dataesr/scientific-paragraphs-categorization huggingface dataset:
One single control sample was not in concordance with the assigned sex and was excluded from further analysis. Differences between probes due to InfI and InfII probe usage as well as between samples were normalized using BMIQ  61  on the 435,803 remaining probes. Differential methylation was determined using a modified version of the champ. dmp function with age, sex, and estimated proportion of non-neuronal cells as covariates in a linear regression analysis based on the limma package  62  adjusting P-values for multiple testing with a Benjamini-Hochberg correction  14. Differentially methylated CpGs were considered significant at adjusted P-values < 0. 05. To account for potential confounding owing to genetic variation, significant probes were filtered for significant mQTLs in adult prefrontal cortex using previously published data, which removed three CpGs all of which associated with GABRA5 (cg01378667, cg03325535, cg10318222) as defined by a SNP in close proximity to the gene (rs7496866)  15.
The circle plot was built with hg19 as reference genome using the OmicCircos R-Package Version 1. 4. 0  63  for R version 3. 1. 3  64. Pyrosquencing. Pyrosequencing was done on a Pyromark-Q24 using PyroMark-Q24 tools and reagents (Qiagen) according to the manufacturer´s protocol. Primers for pyrosequencing (PCR product and sequencing primer) were designed using PyroMark-Assaydesign-2. 0. 1. 15 (Qiagen) software. Primer sequences are shown in Supplementary Table  1. Data were analyzed with the PyroMark-Q24 software. Western blot. White and gray matter from the Gyrus frontalis superior of postmortem tissue was extracted in ice-cold N-PER TM lysate buffer (#87792, Ther-moFisher Scientific, Waltham, MA, USA) using the Dounce homogenizer with 10-20 strokes on ice. Proteins were obtained after removal of cellular debris by centrifugation at 10.
Positive bands were detected by an HRP-conjugated secondary antibody (dilution 1:5000, ab P0447 polyclonal goat anti mouse immunoglobulins, affitinity isolated) and chemoluminescent substrate (see above). Bands were scanned using the ChemiDoc-XRS system and Image Lab software V. 5. 1 was applied to measurement of optical densities (Bio-Rad). Optical densities of anti-DLX1 antibody positive bands were compared to TGX labeled total protein and the housekeeping protein β-actin. DLX1 antibody was validated in positive and negative controls, e. g. , tissue specific human neuronal progenitor cells derived from striatum or protein lysates from the human pancreas. Aperio methodology. DLX1 immunopositive structures on tissue sections were quantitatively measured applying Aperio technology (Leica Microsystems Inc.
0. 1. 5 we generated a literature-derived network that was based on text mining for direct interactions between significantly differentially methylated input genes. The input dataset comprised genes adjacent to the 717 genomic positions (CpG sites) found to significantly differ in methylation between PSP patients and controls. We only analyzed those genes by Network Builder that are known to be expressed in the telencephalon and in interneurons. The GeneRanker program (Genomatix) was used for analysis of association of differentially methylated genes in tissues (Supplementary Data 5)  67. The literature-derived network was curated and extended by manual literature searches as well as by in silico prediction of transcription factor binding sites applying the MatInspector program (Genomatix).
The complete set of differentially methylated genes was further explored using the Pathway Studio software for enrichment in the category "biological process" of the Gene Ontology by applying Fisher's exact test. P-values were corrected for multiple testing by Benjamini and Hochberg  14. Single-cell analysis. We downloaded data on single cells from GEO and processed them according to the recommendations detailed in [GSE67835]  22. We applied the program Prinseq (-min_len 30) to remove very short nonspecific reads. Prinseq also trimmed both ends of the reads in order to eliminate 5´duplicates (-trim_left 10) and to remove low quality 3´ends (-trim_qual_right 25). Furthermore Prinseq filtered reads of low complexity (-lc_method entropy /-lc_threshold 65). The program FASTQC was used to identify sequences that are overrepresented (adapter) in order to exclude them from further analysis.
