dataesr/scientific-paragraphs-categorization huggingface dataset:
) cDNA was obtained from 250 ng of mRNA using IScript ™ Reverse Transcription Supermix (BioRad, USA), following the manufacturer's instructions. qPCRs (10 µL) were prepared using SsoFast ™ EvaGreen ® Supermix (BioRad), primers (100 nM), deionized water, and cDNA. qPCR amplification was carried out for 40 cycles with an annealing temperature of 60 °C in a CFX96 thermocycler (BioRad). Ct data were obtained using the Bio-Rad CFX Manager software. Gene expression levels of the targets (Table  1 ) were calculated using the 2 -ΔΔCt method, relative to the housekeeping gene PPIA (Cyclophilin A).
scCM protein profile The protein content was analysed in two independent batches of scCM using the Proteome Profiler Human Angiogenesis Array kit (R&D Systems, USA), which can detect the expression of 55 angiogenesis-related proteins. Briefly, scCM (150 μg of total protein) was mixed with the biotinylated detection antibodies provided in the kit and incubated (4 °C, overnight) in a nitrocellulose membrane containing the capture antibodies. After incubation, the membranes were washed and the Streptavidin-HRP and chemiluminescent detection reagents were applied. The spot signal was detected with the Luminescent Imaging System Azure c600 (Azure Biosystem), and quantification of the relative densities of the bands was performed using the TotalLab TL 100 1D gel Analysis software. scEBM was used as a negative control.
Western blot detection of ERK1/2 and VEGFR2 phosphorylation on cells treated with either vehicle (DMSO), MEK/ ERK1/2, or VEGFR2, or FGFR inhibitors (e). Proteins classified as involved in angiogenesis  [8] , VEGF-A  [4] , and FGF  [15]  were grouped, then converted in gene identifiers and an interaction network was prepared using Cytoscape software (version 3. 8. 2, released 2020. 10. 24). Such pathways share in common the protein MAPK1 (ERK2) (f). Data represent median (interquartile range) (n = 3-7), *P < 0. 05 vs scEBM; $P < 0. 05 vs scCM vehicle 20 mM ethylenediaminetetraacetate (EDTA), and 0. 5 M dithiothreitol (DTT), and incubated for 1 h at 60 °C. The sulfhydryl groups of the proteins were carbamidomethylated with iodoacetamide used in a 2. 5-fold excess (w/w) to DTT in the dark (20 min, RT).
Data-dependent acquisition by mass spectrometry (DDA-MS) DDA consists in a proteomic approach in which digested peptides are ionized and analysed by MS. Peptide signals that raised in a full-scan mass spectrum with predefined MS parameters were selected for fragmentation, and then, their tandem product (MS/MS) mass spectra were matched to spectra in a protein database. Briefly, two micrograms of peptides from each sample was analysed using an Eksigent nano-LC 2D HPLC system connected to a quadrupole time-of-flight Triple TOF 5600 + mass spectrometer (Sciex, Redwood City, USA). After injection, peptide mixtures were transferred into a nano-HPLC column (ChromXP C18, 3 μm 120 A, nanoLC column, 3C18-CL, 75 μmx15cm, Sciex, USA) at a flow rate of 300 nL/min using the following gradients for solvent B: 2 to 8% solvent B in A (from 0 to 5 min), 8 to 35% B (5 to 90 min), 35 to 40% B (90 to 100 min), 40 to 90% B (100 to 102 min), 90% B (102 to 107 min), 90 to 2% B (107 to 109 min) and finally 2% solvent B in A (109 to 140 min), with a total runtime of 140 min including a mobile phase equilibration.
The following solvents were added in the mobile phase A: 2% acetonitrile/98% of 0. 1% formic acid (v/v) in water; and in the mobile phase B: 98% acetonitrile/2% of 0. 1% formic acid (v/v) in water. The eluted peptides were directly injected for MS data acquisition in a hybrid quadrupole-TOF 5600 + System fitted with a Nanospray III source. Ionization was obtained with an ion spray voltage of 2. 2 kV, curtain gas set at 25 psi, and ion source gas at 3 psi, using positive-ion mode. DDA-MS survey scans were acquired at 250 ms from 400 to 1500 m/z and MS/MS scan from 100 to 2000 m/z (100 ms accumulation time, 50 mDa mass tolerance, rolling collision energy). Peptide and protein identifications were performed using the Protein Pilot software (version 5. 0. 2, Sciex) with a Swiss-Prot/TrEMBL concatenated target-reverse decoy database (downloaded in November 2018), specifying iodoacetamide and methionine oxidation as variable modifications.
