dataesr/scientific-paragraphs-categorization huggingface dataset:
). The abductive approach, inspired by the philosopher Charles S. Peirce  (Peirce 1931) , consists of grouping the content of messages as faithfully as possible by abstraction. This approach then makes it possible to avoid defining categories a priori and thus avoid being far from the observable. Finally, this has the advantage of limiting the biases likely to influence the classification process. The categories of these 100 influential tweets were then applied to the retweets to build a file of edges and nodes to visualize the links between tweeters using Gephi 0. 9. 2 software. The most relevant indicator for this study is the eigenvector centrality  (Bonacich and Lloyd 2001)  because it makes it possible to highlight the links in a graph between the most influential tweeters and thus better visualize the dissemination of information in the social network.
Blots were then incubated with α-rabbit, α-mouse, or α-goat horseradish peroxidaseconjugated secondary antibodies. Immunoreactivity was detected by enhanced chemiluminescence (GE-Healthcare) and quantified by Quantity One software (Bio-Rad). Optical density values of all markers analysed were normalized to either β-actin or GAPDH for variation in loading and transfer. Data are reported as medians, along with the interquartile range (first-third quartiles-IQR). All representative blots shown in the figures arise from cut-out and pasted bands for reassembling the image.
2 Megapixels; Teledyne photometrics, US). The ImageJ software (National Institutes of Health, United States) was used to quantify: (1) the density of TH positive fibers in CPu, (2) the mean intensity and the total number of IBA1 positive cells in the SNc, (3) the mean intensity of IL-1β in the SNc, (4) IBA1-IL-1β colocalization in the SNc. For mean intensity quantification, images were background subtracted, then optical density was determined by measuring the mean grey density in fixed regions representing the dorsal CPu and the SNc. Quantification of IBA1 positive cells was carried out by the manual particle counting option of Image J software, while quantitative analysis of co-localization of IBA1 with IL1β was conducted using the ImageJ plugin JACoP (Just Another Co-localisation Plugin)  (Bolte and Cordelières, 2006)  and calculated as Pearson correlation coefficient (Rr), as previously described  (Pinna et al.
Acknowledgements AU and MM were supported by a grant from  MIUR (Ministero dell'Istruzione, dell'Università e della Ricerca ,  PRIN 2017 - Project nr  2017 M42834  (AU),  2017LYTE9M  (MM)); AU was funded by a grant from  Fondazione Cariplo  (Project nr  2017-0575 ) and by the  Ministry of University and Research (MUR) ,  National Recovery and Resilience Plan (NRRP) , project  MNESYS  ( PE0000006 ) - A Multiscale integrated approach to the study of the nervous system in health and disease  (DN. 1553( DN. 11. 10. 2022 )). M. S. gratefully thanks the  Zardi-Gori Foundation  for the financial support (research grant  2021 ). Authors thank the  Centre for Research University Services (CeSAR)  for the technical support. We thank  Hiroshi Homma  and  Masumi Katane  for the generous gift of a DDO enzyme aliquot for HPLC detection of  D-Asp , and  Dr.
25-0. 75 oil (11506253) objective with 2X electronic magni cation (188. 92 nm pixel size) and lasers set at 488 and 561 nm for excitation of FITC and Cy3, respectively. Stacks were acquired using a 0. 5 µm z-interval. Image stacks were imported into ImageJ for processing as previously described  58, 59. Brie y, to edit the channel window containing Iba1 + cells, an Unsharp Mask lter was used to further increase contrast using a pixel radius of 3 and mask weight of 0. 6, a despeckle step was performed to remove salt-and-pepper noise and outlier pixels were removed with the Remove Outliers function (pixel radius 2; threshold 50). Image stacks were then imported into Imaris Software (Bitplane). Individual microglia were reconstructed in 3D using the lament tracer module and measures of the process length, terminal points, as well as 3D-Sholl analysis were performed to assess their morphology.
