dataesr/scientific-paragraphs-categorization huggingface dataset:
CRC samples For gene expression profiling, we selected 143 tumor samples from 143 patients included in three cohorts: the prospective single-center study REGP (19 patients) (GSE62322)  [19, 20] , the retrospective multi-center study COSIVAL (68 patients) and the prospective multicenter study BIOCOLON (56 patients) (GSE62080 and GSE72970)  [21]. For these three studies, Inclusion criteria were: histologically proven colon adenocarcinoma, advanced and bidimensionally measurable tumor (stage IV), age between 18 and 75 years, and World Health Organization (WHO) performance status ≤2. Before any treatment, all patients underwent surgery for primary tumor resection or endoscopic biopsy. For western blot analysis, 13 additional tumor samples from the prospective single-center study REGP but not included in the 143 samples were used.
Radiolabeling and SPECT-CT imaging Female athymic nude mice (6-8-week-old) were purchased from Harlan. The 6 F6 mAb was radiolabeled with 125 I (Perkin Elmer) at the specific activity of 370 MBq/mg for single-photon emission computed tomography (SPECT) imaging, using the IODO-GEN (Pierce Chemical Co. ) method. After tail vein injection of 16 MBq/50 μg 125 I-labeled 6 F6, whole-body single photon emission tomography/computed tomography (SPECT/CT) images were acquired with a 4-head multiplexing multi-pinhole NanoSPECT camera (Bioscan Inc. , Washington, USA) at various times (48, 72 and 96 h). Concomitantly, whole-body micro-CT images were acquired for anatomic co-registration with SPECT data. Reconstructed SPECT and CT data were visualized and co-registered using Invivoscope®.
Cell cycle and proliferation analysis in spheroids Spheroids were prepared by plating 1000 DiFi cells per well in ultra-low attachment 96-well plates, and growing them in the presence of 100 μg/ml of the 6 F6 mAb or irrelevant mAb (retuximab) for 5 days. For cell cycle analysis, cells were pelleted, trypsinized, washed with PBS, fixed in 75% ethanol, and stained with 40 μg/ml propidium iodide in the presence of 100 μg ml -1 RNAse (Qiagen). The cell cycle distribution was determined with a FC500 Beckman Coulter Flow Cytometer using the FL-3 channel. Cells were gated on a dot plot that displayed the DNA-pulse-peak vs the DNA-pulse area to exclude doublets. Cell cycle distributions were illustrated using the Flow Jo analysis software (Treestar, FLOWJO, Ashland, OR, USA). At day 4 of culture, cell proliferation was measured by incubating cells with 5-ethynyl-2′-deoxyuridine (EdU) for 24 h.
EdU is incorporated into DNA during active DNA synthesis. Then, after cell trypsinization and fixation/ permeabilization in 75% ethanol/PBS, incorporated EdU was labeled and detected with the Click-iT EdU Alexa Fluor 488 Flow Cytometry Assay Kit (Invitrogen). Cells were then incubated with 1 μg/ml of 4′,6-diamidino-2-phenylindole (DAPI) in PBS/0. 1% Triton X100 at 37 °C for 30 min. The Celigo™ "Expression Analysis" (Target 1 + Mask) application was used to quantify the fluorescent signal and for data analysis. Cells were identified using the DAPI nuclear stain and DNA synthesis was quantified by measuring EdU incorporation. Mouse xenograft models 1. 5 × 10 6 SW620 cells or 3 × 10 6 DiFi cells were suspended in culture medium and injected subcutaneously (s. c. ) into the right flank of 6-8-week-old female athymic nude mice from Harlan.
Acknowledgments The authors thank  Imade Ait Arsa ,  Véronique Garambois ,  Christelle Larbouret  and  Charles Vincent  for their help with animal experiments. The animal facility staff is greatly acknowledged. We thank  Patrick Balaguer  for providing the SW620-LUC cell line and  Hervé Watier  for helpful discussions. Flow cytometry analyses were performed using equipment at the Montpellier RIO Imaging facility.
Funding This work was supported by grants from the  French National Research Agency  under the program " Investissements d'avenir "  Grant Agreement LabEx MAbImprove :  ANR-10-LABX-53 ,  SIRIC Montpellier Cancer Grant « INCa-DGOS-Inserm 6045 », Inca grant   INCA-RECF0356 ,  Ligue Régionale Contre le Cancer (comité du Gard)  and  GEFLUC.
