dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: R. L. was funded by the  Medical Research Council  (Ref:  MR/R005370/1 ),  Wellcome/EPSRC Centre for Medical Engineering  (Ref:  WT 203148/Z/16/Z ),  Simons Foundation  ( SFG640710 ) and support from the  Data to Early Diagnosis and Precision Medicine Industrial Strategy Challenge Fund, UK Research and Innovation (UKRI). The authors would also like to acknowledge support from the Data to Early  Diagnosis and Precision Medicine Industrial Strategy Challenge Fund, UK Research and Innovation (UKRI).
Subjects and nicotine administration The study involves a re-analysis of a previously published dataset  Gießing et al. (2013). Eighteen healthy right-handed cigarette smokers were studied in a double-blind, randomized, placebo-controlled, crossover design in which once nicotine was delivered in the form of a 4 mg nicotine polacrilex gum (Nicorette, McNeil AB) and once a placebo in the form of a taste-and size-matched gum. The gum was chewed for 30 min; then the scanning started immediately. Subjects were asked to refrain from smoking 2 h before the experiment.
fMRI data acquisition Scanning was performed with a Siemens MAGNETOM Sonata MRI system (1. 5 T). 1729 T2 * -weighted echo-planar images with BOLD contrast were measured with a repetition time of 1. 5 s and echo time of 50 ms. Detailed information on the investigated sample, nicotine administration, the paradigm, and the fMRI data acquisition can be found in  Gießing et al. (2013).
Preprocessing and time series analysis Preprocessing steps consisted in slice timing offsets correction, removal of head movements artifacts by alignment to the first volume, co-registration with the subjects' anatomical images, spatial normalization to standard stereotaxic MNI space (Montreal Neurological Institute; http://www. mni. mcgill. ca/ ), and detrending. Next, nuisance covariates associated with the six linear head movements parameters, and anatomical masks for the cerebrospinal fluid (CSF) and white matter (WM) were regressed out from the data  ( Hallquist et al. , 2013 ). To remove the possible effects of small and transient head movements on FC, we quantified the framewise displacements (FD) and the root mean square of voxelwise percentage signal change (DVARS) of the preprocessed time series.
In accordance with previously reported methods for "scrubbing "  ( Power et al. , 2012 ) , we removed all frames of data with FD > 0. 5 mm and/or DVARS > 0. 5%. Then, fMRI-BOLD time series were extracted using the Schaefer 100 cortical atlas parcellation  ( Schaefer, 2018 )  and band-pass filtered between 0. 01 and 0. 08 Hz  ( Cordes, 2001 )  with a 3rd order Bessel filter. FC matrices were calculated as pairwise Pearson's correlations, from which the subsequent analysis of functional network properties was performed. Average volumetric PET images were collected for three different cholinergic receptors and transporters across 5 studies with open-dataset  ( Hansen, 2021 ) , which include nicotinic  4  2  ( Hillmer, 2016 ) , muscarinic  1  ( Naganawa, 2021 ) , and the vesicular acetylcholine transporter (VAChT)  ( Aghourian, 2017; Bedard, 2019; Hansen, 2021 ).
Simulations We ran simulations to generate the equivalent of 21 min real-time recordings, discarding the first 60 s. The system of stochastic differential Eq. (  4 ) was solved with the Euler Maruyama method, using an integration step of 1 ms. For model fitting and data augmentation (realizations of the fitted model), we used 100 and 300 random seeds, respectively, which controlled the stochasticity of the simulations. All the simulations were implemented in Python and the codes are freely available at https://github. com/vandal-uv/Nicotine-Whole-Brain. git. The optimized connectivity matrix is the map of the connections (and their weights in the color bar) between brain regions (rows and columns of the matrix). This matrix was generated starting from the original human SC matrix. For each region, the model produces both EEG-like and BOLD-like signals.
The codes for SC optimization can be found in https://github. com/vandal-uv/Nicotine-Whole-Brain. git. To fit the model to the placebo-RS and nicotine-RS blocks, we swept  ∈ [0. 8 , 1. 5] in steps of 0. 005 and searched for the minimum distance between FCs. Using the value of the placebo-RS block (  = 1. 315) , we swept the input slope  ∈ [0 , 0. 06] in steps of 0. 001 for fitting the model to the placebo-task block. Then, employing  = 0. 023 we swept  ∈ [0. 8 , 1. 5] to fit the model to the nicotine-task block.
