dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants were repeating one of three syllables ('ba','da','ga') in each trial. These syllables were chosen to minimally differ acoustically (by a few dozens of ms of voice onset time, VOT) but rely on very different movements at the articulatory levels. This aims at optimizing the differences observed at the production level while limiting potential contamination by exogenous acoustic cues. Participants performed 16-55 trials for each syllable. Stimuli presentation was coordinated with Presentation software (Neurobehavioral Systems). All behavioral recordings were made via a computer on the service tray of a hospital bed using Presentation Software (NeuroBehavioral Systems). Audio recordings were obtained using a microphone connected to the computer, and were synchronized to the onset of the last auditory cue.
Electroencephalographic (ECoG) activity was recorded from intracranially implanted subdural electrodes (AdTech Medical Instrument Corp. ). Recordings included grid, depth and strip electrode arrays. Each electrode had a diameter of 4 mm (2. 3 mm exposure), and the space between electrodes was 6 mm (10 mm center to center). Neural signals were recorded on a 128-channel Nicolet One EEG system with a sampling rate of 512 Hz. Anatomical localization of ECoG electrodes. ECoG electrodes were localized using the iELVis toolbox (http://github. com/iELVis/iELVis)  71. Briefly, each patient's pre-implant high-resolution structural MRI scan was automatically segmented and parcellated using Freesurfer (http://surfer. nmr. mgh. harvard. edu/)  72. A post-implantation high-resolution CT or MRI scan was coregistered with the preimplant MRI scan.
One patient from the first study was not included in the present dataset based on this control, as the patient was whispering words instead of pronouncing them covertly. (iii) Available video recordings of patients during the task were watched by one of the authors to check that no movements of the mouth could be observed during the imagined speech task. Time series were visually inspected, and contacts or trials containing epileptic activity and excessive noise were removed. Trials with overt speech were checked for acoustic contamination by correlating the recorded audio signal and the neural data (Supplementary Fig. 22 )  57. All times series were then corrected for DC shifts by using a high-pass filter with a 0. 5 Hz cutoff frequency (zero-phase Butterworth filter of order 6, zero-pole-gain design).
Acknowledgements This work was funded by  EU FET-BrainCom  project (A. G. ),  NCCR Evolving Language ,  Swiss National Science Foundation  Agreement # 51NF40_180888  (A. G. ),  NINDS R3723115 (R. T. K. ) ,  Swiss National Science Foundation  project grant  163040  (A. G. ),  National Natural Science Foundation of China   32071099  (X. T. ),  Natural Science Foundation of Shanghai   20ZR1472100  (X. T. ),  Program of Introducing Talents of Discipline to Universities ,  Base B16018  (X. T. ),  NYU Shanghai Boost Fund  (X. T. ),  Fondation Pour l'Audition FPA RD-2020-10  (L. A. ),  Swiss National Science Foundation  career grant  167836  (P. M. ), and  Swiss National Science Foundation  career grant  193542  (T. P. ). The authors thanks  Dr. Gerwin Schalk ,  Dr. Dan Friedman , and  Dr. Patricia Dugan  for providing access to the datasets used in this work, and  Dr.
We used the publicly available reduction-utils script by the DSHARP team to (1) determine parameters for phasecentring, which does so by fitting a Gaussian to the inner part of the disc, and (2) determine whether further flux-rescaling was required post-pipeline calibration. We re-scaled e x ecutions 1, 2, and 3 from P2015 due to an initial difference of > 4 per cent in flux. Finally, prior to self-calibration, we generated three concatenated files with (i) the three individual e x ecution blocks of P2015, (ii) the two e x ecutions of P2016 with compact configuration, and (iii) the three e x ecutions of P2016 with extended configuration, using CASA task concat. Self-calibration involved the sequential application of multiple phase-only calibrations to the continuum-only spectral windows (spws), with solution intervals in the order of [length of a scan] > 120 s > 60 s > 30 s > 15 s > 6 s.
For P2015, calibration tables from the single continuum only spw were applied to all line spws. P2015 has a channel spacing of 61 kHz for the line spws, corresponding to a spectral resolution of 71 kHz, due to the correlator applying Hanning smoothing. Similarly, P2016 has a channel width of 122 kHz for line spws with a channel binning of 4. The product images are the combined products of the individually self-calibrated P2015 and P2016 data sets, where CASA tasks fixvis and fixplanets were used for alignment prior to merging using CASA task concat. Imaging was performed using CASA task tclean with the multiscale deconvolver and several weighting schemes Downloaded from https://academic. oup. com/mnras/article/517/4/5942/6772461 by Universite de Liege user on 11 May 2023 Figure  1. Gas rings, gaps, and cavity.
