dataesr/scientific-paragraphs-categorization huggingface dataset:
Beat score analysis Statistical analysis of the beat score was conducted as described previously  (Lauschke et al. , 2020). The different categories were designated a score, "Beat" -3, "Partial Beat" -2, "No Beat" -1, "No EB" -0, to be used in the statistical analysis. Statistical significance was calculated using ordinal logistic regression in R with RStudio (RStudio inc, MA, USA). In this analysis, each EB is considered an individual unit, giving n = 240. Graphs were made using GraphPad Prism 8 (Graphpad Software, USA), showing the pooled distribution of the different beat categories in each treatment group. Fig. 1. PluriBeat protocol and study design. The pluriBeat assay was performed over a period of 8 days (indicated with vertical white lines). For each day, different differentiation medium was added to the culture.
BRB-seq preprocessing and analysis Preprocessing of raw data was conducted according to the quality control pipeline described in  Giacosa et al. , 2021. Initially, 16  bases are read and a quality score > 10 is ensured. The unique sample-specific barcode is assigned to the first 6 bp, and the unique molecular identifier (UMI) is assigned to the following 10 bp. Second reads were aligned using the human reference transcriptome from the UCSC website (hg38) with BWA version 0. 7. 4. 4 and parameter "-l 24". Read mapping to multiple positions in the genome was removed from the analysis. A gene count matrix was made from UMIs associated with each gene (lines) in each sample (columns) following quality control and preprocessing of the data. The UMI matrix was normalized with regularized log (rlog) transformation package from DeSeq2  (Love et al.
Differential gene expression and functional analysis The AMEN suite was used to identify differentially expressed genes (DEGs)  (Chalmel and Primig, 2008). For each stage (early or late) of each cell line (BiONi010-C or IMR90-1) a statistical comparison between controls and the PFOS treated groups were made to identify DEGs. In short, genes that were more highly expressed than the background cutoff (overall median of rlog-transformed UMI dataset, 0. 0) and that exceeded 1. 5-fold change compared to the control were used for further analysis (Fig. 3 , panels A-F). Significantly differentially expressed genes (DEGs) were finally identified by using the empirical Bayes moderated tstatistics implemented into the LIMMA package with an adjusted Fvalues estimated using Benjamini & Hochberg (BH) False Discovery rate approach (p ≤ 0.
05)  (Ritchie et al. , 2015; Smyth, 2004). Partitioning of the DEGs were performed by using the k-means method. Expression profiles of DEGs were displayed as false-colour heatmaps using the pheatmap package. Functional analyses were performed with AMEN  (Chalmel and Primig, 2008)  with FDR-adjusted p value of ≤0. 05. The resulting toxicogenomics signatures were submitted to the TOXsIgN repository with the accession number TSP1268 (https://toxsign. gen ouest. org) (Darde et al. , 2018).
Identification of a shared pattern of PFOS exposure We next tried to identify all DEGs that were affected similarly in both cell lines in at least one stage and one dose in response to PFOS exposure. This strategy identified 135 DEGs (Fig. 6A ). Functional analysis showed that PFOS across cell lines affects the expression of genes related to regulation of developmental process (GO:0050793, p value <0. 02, 35 genes) and cell differentiation (GO:0030154, 9E-3, 50) (Fig. 6B ). Importantly, PFOS exposures also causes transcriptional changes in genes involved in cardiac development and function. Ten of the genes are listed in at least one cardiac or heart related GO term when cross referenced with the Ensemble database  (Cunningham et al. , 2022)  (Fig. 6C ).
Matching muons to tracks measured in the silicon tracker results in a relative p T resolution of 1% in the barrel and 3% in the endcaps for muons with p T up to 100 GeV. The p T resolution in the barrel is better than 7% for muons with p T up to 1 TeV  [23]. Events of interest are selected using a two-tiered trigger system. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a fixed latency of about 4 μs  [24]. The second level, known as the high-level trigger, consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage  [25]. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematical variables, can be found in Ref.