TempO-Seq analysis Targeted transcriptome quantification assay (TempO-Seq, BioSpyder) was performed in CMECs (n = 3 per group) treated with scCM (5 μg/mL) or scEBM (control). Cells were rinsed with sterile PBS-CMF, lysed using a TempO-Seq lysis buffer, and stored at -80° C before shipment to BioClavis (Glasgow, UK), where the TempO-Seq assay was performed. The TempO-Seq analysis was performed as previously described  [27]. Briefly, the toxicity pathway analysis was performed using a list of genes annotated to different stress response pathways (3565 probe-set representing 3257 genes). The FASTQ file from each sample was aligned against the TempO-Seq transcriptome using the Bowtie aligner, generating a table of counts per gene and sample, which was further analysed using the R software. The differential expression analysis was performed by comparing the scCM-treated samples with their suitable control (scEBM).
Statistical analysis All statistics were analysed using GraphPad Prism ® software version 6. 0. The normality of continuous variables was assessed using the Shapiro-Wilk test (n < 30) or Kolmogorov-Smirnov test (n ≥ 30). For variables that were not normally distributed, Mann-Whitney or Kruskal-Wallis tests were performed and the values were expressed as median (interquartile range 25-75). For variables that were normally distributed, Student's t test or one-way ANOVA followed by Tukey's multiple comparisons test was performed, and the values were expressed as mean ± standard deviation (SD). The threshold for statistical significance was set as P < 0. 05 (*).
Next, a DDA-MS analysis of two scCM batches revealed 1041 common proteins (Fig. 2b ). By using the Panther software (Protein Analysis THrough Evolutionary Relationships, version 16. 0), such proteins were converted in gene identifiers and were then classified according to their protein class  (Fig. 2c ), molecular function (Fig. 2d ), and biological process (Fig. 2e ). Besides, a Gene Ontology (GO) enrichment analysis revealed an overrepresentation (fold enrichment) of pathways involved in cell metabolism, such as serine glycine biosynthesis, pentose phosphate pathway, glycolysis, pyruvate metabolism, and TCA cycle (Fig. 2f  and Additional file 14: Table  S1 ).
3c ). Additionally, VEGFR2 and FGFR inhibition reduced the scCMinduced migration (Fig. 3d ). The efficiency of ERK1/2 and VEGFR2 inhibition was confirmed by WB (Fig. 3e ). STRING analysis database (version 11. 0, released 2019. 01. 19 ) was used to classify the scCM proteins as involved with "angiogenesis" (8 proteins), "VEGF-A pathway" (4 proteins), and "FGF pathway" (15 proteins). Among them, most were adapter proteins (14-3-3 superfamily and CRK), GTPases (RHOC, RAC1, and RAC3), or serine/threonine-protein phosphatases (PPP2R1A, PP2CA, and PPP2CB). By creating an interaction map among these proteins, we observed that MAPK1 (ERK2) was central in the network hub (Fig. 3f  ).
scCM upregulates the expression of genes involved in the interferon pathway We further investigated the underlying mechanisms of scCM effects in CMECs by performing an mRNA sequencing by TempO-seq. scCM upregulated the expression of 23 genes (Fig. 4c ). By analysing the upregulated genes using the Panther software, we observed an overrepresentation of genes involved in interferon (IFN) pathways (Table  3  and Additional file 16: Table  S2 ) according to the Reactome database. Given that previous studies have suggested that IFN response can inhibit the Wnt pathway activation  [31] , we investigated the effect of scCM on Wnt signalling. The levels of non-phosphorylated β-catenin, which translocates into the nucleus and transduces the Wnt pathway signalling, were reduced in scCM-treated cells (91.
(b) Proteins present in scCM were converted in gene identifiers and were then grouped according to their biological function/pathway (metabolic pathways, growth factors, inflammatory response, basal membrane (BM) regulation, cytoskeletal regulation, and integrin pathway). An interaction network was prepared using the Cytoscape software (version 3. 8. 2, released 2020. 10. 24). The regulation of MAPK activity might play an essential role in the scCM-induced angiogenesis in human primary ECs. In parallel, scCM promotes vascular maturation of newly formed vessels and protects the BBB integrity under ischemic and inflammatory conditions 64. 8 ± 25. 7%) this effect in BLECs (Additional file 18: Figure  S16c ).