To search for hidden kinematic signatures of an embedded planet(s) in HD 169142 (refer to Figs. 4  and 5  ), we utilize the software EDDY  (Teague 2019 ) , which subtracts a background disc model with parameters determined via the Markov chain Monte Carlo (MCMC) method, where the posteriors explore the azimuthally av eraged v elocity fields from the observations. We fit for disc centre coordinates (x 0 and y 0 ), PA, M , and v LSR using 250 w alk ers to explore the posterior distribution with a total of 10 000 steps, of which the w alk ers were found to hav e conv erged in less than 500 steps. Disc inclination was kept fixed at 13 •. The MCMC fitting was limited to the inner 1. 5 arcsec radial range of the disc, because (1) line emission past this radius was found to be more noise dominated, and (2) the gas surface density was also found to drop drastically past the outermost dust ring and thus could have resulted in an underestimation of the stellar mass if included.
To determine the statistical significance of any detected kinematic structure, we divide the residual velocity maps by the statistical uncertainty on the velocity per pixel ( δv 0 ). To spectrally collapse line emission , we utilize the BETTERMO-MENTS package (Teague & F oreman-Macke y 2018 ), which returns both the projected peak velocity maps (v φ ) and the statistical uncertainty per pixel ( δv φ ). Specifically, we use the Gaussian method to spectrally collapse the cube, as the method has been shown to have minimal statistical uncertainty  (Yu et al. 2021 ). For the kinematic analysis we use maps with a slightly degraded beam of 0. 1 arcsec resolution, using the Briggs robustness factor of 0. 5 (instead of 0) to achieve higher sensitivity. Additionally , a uv-taper is also applied to circularize the beam.
Computing the gas surface density structure The upper and lower bounds of the gas column density structure are computed as per the method outlined in  Garg et al. ( 2021 ) , and we refer the reader there for more thorough discussions on caveats and a means of accounting for them. This technique has also been implemented in  Lyo et al. ( 2011 ) ,  Schwarz et al. ( 2016 ) ,  Perez et al. ( 2015 ) , and  Casassus et al. ( 2021 ). For completeness, we provide the main formulae in Appendix A. We use the Leiden Atomic and Molecular Database (LAMDA)  (Sch öier et al. 2005  ) (accessed in 2020 July), to acquire quantities for the E u and A ul coefficients in equations ( A2 ) and ( A3 ). The computed molecular column density using equations ( A2 ) and ( A3 ) is scaled to represent total gas column density by multiplying by the canonical ISM abundance ratios:  (Stahl, Casassus & Wilson 2008  ), [ 16 O]/[ 18 O] ≈ 500  (Wilson & Rood 1994 ) , and [H 2 ]/[ 12 CO] ≈ 10 4.
Semi-analytical modelling of the wake F or qualitativ e comparison of the kinematic features seen in the observations to those induced by an embedded planet, we employ the semi-analytical method of modelling the w ak e presented in  Bollati et al. ( 2021 ) , with the implementation in WAKEFLOW 1. Via this method, we only consider coplanar orbits and velocity deviations in the plane of the disc, thus neglecting vertical motions. We defer comparisons with full 3D hydrodynamical models for deeper ALMA observations. The semi-analytical model assumes power-law functions for surface density ( ) and sound speed (c) profiles, given by 0 (r) = p ( r/r p ) -δ , (7) and c 0 (r) = c p ( r/r p ) -q , (8) respectively. Subscript p denotes estimates at the location of the planet. Here, we use 1. 0 and 0.
This paper uses the follo wing publicly av ailable programs: MCFOST , https://github. com /cpint e/mcfost ; PYMCFOST , ht tps://github. com/cpint e/pymcfost ; BET-TERMOMENTS , ht tps://github. com/richt eague/bett ermoment s ; EDDY , ht tps://github. com/richt eague/eddy ; and WAKEFLOW , ht tps://github. c om/TomHilder/wakeflow. The polar binary PHANTOM models were provided by Pedro P. Poblete. Finally, we thank Sebasti án P érez for helpful discussions.