Samples produced for the 2016 analysis use the CUETP8M1  [40]  underlying event tune, with the exception of the t t samples, which use CUETP8M2T4  [41]. The 2017 and 2018 samples use the CP5  [42]  tune. The CMS detector response is simulated with the GEANT4 package  [43]. Additional pp interactions occurring in both the same and adjacent bunch crossings (pileup) are simulated as a set of minimum bias interactions that are mixed with the hard scattering event. In the analysis of each datataking year the simulated events are weighted based on the number of pileup events to match the distribution measured in data.
4. The jet momentum is determined as the vector sum of all particle momenta in the jet, and is found from simulation to be, on average, within 5%-10% of the true momentum over the entire p T spectrum and detector acceptance. Pileup interactions can contribute additional tracks and calorimetric energy depositions to the jet momentum. To mitigate this effect, charged particles identified to be originating from pileup vertices are discarded, and an offset correction is applied to subtract remaining contributions. Jet energy corrections are derived from simulation to bring the measured response of jets to that of particle level jets on average. In situ measurements of the momentum balance in dijet, photon þ jet, Z þ jet, and multijet events are used to correct any residual differences in the jet energy scale between data and simulation  [47].
ACKNOWLEDGMENTS We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
The two nonrecurrent variants were both associated with an early-infantile phenotype. Outcome severity and correlation with genotype. Outcomes of individuals with early-infantile phenotypes were severe (n = 35), intermediate (n = 23), and benign (n = 81), as defined by criteria described in the "Methods" section and summarized in Supplementary Table  2 ; there were five unaffected individuals within affected families. All families with unaffected individuals carrying the variant had SCN2A variants that were not present in the Genome Aggregation Database (gnomAD). Seven individuals   were deceased, all severely affected prior to death. Three individuals with seizure onset at/after age 3 months did not have a normal developmental outcome (intermediate phenotype). The SCN2A variant did not consistently predict outcome; clinical severity of the early-infantile phenotype was consistent between individuals for only 18/30 (60%) recurrent variants (Table  1 ).
We implemented two previously published tools, which have utilized voltage clamp data for prediction of biophysical impact or severity of SCN2A variants. The first, a machine learning-based statistical model for function prediction  22  , providing GoF or LoF probability for the individual Na v 1. 2 variants studied here and previously  7. This method resulted in unreliable functional predictions because 12 of the 16 variants were previously included in the training dataset of the model  22  (Supplementary Table  8 ). In the second, we calculated the clinico-electrophysiological severity score of Na v 1. 2 variants (CESSNa + score) to determine the extent of changes of selected biophysical properties relative to wild-type, according to a previously published method  15  (Supplementary Table  4 ).
Although detailed voltage clamp data are still important in some instances, DAPC is a better choice for a rapid and unambiguous assessment of novel SCN2A variants identified in potential candidates for clinical trials, where an Fig. 5  Gain-of-function characteristics of the K905N variant are revealed in the presence of β 2 subunit. a 3D structure of the wild-type Na v 1. 2 channelβ 2 subunit complex (PDB accession no. 6J8E  24  ) showing the four main channel domains DI-IV (DI, gray; DII green; DIII, yellow; DIV, cyan) and the β 2 subunit (blue). b Structure-predicted interactions between the α 1 -subunit S5 DII region and the β 2 -subunit. Expanded views of the helix in segment 5 of domain II (S5 DII ) (boxed area in A) showing the positively charged K905 (wild-type, left) and the polar, uncharged side chain residue N905 (mutant, right).
Methods Standard Protocol approvals and patient consents. The study was approved by the Human Research Ethics Committees of the Royal Children's Hospital and Austin Health Melbourne, University Medical Center Groningen, State Medical Association of Berlin, and the Simons Foundation. Written informed consent was obtained for all individuals whose previously unpublished clinical data is presented here. Identification of variants and patients. The medical literature, SCN2A International Natural History Study (NHS) database, Florey Institute's Ion Channels Laboratory database, and Simons Searchlight database (SSDb) (https://www. sfari. org/resource/simons-searchlight/) were searched to identify all recurrent SCN2A variants with clinical information available on affected individuals. Individuals from the SSDb were only included if the individual had not been previously reported and was not in the NHS, to avoid duplicate reporting.