Pathways overrepresented in CMECs treated with scCM Upregulated genes (23 genes) in scCM-treated CMECs were analysed by the Panther software based on the human database. Pathways (Reactome database) which were overrepresented (fold enrichment) were mainly related to IFN and inflammatory pathways. N: number of genes found in the human genome (Homo sapiens) or scCM; Expected: number of genes expected for a certain pathway based on the Reactome database; Fold enrichment: scCM (n)/scCM (expected).
Acknowledgements Authors would like to acknowledge  Dr. Julien Saint-Pol  for helpful discussions and advices on experimental set-ups,  Lucie Dehouck  and  Emanuel Sevin  for supporting cell culture routine,  Maxime Culote  and  Sara Wellens  for collaborating with mRNA-sequencing experiments, and  Gustavo Henrique de Oliveira Rocha  for the help with the western blots.
Funding This work has been supported under the Euronanomed 8th Joint Call-MAG-GBRIS collaborative project by grants from the  Spanish Ministry of Science and Innovation  ( PCIN-2017-090 ) the  French national agency  ( ANR-ANR-17-ENM3-0005-01 ), the  AC17/00004  grant from  Instituto Carlos III (ISCIII)  with  FEDR  funds, and the  National Centre for Research and Development  ( NCBR 15/EuronanoMed/2018 ). A part of this study has been also funded in the frame of the  NANOSTEM  project, a  Marie Skłodowska-Curie Innovative Training Network (ITN) by receiving grant  from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 764958  and the  Expression of Interest (EoI)  for Collaborative Projects on Regenerative Medicine 2019 P-CMR[C]), and the programs  2017-SGR-1427  and  2017-SGR-765  from the  Generalitat de Cataluny.
Alba Grayston  is supported by the fellowship  FI17/00073  from  ISCIII  with  FEDR  funds. Miguel Garcia-Gabilondo  is supported by the  PERIS  grant  SLT017/20/000197  from  Generalitat de Cataluny. The mass spectrometer of the  Spectrométrie de Masse de l' Artois (SMART)  facilities used in this study was funded by the  European Regional Development Fund (ERDF) , the  Hauts-de-France regional council , and the  Université d' Artois (France).
Methods We performed a systematic search in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We identified all published articles from database inception to April 2020 on the Medline and Cochrane databases using the following Medical Subject Heading (MeSH) terms: "frontotemporal dementia" OR "frontotemporal lobar degeneration" OR "primary psychiatric disorder" OR "bipolar disorder" OR "depression" OR "psychosis" AND "neurofilament" AND "blood" OR "serum" OR "plasma" OR "cerebrospinal fluid" OR "biomarker". Papers were first screened based on titles and abstracts (n = 145) and then preselected articles (n = 40) were read in full. Articles were included in the systematic review using the following criteria: The study (1) was available in English; (2) reported primary data; (3) included individuals diagnosed with FTD or PPD; (4) had included at least 70% of bvFTD patients in the FTD group or primary data were available for bvFTD;  (5)  quantified NfL in human CSF, plasma, or serum using the prominent NfL antibody (mAb47:3, UmanDiagnostics, Umeå, Sweden) either in commercial or homemade kits; and (6) reported NfL concentration as mean and standard deviation, or the median and interquartile range (IQR) or confidence interval.
Methods We performed a systematic search in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We identified all published articles from database inception to April 2020 on the Medline and Cochrane databases using the following Medical Subject Heading (MeSH) terms: "frontotemporal dementia" OR "frontotemporal lobar degeneration" OR "primary psychiatric disorder" OR "bipolar disorder" OR "depression" OR "psychosis" AND "neurofilament" AND "blood" OR "serum" OR "plasma" OR "cerebrospinal fluid" OR "biomarker". Papers were first screened based on titles and abstracts (n = 145) and then preselected articles (n = 40) were read in full. Articles were included in the systematic review using the following criteria: The study (1) was available in English; (2) reported primary data; (3) included individuals diagnosed with FTD or PPD; (4) had included at least 70% of bvFTD patients in the FTD group or primary data were available for bvFTD; (5) quantified NfL in human CSF, plasma, or serum using the prominent NfL antibody (mAb47:3, UmanDiagnostics, Umeå, Sweden) either in commercial or homemade kits; and (6) reported NfL concentration as mean and standard deviation, or the median and interquartile range (IQR) or confidence interval.
