dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and methods Participants. The methods and experimental procedures (except for magnetic resonance imaging [MRI]   procedures, see below) used in the present investigation are described in full in our previous report  10. In brief, we adapted a TMS-memory interference protocol  6  and demonstrated replicability of the induced effects across a two-center study. In the present report we focused on functional MRI (fMRI) activation changes during the recognition phase as a function of the BDNF Val66Met gene variation. From our previous study  10  , we included all participants with available fMRI data (N = 43; mean age: 23. 47 ± 3. 5 years). No additional subjects were recruited. All participants were healthy right-handed young males with at least secondary educational attainment. Subjects were recruited from two centers, Barcelona (BCN; N = 31) and Marseille (MRS; N = 12), where the experimental protocols had been harmonized in the context of the IMI FP7 European 'Pharmacog' research project (https:// www.
Genotyping. All participants underwent genotyping analysis of the functional polymorphism in the BDNF Val66Met gene, causing a valine (Val) to methionine (Met) amino acid substitution at codon 66. Results revealed that 19 participants were Met carriers (Met group; 18 heterozygous and 1 homozygous for the Met allele; Met allele frequency was 44. 2% from the total sample), while 24 participants were homozygous for the Val allele (Val group; frequency of 55. 8% from the total sample; see further information on SM). fMRI data. fMRI data from both centers was analyzed in a single site (BCN) with the FEAT-FSL (FMRIB's Software Library v. 6. 00; http:// fsl. fmrib. ox. ac. uk/ fsl/ 37 ). After preprocessing of all individual fMRI scans, firstlevel analyses  38  were individually customized, and three contrasts of interest: (1) mean hits frontal [HF]; (2) mean hits vertex [HV]; and (3) HF > HV were defined (see also SM).
Statistical analysis. Non-imaging data analyses were performed using IBM SPSS (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27. 0. Armonk, NY: IBM Corp). For cognitive performance data, accuracy (percentage of pictures correctly categorized as indoor or outdoor), and reaction time (RT; time elapsed from the presentation of a picture to the subsequent response) during encoding and hits (percentage of correctly recognized pictures), and RT (time elapsed between picture appearance and yes/no motor response) during recognition, were collected for both experimental conditions (LFC and vertex). For these outputs, oneway repeated measures ANOVAs were conducted with experimental conditions (i. e. , LFC vs. vertex) as withinsubject factor and BDNF Val66Met gene polymorphism (i.
All statistical analyses were two-tailed and α was set at 0. 05 (see SM for further details and analyses). Due to techni-   68  , created for visual purposes, pointed with a yellow arrow). After a 30 min rest, participants completed the recognition of the visual memory task (i. e. , discrimination between seen/not seen items in encoding phase) within the MRI scanner. RMT, resting motor threshold; rTMS, repetitive transcranial magnetic stimulation; LFC, left frontal cortex; fMRI, functional magnetic resonance imaging. www. nature. com/scientificreports/ cal issues during the recording of the encoding phase, data from three subjects was not available for conducting accuracy statistical analyses (see also Table  S1 ).
Funding This work was supported by the  European Commission Seventh Framework Programme  ( FP7/2007-2013 ), the  Innovative Medicine Initiative's (IMI)  ' PharmaCog ' project. This work was also supported by a grant from the  Spanish Ministry of Science, Innovation and Universities (MICIU/FEDER ;  RTI2018-095181-B-C21 ) to D. B. -F. , which was also supported by an  ICREA   Academia 2019 grant award. K. A. -P. was supported by a postdoctoral.
ORAI1 and ABCB1 Gene Expression Varies with the Stage of AML Differentiation and Prognostic Classification In addition to the fresh AML primary samples collected by our laboratory, we analyzed RNA-seq datasets from a cohort of human AML samples at diagnosis (n = 439,  [12] ) available in the public domain (https://www. cbioportal. org) for ORAI1 and ABCB1 gene expression. We first compared AML patient samples according to the stage of leukemic cell differentiation (French-American-British (FAB) classification), i. e. , from the M0 undifferentiated stage to the M5 monocytic differentiated stage. We observed an increase in ORAI1 expression associated with late stages of AML differentiation (one-way ANOVA (F(5. 79) = 18. 07, p < 0. 0001) (Figure  2a ), while in an opposite manner, ABCB1 expression was downregulated in a more advanced stage of differentiation (one-way ANOVA (F(5.