005 µg. µL - 1 , Sigma). Image stacks of apical dendritic segments in CA1 were taken using a confocal laser scanning microscope (SP5, Leica) equipped with a 63x/1. 4 NA objective (oil immersion, Leica) with a pinhole aperture set to 1 Airy unit, pixel size of 60 nm and z-step of 200 nm. The excitation wavelength and emission range were 488 and 500-550 nm for Alexa 488 (i. e. P2Y12 receptor) and 561 and 570-620 for DiI. Deconvolution with experimental point spread function from uorescent beads using a maximum likelihood estimation algorithm was performed with Huygens software (Scienti c Volume Imaging). One hundred fty iterations were applied in classical mode, background intensity was averaged from the voxels with lowest intensity, and signal to noise ratio values were set to a value of 20.
For each mouse, 10 dendritic segments of 20-50 µm in length were analyzed. Dendritic spines were analyzed with Neuronstudio software 61 (version 9. 92; http://research. mssm. edu/cnic/tools. html). Images from the P2Y12 receptor channel were preprocessed in order to enhance the contrast, using an Unsharp Mask lter using a pixel radius of 3 and mask weight of 0. 6, a despeckle step was performed to remove salt-and-pepper noise and outlier pixels were removed with the Remove Outliers function (pixel radius 2; threshold 50). To analyze the interaction between microglial processes and dendritic spines, we measured the percentage of dendritic spines having microglial processes located within 0. 3 µm surrounding the spine head using Fiji-ImageJ software. To assess the presence of microglial processes, for each spine, its 3D coordinates and head diameter measured with Neuronstudio were used to draw a sphere on the image using the DrawShape function of the plugin 3DImageSuite.
Protrusions were counted on secondary branches of apical dendrites. Only protrusions with a clear connection of the head of the protrusion to the shaft of the dendrite were counted. Protrusion density, length and width were analyzed using the Reconstruct software as described by Risher and colleagues  30. Protrusions were classi ed based on their morphology into six classes using the following exclusive criteria  30  : lopodia, when the length value > 2 µm; long thin spines, when the length value > 1 µm and ≤2 µm; thin spines, when the length-to-width ratio (LWR) value > 1; mushroom spines, when the width value > 0. 6 µm; stubby spines, when the LWR value ≤ 1; branched spines, when spines showed more than one head (entered manually by the experimenter). All image analyses were done blind.
Data acquisition was performed using Patchmaster software (Heka Elektronik). The junction potential (-14. 9 mV) was uncorrected. Signals were low pass-ltered at 4 kHz before sampling at 20 kHz. Recordings were ltered o ine at 2 kHz using Clamp t (Molecular Devices) and analyzed using MiniAnalysis (Synaptosoft). During patchclamp recordings and analysis, the investigator was blind to the mouse genotype. Anterograde labeling of retinogeniculate projections P15-16 mice were anesthetized with ketamine-xylazine (100 and 10 mg. kg - 1 , respectively, in 0. 9% saline). Eyes were intravitreally injected using a glass micropipette with 1-5 µl of 0. 2% cholera toxin subunit B (CTB) conjugated to Alexa Fluor 488 or 555 (Invitrogen) diluted in 1% DMSO. After 48 h, mice were deeply sedated (pentobarbital, 140 mg.
kg - 1 i. p. ) and perfused transcardially with 4% paraformaldehyde in PBS. Brains were post xed overnight with the same xative and sectioned with a vibratome (60 µm). All sections containing the dorsal lateral geniculate nucleus of the thalamus (dLGN) were selected and directly mounted in Mowiol containing DAPI (0. 005 µg. µL - 1 , Sigma). 10X epi uorescent images were obtained using a CCD camera (CoolSNAP EZ, Teledyne Photometrics) attached to an upright Leica 6000 microscope. MetaMorph software (Molecular Devices) was used for quantitative analyses. All image analyses were done blindly to the mice genotypes. Quanti cation of eye-speci c segregation was performed on images of Alexa Fluor 488-labeled contralateral/Alexa Fluor 555-labeled ipsilateral projections, as previously described by Rebsam and colleagues 64.
Behavioral assessment Experiments were carried out in the light phase of the light/dark cycle (between 8:00 a. m. and 8:00 p. m. ). For each behavioral test, mice were acclimatized to the testing room for at least 1 hr. Equipment was carefully swabbed with 35% ethanol between each mouse to eliminate odors. Behavioral tests were videorecorded in real-time and analyzed off-line by an experimenter blinded to the experimental groups using the free event recorder JWatcher software (Blumstein & Daniel http://www. jwatcher. ucla. edu/).
