dataesr/scientific-paragraphs-categorization huggingface dataset:
5): 5 -CGTGGGTCAGATGCAGTACA-3 , 5 ACCAGGGT-CAAAATTGGGCT 3 ; sod2 (gene accession number NM_000636. 4): 5 -GCCCTGGAACCTCAC ATCAA-3 ;5 -AGCAACTCCCCTTTGGGTTC-3 ; rpl32 (gene accession number NM_000994. 4): 5 -GTGAAGCCCAAGATCGTCAA-3 , 5 -TTGTTGCACATCAGCAGCAC-3.
In Silico Analyses Homology Modeling of the CeAhR LBD The structural model of C. elegans AhR LBD (residues 267-372) was generated by homology modeling. The X-ray structures of the PASB domains of homologous bHLH-PAS family members sharing the highest sequence identity (about 20%) with the CeAhR PASB were used as templates: the circadian locomotor output cycles kaput (CLOCK, PDB: 4F3L), the neuronal PAS domain-containing protein 3 (NPAS3, PDB: 5SY7), the Hypoxia-inducible factors 2α (HIF2α, PDB: 3H82, 4ZP4, 3F1N) and 1α (HIF1α, PDB: 4H6J). The model was obtained with MODELLER  [70] [71] [72]. The optimal model was selected from among the 100 generated, based on the best DOPE SCORE  [73]. The quality of the models was evaluated using PROCHECK  [74]. Secondary structures were attributed by DSSPcont  [75].
Statistical Analysis Unless otherwise stated, statistical analyses were performed in GraphPad Prism (Version 6. 01) (GraphPad Software, Inc. San Diego, CA, USA). For life/health span assays, statistical analysis was done using OASIS  [53]. Statistical analysis of the microarray data was performed in R (R Foundation (Vienna, Austria)). Boxplots were created in GraphPad Prism (Version 6. 01) (GraphPad Software, Inc. San Diego, CA, USA) and show the median (line), 25-75th percentile (box) and 10-90th percentile (whiskers).
These data suggest that the pro-migratory effect of curcumin is modulated by AhR-independent mechanisms but possibly also by a reduction in AhR activity. We next determined intracellular AhR distribution and the expression of cyp1a1 in curcumin-treated human EC. Curcumin did not affect AhR-nuclear translocation (Figure  5B ) or cyp1a1 expression (Figure  5C ). In search of pathways modulated by curcumin in an AhR-independent manner, we turned back to nematode transcriptomic profiles to find transcription factors regulating genes significantly modulated by loss of ahr-1 or by curcumin treatment in wild-type animals (Table  1 ). This in silico search identified the redox transcription factor SKN-1, the ortholog of human Nrf2 (nuclear factor erythroid 2-related factor 2), whose activation by curcumin  [91]  is often reported as a possible mediator of its anti-oxidant activity  [92, 93].
Scale bar: 50 µm. (F) Human primary EC were transfected with an empty vector (EV) or an expression vector for an shRNA targeting the human AhR transcript (shAhR). Relative Sod2 expression was assessed by qPCR, mean expression in the EV transfected cells was set to 1. Boxplots show data of 7 experiments. # p < 0. 05 vs. respective control. (G,H) Pgst-4::GFP expression in DMSO-or cur-treated wt and ahr-1 nematodes subjected to control or skn-1 RNAi. Representative images (G) and gst-4-driven GFP quantification (H) are shown. Boxplots show pooled data of 103-189 worms/condition in 4 experiments. (I) Juglone stress survival in curcumin-or DMSO-treated wt and ahr-1 nematodes subjected to control or skn-1 RNAi. Kaplan Meier survival curves show pooled data of 100 worms/condition in 4 experiments.
