretest or posttest). The between-subject factor was represented with the condition (helmet or desktop).
In the present study, the choice of the 2D stimuli instead of 3D stimuli was dictated by the need to investigate the possible Lipid layer thickness measurements before and after the game session in the two conditions. Lipid layer thickness averaged across all participants before and after 20 minutes of playing the game wearing virtual reality headset (helmet) and in front of a desktop screen is plotted. Lipid layer thickness increased significantly with time. Also, it increased at a higher rate with the virtual reality headset. Lipid layer thickness is scaled on the ordinate axis, the measurements done before the experiment (pretest, left) and after 20 minutes of playing the game in virtual reality headset (posttest, right). The data in the desktop condition is plotted with a solid line, and the data in the helmet condition is plotted with a dashed line.
Calcium imaging Calcium imaging in astrocytes was performed after patching visually identified astrocytes expressing EGFP (see above). The patch solution contained the cell impermeant calcium indicator Oregon Green™ 488 BAPTA-1, Hexapotassium Salt, (OGB; 100 µM, Invitrogen, USA). The slice was then transferred under the objective of a multiphoton microscope (Bergamo II Series, Thorlabs Inc. , Newton, New Jersey, USA) equipped with a Ti:sapphire laser Chameleon Ultra II Laser (Coherent, Santa Clara, CA, USA) and controlled by ThorImageLS software 3. 0. The Laser was tuned at 950 nm. Fluorescence was split into red and green channels with a dichroic reflector (562nm single-edge) and detected via a 500/550 nm bandpass filters (Semrock, USA) using photomultiplier tubes (Ultrasensitive GaAsP PMTs Positioned Directly Behind the Objective).
For experiments using pharmacology, imaging was performed under the objective of a epifluorescence microscope (BX51WI, Olympus, Japan) with the use of LED illumination (X-Cite® 120LEDBoost, Canada). Recordings were obtained with a Zyla sCMOS Camera (Andor, UK). Pictures were acquired at 8-30 frames/second. Images were analyzed with ImageJ software (National Institutes of Health, USA). Regions of interest (ROIs) were defined offline and quantified with ImageJ (Fiji, www. fiji. sc). Potential bleaching was corrected by subtracting the linear regression of the signal. The relative change of fluorescence signal ΔF/F was calculated as (F 1 -F 0 )/F 0. , where F 1 represents the intensity of the signal at a given time and F 0 the baseline of the signal. A response was considered significant when it exceeded twice the standard deviation of the baseline signal for at least 3 consecutive points.
Measurement of tremor Animals used: 8-14 weeks old wild type (C57BL/6J; Janvier), GFAP-CB1-KO and GFAP-CB1-WT. The mice were placed in a custom-made cage (15cm x 15cm x 20cm) suspended from a metal rack by rubber bands (for details, see  22  ). Underneath the cage was attached an iPhone 7 (Apple, USA). Tremor measurements were obtained with the VibSensor app (v. 2. 0. 0. , Now Instruments and Software, Inc. , USA). The VibSensor app measures acceleration in three perpendicular axes by using the inbuilt accelerometer of the phone. Tremor data were sampled in 10 minutes episodes at 100Hz. Data collection and analysis were not performed blind to the conditions of the experiments. Experimental procedures were randomized by simple randomization according to treatment type and genotype.
Numerical evaluation for immunohistochemistry Semi-quantification of CB1 and GFAP signals was performed using ImageJ version 1. 52a. For automated detection of CB1 particles from the background, binary images were generated using Threshold function that was the same for all analyzed pictures. Coexpression of CB1 particles with GFAP+ cells was counted manually in grey matter of individual spinal cord sections of GFAP-CB1-WT (n=3 mice, 9 sections, 539 astrocytes, 528 CB1 signals) and GFAP-CB1-KO (n=3 mice, 9 sections, 602 astrocytes, 537 CB1 signals) mice using Synchronize Windows and Cell Counter plugins. Data collection and analysis were not performed blind to the conditions of the experiments. Experimental procedures were not randomized.
Analysis Data are represented as mean ± standard deviation of the mean. Data analysis of electrophysiological recordings were performed in Clampfit 10. 5. 1. 0 software (Molecular Devices, USA) and Graphpad Prism 8 (GraphPad Software, USA). Flourescence images and tremor measurements were analyzed with Graphpad Prism 8 (GraphPad Software, USA), Python programming language 3. 8. 5. (Python Software Foundation, https:// www. python. org/) and JupiterLab version 2. 1. 5 (https://jupyter. org).