Statistical analysis For each gure, the n values are summarized at the end of the legend. Statistical analyses were performed using GraphPad Prism 6. 01 software (Graph-Pad Software). Males and females were included and compared to determine if sex impacted our experimental results. All n values represent individual animals, unless stated otherwise (i. e. electrophysiology experiments). Notably, in analyses where multiple microglia were assayed per animal, all microglia analyzed for an individual animal were averaged to generate a single value per animal. When appropriate, animals were randomly assigned to conditions. Where possible, conditions were randomized to account for potential ordering effects. To ensure reproducibility, when relevant, experiments were performed at least three times independently.
To avoid litter bias in the mouse experiments, experimental groups were composed of animals from different litters randomly distributed. All analyses were conducted with blinding to the experimental condition. Data are presented as scatter dot plot with bar, and the line at the mean ± S. E. M. (standard error of the mean). Comparisons between two groups following a normal distribution were analyzed using two-tailed unpaired t-test with or without Welch's correction, comparisons between two groups not following a normal distribution with Mann-Whitney test. Normality was assessed using the D'Agostino & Pearson omnibus normality test. When we compared cumulative distributions, we used the Kolmogorov-Smirnov test. When more than one variable was evaluated, we employed the two-way ANOVA with or without repeated measures (ANOVA and RM-ANOVA, respectively) with Sidak multiplicity-corrected post hoc comparisons to compare cohorts where appropriate.
Acknowledgements We thank  Chris N. Parkhurst  and  Wenbiao B. Gan  for providing the Cx3cr1 creERT2 knock-in mice, the  Cell and Tissue Imaging Facility  of the  Institut du Fer à Moulin  (namely  Theano Eirinopoulou ,  Mythili Savariradjane  and  Xavier Marquès ), where all image acquisitions and analyses have been performed, and the staff of the  IFM animal facility  (namely  Baptiste Lecomte ,  Gaël Grannec ,  François Baudon ,  Anna-Sophia Mourenco ,  Emma Courteau  and  Eloise Marsan ). We warmly thank  Patricia Gaspar ,  Ludmilla Lokmane ,  Sonia Garel ,  Véronique Fabre  and  Jean Christophe Poncer  for the discussion and revision. This work has been supported by grants from the  Agence Nationale de la Recherche  ( ANR-17-CE16-0008 ,  ANR-11-IDEX-0004-02 ), the  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ2014039529 ) and the  Fédération pour la Recherche sur le Cerveau  ( FRC-2019-19F10 ).
His team employed a wide range of experimental techniques, including public database mining, animal models, single-cell RNA sequencing, microbiome analysis, and large-scale chemical screening. They proposed that the herbicide propyzamide may play a role in IBD through a pathway involving T and dendritic cells, which is partly controlled by the AHR. Further epidemiological research is needed to confirm these connections, but these intriguing findings will inspire further investigations into the ways in which environmental chemicals can contribute to the development of IBD. The AHR plays an important role in metabolic diseases, including obesity and fatty liver disease. Rance Nault and Tim Zacharewski from Michigan State University, East Lansing, MI, USA, emphasized the value of sequence-based approaches, including single-nuclei RNA sequencing (snRNA-seq) and spatial transcriptomics  [12, 13].
In addition, IK-175 affects the tumor microenvironment leading to more activated immune cells with an increase in pro-inflammatory cytokines. This immunostimulatory effect of AHR inhibition observed in mouse models was also seen in human T cells, where IK-175 led to a decrease in IL-22 and an increase in IL-2. Studies utilizing a signature of AHR-regulated genes and an immunohistochemistry method for assessing nuclear AHR in human tumors pointed to bladder cancer and head and neck cancer as tumor types, where AHR may play a major role. A phase 1a/b open-label study of IK-175, alone and in combination with nivolumab in patients with locally advanced or metastatic solid tumors and urothelial carcinoma, is in progress (clinicaltrials. gov NCT04200963). AHR activation may provide a benefit for patients by increasing immune suppression in autoimmune diseases, as has been demonstrated with multiple indoles  [73].