Acknowledgments: Most nematode strains utilized in this work were provided by the  Caenorhabditis Genetics Center , funded by the  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). We thank  Thomas Haarmann-Stemmann  ( IUF Düsseldorf ) for providing Cos7 cells,  Katrin Hochrath  ( IUF Düsseldorf ) for advice and  Daniel Puchta , and  Bo Scherer  for expert technical assistance. We further thank Wormbase and the  GENiE network  funded by the  European Cooperation in Science and Technology  ( COST Action   BM1408 ). The research and  APC  were financially supported by funding to N. V. from the  Deutsche Forschungsgemeinschaft  ( DFG VE366/6-1  and  VE366/8-1 ). V. B. was supported by a Ph. D. scholarship from the  Jürgen Manchot Foundation. J. A. and J. H. acknowledge funding from the  Deutsche Forschungsgemeinschaft :  IRTG1902-P1  and  IRTG1902-P2.
Acknowledgments: This work was funded by :  Agence  nationale pour la recherche : SYNTRACK -R17096DJ (AT); Agence nationale pour la recherche:  EXPECT 17-CE37-0022-2  (CL, DP);  European Research Council  :  PLASTINHIB  Project No:  322821  and  MICROCOPS (AT) ;  Human Brain  Project :  HBP SGA2-OPE-2018-0017  (AT);  EMBO  fellowship:  ALTF 362-2017  (MJP); We gratefully acknowledge  Astou Tangara ,  Benjamin Mathieu  and the  IBENS imaging facility (IMACHEM-IBiSA ), member of the  French National Research Infrastructure France-BioImaging  ( ANR-10-INBS-04 ), which received support from the  "Fédération pour la Recherche sur le Cerveau -Rotary International France " (2011) and from the  program « Investissements d'Avenir »   ANR-10-LABX-54   MEMOLIFE. We are grateful to  Amandine Delecourt ,  Eleonore Touzalin ,  Deborah Souchet  for excellent technical assistance.
Screening was performed in duplicate and identical to the HTT TR-FRET experiments described above in 384-well format, except for the following changes: Using robotic pipetting, on Day 2 cells were treated with 3 μl/well compounds (3 μM of each library compound in DMSO) or 0. 1% DMSO for 24 hours. The two first and last plates were treated with DMSO only to ensure technical robustness and to monitor possible signal fluctuations during the screening procedure. On Day 3 robotic pipetting was used to aspirate the medium and to add lysis buffer and the 2B7-Tb/MW1-D2 antibody mix as described above. Plate sealing was performed manually. On Day 4 plates were placed at 4°C, transported on ice to a PHERAstar plate reader (BMG Labtech) and read-out manually. The data obtained during screening required pre-processing to identify hits: First, using KNIME workflow 1 (available upon request) all data generated by the PheraStar plate reader was transformed into a data format which allowed processing using the commercial TIBCO Spotfire (Perkin Elmer) software  [37].
Second, the TIBCO Spotfire software was used to aggregate the data from all screening plates and to calculate the temperature ratios obtained for each well, as described above in the "Huntingtin TR-FRET and data analysis " section. Third, KNIME workflow 2 (available upon request) was used to link the temperature ratio obtained for each well with the treatment condition for this well. In a last step, the data was visualized using the Python Seaborn data visualization library (available upon request)  [38]. One compound was removed during analysis due to outlier fluorescent properties. Compounds which reproducibly scored higher than three times the standard deviation around the DMSO control median 4°C/20°C FRET ratio were considered as hits. The ten strongest hits were chosen for further analysis.
Discussion Glutamine-expanded HTT is regarded as the molecular cause for HD pathophysiology. Reducing mHTT levels in the brain is one strategy to alleviate disease symptoms and severity. Currently, the antisense oligonucleotide (ASO) Tominersen is explored in a clinical trial (Clin-icalTrials. gov identifier NCT03761849) and has been shown to reduce mHTT and wtHTT levels in HD patients. However, ASOs have to be repeatedly injected intrathecally, a potentially risky and unpleasant procedure for patients. Furthermore, the depletion of wtHTT might have unintended consequences by impacting neuronal connectivity  [13]  and caus-ing neurological disease  [14]. Recently, allele selective mHTT depletion has been achieved by a small molecule which binds both to autophagosome protein LC3 and mHTT thus inducing mHTT selective autophagic clearance  [6].