We found that both ABCB1 high and ORAI1 low AML were associated with lower expression of CD33 (Log (2) = 6. 661 vs. 5. 761, p < 0. 0001 and Log (2) = 6. 436 vs. 6. 003, p = 0. 0007, respectively) and CDK4 (Log (2) = 5. 658 vs. 5. 464, p = 0. 0211 and Log (2) = 5. 698 vs. 5. 424, p = 0. 0011, respectively) genes (Figure  3 ). Regarding the CDK6 gene, only ORAI1 low AML displayed lower expression than ORAI1 high AML (Figure  3d, h ). Interestingly, ABCB1 high AML samples displayed an overexpression of several leukemic stem cell markers previously identified and established as the 17-gene stemness signature  [15]  (Table  S2 ) and underexpression of the ORAI1 calcium channel gene (Log (2) = 3. 471 vs. 3. 741, p < 0. 001) (Figure  3i ). [12]  from samples derived from patients with AML. RNA-Seq data were obtained from public domain AML datasets (https://www.
Interestingly, we observed similar behaviors in primary AML cells obtained from patients at the time of diagnosis and presenting the same opposite features. We have previously shown the clinical significance of ABCB1 in a cohort of patients with de novo AML and highlighted the link between ABCB1 expression and activity with LSC gene expression and risk stratification  [2]. However, ABCB1 was not directly linked to drug resistance, prompting us to propose a bystander effect of these particular MDRs in chemoresistance mechanisms. Interestingly, in this study, we present new data obtained from public datasets  [12] , corresponding to the RNA-Seq analyses of a cohort of 439 AML patients at diagnosis, linking high ABCB1 expression and low ORAI1 expression with (i) an undifferentiated stage of AML, (ii) a lower expression of CDK4 and CDK6 (i.
In agreement with our results obtained by screening a public dataset, the slow-cycling immature KG1 AML and AML#1 primary cells displayed high ABCB1 activity and an enrichment of their LSC compartment while underexpressing ORAI1. In contrast, fast-cycling differentiated U937 and AML#2 primary cells exhibited lower ABCB1 activity and a discrete number of LSCs while overexpressing ORAI1. More precisely, we identified two distinct cell subpopulations among KG1 and U937 cells in regard to ABCB1 activity, namely, the Rh123 low and Rh123 high populations. The Rh123 low cell compartment in each cell line shared common characteristics regarding ABCB1 activity and the LSC phenotype characterized herein by expression markers (e. g. , SOX2, KLF4, NANOG), surface markers (CD34 + CD38-) and Ki67 expression levels.
A study on leukemic blasts isolated from AML patients was performed to determine the expression level (RNA-Seq) of NFAT isoforms according to differentiation status (FAB classification). NFATc2/c3 isoforms were found to be overexpressed at the early stage of differentiation. Moreover, it has been shown that NFAT inhibition increases the sensitivity to chemotherapy of primary AML leukemic blasts with an FL3-ITD mutation  [11]. Interestingly, NFAT overexpression was observed in AML primary cells from patients during relapse compared with samples at the diagnostic stage  [9]. While these data point to a possible role of NFAT in AML chemoresistance and AML progression, a majority of these studies only described NFAT gene expression without examining NFAT protein levels and intracellular distribution.
Graphs were plotted using GraphPad Prism software. Immunophenotyping. After rinsing, the cells were stained with fluorochrome-conjugated antibodies (all from Biolegend, San Diego, CA, USA) for 30 min at room temperature (RT) in PBS with 10% SVF. Antibodies were used at a 1:100 dilution (except for the PE-Cy7conjugated CD33 antibody used at a 1. 25:1000 dilution), and matching isotype antibodies were used at the same final concentration. Then, the cells were rinsed and resuspended in PBS before being analyzed by flow cytometry. Compensation beads (Invitrogen) were used to establish a matrix of compensation. Gating was determined based on negative control cell staining with corresponding isotype antibodies. Depending on the protocol, two panels of antibodies were used. If combined with Rh123 exclusion and calcium influx assays, allophycocyanin (APC)-conjugated CD34 and phycoerythrin (PE)-conjugated CD38 antibodies were used.