This search and inclusion criteria are described in the flowchart (Figure  1 ) and resulted in 29 articles selected for the systematic review. Medians and IQR were converted to means and standard deviation following the methodology presented by Hozo et al. [8]. Data extracted from the included studies are summarized in Supplementary Table  S1. Because the retrieved data were not abundant enough to perform a comparative analysis, we made a pooled forest plot analysis. We also examined papers cited in the selected articles and included additional references based on their relevance regarding the scope of this paper. Medians and IQR were converted to means and standard deviation following the methodology presented by Hozo et al. [8]. Data extracted from the included studies are summarized in Supplementary Table  S1.
The membrane was then treated with a blocking buffer (5% skim milk inTBS-T) for 1 h at RT, thereafter, with the anti-mouse AIM polyclonal antibody (rab2) at 4 C O/N. After 3-times washes with TBS/2 mM CaCl 2 , the goat anti-rabbit IgG conjugated with HRP that was diluted in the blocking buffer was loaded on the membrane for 1 h at RT. After 3-times washes, the membrane was treated with the Immobilon Forte Western HRP Substrate (EMD Millipore Corporation), and thereafter, the enhanced luminescence signals were examined using FUSION FX (Vilber-Lourmat). The molecular weight of each signal was calculated by the Evolution Capt software (Vilber Lourmat). Proximity ligation assay (PLA) PLA was performed using Duolink PLA Fluorescence kits according to the protocol of the manufacturer (Sigma Aldrich).
S100A9, HMGB1, and MAP2: Boiled Tris/ EDTA buffer (pH 9. 0) containing 0. 05% Tween-20 for 20 min. PRDX1: 20 mg Protenase K in PBS at 37 C for 20 min. Iba1: Boiled citrate buffer (10 mM, pH 6. 0) containing 0. 05% Tween-20 for 20 min. After stained with DAB, sections were counter-stained with hematoxylin. Oil red-O staining: Brain tissues were fixed in 4% paraformaldehyde (PFA) in PBS for 24 h and 30% sucrose solution for 24$48 h and were frozen with OCT compound. 10 mm sections were stained with Oil red-O solution (Muto Pure Chemicals). The histologic data for human brain AIM were purchased from Genostaff Co. Ltd, who had obtained a commercially available paraffin block of a brain tissue from human stroke patient from BioIVT and stained the serial sections for human AIM and MAP2.
ACKNOWLEDGMENTS We thank  Drs. K. Kurokawa ,  K. Tanabe ,  K. Toyota , and  T. Ago  for helpful advice and discussion;  Drs. S. Takahashi  and  M. Hamada  for providing Lysm-Cre;MafB À/À mice;  Dr. S. Shichita  and  T. Nonomura  for advice about MCAO;  Dr. R. Suzuki  for help with rAIM production;  K. Yasuda  for help with statistical analysis of the data;  E. Hiramoto ,  A. Nishijima ,  T. Yoshida , and  Y. Yoshikawa  for technical assistance; and  K. Ohkubo  and  Y. Noguchi  for helpful technical advice on histology. This work was supported by  AMED-CREST  ( JP18gm0610009 ) and  AMED-LEAP  ( JP20gm0010006 ),  Japan Agency for Medical Research Development  (to T. M. );  Japan Society for the Promotion of Science (JSPS) KAKENHI   Grant-in-Aid for Scientific Research (B)  (grant  20H03446  to S.
Materials Written instructions provided detailed information about the sequence of tasks within the experiment, the mechanics of how participants would perform the tasks, and images illustrating what a typical screen within each task section would look like. The experiment was developed using Matlab and PsychToolbox, and was conducted entirely in French. The stimuli for this experiment were 148 digital images, each representing a distinct food item (50 fruits, 50 vegetables, 48 various snack items including nuts, meats, and cheeses). Food items were selected such that most items would be well known to most participants. Eye gaze position and pupil size were continuously recorded throughout the duration of the experiment using The Eye Tribe eye-tracking devices. Participants' head positions were fixed using stationary chinrests.
e. , choice confidence, change of mind, spreading of alternatives, and certainty gain), and vice versa? To address this question, we performed two partial model fits: (i) with decision-related variables only, and (ii) with effort-related variables only. In both cases, out-of-sample predictions for the remaining dependent variables were obtained directly from within-subject parameter estimates. For each subject, we then estimated the cross-trial correlation between each pair of observed and predicted variables. Figure  4  below reports the ensuing groupaverage correlations, for each dependent variable and each model fit. In this context, the predictions derived when fitting the full dataset only serve as a reference point for evaluating the accuracy of out-of-sample predictions. For completeness, we also show chance-level prediction accuracy (i.