Acknowledgements All authors would like to thank  Lee Varban  ( Sanofi, Cambridge, MA, USA ) for technical advice concerning HTT TR-FRET in fibroblasts,  Robert Godemann  ( Evotec, Hamburg, Germany ) for technical advice,  Douglas MacDonald  ( CHDI foundation ) for conceptual project discussions and the 2B7 antibody,  Cécile Capdevila  ( Sanofi, Vitry-Sur-Seine, France ) for antibody purification, and  Nicolas Muzet  and  Rama Heng  ( Sanofi, Strasbourg, France ) for help in processing compound annotations. The pS13 HTT antibody and HTT DNA plasmids were provided by the  Coriell Institute biobank. The MW1 anti-body developed by the  late Patterson, P. H. ( California Institute of Technology)  was obtained from the  Developmental Studies Hybridoma Bank , created by the  NICHD  of the  NIH  and maintained at The  University of Iowa.
MEG acquisition and pre-processing Data were acquired using a MEG system composed of 154 magnetometers SQUID (superconductive quantum interference device) and 9 reference sensors. The acquisition took place in a magnetically shielded room (ATB, Biomag, ULM, Germany) to reduce external noise. To define the position of the head under the helmet we used Fastrack (Polhemus®), that digitised the position of four anatomical landmarks (nasion, right and left pre-auricular points and vertex of the head) and the position of four reference coils (attached to the head of the subject). Each subject was recorded twice (3. 5 min each) with a one-minute break, in resting state with closed eyes. We also recorded the cardiac activity and the eyes movement to remove physiological artefacts. After applying an anti-aliasing filter, data were sampled at 1024 Hz.
Data preprocessing was performed similarly to Liparoti et al. (Liparoti et al. , 2021). Briefly, the MEG data were filtered in the band 0. 5-48 Hz through the implementation of a 4 th -order Butterworth IIR band-pass filter, using the Fieldtrip toolbox in the MATLAB environment. Moreover, a Principal Component Analysis (PCA) was carried out to reduce environmental noise. Finally, we performed a supervised Independent Component Analysis (ICA) to remove physiological artefacts from the ECG (one component per participant) and the EOG (no component per participant, rarely one)  (Romano et al. , 2022; Rucco et al. , 2022).
Similarly to Sorrentino et al. ,  (Sorrentino et al. , 2021)  we estimated the suitable time bin length by computing the branching ratio σ for each subject, each neuronal avalanches and each time bin duration  (Haldeman & Beggs, 2005). Indeed, a branching ratio ~1 typically indicates a critical process. Specifically, the branching ratio was calculated as: 𝜎 𝑖 = ∏ 𝑁 𝑏𝑖𝑛 -1 𝑗=1 ( 𝑛 𝑒𝑣𝑒𝑛𝑡𝑠 (𝑗 + 1) 𝑛 𝑒𝑣𝑒𝑛𝑡𝑠 (𝑗) ) 1 𝑁 𝑏𝑖𝑛 -1 where σ is the branching parameter of the i-th avalanche in the dataset, Nbin is the total number of bins in the i-th avalanche, nevents (j) is the total number of events in the j-th bin. We then (geometrically) averaged the results over all avalanches as follow: 𝜎 = ∏ 𝑁 𝑎𝑣𝑎𝑙 𝑖=1 (𝜎 𝑖 ) 1 𝑁 𝑎𝑣𝑎𝑙 However, we repeated our analysis exploring different time bins, ranging from 1 to 5 obtaining similar results (see supplementary materials 2).