BiOLOGiCAL AnD CLiniCAL FEATURES OF pATiEnTS WiTH AUTOiMMUnE pSYCHOSiS Autoimmune psychosis: Genetic and Environmental Risk Factors Several genome-wide association studies (GWAS) confirmed an association between the MHC region (chromosome 6) and psychosis  (35, 36). Moreover, a recent landmark GWAS analysis produced by the largest consortium on genetics of schizophrenia has shown that, like in autoimmune disorders, the MHC region was the most strongly associated (best p-value: MHC-region: p = 3. 86e-32; 36,989 cases and 113,075 controls)  (37, 38). The consequences of these mutations are still to be fully understood because some of them are found in non-coding region. However, in a matter of interest, the MHC region include the human leukocyte antigen (HLA) cluster, which is the most polymorphic and gene-dense genomic part of the human genome  (39)  encompassing more than 250 genes (4 Mb) and 14,000 alleles as reported to date (IMGT/HLA database; http://www.
Size Exclusion Chromatography -Multi-Angle laser Light Scattering (SEC-MALS) analysis of the refolded protein Protein oligomeric state was studied using a Jasco PU-2080 Plus system consisting of a pump, a vacuum degasser, an autosampler, and a Silica Gel KW804 column (Shodex). Detection was performed using a triple-angle light scattering detector (Mini-DAWN™ TREOS, Wyatt Technology), a Shimadzu refractometer (RID-10A) and a SpectraSeries UV100 detector. Molecular weight determination was performed using the ASTRA VI software (Wyatt Technology) using a dn/dc value of 0. 185 ml/g. The column used for the SEC-MALS analysis was equilibrated with SEC buffer (50 mM Tris-HCl pH 7. 5, 300 mM NaCl, 0. 1 mM DDM, filtered through 0. 22 μm pore size filters (Millipore) at 0. 5 ml/min. One hundred microliters of protein solution (10 μM) was injected.
Ligand binding assay and data analysis The intrinsic fluorescence of cT1R1-NTD was measured as previously described by Nie et al. [37]  using a Cary Eclipse spectrofluorometer (Varian Instruments) equipped with a Peltier temperature control unit. Sample proteins were excited at 295 nm and emission spectra were recorded from 300 to 400 nm, with 5-nm and 10-nm bandwidth for emission and excitation, respectively. For titration experiments, fluorescence spectra were recorded using 0. 5 μM protein solution in 50 mM Tris-HCl pH 8. 0, 150 mM NaCl, 1mM DTT and 0. 1 mM DDM. Ligand solutions were freshly prepared in the same buffer. Successive aliquots of potential ligands were added to 400 μL of cT1R1-NTD solution. The temperature was kept constant at 20˚C. Fluorescence measurements were corrected for dilution, bleaching and nonspecific buffer quenching.
K d values and standard errors were calculated from a plot of the ratio between the fluorescence intensity variation and the maximum of fluorescence intensity variation versus concentration of total ligand, obtained with a standard non-linear regression method and using one site saturation ligand binding equation using SigmaPlot software. To measure the synergism between L-amino acids and IMP, the intensities of fluorescence of cT1R1-NTD were normalized to 0% in presence 0. 3 μM IMP alone and 100% at highest concentrations of L-amino acids in absence of IMP. The K d values of ligand-receptor interactions were determined using Eq (1) DF DFmax ¼ B max Â ½L ðK d þ ½LÞ ð1Þ Where K d value is the apparent dissociation constant, ΔF is the difference between the fluorescence intensity at a given concentration of ligand and the fluorescence intensity in the absence of ligand, and ΔFmax is the difference at infinite ligand concentration [L], B max is the maximum fluorescence signal, [L] corresponds to the concentration of ligands.
Acknowledgements C. M. acknowledges nancial support from the  German Academic Exchange Service (DAAD)  and  Fonds der Chemischen Industrie. M. D acknowledges funding from the  French National Research Agency (ANR) Paris-Science-Lettres Program  ( ANR-10-IDEX-0001-02 PSL ). M. D. acknowledge funding from  Labex CelTisPhyBio  (No. ANR-10-LBX-0038 ). This project has received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No. 686841  (MAGNEURON).
Immunostaining All analyses for Efnb2 cKO were performed on control and mutant littermates collected from at least two different litters. On the other hand, control and Efnb3 mutant embryos were collected from independent litters. The number of embryo analyzed for each immunostaining and each developmental stage is indicated in the figure legends. To avoid bias in rostro-caudal axis, data was collected on thick vibratome sections covering the entire brachial region (600 μm). Antibody staining was performed following standard protocol on 70μm vibratome sections of mouse embryos at brachial level. For BrdU incorporation, pregnant dams were injected with BrdU (10mg/ml; 100mg/kg) with intraperitoneal injection. After 1 h, embryos were dissected in cold PBS and processed for subsequent immunostaining.
