dataesr/scientific-paragraphs-categorization huggingface dataset:
3D-chemical structure dataset preparation and in silico profiling The molecular structures of the 81 identified drugs were downloaded from the DrugBank website  (Wishart et al. 2018 ) (https:// go. drugb ank. com/) as SD-files containing 2D atom coordinates. Stereoisomers were elaborated since some drug formulations have racemic mixtures. The individual drug SD-files were concatenated to a single file for processing by the software Flipper (Flipper 3. 1. 1. 2: OpenEye Vol. : (0123456789) Scientific Software, Santa Fe, NM. https:// docs. eyeso pen. com/ appli catio ns/ omega/ flipp er. html) (version 3. 1. 1. 2, with default settings for all non-mandatory options) in order to enumerate all possible stereoisomers of drugs having one or more undefined stereocenters. The resulting output SD-file contained a total of 111 structures which were then subjected to the program OMEGA  (Hawkins et al.
From this data set, we extracted all records for the drugs from our drug list (81 drugs) and the MedDRA terms as defined above. All records meeting commonly used criteria, specifically proportional reporting ratio (PRR) ≥ 2, the lower boundary of the CI 95% of the information component (IC025) had to be positive, and at least 5 reports in the dataset were used for further analysis. Except for the number of reports, these criteria correspond to those used in  Andronis et al. (2020). Visualization of the pharmacovigilance data was done using Python 3. 10 with the plotly 5. 5. 0 and pandas 1. 4. 0 libraries. In Fig. 4 , the percentages of the bar charts were calculated as follows: For the upper bar plot: Drug AE % = Drug AE reports / Drug total reports * 100. For the lower bar plot: AE % = Drug AE reports / Total reports for seizures incl subtypes (In total 46,285 reports) * 100.
Adding of dotting medium: for 240,000 cells/8 µL (Gluta Neurons). Adding of dotting medium: for 27,000 cells/3 µL (Astrocytes). Cell suspensions: GlutaNeurons und Astrocytes mixed 102,000 GlutaNeurons/5 µL and 13,800 Astrocytes/5 µLratio ~ 12% 0. 5 µL cell suspension/well added directly to the electrode area of the wells. Incubated for ~ 1 h at 37 °C, 5% CO2, and > 90% humidity, add the filling medium (200 µL). Medium change every other day for 14-21 days. Vol. : (0123456789) Regarding data acquisition, all recordings were executed with a Multiwell-MEA System (Multichannelsystems, MCS). The MEA system was pre-warmed to 37 °C, and plates were covered with 5% CO2 during the whole measurement. 96-well plates were placed into the device, and the cells were left to rest for 10 min. The electrical activity of the co-cultures was measured with a sampling rate of 20,000 Hz for 5 min intervals before compound addition and 2, 10, and 60 min after compound addition.
The following filters were used: High-Pass: 2nd Order Butterworth filter (10 Hz) and Low Pass: 2nd Order Butterworth filter (3500 Hz). A burst was defined to be > 50 ms with > 10 spikes. The experimental endpoints analyzed were spike count, spike rate, burst count, mean burst duration, mean burst spike count, mean burst spike rate, percentage spikes in bursts, mean interburst interval, network burst count, mean network burst duration, mean network burst spike count, mean network burst spike rate, percentage spikes in network burst, and mean network interburst interval. All analysis was done with Multichannel systems Multiwell Analyzer software Version 2. 0. Raw data were exported for further analysis in Excel. The values measured by each electrode were normalized with predose values of the same electrode, and the percentage of change was calculated for the 5 min intervals at 2 min, 10 min, and 60 min after compound administration.
1 mM Mg 2 + and 0. 1% DMSO) and positive reference (4 Aminopyridine or 4-AP at 30 µM) are also added on this "product plate. " The results for 4-AP are 146% for frequency and 95% for amplitude. For fluorescence detection, µCell FDSS instrument (Hamamatsu®) is used, a kinetic plate reader with an integrated dispensing head and imaging-based detector. Ca 2 + flux oscillations variations are detected live for 15 min, the injection starting at 7 min 30 s after the beginning of the recording. During this recording, two parameters are measured in each well: oscillation frequencies and amplitudes (before and after compound injection). Data output for frequency and amplitude are obtained with Waveanalysis® software Vol:. (  1234567890 ) from Hamamatsu© and are given in Reference Fluorescence Unit (RFU) and used for data analysis.