We used the Prinseq tool to remove orphan pairs less than 30 bp in length followed by removal of nextera adapters using Trim Galore (--stringency 1). Reads were aligned to the hg19 genome with STAR using the following options (-outFilterType BySJout/--outFilterMultimapNmax 20/--alignSJoverhangMin 8/--alignSJDBoverhangMin 1 /--outFilterMismatchNmax 999/--outFilterMismatchNoverLmax 0. 04/--alignIntronMin 20 /--alignIntronMax 1000000 /--alignMatesGapMax 1000000 /--outSAMstrandField intronMotif). Aligned reads were converted to counts for each gene using HTSeq (-m intersection-nonempty /-s no). The human Ensembl General Feature Format (GTF) annotation file (version 2013-09) necessary for HTSeq was extended by DLX1AS splice variants represented by the following Genbank identifier KU179668. 1, KU179669.
1, KU179670. 1, KU179671. 1, and KU179672. 1. For all analyses Genome_build hg19 was used. Finally, counts were converted to FPKM (Fragments per kilobase of transcript sequence per million mapped fragments) values. Out of 466 available single-cell datasets, we selected 251 datasets that represent specific cortical cell types (astrocytes, microglia, oligodenrocytes, endothelia, neurons, oligodendrocyte precursor cells, see [https://www. ncbi. nlm. nih. gov/ Traces/study/?acc = SRP057196]). Hybrid cells and fetal quiescent cells were not considered. In silico promoter analysis. All sequences analyzed are from the promoter sequence retrieval database EIDorado 12-2016 (Genomatix) that is based on NCBI build GRCh38. The following Genomatix / Entrez Gene identifiers for MAPT were used: GXP_10577 / 4137 (human), GXP_7225047 / 574327 (rhesus_monkey), GXP_5397266 / 100054638 (horse), GXP_3861378 / 281296 (cow).
Promoter sequences of MAPT from four mammalian species were aligned using the DiAlign TF program of the Genomatix software suite GEMS Launcher in order to evaluate overall promoter similarity and to identify DLX1-binding sites. The corresponding position weight matrices V$DLX1. 01 and V$DLX1. 02 were applied to promoter analysis according to the Matrix Family Library Version 10. 0 (October 2016). Binding sites were considered conserved if promoter sequences could be aligned in the region of the DLX1-binding site using the DiAlign TF program. Statistics. Statistical tests were performed within the R computing environment  64  [http://www. r-project. org]. In order to calculate Pearson's product moment correlation the R function cor. test was used by setting alternative = "two-sided" and method = "pearson".
We applied the function t. test to two sample t-tests with Welchs's correction and the arguments paired = "FALSE", alternative = "two. sided" and var. equal = "FALSE". Mann-Whitney tests were performed by using the R function wilcox. test in conjunction with paired = "FALSE" and alternative = "two-sided". Test of normality according to Kolmogorov-Smirnov was applied separately to CpG-control and CpG-PSP samples for one specific gene from pyrosequencing analysis. If one of both tests was significant the corresponding dataset was defined as not normally distributed. If only one of all CpG samples for a specific gene are not normally distributed the unpaired t-test with Welch's correction was performed otherwise the Mann-Whitney test. In case of the densitometry of DLX1-immunoreactivity experiment the Mann-Whitney test was used because the corresponding datasets were not normal distributed according to Kolmogorov-Smirnov.
Acknowledgements We gratefully acknowledge technical support of  Claudia Röhrsheim ,  Michaela Weis , and  Stefanie Teschler  (RT-qPCR, pyrosequencing),  Monica Castanedes-Casey  (immunohistochemistry), and  Drs. Melissa Murray  and  Amanda Serie  (Aperio technology). We thank  Drs. Eilis Hannon  and  Jonathan Mill  ( University of Exeter Medical School ) for the mQTL data of the adult prefrontal cortex. We also thank  Drs. Per Hoffmann  and  Stefan Herms  for advice on data analysis. Dr. Hong Xu  is acknowledged for his help with DLX1 siRNA and tau overexpression experiments. We are indebted to  Dr. Ellen Gelpi  and  Dr. Laura Molina  at the  Neurological Tissue Bank of the Biobank-Hospital Clinic-IDIBAPS, Barcelona , for data and sample procurement. We thank  Dr. Hagen Scherb  ( Institute of Computational Biology, Helmholtz Center Munich, Germany ) for the support regarding statistical questions.