1k-m ). Conversely, expression of Efnb3 is highest in the ventral-most region of the spinal cord at all stages analyzed, with a lower expression extending more dorsally (Fig. 1n-p ). Because Efnb2 and Efnb3 were expressed in distinct progenitor domains of the spinal cord, we asked whether these corresponded to progenitors with distinct identities, namely pMN progenitors expressing Olig2 and p3 progenitors expressing Nkx2. 2. Since the expression of Efnb2 in progenitors of the ventral neural tube detected by in situ hybridization was low, we took advantage of a reporter mouse line that expresses H2BGFP under the control of the Efnb2 endogenous promoter  [22]. The benefit of this reporter strategy is that H2BGFP accumulates in the nucleus thus highlighting low domains of expression and facilitating co-expression analyses.
Efnb2 interacts with Shh to control the ratio between pMN and p3 progenitors To characterize the underlying causes of the decreased pMN number in ephrinB2 mutants, we first tested whether this reduction was due to altered rates of proliferation of pMN. We performed BrdU incorporation and immunostaining for Olig2 and quantified BrdU + /Olig2 + progenitors at two different developmental stages to monitor proliferation. At both stages, no difference in pMN proliferation was observed in Efnb2 cKO compared to control embryos (Fig. 4a-f  ). We also assessed apoptosis and observed no change in the number of cleaved caspase positive pMN at E10. 5 and E11. 5 (data not shown). In addition, to confirm that the rate of pMN differentiation was unchanged in Efnb2 cKO, we performed co-immunostaining for Olig2 and for the motor neuron (MN) marker Islet1/2 and quantified the fraction of Islet1/2 + nuclei at the basal side of the ventricular zone (intermediate zone) which represent newborn MN.
Acknowledgements The Islet 1/2 antibody was obtained from the  Developmental Studies Hybridoma Bank developed  under the auspices of the  NICHD  and maintained by the  University of Iowa ,  Iowa City , IA 52242. We thank  Dr Novitch  for sharing the Olig2-Cre mice. Dr Kania  and  Dr Henkemeyer  provided some molecular reagents used in this study. We are grateful to  Brice Ronsin  for his help with confocal microscopy ( TRI Imaging Core Facility ).
Funding Research in the Davy team is financed by the  CNRS , by the  Fondation ARC  and by  ANR  ( ANR-15-CE13-0010-01 ). JL received support from the  French Ministère de l'Enseignement Supérieur et de la Recherche  and from the  Fondation pour la Recherche Médicale  ( FDT20140931010 ). DJL is funded by the  Miami Project  to  Cure Paralysis  and PAN by  NIH/NINDS  ( NS089325 ).
Uncompensated access resistance was monitored throughout the recordings. Recordings were digitized online (10 kHz) with an interface card to a personal computer and acquired using Axoscope 7. 0 software. For most stimulation experiments, the movie acquisition time was separated evenly between (1) a 3 min resting period during which the cell was held close to Vrest (i. e. , zero current injection); (2) a 3 min stimulation period during which phasic stimulation protocols were applied; and (3) a 3 min recovery period where the cell was brought back to resting membrane potential. Stimulation protocol: suprathreshold current pulses (amplitude: 100-200 pA, duration: 200 ms) repeated at 0. 1 and 0. 2 Hz.
2017 ). Although we did not sample from any stellate cell in our glutamatergic cell dataset, we did not find any evidence that single-glutamatergic neurons could influence network dynamics, even when they were identified as functional hubs (Fig. 2A ). This is in agreement with previous work, where early-born CA3 glutamatergic neurons were shown to be active at the onset of network bursts, but nevertheless were not drivers unless stimulated as a population  (Marissal et al. 2012 ). It will therefore be of major interest to study the interaction between stellate cells and the driver hub GABA neurons described here, as the former may well, as a population, feed the activation of the latter. This finding adds to a number of additional differences previously observed during the development and integration of GABAergic microcircuits into functional networks in the neocortex and hippocampus.