Raw data from µCell FDSS are processed with Waveanalysis® software from Hamamatsu© in combination with a template of product plate scheme. The software enables counting the number of peaks detected and their height. Data output of oscillations frequency and amplitude for each well after compound injection are reported as ratio values, i. e. , normalized to 100% in comparison to values before injection (initial state) per well and versus vehicle control. Data obtained for the test article at different concentrations can be used for EC50 estimation using speed HMTS, a software designed by Sanofi with the main objective of drawing dose-response fitting for screening throughput activities. EC50 can be estimated for each parameter and for each compound with several tested concentrations.
Clampex (Axon Instruments, version 9. 2) was used to control the amplitude and frequency of the voltage stimulus and to record the evoked response at the recording electrode. The assay has been pharmacologically validated as shown in  Easter et al. (2007)  and in the supplementary information with bicuculline (Supplementary Figure  S13 ). Data were expressed as % difference versus vehicle. For statistical testing, raw concentration data were fit with a linear mixed effects model in R using the package lme4 (version 1. 1-31). Models were specified with a fixed effect of dose, and a random effect of slice nested within the animal to account for the hierarchial structure of the experiment. On occasion, there were multiple technical replicates per slice (i. e. , a single slice was tested twice).
Where this occurred, the data across the replicates was summarized as a mean value per slice. Where there was a significant main effect of dose as indicated by analysis of variance using Sattherwaite's method for degrees of freedom estimation, a Dunnett's posthoc analysis was carried out to compare all doses to vehicle using the R package emmeans (version 1. 8. 3). A significance threshold of p < 0. 05 was used throughout. Model fit was evaluated via visualization of the residuals. For the bicuculline data, there was clear non-linearity in the residuals, and the raw data were log transformed to improve model fit.
Electrodes with 100 or more spikes over the 15-min post-treatment period (~ 7 spikes/min) were determined to be "active," and only wells with 5 or more active electrodes were used in the final analysis. If a treated well fell below the activity threshold due to compound effect, only spike count was determined and reported. All other parameters were not calculated. The following 12 endpoints, calculated using custom MATLAB (MathWorks, Natick, MA) scripts, were reported: firing rate (spikes/second), burst rate (bursts/ sec), number of spikes in burst, percent isolated spikes, coefficient of variation (CV) of the inter-spike intervals (ISI) (indicator of the burstiness of the spike train), normalized burst duration IQR (indicator of burst duration regularity), burst duration (s), interburst interval (s), mean of ISI-distance (synchrony endpoint), normalized mean absolute deviation (MAD) burst spike number (indicator of statistical dispersion of the spikes in bursts), median/mean ISI (indicator of spike organization within bursts/burst deterioration), and median ISI.
Funding Open access funding provided by Austrian Science Fund (FWF). The authors would like to acknowledge financial support from the European Community: The NeuroDeRisk project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 821528. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and EFPIA. This study has also received funding from the Austrian Science Fund with projects W1232 and DOC 33-B27.
We analyzed task-based functional brain connectivity under well-defined conditions while participants performed a semantic relatedness judgment task. This approach allowed us to assess brain connectivity while all participants were performing the same task but yet independent of task-related brain activation (see Methods). Importantly, the functional connectivity patterns during task conditions improves the predictions beyond resting-state functional connectivity  (Cole et al. , 2021). Finally, we applied the predictive model built on task-related functional connectivity data to the resting-state data of the participants as an internal validation and evaluation of the generalization of the predictive model to rest data. The advantage of using the CAT, rather than the classic RAT, is that creative performance in trials with lower and higher associative remoteness can be assessed separately by varying associative distance between cue and solution words across CAT trials.
Participants Data collected for this study was part of a larger study examining the role of neural and cognitive networks in creative thinking (Ovando-Tellez,  Benedek, et al. , 2022; Ovando-Tellez, Kenett, et al. , 2022). We recruited 101 healthy right-handed participants (48 women, aged 22 -40 years, mean age 25. 6 ± SD 3. 7) without neurological disorder, cognitive disability or medication affecting the central nervous system. All participants were French native speakers and had normal or corrected-to-normal vision. Eight participants were excluded from the fMRI analysis due to the discovery of MRI brain abnormalities (6 participants), falling asleep during the acquisition of the data (1 participant) and claustrophobia episode at the beginning of the MRI scanning (1 participant). The participant that experienced the episode of claustrophobia performed the task outside the scanner and this data was included in the behavioral analysis.