, 2007). We note that the above variational Laplace approach is implemented in the opensource VBA toolbox  (Daunizeau et al. , 2014). In what follows, we use Monte-Carlo numerical simulations to evaluate the ability of this approach to recover MCD parameters. Our parameter recovery analyses proceed as follows. First, we sample a set of model parameters  under a standard i. i. d. normal distribution. Here, we refer to ij  as i th element of  at the j th Monte-Carlo simulation. Second, for each of these parameter set j  g , we simulate a series of N=100 decision trials according to Equations A14-A15 above (under random prior moments of value representations). Note that we set the variance of model residuals ( in Equation  A14 ) to match the average correlation between MCD predictions and empirical data (about 20%, see Figure  4  in the main text).
Data descriptive statistics and sanity checks Recall that we collect value ratings and value certainty ratings both before and after the choice session. We did this for the purpose of validating specific predictions of the MCD model (in particular: choice-induced preference changes: see Figure  10  in the main text). It turns out this also enables us to assess the test-retest reliability of both value and value certainty ratings. We found that both ratings were significantly reproducible (value: mean correlation=0. 88, s. e. m. =0. 01, p <0. 001, value certainty: mean correlation=0. 37, s. e. m. =0. 04, p <0. 001). We also checked whether choices were consistent with pre-choice ratings. For each participant, we thus preformed a logistic regression of choices against the difference in value ratings.
Appendix-Figure  8  below summarizes this analysis, in terms of the baselinecorrected time series of regression coefficients. ===== Insert Appendix-Figure  8  here. ===== We found that the correlation between subjective effort ratings and pupil dilation became significant from 500ms after stimulus onset onwards. Note that, using the same approach, we found a negative correlation between pupil dilation and pre-choice absolute value difference |ΔVR 0 |. However, this relationship disappeared when we entered both |ΔVR 0 | and effort into the same regression model. Our eye-tracking data also allowed us to ascertain which item was being gazed at for each point in peristimulus time (during decisions). Using the choice responses, we classified each time point as a gaze at the (to be) chosen item or at the (to be) rejected item.
The density of the D2 receptor in the cortex is so low that it is not detectable with the method used here. To compare the gradient of D1 receptors with other known gradients of anatomical organization in the monkey cortex, we carefully mapped the receptor data (Figure  1A ) as well as data on neuronal density (Figure  1B ;  Collins et al. , 2010)  and spine count (Figure  1C ;  Elston, 2007)  onto the Yerkes19 common cortical template, to which anatomical tract tracing data (Figure  1D , i) has been mapped previously  (Donahue et al. , 2016). Here we include retrograde tracing data from 40 regions, quantified using the same protocol as in previous publications  (Markov et al. , 2014b). This expands the number of injected cortical areas by 33%, with connections to areas 1, 3, V6, F4, F3, 25, 32, 9, 45A, and OPRO (orbital proisocortex) now included in the database (downloadable from core-nets.
STAR★METHODS RESOURCE AVAILABILITY Lead contact-Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Xiao-Jing Wang (xjwang@nyu. edu). Materials availability-This study did not generate new unique reagents. Data and code availability-Dopamine D1 receptor per neuron and tract-tracing connectivity data have been deposited at at BALSA: 7qKNZ and core-nets and are publicly available as of the date of publication. Accession numbers are listed in the Key resources table. All original code has been deposited at GitHub: seanfw/dopamine-dist-wm and Zenodo: https://doi. org/10. 5281/zenodo. 5507279 and is publicly available as of the date of publication. DOIs are listed in the Key resources table. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
For visualization purposes solely, autoradiographs were subsequently pseudo-color coded by linear contrast enhancement and assignment of equally spaced density ranges to a spectral arrangement of eleven colors. Cortical areas were identified by cytoarchitectonic analysis and receptor densities measured at comparable sites in the adjacent sections processed for receptor visualization. The mean receptor density for each area over a series of 3-5 sections per animal and receptor was determined by density profiles extracted vertical to the cortical surface using MATLAB based in house software (Palomero-Gallagher and  Zilles, 2018). Retrograde tract-tracing-The inter-areal connectivity data in this paper is part of an ongoing effort to map the cortical connectome of the macaque using retrograde tract-tracing  (Markov et al.