, 2003; Jensen and Mazaheri, 2010). Otherwise, unseen correct and incorrect trials were indistinguishable in their power spectra and similar to the target-absent control condition. Second, there was no clear evidence for a shared discriminative decoding axis between the seen and the unseen correct trials: Generalization was entirely unsuccessful when the classifier was trained on the time-frequency data, and highly dissimilar from the original visibility decoder when trained on the ERFs. While it is impossible to draw definitive conclusions just from the current dataset and future research should replicate these results, the majority of our evidence thus points against an interpretation, in which the unseen correct trials constituted either just a subset of seen trials, or arose from the conscious maintenance of an early guess.
Future research should thus aim at replicating the present findings with larger datasets or with more sensitive techniques, such as intra-cranial recordings. In particular, it might be interesting to further probe the relationship between seen, unseen correct, and unseen incorrect targets: A specific prediction of the proposed model is that unseen correct trials should possess enough activity to modify synaptic weights in high-level cortical circuits, yet without crossing the threshold for sustained activity and consciousness ('failed ignition'). Unseen correct trials should thus share some of the processes that are found on seen trials and future research is necessary to directly test this hypothesis.
Behavioral analyses We analyzed our behavioral data in Matlab R2014a (MathWorks Inc. , Natick, MA; code available upon request) and SPSS Statistics Version 20. 0 (IBM, Armonk, NY), using repeated-measures analyses of variance (ANOVAs). Only meaningful trials without missing responses were included in any analysis. Distributions of localization responses were computed for visibility categories with at least five trials per subject. Objective working memory performance was quantified via two complementary measures. The rate of correct responding was defined as the proportion of trials within two positions (i. e. , ±36 ˚) of the actual target location and served as an index of the amount of information that could be retained. Because 5 out of 20 locations were counted as correct, chance on this measure was 25%.
(2015). Using MaxFilter Software (ElektaNeuromag, Helsinki, Finland), raw MEG signals were first cleaned of head movements, bad channels, and magnetic interference originating from outside the MEG helmet  (Taulu et al. , 2004) , and then downsampled to 250 Hz. We conducted all further preprocessing steps with the Fieldtrip toolbox (http:// www. fieldtriptoolbox. org/; Oostenveld et al. , 2011)  run in a Matlab R2014a environment. Initially, MEG data were epoched between À0. 5 and +2. 5 s with respect to target onset for all stimuluslocked, and between À0. 5 and +0. 8 s with respect to the onset of the response screen for all response-locked analyses. Trials contaminated by muscle or other movement artifacts were then identified and rejected in a semi-automated procedure, for which the variance of the MEG signals across sensors served as an index of contamination.
The classifier was then trained on the resulting data and applied to the left-out trials in order to identify the hyperplane (i. e. , topography) best suited to separate the classes. This sequence of events (univariate feature selection, normalization, training and testing) was repeated five (two) times, ensuring that each trial would be included in the test set once. Within the same cross-validation loop, we also evaluated the ability of each classifier to discriminate the experimental conditions of interest at all other time samples (i. e. , generalization across time). This kind of MVPA results in a temporal generalization matrix, in which each entry represents the decoding performance of each classifier trained at time point t and tested at time point t', and in which the diagonal corresponds to classifiers trained and tested on the same time points  (King and Dehaene, 2014).
Acknowledgements We gratefully acknowledge  Henrik Ueberscha ¨r ,  Leila Azizi , and  Virginie Van Wassenhove  for their invaluable daily support and stimulating discussion. Author contributions DT, Conceptualization, Data curation, Formal analysis, Funding acquisition, Visualization, Methodology, Writing-original draft, Project administration, Writing-review and editing; SM, Conceptualization, Formal analysis, Supervision, Visualization, Writing-review and editing; AO, Conceptualization, Formal analysis, Methodology;  J-RK , Conceptualization, Methodology, Writing-review and editing; YM, MT, Methodology, Writing-review and editing;  SD, Conceptualization, Resources, Supervision , Funding acquisition, Writing-review and editing.
The original stimulus set also included RAND20 sequences, sampling the entire frequency pool (alphabet size of 20), which were left out of the present analysis to ensure a balanced design. Specifically, in subsequent modeling (see Dynamic causal modeling), we wanted to preclude unmatched experimental conditions from confounding our modeling of the differences between the conditions. Per participant and condition, 104 different sequences were generated. The order of sequence presentation was randomized, such that the sequence type was unpredictable on a given trial. Sequences were separated by a random interval between 700 and 2000 ms. Stimulus presentation was controlled via the Cogent toolbox (http://www. vislab. ucl. ac. uk/ cogent. php) for MATLAB (MathWorks). Sounds were rendered offline, stored as 16 bit WAV files at 44.