For subjects/seeds, the averaged within module connectivity (average across modules) is defined as 𝐹 𝐶 𝑊 = 𝑀 ∑ 𝑘 =1 1 | 𝑚 𝑘 | 𝑛 ∑ 𝑖 ∈𝑚 𝑘 𝑛 ∑ 𝑗∈𝑚 𝑘 𝑎 𝑤 𝑖𝑗 (14) with   being a module of the  = 8 total number of modules, and |   | the number of links that belong to the module. Similarly, averaged between module connectivity is computed as 𝐹 𝐶 𝐵 = 𝑀 ∑ 𝑘 =1 1 | 𝑚 𝑐 𝑘 | 𝑛 ∑ 𝑖 ∈𝑚 𝑘 𝑛 ∑ 𝑗∉𝑚 𝑘 𝑎 𝑤 𝑖𝑗 ( 15 ) where |    | is the number of links that do not belong to the module. Details of the method and calculations can be found in  Wang (2019)  ,  Wang et al. (2021). We used our own implementation in Python ( https: //github. com/vandal-uv/Integration _ Segregation _ Methods ) of the original codes of  Wang et al. (2021)  ( https://github. com/TobousRong/ Hierarchical-module-analysis ).
Network-based statistics Beyond the global network analysis described in the last subsections, key connections that change under the effects of nicotine can be characterized using network-based statistics (NBS)  ( Zalesky et al. , 2010 ). The method identifies subsets of links that differ between two conditions and avoids the multiple comparisons problem when performing paired tests at the level of single links. We used the NBS implementation of the Brain Connectivity Toolbox for Python ( https://github. com/fiuneuro/ brainconn )  ( Rubinov and Sporns, 2010; Zalesky et al. , 2010 ). A one-tail paired t -test was used for ranking the connections, under the hypothesis that nicotine decreases functional connectivity (and then the strength of the links). A threshold of  = 3 was selected to obtain the connections that change the most under the effects of nicotine.
Effects of nicotine on functional network topology are context-dependent We first analyzed the empirical fMRI data  ( Gießing et al. , 2013 ) , under the hypotheses that 1) nicotine promotes functional segregation, and 2) the effects are context-dependent. Recordings were taken from 18 healthy smokers in two different conditions: placebo, or under the effects of nicotine. BOLD-fMRI was measured in RS and during a Go/No-Go attentional task. The original data published in  Gießing et al. (2013)  comprises multiple RS and task periods (consecutive recordings). In this work, we principally focused our analysis and computational modeling on the fMRI and behavioral data of the first RS and task periods to avoid fatigue effects (see below). There is a trend of nicotine to decrease the mean values within the FC matrices (referred to as overall FC, Fig.
Acknowledgments We thank  Gustavo Deco  and  Andrea Luppi  for kindly providing the structural connectivity matrices used in the model. This work was supported by  Fondecyt  Grants  1211750  and the  Advanced Center for Electrical and Electronic Engineering (FB0008 ANID, Chile) (PO) , and the  Human Brain Project ,  H2020-945539  (RC). The  Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)  is supported by  ICM-ANID , Grant  ACE210014. CC-O is funded by  Beca Doctorado Nacional   ANID 2018-21180995. CC-O and VM are funded by the  Latin American Brain Health Institute (BrainLat). The analyzed data derive from a study of [32] funded by  Deutsche Forschungsgemeinschaft  Grant  GI 682/2-1  (CG).
Data availability statement All the codes used to perform the simulations are freely available in the next github repository https://github. com/vandal-uv/ Nicotine-Whole-Brain. The graph analysis was performed using the Brain Connectivity Toolbox for Python ( https://github. com/fiuneuro/ brainconn )  ( Rubinov and Sporns, 2010 ). Codes for HMA can be found in https://github. com/vandal-uv/Integration _ Segregation _ Methods. Data are available on request from Dr. Carsten Gießing, University of Oldenburg (email: carsten. giessinguni-oldenburg. de). A formal data sharing agreement is required.
30 ± 0. 92 MBq). Animals were placed in the U-SPECT-II scan (n = 4; MiLabs). A dynamic SPECT scan (60 × 1-min frames) was acquired starting with radiotracer injection. SPECT images were reconstructed using a P-OSEM (0. 4 mm voxels, 4 iterations, 6 subsets) method with a radioactive decay correction. Dynamic images were manually co-registered to the T2 Schwartz template of the small animal molecular imaging toolbox (SAMIT  [21] ) using PMOD software (version 4. 2, PMOD Technologies Ltd). A factorial analysis correction was applied to reduce noise in SPECT data using Pixies (Apteryx) as previously described  [22, 23]. Then, time activity curves were generated from the Px Rat (Schwartz) template using PMOD. Non-displaceable Binding potentials (BP ND ) values were estimated using the Logan approach  [24] , with an explicit mask on the signal coming from outside of the brain and whole cerebellum as reference region.