Statistics Statistical significance was assessed by Student's t test, Mann-Whitney test and Wilcoxon matched-pairs signed rank test. Due to the small sample size, data obtained from elecrophysiological and behavioral experiments were not assumed to be normally distributed and non-parametric tests were used. For immunohistochemistry data distribution was assessed using D'Agostino & Pearson normality test. Significance levels are: * P<0. 05, ** P<0. 01, *** P<0. 001. No significant effect of vehicle (Wilcoxon matched-pairs signed rank test, n = 6, p = 0. 99). d, When animals were pretreated with CB1 receptor antagonist AM281 (0. 5mg/ kg), WIN55,212-2 did not decrease tremor significantly. e, Pretreatment with AM281 and subsequent intrathecal injection of a vehicle had no effect on tremor. f, Area under the power spectra curve (8Hz-25Hz).
Acknowledgements We thank  Professor Frank Kirchhoff  ( University of Saarland ) for providing the Tgn(hgFAPEGEP) mice,  Dr Marco Beato  ( UCL ) for his help with the adult spinal cord slice preparation,  Dr Alex Fletcher-Jones  and  Dr Abel Eraso  for technical support and  Lesley Anson  for her help in editing the manuscript. The study was funded by  Offerfonden (JFP) ,  Aase og Ejnar Danielsens Fond (EMC) ,  Den Owensenske Fond (JFP) ,  Laege Sofus Carl Emil Friis og hustru Olga Doris Fond (JFP) ,  Danmarks Frie Forskningsfond  ( 9039-00072B  to JFP),  European Research Council  (grant MiCaBra,  ERC-2017-AdG-786467  to GM) and  Fondation pour la Recherche Médicale  (grant no. FRM SPF201809006908  to US).
For this purpose, recent literature and tumor databases were analyzed to show correlations between NRP1 and CD15 (a stem cancer cells marker), and between NRP1 and PDL1, for various pediatric brain tumors, such as high-and low-grade gliomas, medulloblastomas, and ependymomas. Finally, this review suggests a relevant role for NRP1 in pediatric brain tumors progression and identifies it as a potential diagnostic or therapeutic target to improve survival and life quality of these young patients.
Breast cancer CSCs overexpress VEGF-A and NRP1. Zhang et al. demonstrated, using a CSC KO model for NRP1 or VEGF-A, that this signaling axis was necessary to ensure the specific traits of CSCs in vitro and in vivo, and that the VEGF-A/NRP1 axis conferred the strain phenotype to breast cancer cells by activating the WNT/bcatenin signaling pathway  (108). In the same way, Liu et al. have shown that NRP1 expression is induced by Wnt/b-catenin signaling in mammary stem cells, and its suppression leads to decreased tumorigenesis in vivo models  (107). Furthermore, analysis of pediatric brain tumors databases shows a significant correlation between NRP1 and CD15 for the four analyzed tumor types. This correlation seems especially high for high grade gliomas, and so reinforces the possible role of NRP1 in tumor progression (Figure  3 ).
However, many unknowns remain to be clarified, and the involvement of NRPs in the antitumor immunity seems to be unclear. MBs had the lowest amount of PD-L1 and cytotoxic lymphocytes of all pediatric brain tumors and, overall, a very small amount of infiltrating immune cells  (144). This suggests that the tumor either actively modulates the immune response or simply has little immunogenicity, as it is suggested by the relatively low mutational burden  (144). Analysis of pediatric brain tumors database shows a significant correlation between NRP1 and PDL-1 for three tumor types, such as HGGs or LGGs, as well as EPNs, and a weak correlation for MBs (Figure  4 ). This correlation highlights the possible role of NRP1 in the regulation of tumor immune system. In the context of cancer, LTreg infiltration is a critical step in tumor development and progression.
CONCLUSION NRP1 were known since the end of the 1990s to play a role in axonal guidance and vascular biology, acting as a co-receptor for plexin and VEGF receptors families respectively. Furthermore, this co-receptor is also expressed by tumor cells and may be involved in the progression of tumors. In this review, we have discussed the involvement of NRP1 in pediatric brain tumors, such as gliomas, MBs, or EPNs. Indeed, the NRP1 signaling pathway is clearly involved in the cancer stem cells maintenance in pediatric tumors, and a significant correlation between NRP1 and CD15 (stem cancer cells marker) has been observed in pediatric tumors database. Moreover, NRP1 is involved in the activation of the immune cells, in particular in the interaction between T cells and DCs. NRP1 may also play a role in the regulation of immunogenicity of tumor cells, highlighted by the correlation between NRP1 and PDL-1.