Then, the cells were kept on ice until analysis by flow cytometry. Cell sorting. Subsequently, for the Rh123 exclusion assay, U937 and KG1 cells were washed and resuspended to a concentration of 5 × 10 6 cells per milliliter in culture cell medium without phenol red and 1% SVF (Sigma-Aldrich). Rh123 low and Rh123 high cells were sorted on a FACSAria flow cytometer (BD Biosciences). Gating was determined based on the positive control loaded with the Rh123 probe without the exclusion step. After sorting, the cells were kept at 37 • C before being used in further experiments. Flow cytometry data analysis. Data were analyzed using FlowJo software (TreeStar). Live cells were discriminated from dead cells using a viability probe (DyeTM 750/777). Cell subpopulations were discriminated according to their rhodamine exclusion and/or cell-surface marker expression.
Negative controls were performed by replacing the primary antibody and fluorochrome-conjugated antibody with irrelevant antibodies of the same isotype. To visualize nuclei, 10 µg/mL Hoechst (Molecular Probes, Eugene, OR, USA) was added for 10 min at RT. The coverslips were mounted on microscope slides with Fluoroshield mounting medium (Abcam). For confocal microscopy, cell images were obtained using a confocal laser scanning microscope (LSM 700, Carl Zeiss MicroImaging GmbH, Jena, Germany) with a Plan Apochromat 40×/1. 3 numerical aperture oil immersion objective (Hoechst, Frankfurt, Germany, λex 343 nm, λem 483 nm, NFAT λex 568 nm, λem 605 nm and Ki67 λex 488 nm, λem 532 nm). Images were analyzed with Zeiss LSM Image Browser software. siRNA-mediated gene knockdown. To evaluate the role of the ORAI1 calcium channel in SOCE, five million KG1 or U937 cells were transfected by electroporation in transfection buffer (LONZA Amaxa ® Cell Line Nucleofactor™ Kit V, Basel, Switzerland) with 100 nM control or ORAI1-specific siRNA (Santa Cruz Biotechnology, Dallas, TX, USA) to downregulate ORAI1 expression.
Duplex qPCR was performed, and discrimination of the genes of interest from the reference gene was allowed owing to 2 distinct fluorophore dyes, FAM and VIC probes for the genes of interest and the reference gene, respectively. The relative expression ratio for each gene was calculated by the 2 -∆∆Ct method. The calculated values represent the expression level for each gene relative to the expression of B2M, which was used as the endogenous control. No expression denotes no detection in 50 ng of cDNA. Analysis of public datasets. RNA-seq datasets of human AML samples at diagnosis (n = 439)  [12]  are available in the public domain (https://www. cbioportal. org, accessed on 15 March 2022). The RNA-seq expression unit used was counts per million reads mapped (CPM). Raw data were extracted and analyzed as previously described  [13, 14].
Statistical analyses. All results are expressed as the means ± SEM of at least 3 independent experiments with 3 replicates each. For public dataset analysis, the number of patient samples is indicated. Comparisons between means were assessed using the Student's t test for unpaired data or one-way ANOVA. For the Student's t test, if unequal variance was observed, then Welch's correction was applied. Statistical analyses were performed using GraphPad Prism software. A p value ≤ 0. 05 was considered significant. Asterisks denote * p < 0. 05, ** p < 0. 01 and *** p < 0. 001. The leukemic stem cell (LSC) score was determined as described previously  [15]. University of Miami, University of Colorado, University of Florida, National Institutes of Health (NIH), Fox Chase Cancer Center and University of Kansas (KUMC).
MEG system The MEG system was developed by the National Research Council, Pozzuoli, Naples, at the Institute of Applied Sciences and Intelligent Systems "E. Caianiello"  [31]. The system is equipped with 163-magnetometers and placed in a magnetically shielded room (ATB, Ulm, Germany)  [32]. Acquisition, pre-processing, and source reconstruction were carried out as previously described  [20]. Before the acquisition, four position coils were placed on the subject's head and digitalized using Fastrak (Polhemus®). The coils were activated, and localized, at the beginning of each registration segment. The subject was seated on a comfortable armchair placed in the shielded room. Electrocardiographic and electrooculographic signals were co-recorded to aid artefact removal  [31]. After an anti-aliasing filter, MEG data were acquired with a sampling frequency of 1024 Hz.