BP ND images were flipped to allow statistical parametric mapping using SAMIT and SPM12 software of both hemispheres of the same animal. A paired t-test was realized on smoothed images with an implicit mask (eliminating signal from outside of the brain) and an explicit mask for the left hemisphere. A p value threshold of 0. 01 was used. A volume-of-interest (VOI)-based analysis on BP ND images was then performed to compare TSPO binding in both striata using the Px Rat (Schwartz) SAMIT template using PMOD. Cell dissociation. After SPECT acquisition, or 1 h post [ 125 I]CLINDE injection, animals were euthanized by decapitation while still under isoflurane anesthesia. The brain was removed and dissected on ice to isolate the left and right striata. Samples were then prepared for FACS using the previously described protocol  [25].
Cell numbers were determined for each cell population during cell sorting. Samples were sorted on a MoFlo Astrios (Beckman Coulter), and data were analyzed with FlowJo 10. 4 (FlowJo LLC). Ex-vivoradioactivity counting. Radioactive concentrations in rat striatum (before cell dissociation) and in isolated cell types were measured on an automatic γ counting system (Wizard 3", PerkinElmer). Data are expressed in percentage of the injected dose (% ID)/g of tissue for each animal. Immunohistology. Animals of the second cohort were euthanized with an overdose of sodium pentobarbital and perfused intracardially with 4% paraformaldehyde. Then, brains were extracted and cryoprotected with a 30% sucrose solution. Thirty microns coronal slices were prepared on a freezing microtome and stored at -20 °C until use for histology.
Then, a mask was defined on the GFAP + area in the ROI and applied on the TSPO image with the intensity threshold to quantify the TSPO + /GFAP + area. Two or three slices were quantified per animal. Data are expressed as a mean of values obtained per animal. Statistical analyses. Radioactivity was expressed as BP ND , as a percentage of the injected dose/g tissue weight (%ID/g) or as a percentage of the injected dose/ cell (%ID/cell). Normality of residues was validated with the Shapiro-Wilks test. Data were analyzed using a twotailed paired t-test when comparing left and right striata. Two-way ANOVA (Viral vector and cell type as between factors) and Bonferroni's post hoc test were used to compare median fluorescence intensity (MFI) values and the number of cells sorted/g of tissue. All analyses were performed using Prism software (Prism 9.
Acknowledgements We are grateful to  Maria Surini ,  Pia Lovero  and  Jean-Pierre Aubry-Lachainaye  for the technical assistance. We are also grateful to  Nicole Déglon  for providing viruses used in the second cohort and  Martine Guillermier  for her help for virus injections. Finally, we are extremely grateful to  Dr. Makoto Higuchi  ( National Institutes for Quantum Science and Technology, Chiba, Japan ) for sharing the TSPO antibody.
Funding This work was supported by the  Swiss National Science Foundation  (grant no. 320030_184713 ) and  ANR  ( 2010-JCJC-1402-1 , to CE). KC and BBT are also supported by the  Velux foundation  (grant no. 1123). VG was funded by the  Swiss National Science Foundation  (projects  320030_169876  and  320030_185028 ), the  Aetas foundation  and the  Schmidheiny foundation. TC, FF, and FC are grateful for financial support provided by the  Regional Council of Normandy, FEDER , the  University of Caen  and  Carnot I2C. Open access funding provided by  University of Geneva.
Image analysis and processing The MIMS analysis technique collects chemical information from across the entire analysis area, creating an image where each pixel contains the mass spectra of all secondary ions collected at each particular location. However, in order to fully examine the MIMS data from each of these samples, it was first necessary to convert each image into a text file, so that the intensity of each pixel could be easily retrieved. This allows for mathematical operations and statistical tests to be performed on the image as a whole. It also allows the image to be examined on image processing software such as FIJI/ImageJ (NIH, Bethesda, MD, United States) and for the easy examination of regions of interest (ROIs). A more in-depth discussion on MIMS image generation and an overview on current practices of MIMS image have been published previously  (Lechene et al.