Exploratory novel digital technologies will also be used to measure change, examining simultaneously the validity of digital biomarkers in natural environments. The safety and tolerability of the drug will also be examined. Our protocol is very closely aligned with a parallel Canadian trial of 90 participants (ARBA Study, US NCT number: NCT03887676) to allow for secondary combined analyses. Outcomes will be compared using both an Intent-to-reat and Per Protocol approach. Discussion: The outcomes of this trial, combined with the parallel Canadian trial, will contribute to the evidence base for medications used to help social difficulties among young autistic individuals; demonstrate the capabilities of the AIMS-2-TRIALS network of academic centers to deliver clinical trials; and support future drug development.
Randomization and Allocation Randomization of participants will be performed 1:1 into arbaclofen vs. placebo by a randomization website (Interactive Web Response System-IWRS) developed by the Data Management Department of the Julius Center, University Medical Center Utrecht (UMCU). Allocation will be sent to an unblinded dedicated pharmacist at each individual site as specified in a separate Pharmacy Manual. A central unblinded monitor will double check dispensation regularly. The rest of the study members are blind to allocation. Randomization will be stratified by site and age group (5-11 years old; 12-17 years old). Unblinding will only happen if the information can help treat an adverse event and for safety reasons.
Recruitment Recruitment of participants will be through referral via local autism diagnostic teams and pediatric and Child and Adolescent Mental Health Services at each study site. Consent databases/registers, support groups and advertisements on relevant websites may also be used. Potential participants can also self-refer. After initial contact by attending physician, and provided the participant/legal tutor give consent to be contacted, a study member will contact with the parent/carer and potential participant for pre-screening for eligibility, and, if appropriate, families will be invited to attend a screening visit to be assessed for eligibility and discuss consent. Principal investigator or delegated research team physicians will explain and subsequently obtain informed consent/assent.
Age will be adjusted for and baseline scores will be used as covariates. Hypothesis testing will be performed at the 5% level of significance for 2-sided tests. Safety analyses will be conducted on all participants taking at least one dose of study medication by calculating the incidence of adverse events in each arm and summarizing laboratory and ECG assessments, physical examinations and vital signs. A secondary analysis will be conducted by combining data from AIMS-CT1 and the ARBA Study to ensure statistical power for the assessment of efficacy of the primary outcome variable. Additional analyses will consider variables or subvariables that will be different from or derivative of the primary and secondary hypotheses. Another level of analysis will look at data of individual sites. Power calculation was made for the primary outcome (VABS-3 social function domain), which justifies the a priori planned combination of the data of this study with that of ARBA Study in Canada.
and is summarized in Figure 1. In collaboration with the Province of Ontario Neurodevelopmental Disorders Network (POND) our protocol is very closely aligned with a parallel Canadian trial of 90 participants (ARBA Study: NCT03887676) for subsequent combination of data. Participants Inclusion Criteria • Male and female participants aged between 5:0 and 17:11 years at time of consent, with a diagnosis of ASD according to the DSM-5 (3) criteria and complex verbal language [defined as qualifying for an Autism Diagnostic Observation Schedule-2 (ADOS-2) Module 3 or 4 assessment (27), as determined by a clinically-certified ADOS-2 rater supervised by the local research-reliable ADOS-2 lead (see Requirements in Additional Material 5)]. • The parent/carer (hereafter parent; for brevity) can speak and understand the local language.
RT-qPCR After 24 h of treatment with PTX2 (100, 150, and 200 nM) and OA (75, 125, and 175 nM), genes expression in Caco-2/HT29-MTX cells in one hand and in EGC in the other hand was evaluated as previously described  (Reale et al. , 2019)  with the following modifications. For Caco-2/HT29-MTX cells, reverse transcription was performed with 2. 5 mg of total RNA, and quantitative PCR reactions were carried out in a total volume of 5 mL containing 8. 33 ng cDNA. For EGC analysis, reverse transcription was performed with 2 mg of total RNA, and quantitative PCR reactions were carried out in a total volume of 5 mL containing 6. 67 ng cDNA. All primers were purchased from Sigma-Aldrich. Using NormFinder software, the genes RPLP0 and ACTB were chosen as reference gene for Caco-2/HT29-MTX cells and for EGC, respectively, since they did not exhibit any significant variation of expression among the samples.