Statistical analysis All statistical analyses were performed in Matlab (Mathworks®, version R2013a). To compare the two groups, for each frequency band and each parameter, we used permutation testing  [48] , where the null distribution for between-group differences is derived from the data. In detail, assuming no group differences, the labels of the subjects were permuted 10000 times. Each time, the differences between the averages of the two groups were computed, obtaining the null distribution for between-group differences. Such distribution was used to define the statistical significance of the observed difference between the two groups. We used the False Discovery Rate (FDR)  [49]  to correct for multiple comparisons. A significance level of p ≤ 0. 05 after FDR correction was used. Finally, we tested whether the results were stable when randomizing the choice of epochs one hundred times (see supplementary information (SI) for more details).
To explore the relationship between neural network dynamics and decision-making in Drosophila larvae, we mix Bayesian induction techniques and physical modeling on biological data. By combining video measurement experiments of larval behavior with advances in modern optogenetics that allow the activation/inactivation of individual neurons, a database of millions of larvae responding to the activation of single neurons has been constructed. Larva images, especially taken at low resolution to record large amount of them, remain ambiguous and prone to multiple artifacts which impairs behavior detection and can lead to significant uncertainties during complex consecutive sets of actions. We design a new approach that combine physics informed prior to Bayesian inference to infer the real shape of the larva and detect behavior.
Funding This work was supported by the  French National Research Program for Environmental and Occupational Health of Anses  under Grant  2015/2 RF/19 , by the  European Union 's  Horizon 2020 Research and Innovation Program  under Grant  737164  and by the  Region Nouvelle-Aquitaine  under Grant  AAPR2020A-2019-8152210. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All participants had an APGAR score between 7 and 10 at 5 min. Parental informed consent was obtained in accordance with the Declaration of Helsinki. The study was approved by the ethics committee CPP Nord-Ouest III, France. Promotion and quality control were carried out by the University Hospital of Caen, France. The protocol was pre-registered before patient inclusions with the Agence nationale de sécurité du médicament et des produits de santé (ANSM, France) (ID-RCB: 2014-A01762-45), and at the US National Institute of Health (NIH) registry of clinical trials (NCT02880696). The initial sample was composed of 40 participants randomly assigned to two groups: Fixed ISI or Jittered ISI. Sex, GA and weight at birth, and presence of intrauterine growth retardation (IUGR) were retrieved from medical records.
, 2014). DCS offers a better temporal resolution than traditional fNIRS because blood flow increases immediately with neuronal activity  (Hoge et al. , 1999 (Hoge et al. , , 2005)). This was confirmed in preterm neonates  (Roche-Labarbe et al. , 2014). Blood flow is a more direct and less ambiguous proxy of neuronal activity than hemoglobin concentration. We used a commercial device, αιμα-FloMo (Hemophotonics S. L. , Barcelona, Spain, http://hemophotonics. com) instrument with one light source at 785 nm (intensity < 30 mW) and four single-photon counting avalanche diodes for detection, an internal hardware correlator, and input ports for a posteriori synchronization with markers from the stimulation computer. Stimulation markers were placed in DCS data files by the stimulation device at each stimulus onset.
Data processing The auto-correlation curves that were saved from each detector were first evaluated for data quality based on the initial intercept (~0. 48), tail (~1), and count-rate (>10 kHz) discarding those that did not fit these criteria  (Cortese et al. , 2021; Durduran and Yodh, 2013). The remaining curves were fitted with the appropriate solution of the correlation diffusion equation and averaged over the four detector channels placed at the same spot on the skin. This provided us with a time trace of BFi. Using an in-house Java software (TiSerVA is open-source and available for download at https://gitlab. ecole. ensicaen. fr/rclouard/TiSerVa), we applied a zero-phase-lag Butterworth bandpass filter (order 10, 0. 03-0. 3 Hz) on this time series and segmented the data around markers from -2 s to + 7 s.
We then discarded segments comprising values exceeding three standard deviations (movement artifacts). Out of measurements, we discarded three datasets for insufficient quality (less than 60 Stimuli segments out of 84 or six Omissions segments out of passed artifact rejection). In MATLAB® (The Mathworks, Inc. ), segments were baseline corrected by subtracting the value at t 0 and standardized by dividing them by their standard deviation, before averaging. We thus report relative values.