, 2006). In this study, MIMS images were processed using a custom Matlab script (the Mathworks Inc. , Natick, MA, United States), adapted partially from the previous analysis scripts  (Saka et al. , 2014; Bonnin et al. , 2021). In cases where more than one image was taken at a given location (such as in the high-resolution images), the intensities of each component image were summed together into a single image matrix. The 15 N/ 14 N ratio images were then obtained by dividing each data point in the numerator isotope with each data point in the denominator isotope. The resulting matrices were saved as text files. Images were then generated using FIJI/ImageJ (NIH, Bethesda, MD, United States). In the case of the low-resolution images, where multiple images were used to examine a large area, images were stitched together manually by aligning each image along overlapping regions.
Acknowledgments The technical assistance of  M. Klewer ,  K. Schmitt , and  K. Karg  was gratefully acknowledged. HHa has served on the scientific advisory board of  Angelini , Corlieve,  Eisai ,  GW/Jazz ,  Sandoz ,  UCB Pharma ,  UNEEG  and  UCB/Zogenix. He served on the speakers' bureau of or received unrestricted grants from  Ad-Tech ,  Alnylam ,  Angelini ,  Bracco ,  Desitin ,  Eisai , GW,  Micromed ,  Nihon Kohden ,  Novartis ,  Pfizer , and  UCB Pharma. T-DW and J-LG-K thank the CurieCoreTech, the technological plat-forms at the  Institut Curie , for the use of the  Curie-NanoSIMS  instrument.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. EB and SR acknowledge funding from the Deutsche Forschungsgemeinschaft (DFG), under Germany's Excellence Strategy, EXC 2067/1-390729940, and RI 1967/10-1 (NeuroNex). HHu was supported by a grant from the German Research Foundation (DFG Hu1961/2-1). CD and KA acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 509149993, TRR 374, SP-C2. CR was supported by EU Joint Programme-Neurodegenerative Disease Research (JPND): SOLID JPND2021-650-233; Federal Ministry of Education and Research: 01ED2207 and the initiative the "Lung-brain axis in health and disease" at Research Campus Mid-Hessen (FCMH). AT was supported by The European Commission Horizon 2020 Framework Programme (Project 856871-TRANSTEM).
Introduction While mechanisms driving the acquisition of specific neuronal class and subtype identities are increasingly investigated, the maintenance of such identity and conversely their degree of plasticity remains rather enigmatic  [1]. Work over the last decade has challenged the view that neural cell identity is irrevocably fixed by demonstrating that fate-restricted neuronal progenitors and even early postmitotic neurons can be coaxed into neurons of distinct identities when appropriate transcriptional cues are provided  [2] [3] [4] , an experimental approach referred to as direct lineage reprogramming  [5]. Likewise, different classes of glial cells, i. e. , astrocytes, oligodendrocyte progenitor cells, and microglia can be converted into induced neurons by forced expression of neurogenic transcription factors (TFs) or regulatory RNAs, known to act as key regulators of cell fate during development  [6, 7].
Microscopic imaging and image analysis For whole brains and tract tracing imaging, mosaic microscopic images were acquired using an Axio Imager M2 epifluorescence microscope (Carl Zeiss Microscopy GmbH, Jena, Germany) equipped with a halogen lamp, MCU 2008 motorized stage, and an EC Plan-Neofluar 10×/0. 30 and an AxioCam MRm camera. ZEN blue software was used for imaging and automatic stitching. For molecular analyses, imaging was performed using a Zeiss 710 confocal microscope equipped with a 405 nm diode, an argon ion, a 561 nm DPSS, and a 647 HeNe lasers. For molecular studies as well as morphological characterization of retrovirus-infected glial cells, Z-stacks of fixed cortical sections were imaged using a LD-LCI Plan-Apo 25×/0. 8 NA. Images from immunostaining experiments were analyzed by using Fiji-ImageJ Software.
Statistical analysis Graphs and statistical analysis have been performed with Graphpad Prism 7. The results of the molecular analyses (Figs  1 2 3 4 ) are represented as percentages and error bars were calculated as standard error of the mean (SEM). For comparisons between plasmids, one-way ANOVA was applied, followed by Tukey post hoc test. For grouped comparisons between areas and plasmids, two-way ANOVA followed by Tukey or Sidak post hoc test was used. For each experiment, between 3 and 4 sections per brain were imaged in the somatosensory or motor cortex of electroporated brains. Quantification of GFP+ cells expressing a specific combination of markers were averaged for each brain, and then pooled together with other 2 independent brains, each of which had 3 sections. In total, 9 sections (3 per brain) were quantified to generate the data shown in the histograms and data sets.
For normally distributed data, Levene test was used to test homogeneity of variances and one-way ANOVA (in the case of equal variances) or Welch-ANOVA (in the case of non-equal variances) followed by a Tukey post hoc test was used for comparison of means. For non-normally distributed data, the Kruskal-Wallis test by rank was used for comparison of means. All original data are listed in S1 to S6 data sets.