, 2004 ) defined as: 𝜔(𝜃) = 1 2𝑇 ∑ ( 𝑃 𝑘 (𝜃) -𝑃 𝑘 𝑒𝑥𝑝 𝑃 𝑚𝑎𝑥 𝑒𝑥𝑝 ) 2 𝑇 𝑘=1 , (2. 8) for one displacement controlled nanoindentation test. is the number of data points, thus the number of measured numerical   and experimental    force values. is the maximum experimental force. The number of data points  is equal to 800 with the same number of data points for the loading and unloading part, thus equal to 400. The normalisation of the sum term by    allows to consider dimensionless quantities and an absolute uncertainty on. A variant of the Levenberg-Marquardt algorithm  (Levenberg, 1944; Marquardt, 1963) , is used to minimise  with high convergence speed while constraining parameters to positive-only values. This optimization algorithm is implemented in MIC2M (http://mic2m. univ-fcomte. fr/) software  (Richard, 1999 (Richard, , 2017)).
5 days and 10 GB per sample, respectively. Image processing-The resulting Z-stacks were flat-field corrected using a custom macro in ImageJ and stitched with the Grid/Collection plug-in  [20]  in ImageJ, using the stage coordinates as initialization. Stage coordinates were extracted from the metadata of the Zeiss (. czi) file using a custom Python script. Methoxy-XO4 has a very wide emission spectrum, therefore amyloid plaques were visible in both the green and red channel (data not shown). DyLight-594 has a narrow emission spectrum and was thus visible in the red channel only. To remove the methoxy-XO4 fluorescence from the red channel, the green channel was first subtracted from the red channel, also for the WT mice for the sake of comparability. Vessel segmentation was performed with the subtracted data using an Otsu threshold.
Statistics For both the microscopy and the MRI data, a mixed ANOVA was used to test whether there was a significant effect of either the genotype or the brain region on the outcome measures mCBV, CBF and ATT. Because Mauchly's test of sphericity was significant in the CBF data, the degrees of freedom for the F-distribution were Greenhouse-Geisser corrected. For ATT and mCBV data, Mauchly's test was not significant, thus sphericity was assumed for the degrees of freedom of the Fdistribution. Statistical testing was done using IBM SPSS statistics 23 software (Armonk, New York, NY, USA).
Both pCASL scans were followed by a 3-slice coronal spin echo EPI readout with identical parameters (17 ms echo time, 0. 224 × 0. 224 mm² in-plane resolution, 1. 5 mm slice thickness and a 1 mm slice gap). To aid quantification, an inversion-recovery scan and a flowcompensated, pCASL-encoded FLASH sequence were additionally acquired for every mouse to measure the tissue T 1 and the labeling efficiency, respectively. One mouse (TG, female) had to be excluded from further analysis, as it died during the scan, probably a result of fragility due to old age. Image processing-The individual EPIs from both ASL sequences were first aligned to the first EPI of the standard pCASL scan to compensate for motion during the scans. Six different ROIs (auditory/visual cortex, sensory cortex, motor cortex, hippocampus, thalamus and striatum) were manually drawn on the anatomical images of one arbitrarily chosen mouse (reference dataset), and propagated to the anatomical images of the other animals after registration to the reference dataset.
The fluoropolymer CYTOP was investigated in order to evaluate its suitability as a coating material for ultracold neutron (UCN) storage vessels. Using neutron reflectometry on CYTOP-coated silicon wafers, its neutron optical potential was measured to be 115. 2(2) neV. UCN storage measurements were carried out in a 3. 8 l CYTOP-coated aluminum bottle, in which the storage time constant was found to increase from 311(9) s at room temperature to 564(7) s slightly above 10 K. By combining experimental storage data with simulations of the UCN source, the neutron loss factor of CYTOP is estimated to decrease from 1. 1(1)×10 -4 to 2. 7(2)×10 -5 at these temperatures, respectively. These results are of particular importance to the next-generation superthermal UCN source SuperSUN, currently under construction at the Institut Laue-Langevin, for which CYTOP is a possible top-surface coating in the UCN production volume.
5 mm s -1 and 1 mm s -1 , respectively. The samples were subsequently dried in air for 15 min, and then baked at 130 • C for 45 min to harden and achieve polymer cross-linking. Raw reflectivity data were corrected for background and instrument effects using the LAMP/COSMOS software  [37]. These refined data are plotted in Fig. 1  as a function of the wave-vector transfer Q. Vertical error bars represent statistical uncertainties, while horizontal error bars are the fullwidth half maxima (FWHM) of Gaussian approximations to the "resolution functions" appropriate for a given configuration of the D17 instrument. A resolution function f Q (Q ) describes the range of physically different Q values that contribute to the reflectivity at a nominal value of the momentum transfer Q on a real instrument.