1 kHz, and delivered using (EARTONE 3A 10 ⍀, Etymotic Research) headphones at a comfortable listening level (self-adjusted by each listener). Details of statistical analyses are reported separately for sensor-level data (below), time-frequency data (see Time-frequency analysis), and dynamic causal modeling (see Dynamic causal modeling). MEG data acquisition and sensor-level analysis. MEG signals were acquired in a 275-channel whole-head setup with third-order gradiometers (CTF Systems) at a sampling rate of 600 Hz. All analyses were performed using a MATLAB (MathWorks) toolbox SPM12 (Wellcome Trust Centre for Neuroimaging, University College London). Continuous data were high-pass filtered at 0. 1 Hz, notch-filtered at 50 Hz and harmonics, and epoched into segments time-locked to stimulation window onset (from Ϫ700 to 3700 ms peristimulus time).
The early time window, in which responses to all signals are overlapping, was chosen for source localization to ensure that condition-specific differences did not bias the selection of sources, but instead reflect generic processing of sequences. Evoked power was calculated by subtracting the baseline from the poststimulus activity map. Sources of the largest evoked activity were identified as clusters of voxels whose evoked power was larger than the mean ϩ 2 SD of all positive values in the evoked power map. These clusters were then used to extract individual subjects' source-level time-series using a linearly constrained minimum variance beamformer  (Van Veen et al. , 1997) , as implemented in the Data Analysis in Source Space (DAiSS) toolbox for SPM12 (https://github. com/SPM/DAiSS), separately for each condition.
The voltage and current of each population m are denoted by V m and I m respectively, with a synaptic rate constant m. The postsynaptic potential is transformed into the firing rate by a sigmoid operator. A F and A B denote the forward and backward (extrinsic) connections between regions and ␥ m¡n denote the (intrinsic) connec- tion from population m to n within a region. Finally, spiny stellate cells receive endogenous input E(x). In DCM for cross-spectral density, this endogenous input is modeled as a linear mixture of firing rates (V m ) in all populations. In the first step, to provide a good fit of the models to the data, prior rate constants m and intrinsic connectivity parameters ␥ m¡n of the canonical microcircuit model were optimized to reproduce the spectral peaks observed in our dataset, averaged across both regions and experimental conditions.
To verify that the lower-(Ͻ8 Hz) and higher-frequency (8 -128 Hz) data components are indeed independent in our dataset, we correlated the time course of the grand-mean RMS during acoustic stimulation (averaged across conditions) with the time course of the first principal component (PC) of induced 8 -128 Hz power (averaged across conditions; first PC explaining 99. 11% of the spectral variance over stimulation time). The ensuing correlation was not significant (r ϭ Ϫ0. 136, p ϭ 0. 341). Thus, mean RMS (Ͻ8 Hz) did not correlate with mean induced power (8 -128 Hz). Similarly, the SD of the RMS across conditions did not correlate with the first PC of the SD of induced power across conditions (PC: 92. 78% of spectral variance; r ϭ 0. 0133, p ϭ 0. 352), suggesting that lower-and higher-frequency differences between experimental conditions explained largely non-overlapping variance components.
The eight predictors (2 experimental factors ϫ 4 modulatory parameters) were up-sampled to match the sampling rate of the sensor-level data (600 Hz) using MATLAB's resample function with nearest-neighbor interpolation, and smoothed with a Gaussian kernel (SD 150 ms). In the regression, we set the elastic-net mixing parameter ␣ to 0. 95 (yielding a relatively sparse predictor structure). This analysis returned an ordering of the eight DCM parameters according to how well they explain sensor-level effects.
The total amount of mTOR did not change during the experimental time course in either the CON or FED groups (data not shown). mTOR and LAMP2 were found to be highly colocalised in human skeletal muscle in both basal and exercise stimulated conditions suggesting that mTOR is located in close proximity to the late-lysosome in basal skeletal muscle (Fig. 4A ). In both CON and FED we observed relocation of mTOR from cytosolic regions to the sarcolemmal membrane following resistance exercise as quantified by mTOR Regionality of staining is provided through co-staining with WGA or Dystrophin as a marker of the cell membrane. Competition (COMP) assays were performed to demonstrate antibody specificity of TSC2, eIF3F and Rheb. Non-specific binding/antibody efficiency was examined through imaging samples in which secondary antibody was used without prior primary antibody incubation (No Ab).