Forty-one recurrent SCN2A variants were identified. Three variants were excluded from further study. One variant (R187W), identified in two individuals, was excluded because no clinical information was available for one (father of proband), and the parents of the proband (who had febrile seizures plus) both had febrile seizures, raising the possibility of additional contributing factors for the proband's phenotype  35  , and for two variants (R379H and C959*), there was no available clinical data for any individual with that variant (two individuals for each variant). For three additional variants (R1629H, R1626Q, R853Q), two individuals with each variant were excluded due to insufficient clinical information. Thirty-eight recurrent SCN2A variants were included with clinical information available on at least two individuals per variant.
Eleven recurrent variants were selected for biophysical analysis and 3D structural modeling, spanning both earlyinfantile and later-onset SCN2A-related epilepsy phenotypes. Seven variants have not been previously studied biophysically, whereas the remaining four (A263V, E999K, E1211K, and R1319Q) have been studied with voltage clamp (Supplementary Data 2). To further understand the relationships between electrophysiological findings and severe phenotypes, we also analyzed two nonrecurrent variants (K905N, R1629L), each identified in a single individual with early-infantile-DEE  1, 36. Clinical data. Clinical information for 18 new, and updated data on 3 reported, individuals was obtained from interviews with parents and review of home videos by a pediatric epileptologist (KBH), and medical records.
Clinical data for the remaining patients were obtained from the literature (N = 159) or Simons Foundation database (N = 1). Patients were allocated to one of three phenotypic groups; early-infantile, lateronset or ID/ASD (without epilepsy) (Supplementary Table  1 ), using the previously reported phenotypic spectra for each group  2, 3, 5, 7. Group allocation was done twice, using different criteria. In the first allocation, age of seizure onset was used as the major criterion. Early-infantile was considered to be prior to age three months, later-onset at or after three months, and ID/ASD as having no seizures, given the three months age cut-off has been previously reported as distinguishing these groups, at least for SCN2A-related DEEs 3. If age of seizure onset was unknown, we considered individuals with early-infantile epileptic encephalopathy (EIEE), epilepsy of infancy with migrating focal seizures, or S(F) NIS (previously known as benign (familial) neonatal-infantile seizures (B(F)NIS)) to have an early-infantile phenotype, and individuals with West syndrome or Lennox-Gastaut syndrome at presentation, a later-onset phenotype.
5 °C) 48-72 h after transfection. The cells were super-fused at a constant rate (~0. 2 ml/min) with extracellular solution containing 145 mM NaCl, 5 mM CsCl, 2 mM CaCl 2 , 1 mM MgCl 2 , 5 mM glucose, 5 mM sucrose, 10 mM Hepes (pH = 7. 4 with NaOH), and intracellular solution containing 5 mM CsCl, 120 mM CsF, 10 mM NaCl, 11 mM EGTA, 1 mM CaCl 2 , 1 mM MgCl 2 , 2 mM Na 2 ATP, 10 mM Hepes (pH = 7. 3 with CsOH). Sodium currents were recorded using an Axopatch 200B amplifier (Molecular Devices, Sunnyvale, CA) controlled by a pCLAMP 10/DigiData 1440 acquisition system (Molecular Devices). Patch electrodes were pulled from borosilicate glass capillaries (GC150TF-7. 5, Harvard Apparatus Ltd. ). Patch electrodes exhibited resistance values of 1. 1-1. 5 MΩ; series resistance values, typically of 2-2.