Combined Associates Task (CAT) The CAT  (Bendetowicz et al. , 2017; Bendetowicz et al. , 2018)  is an adaptation of the RAT  (Mednick, 1962)  and measures the ability to combine remotely associated words. In this task, three cue words with no obvious link are presented to the participants and they have to come up with a fourth word that is semantically related to all the three cue words. CAT trials vary according to the mean associative distance between each of the three cue words and the expected solution, based on French association norms  (Debrenne, 2011). This database enables to compute an associative strength (or frequency) that quantifies the percentage of native French speakers (n > 450) who produced the word B when they were given the word A in a free word association task. Therefore, each trial had a value measuring the mean associative strength between the cues and the expected solution, where lower values represent higher remoteness (i.
MRI data acquisition and Preprocessing Participants completed the RJT task in six separated runs during the fMRI acquisition data. FMRI data were collected on a 3T MRI scan (Siemens Prisma, Germany) with 64-channel head coil using multi-echo echo-planar imaging (EPI) sequences. Each run included 335 whole-brain volumes acquired with parameters as follows: repetition time (TR) = 1,600 ms, echo times (TE) for echo 1 = 15. 2 ms, echo 2 = 37. 17 ms and echo 3 = 59. 14 ms, flip angle = 73°, 54 slices, slice thickness= 2. 50 mm, isotropic voxel size 2. 5 mm, field of view (FoV) = 210 mm, Ipat acceleration factor 2, multi-band 3 and interleaved slice ordering. At the end of the six functional runs, a T1-weighted structural image was acquired with the parameters as follows: TR = 2,300 ms, TE = 2. 76 ms, flip angle = 9°, 192 sagittal slices with a 1 mm thickness, isotropic voxel size 1 mm, FoV = 256 mm, Ipat acceleration factor 2 and interleaved slice ordering.
A 15-minute resting scan was collected after the T1 acquisition. No dummy scan was recorded during the acquisition; therefore, we did not discard any volume. The fMRI data was preprocessed in three different steps. First, we preprocessed the fMRI data for each run separately using the afni_proc. py pipeline from AFNI  (Cox, 1996). The AFNI pipeline included despiking, slice timing correction and realignment to the first volume (computed on the first echo). The second preprocessing step consisted of denoising the data using the TE-dependent analysis of multi-echo fMRI data (TEDANA), version 0. 0. 9  (Kundu et al. , 2013; Kundu et al. , 2012). The acquisition of multi-echo EPI sequences allowed us to preprocess the data by assessing the BOLD and non-BOLD signal through the ICA-based denoising method.
The last step of the preprocessing was performed with the Statistical Parametric Mapping (SPM) 12 package running in Matlab (Matlab R2017b, The MathWorks, Inc. ,USA). The denoised data was co-registered on the T1-weighted structural image 12 package running in Matlab (Matlab R2017b, The MathWorks, Inc. , USA). We then normalised the data to the Montreal Neurological Institute (MNI) template brain, using the transformation matrix computed from the normalisation of the T1-weighted structural image, performed with the default settings of the computational anatomy toolbox  (CAT 12; Gaser et al. , 2022)  implemented in SPM 12. To covary out the task-related signal from each run, we entered the data in a general linear model (GLM) in SPM. For the GLM analysis, we entered the 24 motion parameters (standard motion parameters, first temporal derivatives, standard motion parameters squared and first temporal derivatives squared) and the onsets and durations of the task related events as confounds to be regressed from the BOLD signal.
Finally, we concatenated the residuals of each task run and we standardized and detrended the resulting data before concatenating the six runs. The rest periods between runs (6 volumes in total) were removed from the concatenation. This method follows the background connectivity approach  (Cole et al. , 2019). This choice was motivated by previous studies showing subtle differences in intrinsic networks during task performance compared to rest, which are largely individual-specific  (Gratton et al. , 2018)  and facilitate the prediction of individual traits and differences in brainbehavior relationships  (Cole et al. , 2021; Greene et al. , 2018; Jiang et al. , 2020). The final dataset composed of the concatenated residuals of the six task-runs was used as input for the subsequent task-based functional connectivity analyses.