Acknowledgments We would like to thank  Qiang Lu  and  Paola Arlotta  for the Cdk5r-Ctrl GFP and Cdk5r-Fezf2-IRES-GFP plasmids, respectively. We acknowledge support from the  Microscopy Core Facility of the Institute of Molecular Biology (IMB) Mainz  and of the iBV PRISM imaging platform.
This work was funded by an  AFM-Telethon grant  ( 20899  to MS), by the  French Government (National Research Agency, ANR)  through the ' Investments for the Future' programs   LABEX SIGNALIFE  ( ANR-11-LABX-0028-01  and  IDEX UCAJedi   ANR-15-IDEX-01  to MS), by an  ERA-NET Neuron grant  ( Brain4Sight ) ( ANR-21-NEU2-0003-03  to MS;  01EW2202  to BB); by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( 101021560 ,  IMAGINE  to BB), by the  Wellcome Trust  ( 206410/Z/17/Z  to BB), by the  German Research Foundation  ( BE 4182/11-1 ,  357058359  to BB), by the research initiative of  Rheinland-Pfalz  at the  Johannes Gutenberg University Mainz  (ReALity to BB); by a  SIGNALIFE Ph. D. contract to TF and by the  Inneruniversita ¨re Forschungsfo ¨rderung Stufe  I of the  Johannes Gutenberg University Mainz  to SP.
Overexpression of GDP-bound, active, RAGC S75N mutant rescued the inhibitory effect of CK-666 treatment both on p4E-BP1 phosphorylation and vesicular mTOR distribution, in contrast to inactive RAGC Q120L mutant that failed to do so (Fig. 4 , G to I, and fig. S5, A  and B ). Together, our data support an interplay between Factin puncta and stable association and activity of mTORC1 on endolysosomes and suggest an epistatic relationship between endolysosomal F-actin and the RAGC GTPase.
All numerical data and statistical tests are provided in table  S5. endolysosomes. Several key mTORC1 signaling components including mTOR kinase and its regulators, RAGC and LAMTOR complex subunits (LAMTOR1 and LAMTOR4), displayed a punctate localization in submicrometric domains in the vicinity of and partially overlapping with WASH/Arp2/3 complex-enriched Factin puncta on the surface of juxtanuclear endolysosomes. Analysis of the RAGC-associated protein interaction network provided a first hint for an intersection with branched actin dynamics as RAGC interacted and its localization overlapped with actin-capping CAPZA/ B proteins on endolysosomes  (31). A functional regulatory role in barbed end capping was supported by the observation that RAGC silencing led to branched actin puncta enlargement on endolysosomes, reminiscent of actin network elongation observed upon loss of CAPZA/B protein function, although the effect of RAGC KD was milder  (41) (42) (43).
In addition, loss of perinuclear F-actin puncta upon expression of GDP-bound RAGC S75N (but not GTPbound RAGC Q120L ) suggested that active RAGC promoted actin filament capping and prevented filament elongation on endolysosomes, possibly bringing CAPZ CPs and positive regulators, CD2AP and SH3KBP1, together. RAGC, depending on its nucleotide-binding status, may thus influence actin-dependent processes on endolysosomes such as vesicle movement and sorting of endocytic receptors in the recycling pathway  (25, 26, (41) (42) (43) (44) (45) (46). Moreover, branched actin networks can deform the underlying lipid bilayer; a property that is in line with the role of retromer and WASH complexes in the fission of endocytic recycling transport intermediates  (15, 24, 47, 48). Together, our data suggest potential previously unidentified functions for RAGC in endocytic recycling of membrane receptors that will deserve further investigation.
On a mechanistic standpoint, in contrast to low-nutrient conditions, in which RAGC remains stably anchored to the endolysosomal surface [Fig. 4 , E and F and  (2, 5, 6, 40) ], perturbation of endolysosomal actin dynamics under nutrient replete conditions did not affect LAMTOR distribution but led to the detachment of RAGC from endolysosomes in line with mTORC1 inhibition. Similarly, lysosome dissociation of RAGC and mTOR has been previously reported in retromer-deficient cells  (30). A mechanism was proposed that implicated the regulation of Rab7 localization and activity by retromer that could impinge on mTORC1 function  (30). We believe that these observations could also be interpreted in light of the key role of retromer in WASH complex recruitment and F-actin assembly on endolysosomes and based on our data linking mTORC1 function to the integrity of the endolysosomal actin network  (13, 20, 21).