Physiology DCS measures blood flow changes in the tissues sampled by infrared light, reflecting brain activity underneath the probe. However, as light travels through the skin and skull, it is possible that part of the variance in the signal results from systemic changes occurring in superficial layers, that are not from a neuronal origin but the result of brain activity on the body's activity. Although such changes would still indicate stimulus processing and allow similar conclusions to be drawn regarding SP, we aimed to interpret our data as being of cortical origin. Optical imaging studies in infants show that contamination from superficial layers is negligible compared with adults because skin and skull are thinner  (Emberson et al. , 2016). Since they are even much thinner in premature neonates, we did not expect contamination.
However, because most participants were monitored for clinical purposes, we recorded data from the baby's clinical monitoring device (heart rate, respiratory rate, and systemic blood saturation) during acquisitions. Monitoring was available for 10 out of 11 subjects in group Fixed and 7 out of 9 subjects in group Jittered. We synchronized these time series with BFi (interpolating for sampling rate offset) using our TiSerVA software and performed the same analysis on these time series as described above for BFi data.
We had planned to assess the effect of cGA and NICU stay on SP. However, there was too much attrition at 35 weeks cGA for two reasons. First, participants moved more and had a thicker skull, leading to poorer data quality. The signal-to-noise ratio of DCS is lower than that of NIRS, which makes DCS even more sensitive to these issues. Despite using the channel averaging method described in Section 2. 3. , we could not keep enough data. Second, some participants were transferred to a different hospital closer to their parent's home between the two measurements. We chose not to report data on the remaining small sample as conclusions would be too uncertain, and because we are currently conducting a study that should address this better using a protocol including neonates at all GA at birth and following them up into toddlerhood.
Acknowledgments The authors wish to thank  Dr. Udo Weigel at HemoPhotonics  for support with αιμα-FloMo set-up and upgrades, and  Dr. Marc Aguert  for helpful conversations on Statistics. We are very grateful to all the babies and their parents for their participation, and the nurses, physicians, and staff in the  Neonatology Unit at the Caen University Hospital  for their unwavering help and support.
Funding This research was supported by the  PREMATEMP  grant # FDF-00041933  from the  Fondation de France  (http://www. fondationdefrance. org) to NRL, the  NEOPRENE  grant # ANR-19-CE37-0015  from the  Agence Nationale de la Recherche  (www. anr. fr) to NRL, and a Ph. D. grant from the  Region Normandie  (http://www. normandie. fr) to VD.
Eye Movement Analysis The data recorded with the Pupil Labs eye tracker was analyzed with the AIDEAL software (pending international patent application: PCT/EP2021/062224 7 May 2021). The signal was derived by calculating the difference between the two eyes from the individual calibrated eye position signals (i. e. , left eye-right eye). The onset and the offset of the saccades were defined as the moments where the velocity went above or below 10% of the peak velocity. The onset and the offset of the vergences were defined as the moments where the velocity went above or below 5 • /s. These criteria are standard, and were applied automatically by the AIDEAL software. Trials with blinks were excluded. The results are given in an excel spreadsheet and graphs (Figure  3 ).
In this article, the selected version of the Stroop test is the French Stroop Victoria  [46]. This version of the Stroop test was chosen because of its short administration time, being appropriate for usage in an elderly population, and the provision of a normative database on 244 healthy community-dwelling adults living in Montpellier and Lille (mean age 65. 83 SD = 10. 71). In this version, the participant has to list the color of 24 items as quickly as possible (6 lines of 4 items) in three different conditions. The possible colors of the items are blue, green, yellow or red. The first condition is the "Dot" condition, where the items are dots. The second condition is the "Word" condition, where the items are the French words "mais" (but), "pour" (for), "donc" (thus) and "quand" (when).
Data Analyses Aging's effects on hearing, eye movement latency and Stroop scores are measured with simple linear regressions and correlations: Hearing ~Age, Latency ~Age, and Stroop ~Age. These results are presented in the Section 3. 3-Aging Effects. The relationships between hearing, eye movement latency and Stroop scores are investigated two by two: hearing VS eye movement latency, hearing VS Stroop scores and latency VS Stroop scores. As all of these parameters deteriorate with age, the results may be skewed by their confounding effects. In order to avoid this, the simple linear regression will be abandoned in favor of multiple regression analysis, adding age as an explanatory variable: Hearing ~Latency + Age, Hearing ~Stroop + Age, and Latency ~Stroop + Age. These results are presented in the Section 3.