This work was supported by a grant (# 518 -14 ) from  CurePSP  (to G. U. H. , and U. M. ), a  German Federal Ministry of Education and Research (BMBF) -and  French National Research Agency (ANR) - funded project on "Epigenomics of Parkinson's disease " ( O1KU1403A/ ANR-13-EPIG-0003-05 EpiPD  to G. U. H. , J. T. and U. M. ), the  BMBF-funded project  " HitTau " ( 01EK1605A  to G. U. H. ), the  Deutsche Forschungsgemeinschaft (DFG ,  HO2402/6-2   & Munich Cluster for Systems Neurology SyNergy ) (to G. U. H. ), the  NOMIS foundation  ( FTLD  project to G. U. H. ), the  German Science Foundation Collaborative Research Center (CRC)   870  and the  Helmholtz Portfolio Theme ''Supercomputing and Modeling for the Human Brain'' (SMHB)  (to W. W. , D. T. and D. V. W. ), and the  Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst within Bavarian Research Network "Human Induced Pluripotent Stem Cells" (ForIPS)  (to G.
Data availability. Normalized and raw BeadChipArray data have been deposited in NCBI-Gene Expression Omnibus (GEO) with the accession code GSE75704. DLX1AS transcript variants have been uploaded to NCBI under accession numbers KU179668, KU179669, KU179670, KU179671, and KU179672. All other data are available within the paper and its associated supplementary material or upon reasonable request from the corresponding authors.
The primary outcome measure is the occurrence of a severe postoperative opioid-related adverse event within the first 48 hours after extubation defined as: postoperative hypoxaemia or postoperative ileus or postoperative cognitive dysfunction. In addition, each component of the primary outcome measure will be analysed separately. Data will be analysed on the intention-to-treat principle and a per-protocol basis. Ethics and dissemination The POFA trial has been approved by an independent ethics committee for all study centres. Participant recruitment begins in November 2017. Results will be published in international peer-reviewed medical journals. trial registration number NCT03316339; Pre-results.
They will be blind to the treatment. The anaesthesiologist and the nurse anaesthesiologist (care providers) will be the only ones not blinded. They will not participate in the assessment of the patients at any time. At each participating centre, data will be collected and entered into the electronic web-based case report form (eCRF) by personnel trained for the trial or clinic (clinical research associate), blinded to the allocation group, under the supervision of the trial site investigators.
Ancillary immunological study An ancillary study will be performed in 42 patients enrolled in the coordinating centre to confirm or not preclinical data suggesting opioid-induced immunodepression  30  and therefore a potential interest of OFA in sparing immune function. Blood samples will be drawn at D0 (morning before the surgery) then D2 (morning 2 days later) and D7 (before surgery) then D2 and D7 (or the day the patient leaves the hospital if scheduled before D7). Three tubes of 5 mL of blood in agar-free lithium heparinate (green top) tubes will be drawn at each of the three time points. The tubes must be shipped within 2 hours at room temperature to spare unstable factors such as plasma cytokines or HLA-DR expression levels. Two immunophenotyping analyses will be done by flow cytometry on the blood samples: quantification of regulatory T cells and measure of their activation markers, quantification of the circulating myeloid subpopulations, notably the suppressive myeloid cells, and the measure of HLA-DR expression.
data registration Data will be entered into the eCRF by trial or clinical personnel under the supervision of the trial site investigators at each participating centre. From the eCRF the trial database will be established. Data collection will be monitored by trained research coordinators. The following data will be registered: Baseline characteristics at randomisation Demographic data (age, height, weight, gender and body mass index); American Society of Anaesthesiologists physical status; type of surgery; significant comorbidities (cardiovascular, respiratory, neurological, psychiatric and/or abdominal disease, cancer, preoperative chemotherapy or radiotherapy).