5 MΩ, were 85-90% compensated. Currents and potentials were low-pass filtered at 10 kHz and digitized at 50 kHz. CHO cells of 12-25 pF cell capacitance and expressing 2-10 nA peak Na v 1. 2 currents were included in data analysis; cells with small peak amplitude sodium currents (<2 nA, ~20% of cells) or large peak amplitude sodium currents (>10 nA, ~20%) were excluded  7. Leak and capacitive currents were corrected using -P/4 subtraction, except when determining the steady-state inactivation and the recovery from fast inactivation. The voltage protocols are depicted in the figures and explained in the Results section. The current-voltage (I-V) relationships were determined by measuring peak sodium current amplitudes against depolarizing voltage steps in the voltage range between -80 to +60 mV.
The firing frequency, rheobase, upstroke velocity, amplitude, width, and decay time were evaluated using the Clampfit module of pCLAMP 10, whereas the threshold was calculated in Axograph X (Axograph Scientific, Sydney, Australia). Firing frequency (in Hz) during step current injections or in the presence of scaled synaptic current was calculated as the number of action potentials per 1 s. The first action potential elicited by a current step 2 pA above rheobase was selected for action potential morphology analysis. Rheobase (in pA) was determined as the lowest value of injected current that yielded at least one action potential. Threshold (in mV) was defined as the point on the action potential rising phase where the first derivative (dV/dt) of the voltage trajectory reached 20 mV/ms. Upstroke velocity (in dV/dt) was defined as the maximum value of the first derivative of the action potential waveform.
Amplitude (in mV) was determined as the most depolarized value of the action potential relative to the baseline (mean membrane voltage in the absence of firing). Width (in ms) was measured as the time between the halfamplitude points of the rising and decaying phases of the action potential. Decay time (in ms) is the time between the trace's crossing 90% and 10% of the baselineto-peak amplitude range in the decay stage of the action potential. Statistics and reproducibility. Correlations between genotype, biophysical profile, and phenotype were examined for all recurrent variants. Electrophysiological data were analyzed in Clampfit 9. 2 (Molecular Devices) and plotted in Origin 2022 (Microcal Software Inc. , Northampton, MA, USA). Mean and standard error of the mean (SEM) were used to describe the variability within groups; n values represents the number of independent experiments.
One-way ANOVA or two-way ANOVA followed by Dunnetts' post hoc test were performed for the statistical evaluation of data in GraphPad Prism 9. 0 (La Jolla, CA, USA). Sample sizes and P values are included in figure legends and in the Supplementary tables. Differences between groups were considered statistically significant if P < 0. 05. 3D structural modeling of Na v 1. 2 variants. In silico pathogenic variants of the human Na v 1. 2 channel in the absence or presence of the auxiliary subunit β 2 (PDB accession no. 6J8E)  24  were made and visualized in PyMOL 2. 3. 2. software (Schroedinger LLC, New York, USA).
Acknowledgements We are grateful to all patients and their families participating in this research. We acknowledge the  Simons Foundation /Simons Searchlight database in providing data for this study. We are grateful to the  Simons Searchlight families , as well as the  Simons VIP (Simons Searchlight) Consortium. We appreciate obtaining access to phenotypic and genetic data on SFARI Base. Approved researchers can obtain the Simons Searchlight population dataset described in this study by applying at https://base. sfari. org. This study was supported by an  Australian Research Council Centre of Excellence for Integrative Brain Function  grant ( CE14010007 ),  National Health and Medical Research Council (NHMRC)  program grant ( 10915693 ) to S. P. , S. F. B. , and  I. E. S. ,  Medical Research Future Fund Genomic Health Futures Mission Project Grant  to G.
B. , K. B. H. , S. F. B. , I. E. S. , and S. P. , and project funding to the SCN2A Natural History study by  RogCon, Inc. and  Praxis Precision Medicines  to K. B. H. S. P. is supported by an  NHMRC  Fellowship, I. E. S. by an  NHMRC Practitioner Fellowship and Investigator Fellowship , and K. B. H. by an  NHMRC   Early Career Fellowship  and a clinician-scientist fellowship from the  Murdoch Children's Research Institute (MCRI). G. B. was partly funded by  RogCon, Inc. J. H. is supported by an  Australian   Government Research Training Program  stipend and The  Florey Institute of Neuroscience and Mental Health. The  Florey Institute of Neuroscience and Mental Health  and MCRI are supported by a  Victorian State Government Operational Infrastructure Support Program.