Acknowledgments: We acknowledge all meeting participants whose presentations, posters, and discussions at the meeting contributed to the meeting's success. We apologize to those we could not mention due to lack of space. We thank  Jakub Gruszczyk  and  William Bourguet  for the AHR Cryo-EM figure. The next AHR meeting is slated for 2024, to take place in Düsseldorf, Germany, organized by the  IUF-Leibniz Research Institute for Environmental Medicine, Deutsche Forschungsgemeinschaft (CE). We also thank  Iain A. Murray  for his critical review of this manuscript.
Post-hoc pairwise comparisons were then conducted using Bonferroni adjustment for multiple comparisons. Fixed effects estimates, confidence limits, and random effects standard deviations of these LMMs are provided in Supplementary Table  1. When preflight measurements were not available in the astronauts (perceived durations since beginning of workday and lunch), an independent Sample Mann-Whitney test was used to compare their inflight responses with those of the control subjects on the ground. When these responses were not available in the control subjects neither (perceived durations since last docking and last EVA) a one-sample t-test was used to determine if the time errors during the flight were different from zero. Statistical analysis was performed with JASP 0. 16. 1. 0 and IBM SPSS Statistics 27.
ACKNOWLEDGEMENTS The author thank  Jean-François Clervoy  for sharing his thoughts on time perception during space travel;  Robert Macgregor ,  François Derache , and  Maurice Marnat  for their help in implementing this experiment; and  Kerry George  for editing the manuscript. European Space Agency provided access to the astronauts and the  International Space Station  (no grant number) and  Centre National d'Etudes Spatiales  provided the equipment and travel support for the investigators (grant #  2021/480001115 ).
Methods: We present here a method to predict the return to consciousness and good neurological outcome based on classification of auditory evoked potentials obtained during an oddball paradigm. Data from event-related potentials (ERPs) were recorded noninvasively using four surface electroencephalography (EEG) electrodes in a cohort of 29 post-cardiac arrest comatose patients (between day 3 and day 6 following admission). We extracted retrospectively several EEG features (standard deviation and similarity for standard auditory stimulations and number of extrema and oscillations for deviant auditory stimulations) from the time responses in a window of few hundreds of milliseconds. The responses to the standard and the deviant auditory stimulations were thus considered independently. By combining these features, based on machine learning, we built a two-dimensional map to evaluate possible group clustering.
We used data already acquired from a homogeneous cohort of patients admitted in the intensive care unit after CA and who all had EEG, SSEP and AEP recordings within 6 days after admission. We identified specific features from AEP, considering responses to standard and deviant auditory stimulations independently. Using a step-by-step data processing, we finally reported combined features in two-dimensional map where we observed that patients were clustered into two groups corresponding to a different outcome at discharge whether they were able to follow verbal command or not. We then estimated the probability for a patient to be classified into one of the two groups at the acute phase using several classifiers.
Signal processing Auditory evoked potential obtained with standard and deviant auditory stimulations were exported in the European Data Format (EDF), which is a simple and flexible format for storage of multichannel biological and physical signals, then anonymized through a specific software we designed. Analyses were performed on all four active electrodes then on one single Cz (central) electrode in order to see if we could obtain similar results with a simplified electrodes setting. To quantify the auditory evoked responses recorded from post CA patients in the intensive care unit, we studied separately standard and deviant responses (Figure  1 ), which is a novel and different paradigm compared to the classical MMN. We took into account the total 20 min extracted data, instead of the short interval response occurring in [100-300] ms following auditory stimulation.
To study the maps defined above as predictive tools, we used three independent statistical classifiers (SVM, Gaussian estimator, K-nearest neighbors). Because the present database did not contain many patients and to guarantee the robustness of our approach, we decided to use three classifiers (SVM, Gaussian mixture, and k-neighbors). As a small size database is also associated with overlearning, and to overcome this difficulty, we chose to use simple models for classification: Support Vector Machines (SVM) seems to be particularly suitable, as its classification is dependent only on a reduced number of patients. In fact, we wished to assign a good neurological outcome probability to any point that would be added on the map based on the ensemble of previous data points already classified. Using the assumption that statistics associated to patients (features) are independent from each other, we used a Bayesian classification.