Patient withdrawal A participant who no longer agrees to participate in the clinical trial can withdraw the informed consent at any time without need of further explanation. Participants who will withdraw from the study will be followed up, according to routine clinical practice in each participating centre. In order to conduct intention-to-treat analyses with as little missing data as possible, the investigator may ask the participant which aspects of the trial he/she wishes to withdraw from (participation in the remaining follow-up assessments, use of already collected data). Whenever possible, the participant will be asked for permission to obtain data for the primary outcome measure. All randomised patients will be reported, and all data available with consent will be used in the analyses.
The following events: postoperative hypoxaemia, POI and POCD will be recorded as primary evaluation criteria in the case report form. In order to avoid collection duplication, they will not be reported on the 'adverse event' page of the case report form. As planned in the study, they will be analysed at the time of interim analyses (two interim analyses after inclusion of a third and two-thirds of the patients) which will permit to show potential difference between the two groups during the study. In addition, serious adverse events will be submitted to the data monitoring and safety committee (DMSC). The DMSC is independent of the trial investigators and will perform an ongoing review of safety parameters and overall study conduct. The DMSC comprises three independent clinicians (anaesthesiologists), a physician pharmacologist and a methodologist.
If they are unexpected, they are qualified as being Suspected Unexpected SAR (SUSAR) and will be notified in a report by the sponsor to Eudravigilance (European pharmacovigilance database) and to the local regulatory agency within the regulatory time periods for reporting: immediate declaration if seriousness criteria is death or life-threatening condition, declaration within 15 days for other seriousness criteria. data handling and retention Data will be handled according to the French law. All original records (including consent forms, reports of suspected unexpected serious adverse reactions and relevant correspondences) will be archived at trial sites for 15 years. The clean trial database file will be anonymised and maintained for 15 years.
EthICs And dIssEMInAtIon Ethical and legislative approvals The POFA trial is registered in the European Clinical Trials Database (EudraCT 2017-001907-61) and at ClinicalTrials. gov with the trial identification number NCT03316339. Trial methods and results will be reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2010 guidelines. 31 blication plan Scientific presentations and reports corresponding to the study will be written under the responsibility of the coordinating investigator of the study with the agreement of the principal investigators and the methodologist. The coauthors of the report and the publications will be the investigators and clinicians involved, on a pro rata basis of their contribution in the study, as well as the biostatistician and associated researchers.
However detailed databases of passive scalar concentration within such confluences and downstream branches is lacking in the literature and thus does not permit to validate the 3D numerical codes, either advanced (LES or DES) or more operational codes (RANS) regarding their capacity to fairly reproduce the mixing processes. Mixing processes in confluences are governed by the local hydrodynamics, which is itself governed by large scale three-dimensional secondary flow structures. Even for an idealized 90° angle subcritical confluence with rectangular channel cross-sections of equal widths, the flow pattern comprises  [8] ,  [9] ,  [1] : i) a recirculation region with low velocities near the bank of the downstream branch, ii) a contraction zone with a flow acceleration on the side of the recirculation region, iii) a mixing-layer at the interface between the two incoming flows and iv) downstream secondary currents.
Numerical model The reference for the downstream axis is the downstream corner of the confluence, indicated with a red point on Fig. 1. The distances are made non-dimensional using the width of the channel b, which is the same for all the branches of the confluence. The two inlets have a length of 25b and the downstream branch has a length of 50b, which permits to describe the mixing downstream of the confluence correctly. The flow and mixing at the confluence are computed numerically using the commercial software of Computational Fluid Dynamics (CFD) Star-CCM+ ©, solving RANS equations coupled with the advection-diffusion equation. The turbulence model used is the Reynolds Stress Model (RSM). The passive scalar properties correspond to a chemical pollution: the molecular Schmidt number is high (666).