Statistical analysis Statistical analysis was carried out in MATLAB 2021a. The comparison between patients and HC was performed through permutation testing, shuffling the data labels (i. e. , MCI or HC) 10. 000 times. At each iteration, we recorded the absolute difference between the two randomly generated groups in order to generate a null distribution of absolute differences. Then, the actual absolute difference between patients and HC was compared to the random distribution to obtain a statistical significance. Finally, a possible relationship between the topological features of brain dynamics and the clinical conditions (hippocampal volumes and cognitive impairment) of the patients was explored through Spearman's correlation. The results were corrected for multiple comparisons across both frequency bands and parameters using the False Discovery Rate (FDR) method  (Benjamini and Hochberg, 1995)  and the significance level was set at p value < 0.
FUNDING This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 945539  (SGA3);  Human Brain Project ,  Virtual Brain Cloud  No. 826421  and  Ministero Sviluppo Economico; Contratto di sviluppo industriale "Farmaceutica e Diagnostica " ( CDS 000606 ) and  European Union "NextGenerationEU" , ( Investimento 3. 1. M4. C2 ) of PNRR.
| Data preprocessing The preprocessing was performed by the HCP as described in  Glasser, et al. (2013) , based on the updated 7T data pipeline (v3. 21. 0, https:// github. com/Washington-University/HCPpipelines), including gradient distortion correction, head motion correction, image distortion correction, spatial transformation to the Montreal Neurological Institute space using one step spline resampling from the original functional images followed by intensity normalization. In addition, the HCP took an approach using ICA (FSL's MELODIC) combined with a more automated component classifier referred to as FIX (FMRIB's ICA-based Xnoisifier) to remove non-neural spatiotemporal artifact  (Griffanti et al. , 2014; Salimi-Khorshidi et al. , 2014; Smith, et al. , 2013). This step also used 24 confound timeseries derived from the motion estimation (6 rigid-body parameter timeseries, their backwards-looking temporal derivatives, plus all 12 resulting regressors squared;  Satterthwaite et al.
ACKNOWLEDGMENTS The neuroimaging data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. Dr Wei Cheng  and  Shitong Xiang  of  ISTBI, Fudan University, Shanghai  are thanked for parcellating the data into HCP-MMP1 surface-based space (Glasser, Coalson, et al. , 2016) and reordering it into HCPex order (Huang et al. , 2022). Roscoe Hunter  of the  University of Warwick  is thanked for contributing to the description in the Supplementary Material of the Hopf effective connectivity algorithm.
Funding information  EU   Horizon 2020 FET Flagship , Grant/Award Number: 945539;  Fundacio La Marato TV3 , Grant/Award Number: 201725. 33;European  Research Development Fund Operational Program of Catalonia 2014-2020 , Grant/Award Number:  001-P-001682 ;  EU H2020 MSCA-ITN Innovative Training Networks , Grant/Award Number: 860563; EU  H2020 FET Proactive Programme , Grant/Award Number:  101017716 ;  Catalan Agency for Management of University and Research Grants (AGAUR) , Grant/Award Number:  2017 SGR 1545 ;  Spanish Ministry of Science, Innovation and Universities (MCIU) ,  State Research Agency (AEI) , Grant/Award Numbers:  PCI2018-092891 ,  PID2019-105772GB-I00 MCIU AEI ;  Shanghai Municipal Science and Technology Major Project ,  ZJLab , and  Shanghai Center for Brain Science and Brain-Inspired Technology ,.
Immunoreactivity After the last imaging examination, mice were sacrificed and perfused with 0. 9% NaCl and 4% PFA. Brains were fixed overnight in 4% PFA, embedded in paraffin, cut in coronal sections and processed as previously described  (17). To characterize the TME and therapy-induced modifications, CSF-1R/Iba1 and TSPO/Iba1 dual-labeling with fluorescent antibodies was performed in PM and RM studies. Quantification of positive cells were made in biological triplicates counting manually or using the bioimage analysis software QuPath (https://qupath. github. io) (18). Antibodies are available in Supplemental Table  1.