K-nearest neighbors classifier and weighted K-nearest neighbors To classify the standard stimuli, we used the K-nearest neighbors classifier. We computed the ratio for the probability of belonging to a class. For a given point X, the probability to belong to class C 1 ("good neurological outcome") given the distribution of point x is computed empirically as the number of neighbors out of a total of K. p (X ∈ C 1 | x) = k r K (8) where k r is the number of neighbors that belong to the class "good neurological outcome at discharge" among K closest points. To classify deviant stimuli, we used a variant of the K-neighbors method by adding distance-relative weights to the points inside the dataset. The two classes labeled "bad neurological outcome" and "good neurological outcome" are defined as C 1 and C 2, respectively.
Cross-validation We used a Leave-one-out cross-validation approach to validate the classification algorithm: we excluded a patient at a time and computed the probability of a good neurological outcome at discharge, based on the remaining elements in the data basis  (Kohavi, 1995). In other words, we separated the patient database into a testing and a training group, with one patient out, 28 in the other group and ran this test 28 times so that each of the 29 patients was alternatively included in the 1 group patient. We then computed this probability using the three classifiers, SVM, Gaussian estimators and K-neighbors and compared the result to the true result. We followed the protocol: 1-a patient P i , i = 1. N is selected inside the data basis; 2-we trained the classification algorithm on the database of all patients {P k , k = 1.
N} -P i. We evaluated the prediction of the model on the excluded patient, leading to a score s i. We recall that s i = 1 if the prediction is correct, otherwise, s i = 0. We then replaced the patient P i inside the database and reiterated the procedure until each patient has been exactly excluded once. This allowed us to reclassify with a given probability for each patient outcome based on the new map determined by the other patients. The final score of the model is computed as s = 1 n n 1 s i. ( 12 ) Finally, the confusion matrix defined as C = T p F N F p T N ( 13 ) for the true positive T p (number of patients who have a good neurological outcome at discharge and are classified correctly), true negative T n (number of patients who have a bad neurological outcome and are classified correctly) and false positive F p (number of patients who have a good neurological outcome and are classified incorrectly) and false negative F n (number of patients who have a bad neurological outcome at discharge and are classified incorrectly).
Overall patient characteristics Data of twenty-nine consecutive patients were analyzed. Seven patients out of twenty-nine survived, but only 6 out of 7 were able to follow verbal command at hospital discharge. None of the patients was lost of follow-up. The last patient returned home but the degree of disability is unknown. At 3-6 months, GOS-E was scored at 3 for the patient who was unable to follow verbal command at discharge and died 27 months later without neurological improvement. GOS-E was scored at 4 for one patient, at 5 for one patient, at 6 for one patient and at 8 for the last two patients. Age, sex, medical history, characteristics of CA and electrophysiological features are presented in Table  1. At the time of recording, all patients were still hypothermic (<35 • C). Sedation was present in 11 out of the 29 patients (38%) at the moment of the electrophysiological recordings.
It is to note that one third of the patients with bad outcome and 50% of the patients with good outcome were under sedation at the time of recording, which is known to impede electrophysiology interpretation. Our study was not designed to compare our tool with classical electrophysiological examinations but sensitivity and specificity were higher in that small cohort that needs to be validated in a larger cohort. The total amount of data we collected for all epochs during the 20 min of auditory stimulations and not only during the time window used for MMN might explain our more sensitive results. Our present study has several limitations. First, as a retrospective study, neurological prognosis was evaluated on the ability of the patient to follow verbal command at discharge, which remains a subjective assessment that may have led to patient's misclassification.
US-induced tissue-heating simulations A three-fold process was used for the estimation of thermal effects: 1) simulation of the acoustic fields generated by the three transducers, with realistic acoustic parameters, 2) verification that non-linear acoustics did not play an important role in heat transfer and 3) realistic simulations of heat transfer and temperature rise induced at the focus by US in a linear regime for parameters used in this study. For non-linear simulations, we used Matlab's toolbox kWave, by defining the geometry of the transducer in 3D, and using the following parameters for the propagation medium (water): sound speed  = 1500 m s -1 , volumetric mass  = 1000 kg m -3 , non-linearity coefficient B/A = 5, attenuation coefficient  = 2. 2 10 -3 dB cm -1 MHz -y , and frequency power law of the attenuation coefficient y = 2  51.