75 mg/cm 2 final thickness. We note that this was the maximum Carbon thickness we could reach keeping at the same time a high quality of the target. The deexciting γ rays were registered with the GASP array  [6]  in its configuration II. Charged particles were detected with the EUCLIDES silicon ball  [7]. Gain matching and efficiency calibration of the Ge detectors were performed using 152 Eu and 56 Co radioactive sources. The data were sorted into coincidence γγ matrices whereby the detection of protons was required to construct the matrices for 31 P, since it is 1p exit channel of the reaction. -Next to necessity of a precise determination of the branching rations and lifetimes of excited nuclear states it is very important to determine the multipole mixing ratios. Our approach for their determination is presented below.
The best way to obtain this information is to perform a Monte-Carlo (MC) simulation. For the MC simulation of the slowing-down histories of the recoils we used a modified  [10, 11]  version of the program DESASTOP  [12]  created by G. Winter. In this version, the time-evolution of the recoil velocities in the target and stopper is followed in three dimensions. A crucial point for the correct MC procedure is to determine precisely the stopping powers. In some cases the uncertainty of the final value of the lifetime could be about 10-20 %, coming from the error of determination of the stopping powers. In the present analysis, the electronic stopping powers used were obtained from the Northcliffe and Schilling tables  [13]  with corrections for the atomic structure of the medium along the lines discussed in Ref.
[14]. As suggested in Ref. [15] , an empirical reduction of fn = 0. 7 was applied to down-scale the nuclear stopping power predicted by the theory of Lindhard, Scharff and Schiøtt  [16]. According to the calculations performed, the mean velocity of the recoils when leaving the target was about 3. 7 % of the velocity of light, and they needed in average 1. 1 ps to come to rest. The evaporation of charged particles, which is of importance for the velocity distribution of light residual nuclei, was taken into account in the MC simulation and led to better fits of the spectra. The database of about 10 000 velocity histories was additionally randomized with respect to the experimental setup by taking into account the exact position of the GASP detectors and their finite size. Complementary details on our approach for Monte-Carlo simulation can be found in Refs.
As a test of our lifetime approach we determined some lifetimes in 31 P which are already known and published. The values we derived are in a very good agreement with the results from earlier measurements  [19, 21]. For example we could compare the value of the lifetime of the 3/21 + state reported in the literature  [21]  τ = 745(35) fs with the value derived by us τ = 736  (24)  fs. The agreement is perfect, but in our measurement due to the excellent statistics the error is smaller. The quality of the data is well seen in Fig. 4. Different statistics for the two cases presented in Figs. 4  and 5  is due to the different portion on which the gates on the feeding transitions were set. Due to close transition energies in the mirror nuclei the gates should be chosen very carefully in order not to include any contamination from the mirror nucleus.
Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (T B ) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models.
, 2004). These approaches appear to be very promising to evaluate and predict water resources but are still under development for further use in operational hydrological applications  (Xu et al. , 2014). Larue et al. (2017)  showed that the GlobSnow-2 SWE product  (Takala et al. , 2011) , which assimilates both T B satellite data and local snow depth observations, was not accurate enough for hydrological modeling, mainly because of its dependence on in situ data in remote areas. The main difficulty in the assimilation of PMW satellite observations in boreal forest areas is the quantification of all the contributions that affect the measured signal. PMW satellite observations have a low spatial resolution (∼ 10 × 10 km 2 ) and satellite sensors measure many contributions in addition to the PMW emission from the volume of the snowpack (vegetation canopy, ice crust, frozen/unfrozen soil, lakes, moisture in the snow, topography, etc.
, 2016). However, the assimilation of PMW must be used with care, and a good understanding of the interactions between the properties and microwave emission of the snowpack is crucial to avoid degradation of the SWE estimates. For instance, the assimilation of passive microwave in wet snow conditions can introduce large uncertainties since the presence of liquid water in the snowpack increases T Bs , whereas increases in snow grain size decrease the brightness temperature independent of any change in SWE  (Klehmet et al. , 2013). The assimilation of PMW thus can help to adjust the modeled snowpack states during the winter, but it cannot be used at the beginning and at the end of the season (snowmelt periods). This paper aims at developing and validating the assimilation of PMW satellite observations for SWE improvements over Quebec by adjusting meteorological forcing data and simulated snowpack states without using any surface-based observations.