Building the task-based functional connectivity matrices We estimated the task-based functional connectivity matrices of each participant using Nilearn v0. 3  (Abraham et al. , 2014)  in Python 2. 7. To define the region of interest (ROIs), we used the Schaefer brain atlas consisting of 200 ROIs distributed into 17 functional subnetworks across 8 main functional networks  (Schaefer et al. , 2017). We selected the Schaefer atlas because the definition of the different parcels was based on resting-state functional connectivity by integrating two approaches of local gradient and global similarity, and because it has more functionally homogeneous brain parcellations than other atlases, both when exploring restingstate and task-based functional connectivity data  (Schaefer et al. , 2017). We extracted the BOLD signal during the RJT task for each ROI separately (averaged across voxels) and computed the Pearson correlation coefficients between all pair of ROIs.
Connectome-based predictive modeling (CPM) The CPM analyses were performed using Matlab Statistical Toolbox (Matlab R2020a, The MathWorks, Inc. , USA). The pipeline for the CPM is adapted from the Shen protocol  (Shen et al. , 2017). Using the CPM approach  (Beaty et al. , 2018; Rosenberg et al. , 2018; Shen et al. , 2017) , we explored whether creativity scores measured by the CAT task can be predicted from patterns of functional connectivity during the RJT task. We performed independent CPM-based predictions of the CAT scores (CAT_all, CAT_close, CAT_distant, CAT_all_RT, CAT_close_Rl, CAT_distant_RT and CAT_eureka) and of the individual's intercept (CAT_ability) and slope (CAT_sensitivity) estimated from the logistic regression analysis. By following the method described in Shen (  2017 ), we used the leave-one-out cross-validation that consisted in building the predictive model iteratively on N-1 participants (training) and test the prediction on the left-out participant.
The highest degree node was found in the left hemisphere being part of the DAN (superior parietal, k = 9). All other highest degree nodes were found in the right hemisphere being part of the somatomotor network (k = 8), ECN (inferior parietal, k = 7), DMN (inferior parietal, k = 5) and visual (extrastriate cortex, k = 3) networks (Figure  8b ). The information of the main node's location, the functional network they belong to, and MNI coordinates is provided in Supplementary Tables S5. Internal validation: Prediction of the CAT performance from resting-state functional connectivity As an internal validation, we explored whether the predictive models of CAT performance -CAT_all, CAT_close, CAT_distant, CAT_ability and CAT_sensitivity -trained in the taskrelated fMRI data can be generalized to independent resting state connectivity data.
Acknowledgments The research was supported by  "Agence Nationale de la Recherche " [grant number  ANR-19-CE37-0001-01 ] (E. V. , M. B. , and Y. N. K. ), the " Fondation pour la recherche medicale " [grant number:  DEQ20150331725 ] (E. V. ), and received infrastructure funding from the  French program  " Investissements d'avenir "  ANR-10-IAIHU-06  (E. V. ). This work was also funded by  Becas-Chile of ANID-CONICYT (MOT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank the participants for making this work possible.
Code availability Analyses were conducted using open software and toolboxes available online as described in the Methods section (SPM 12: https://www. fil. ion. ucl. ac. uk/spm/software/spm12/; AFNI: https://afni. nimh. nih. gov); Nilearn: https://nilearn. github. io/stable/index. html; TEDANA: https://tedana. readthedocs. io/en/stable/; CPM: https://www. nitrc. org/projects/bioimagesuite/; Network metrics computation: https://sites. google. com/site/bctnet/Home/functions). The scripts written for this paper are available upon request from the corresponding author.
2. 3 and EG. 5. 1 sub-lineages. Structural studies of P4J15 in complex with Omicron XBB. 1 Spike show that the P4J15 epitope shares ∼93% of its buried surface area with the ACE2 contact region, consistent with an ACE2 mimetic antibody. In vitro selection of SARS-CoV-2 mutants escaping P4J15 neutralization showed reduced infectivity, poor ACE2 binding, and mutations are rare in public sequence databases. Using a SARS-CoV-2 XBB. 1. 5 monkey challenge model, P4J15-LS confers complete prophylactic protection with an exceptionally long in vivo half-life of 43 days. Conclusions: The P4J15 mAb has potential as a broad-spectrum anti-SARS-CoV-2 drug for prophylactic protection of at-risk patient populations.