Multiparametric Flow Cytometry (FACS) Myeloid-derived cells were isolated after processing of the tissues as described before  (17). The cells were stained with a panel of directly labeled monoclonal antibodies (mAbs) (Supplemental Table  2 ). All samples were analyzed using the NaviosTM flow cytometer and the Kaluza 2. 1 Software (Beckman Coulter, Krefeld, Germany). See Supplemental M&M for additional data.
Statistical Analysis Statistical analysis was performed using Prism 6 (GraphPad Software, Inc. , USA). Differences over time in radiotracer uptake ratios and tracer uptake volumes intra-and inter-groups were tested using either one-way ANOVA with multiple comparisons corrected with Holm Sidak´s test, or a t-test followed by Mann-Whitney U test on ranks and Wilcoxon test, with Bonferroni correction for multiple measurements. Correlation between tumor and tracer volumes was tested using Pearson correlation and linear regression. Significance levels were set at p < 0. 05. All results are shown as mean ± SE or mean ± SD. Only animals with full dataset were considered. Outliers were automatically excluded.
Treatment with Subsequent Repopulation (RM) We then assessed the possible therapeutic effects of acute treatment and subsequent microglial repopulation. In NT animals, Gd-CE T1w-MRI, 18F-FET-and 18F-DPA-714-PET showed significant increasing in TV and both tracer volume over time. In the PLX5622-treated group, Gd-CE T1w-MRI and 18F-FETderived volumes significantly increased between d7 and d21 while 18F-DPA-714-derived volume significantly increased between d7 and d14 (Figure  4B  and Figure  5 ). Therapy effect was observed at day 21, where repopulated animal showed significantly reduced TV, 18F-FET and 18F-DPA-714-derived volumes compared to NT animals. See supplementary files for additional data (Supplemental Table  5 ). In the NT group, 18F-FET Tmean/Bmean significantly increased between d7 and d21 while in the PLX5622+Repopulation group it increased between d14 and d21.
As reported, single-agent therapy with CSF-1R inhibitors has demonstrated only modest results in glioblastoma clinical trials, showing no significant improvement of the progression-free survival of the patients  (27). Currently, other studies are ongoing combining CSF-1R therapy and immune-checkpoint inhibitors in different types of tumors  (28). One pilot study evaluating the TSPO-PET tracer 11C-PBR28 in patients with primary GLIOBLASTOMA MULTIFORME or melanoma brain metastasis, treated with chemoradiation or immunotherapy, has been completed recently and results are to be determined (NCT02431572)  (29). The validation of TSPO-PET tracers in clinical settings is necessary to improve the understanding of glioma-associated inflammation and therapy resistance mechanisms.
This work has received support from the  EU   7th Framework Programme  ( FP7/2007-2013)  under grant agreement  n°278850  ( INMiND ), the  Horizon2020 Programme  under grant agreement n°  675417  ( PET3D ), the  EU/EFPIA/Innovative Medicines Initiative 2 Joint Undertaking (Immune-Image GA831514)  under grant agreement n°  831514 , the ' Cells-in-Motion´ Cluster of Excellence (CiM) Graduate School , by the  Interdisciplinary Center for Clinical Research (IZKF core unit PIX), Münster, Germany , and by the  Herbert-Worch-Foundation, Bonn, Germany.
Image Processing Anatomical images were skull stripped using the default parameters in FSL BET  (Smith, 2002). Brain-extracted anatomical and functional images were submitted to ME-ICA (version 3. 2 beta; https://github. com/ME-ICA/me-ica; Kundu et al. , 2011; Kundu et al. , 2013). ME-ICA relies on the TE-dependence model of BOLD signal to determine T2* in every voxel and separates BOLD signal from non-BOLD sources of noise. Prior to TE-dependent denoising, time series data were minimally preprocessed: the first 4 volumes were discarded, matrices were computed for de-obliquing, motion correction, and anatomical-functional coregistration, and volumes were brought into spatial alignment across TEs. The T2* maps were then used for anatomical-functional coregistration. Gray matter and cerebrospinal fluid compartments are more precisely delineated by the T2* map than by raw EPI images  (Speck et al.