The surface of the chip was cleaned immediately before the measurement by rinsing with acetone and isopropanol and drying in an air stream. Three independent chips were used and the measurement was repeated three times on each chip. The contact angle was determined using the software Scan20. A baseline correction and 5 points ellipsoid fitting procedure was used. Data processing and simulations. Data analysis was performed with OriginPro 9. 1 and in-house codes running on Matlab 2012. 4] [25]  ENBIOS solves self-consistently the PB-PNP equations in DC and small-signal AC regimes also accounting for the salinity, temperature and frequency dependence of the electrolyte. Nanoelectrodes were modeled as disks on the simulation boundary; Dirichlet boundary conditions set the electrodes' voltage.
After extracellular staining, cells were fixed and permeabilized with a Cytofix/Cytoperm kit (BD Biosciences) and then stained with antibodies directed against IFN-γ, TNF-α, CCL3, and IL-17A. GZMA, GZMB, TBET, FOXP3, and RORγt staining was performed after cells were fixed and permeabilized with an eBioscience Foxp3/Transcription Factor Staining Buffer Set. For p-STAT5 intracellular staining, cells were fixed with 2% paraformaldehyde (EMS Company) for 10 minutes at room temperature and then permeabilized with ice-cold methanol for 30 minutes before intracellular staining. Flow cytometry data were acquired on an LSR Fortessa using DIVA software and analyzed by FlowJo software (all from BD Biosciences). RT-PCR. RNA was isolated with the RNeasy mini kit (Qiagen) and reverse transcribed using an iScript cDNA synthesis kit (Bio-Rad).
Quantitative RT-PCR was performed using LightCycler 480 SYBR Green Master (Roche) and different sets of primers on a LightCycler 480 Real-Time PCR System (Roche). Samples were normalized to Gadph and analyzed according to the ΔΔCt method. Primer sequences are provided in Table  2. Bioinformatic analyses. All genomic data were analyzed with R version 3. 6. 3 (https://cran. r-project. org/) and Bioconductor (http://www. bioconductor. org/. ). RNA-seq. Illumina sequencing was performed on RNA extracted from triplicates of each condition. Standard Illumina bioinformatics analyses were used to generate fastq files, followed by quality assessment, trimming, and demultiplexing. Rsubread v1. 34. 6 was used for mapping to the hg38 genome and creating a matrix of RNA-seq counts. Next, a DGEList object was created with the edgeR package v3.
26. 7  (48). After normalization for composition bias, gene-wise exact tests were computed for differences in the means between groups, and DEGs were extracted based on an FDR-adjusted P value of less than 0. 05. GSEA was performed using the Molecular Signature Database of the Broad Institute  (49). To combine 2 data sets from different batches, count data from the 2 data sets were merged and the batch reference was included as a term in the EdgeR linear model without data transformation. For purposes of visualization (heatmaps), the limma package function removeBatchEffect was applied to the log-transformed counts per million (cpm) values before plotting. ChIP-seq. ChIP libraries were prepared and sequenced (Illumina NextSeq 500) using a standard workflow (Active Motif). Resulting with 9 g/L NH 4 Cl complemented vol/vol with culture medium.
Acknowledgments We thank all the past and present members of the  Marie lab  for their help and discussion. We sincerely thank the platforms of the CRCL: flow cytometry, histology (PIC), genomic, and animal facility (Anican/P-PAC); for their advice and assistance. We thank  Brigitte Manship ,  Anne-Marie Schmitt-Verhulst ,  Lee Leserman , and  Gregoire Lauvau  for critical reading of the manuscript. This work was supported by the  Association pour la Recherche sur le Cancer (ARC)  ( PJA20181207928-PJA20151203509-ARCDOC42020070002550 ).