However, the infectivity of select VLPs was reduced with Spike proteins harboring many of the escapee mutations (Fig. 3d ) including A475D and N487D in the BA. 2. 75. 2 Spike and N487D, N487K and N487T in BQ. 1 Spike. Furthermore, using the ACE2-RBD interactive tool developed by Jesse Bloom's lab,  24  it was found that all the Spike escape mutations in RBD induced a strong, 1-to 3-log reduction in binding affinity for ACE2 relative to the Omicron BA. 2 Spike reference strain (Fig. 3e ). The reduced binding affinity of ACE2 for the Spike RBD escape mutations was expected based on our structural data in Fig. 2e-g , as these residues are important for both P4J15 and ACE2 binding to the RBD. Furthermore, we examined the GISAID EpiCoV database to determine the frequency of the Spike mutations mediating escape to P4J15 neutralization.
27, 28  Indeed, similar to ACE2 and consistent with having ACE2 mimetic properties, we see that P4J15 has improved neutralizing activities against post-Omicron variants (EC 50 values of 5-14 ng/ml) compared to the ancestral 2019-nCoV (EC 50 of 41 ng/ml) in the Spike pseudotyped lentiviral neutralization assays. Even with the highly overlapping binding epitope of P4J15 and ACE2 on the Spike RBD, we confirmed that the development of resistance is inevitable when a virus is under selective pressure, at least in vitro. Mutations centered at F456, A475, G476, N477, N487, and Y489 did confer escape to neutralization by P4J15 but also reduced binding to ACE2 by 1-to 3-logs, indicating that the virus incurs a significant fitness penalty in developing resistance. Although epistasis is always possible to complement for deleterious mutations,  29  the almost complete absence of these specific resistance mutations within the GISAID database confirms that the virus cannot easily escape the type of inhibition imposed by P4J15 without compromising its ability to spread in the population.
Of note, substitutions at some of the incriminated positions are detectable in public sequence databases, albeit at very low frequency, but these mutations do not confer resistance to neutralization by P4J15, which requires very specific amino acid substitutions. We note that in select Spike mutations, there are discrepancies between the infectivity data obtained in the VLP-based assay and the predicted binding affinity of the resistant Spike for the ACE2 receptor, illustrating the limitations of any in vitro assay. The relative ACE2 binding data were obtained from Jesse D. Bloom's online interactive tool. While this deep mutational scanning approach is a highly valuable and well controlled approach for predicting the potential effects of mutations in the RBD, it has shortcomings, as acknowledged by its inventors themselves.
1. 5 virus. h) Area under the curve (AUC) analysis of gRNA detected between days 0 and 15 of the study. Individual data for nasopharyngeal fluids collected from left and right nostril for each of the timepoints (n = 12) and tracheal fluids (n = 6) were plotted in left and right panels, respectively. i) AUC analysis of sgRNA detected between days 0 and 15 of the study for samples as indicated in h. j) Pharmacokinetic evaluation of P4J15 LS serum concentrations with the terminal phase half-life determined using a two-phase exponential decay analysis. Mean values ± SD are shown, and Mann-Whitney two-sided tests were performed to compare study groups in panels d, h, i with P values of 0. 0022 (**) for d; and p < 0. 0001 (****) and 0. 0022 (**) of h and i. Lower limit of detection were 2. 76-and 2.
SARS-CoV-2 live virus stocks All biosafety level 3 procedures were approved by the Swiss Federal Office of Public Health. The SARS-CoV-2 BA. 2. 75. 2 (EPI_ISL_14795784) and BQ. 1 (EPI_ISL_15369810) isolates were a kind gift from I. Eckerle, Geneva University Hospitals. Viral stocks were prepared in EpiSerf (ThermoFisher Scientific, USA) with a single passage on VeroE6 cells, aliquoted, frozen, titrated on VeroE6 cells by conventional plaque forming units, and sequence verified. BA. 2. 75. 2 isolate differed from our cloned BA. 2. 75. 2 ORF by 1 supplemental mutation (G181V) already found in the original virus isolated from the patient.
Viral escapees sequencing and mapping Viral RNA was extracted from passage 5 supernatants and deepsequenced. Sequencing reads were mapped to the SARS-CoV-2 WuhCor1 strain downloaded from the UCSC database, using botwie2 in sensitive mode with read gap penalties 5,1. 9. The perbase package (https://github. com/sstadick/perbase) was then used to obtain the nucleotide depth for each base in the genome. Only mutations found in more than 20% of the reads were taken into account.