Gradients & Manifold Eccentricity Cortical gradients were computed using functions from the BrainSpace toolbox (https://github. com/MICA-MNI/BrainSpace; Vos de  Wael et al. , 2020) , as implemented in MATLAB. For each participant, the 400 x 400 GPIP functional connectivity matrix was thresholded row-wise to the upper 10% of connections to retain only the strongest positive connections  (Hong et al. , 2019; Margulies et al. , 2016). Cosine similarity was computed on the sparse matrix to input to the diffusion map embedding algorithm employed below, generating a matrix that captures similarity in whole-brain connectivity patterns between vertices  (Hong et al. , 2019; Margulies et al. , 2016). We then applied diffusion map embedding, a non-linear dimensionality manifold learning technique from the family of graph Laplacians  (Coifman et al.
All cells were then washed twice with PBS-CM and fixed with ice-cold methanol. Nuclei were counterstained using DAPI. FITC-POS and DAPIlabeled nuclei were quantified by fluorescence plate reading (Infinite M1000, Magellan version 6 software, Tecan France SASU, Lyon, France). Binding ratios were calculated by subtracting results obtained in internalization (trypan blue-treated) from total phagocytosis (untreated) wells. Corresponding standard deviations (s. d. ) were calculated using the following formula: s. d. binding = √ ((s. d. total 2 /n total ) + (s. d. intern 2 /n intern )).
Next, non-specific sites were blocked with 1% BSA in PBS-CM. Primary antibody incubation for intracellular labeling were performed overnight at 4 • C or for 2 h at RT. Cells were washed 3 times with PBS-CM then with 1% BSA in PBS-CM. AlexaFluor secondary antibodies (Molecular Probes) were incubated on cells for 1-2 hours at RT, followed by washing with PBS-CM, labeling cell nuclei with DAPI and mounting onto glass slides with FluoroMount-G Mounting Medium (Interchim). All fluorescent images were acquired with an upright Olympus FV1000 confocal microscope using the Fluoview version 2. 1c software (Olympus, Rungis, France). Equivalent stacks of images were compiled for each series and further treated equally for signal output levels using NIH ImageJ (version 1. 53o, https://imagej. nih. gov/ij/, last accessed on 31 January 2022).
This work was funded by the  Agence Nationale de la Recherche  [ PRC program ,  ANR-17-CE14-0044-01 , Project Grant to E. F. N. and T. H. ], by  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ] and  Labex LIFESENSES  [ ANR-10-LABX-65 ] supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program  [Project Grants to E. F. N. ], and  Centre National de la Recherche Scientifique (CNRS)  [Tenure Position to E. F. N. ]. Additionally, the  Institut de la Vision  is funded by  Institut National de la Santé et de la Recherche Médicale ,  Sorbonne Université ,  Centre National de la Recherche Scientifique  and  Région Ile-de-France. Institutional Review Board Statement: Ths study  was conducted according to the  Association for Research in Vision and Ophthalmology (ARVO) Statement  for the  Use of Animals in Ophthalmic and Vision Research , and approved by the  Charles Darwin Animal Experimentation Ethics Committee from Sorbonne Université  and the  French Ministry for Education, Higher Studies and Research  (APAFIS # 1631-2015090415466433  and # 20191-2019040311402311 ).
Thus, a systematic comparison of imaging data with the gold standard of electrophysiological data derived from intracerebral stereoelectroencephalography (SEEG) recording is essential to test the potential contribution of any imaging modality toward better definition of the epileptogenic zone (EZ) in patients suffering from drugresistant focal epilepsy who are candidates for surgery. Moreover, the need to characterize the variable manifestations of pathology suggests a clear need to exploit multimodal imaging, combining metrics from different modalities. Insight into alterations of ionic homeostasis and metabolic function can be probed by sodium ( 23 Na) MRI and 1 H-MR spectroscopic imaging (MRSI), respectively. 23 Na-MRI provides a unique opportunity to noninvasively image sodium signal in the brain  (Madelin et al.