Glial cell markers After 24 h of treatment, the immunofluorescence detection of iNOS and GDNF was performed as previously described  (Reale et al. , 2019)  with the following modifications. Antibodies were purchased from Abcam: mouse anti-iNOS (1/350, ab49999), rabbit anti-GDNF (1/200, ab18956), goat anti-mouse IgG (H&L) Alexa Fluor 1 555 (1/1000, ab150114) and goat anti-rabbit IgG (H&L) Alexa Fluor 1 647 (1/1000, ab150079). The Target Activation Module of the BioApplication software (Arrayscan VTI HCS Reader, Thermo Scientific) was used to quantify iNOS and GDNF in the cytoplasm. Cytomix (50 ng/mL TNFα (BD Pharmigen Biosciences) +50 ng/mL IL1β (Sigma Aldrich) +50 ng/mL IFNg (Thermo Fisher, Walthan, MA, USA)) was used as positive control for iNOS. For each well, 6 fields (20X magnification) were analyzed.
Statistics GraphPad Prism software was used for statistical analyses. One sample T-test was performed for cytotoxicity and HCA assays. An analysis of variance (ANOVA) was performed for TEER, LY crossing, IL-8 release and RT-qPCR assays, and, if the effect of concentration was significant (P < 0. 05), the values were compared to the control using the Dunnett's test. Differences were declared significant at P < 0. 05. The values presented are means AE SEM.
ACKNOWLEDGMENTS. This work was supported by the program " Investissements d'Avenir " from the  French Government , implemented by  Agence Nationale de la Recherche , references:  ANR-10-LABX-54   MEMOLIFE ,  ANR-11-IDEX-0001-02   PSL* Research University. M. S. was funded by the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Aard-en Levenswetenschappen  ( ALW-Veni ) and  European Research Council   Starting Grant  ( ERC-Stg ). C. I. D. Z. was supported by  FP7-C7   European Commission ,  ZonMw ,  NWO-Exacte en Natuurwetenschappen (ENW-Klein ),  European Research Council  ( Advanced Grant and Proof of Concept Grant ),  Medical NeuroDelta Programme, Topsector Life Sciences & Health (Innovative Neurotechnology for Society) , and the  Albinism Vriendenfonds Netherlands Institute for Neuroscience.
G. B. was funded by  Région Ile de France, Fondation pour la Recherche Medicale (FRM) , and  Labex MEMOLIFE. A. E. G. was supported by the  National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP)  and the  University of California, San Francisco Discovery Fellows Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank  R. de Avila Freire  and  L. Post  for technical assistance, and  B. Barbour ,  R. S. Larsen ,  P. L. Reeson ,  E. Jones ,  M. Mukundan , and  C. Wang  for comments on the manuscript.
Methods and Materials A detailed description of all methods and materials can be found in the SI Appendix. All experiments were conducted in accordance with European and French directives on animal experimentation and with local ethical committee approval. Electrophysiological recordings and behavioral analyses were performed on adult Wistar and Lister hooded rats as well as serine racemase knock-out mice (SR -/-) and control littermates (SR +/+ ) while in vivo microdialysis and phMRI experiments were performed only on mice. Only males were used. All quantitative data are expressed as mean ± SEM. Statistical analyses performed are detailed in the SI Appendix and in figure legends. The authors thank Dr Philippe Fossier for sharing electrophysiological equipment. We are grateful to Gérard Sadoc for his help using Elphy TM.
Thanks to Julien Moulard for his help in analyzing cell excitability. This work was supported by operating grants from Centre National de la Recherche Scientifique and INSERM (to J-P. M. ), Université Aix-Marseille (to J-P. M. ), Agence Nationale de la Recherche (ANR-09-MNPS-022-01 to J-P. M. ), Institut de Recherche Servier (to J-P. M and KCFF), and the Fondation pour la Recherche Médicale (Equipe FRM DEQ20150331734 to J-P. M. ). NM and AC were supported by the Institut de Recherche Servier, the Centre National de la Recherche Scientifique (CNRS) and the University of Bordeaux. LP and SS were supported by a grant from Fondo di Ateneo per la Ricerca.
Subjects We acquired a set of 60 PETRA volumes that came from 20 different subjects, ranging from 2 to 5 images per subject acquired at different sessions (implying a new positioning of the EEG cap), all varying between two quality levels: 30k and 60k spokes. These volumes were divided into two datasets. The first one was used to train a segmentation model, and the second one was used to test the performance of this model. We decided to separate the data by taking 12 subjects for the training dataset and 8 subjects for the test dataset, resulting in 37 training volumes and 23 test volumes.