All storage data presented in this work were taken after the additional pump was installed. Plots of the source output rate over time during Cycles 188 and 189 can be found in Figure  3. 5 of Ref. [54]. During Cycle 188, the source was operated only in continuous mode, and a background rate of approximately 0. 1 s -1 was observed. During Cycle 189, this background rate was measured to be approximately 0. 04 s -1. This decrease resulted from improved shielding of the detector and from a smaller source output rate at lower reactor power; despite the titanium coating on the interior of the switch housing, there is still a small leak of UCN through the switch to the detector. For accumulation mode measurements, a background rate of approximately 0. 02 s -1 was observed. Plots of the background rate over time during Cycles 188 and 189 can be found in Figure  3.
Conclusion The fluoropolymer CYTOP promise as a coating for UCN storage, especially in cryogenic applications. No evidence of loss of UCN storage performance due to repeated warming and cooling of the bottle was observed. The storage data are well described by a single storage time contant, τ s , which was found to increase from 311(9) s at room temperature to 564(7) s slightly above 10 K. These values were largely unaffected by a gradual decrease in UCN counts during some measurement series. It is suspected that this effect is associated with residual gases freezing on the cold walls of the experiment at low temperatures. Estimates of the loss factor of CYTOP were made by combining experimental storage data with simulations of the UCN output of the SUN-2 source. The results are η = 1. 1(1) × 10 -4 at 295.
Covariates We adjusted for maternal smoking, maternal age, maternal education, child sex, child age at sleep assessment, cell counts, and surrogate variables for batch adjustment. Maternal smoking and education were coded and categorized according to data availability as explained in Additional file 2: Methods. Cell counts were estimated using the Houseman method  [24]  with the Bakulski reference panel  [25]  (cord blood samples) and Reinius reference panel  [26]  (childhood samples). In cord blood DNAm analyses, we additionally adjusted for gestational age at birth, and in childhood DNAm analyses, we adjusted for child age at venepuncture, if different from age at sleep assessment (Table  S1 ). Covariates were chosen based on the previous literature to increase precision and to address potential confounders such as maternal smoking and socio-economic status  [2, 8, 27, 28].
Statistical analyses Cohort-level EWAS Each cohort-level EWAS was performed according to a predefined analysis plan. We used multivariate linear and logistic regression, for continuous (sleep duration and actigraphy-estimated outcomes) and binary (parentreported sleep initiation and fragmentation) outcomes, respectively. All p values were two-sided. We excluded participants with missing data and siblings (one sibling per sibling pair, chosen at random). For more cohortlevel information, please see Additional file 2: Methods.
Sensitivity analyses We reran the primary meta-analysis when including only (1) cohorts based in Europe, and (2) cohorts where sleep was assessed on school-aged children (i. e. mean age ≥ 7 years, corresponding to the highest primary school entrance age in the studied populations, http:// uis. unesco. org/ sites/ defau lt/ files/ docum ents/ indic atorefa-offic ial-entra nce-age-to-prima ry-educa tion. xlsx, accessed 2021/06/09), to increase precision at the cost of sample size. Individual variations in cord and peripheral blood DNAm are only partly stable from birth to school age  [32]. To check if the timing of the DNAm measurement changed the findings, cohorts with available data (re-) ran the EWAS using DNAm data from blood samples collected at school age, and we then reran the metaanalysis on these cohort-level results.
Study characteristics Associations between newborn DNAm and child sleep were assessed in 11 cohorts, whose participants are described in Table  1. Of these 11 cohorts, 10 had data on parent-reported sleep outcomes and 3 had data on actigraphy-estimated outcomes. Two of these cohorts (Generation R, INMA) had both parent-reported and actigraphy-estimated sleep data: correlations across all different sleep outcomes within these cohorts are shown in Additional file 1: Figure  S1. Associations between childhood DNAm and child sleep were assessed in 8 cohorts, described in Additional file 1: Table  S1. Five of these cohorts had DNAm data available among both newborns and in childhood and thus contributed to both analyses.
DNAm and actigraphy-estimated sleep outcomes Newborn DNAm and actigraphy-estimated sleep data were available among 582 school-aged children from 3 cohorts. Additional file 1: Figure  S4 , S5 and S6 show meta-analytic associations between newborn DNAm and actigraphy-estimated sleep duration, sleep onset latency (time between lying down in bed and falling asleep) and wake-after-sleep-onset duration (time awake between falling asleep and final awakening), respectively. We identified two statistically differentiated CpGs (p < 4. 0 × 10 -8 ) in analyses of newborn DNAm and child sleep, and none in the analyses of DNAm in childhood and child sleep. These two hits are described briefly below: for details, see Additional file 1: Table  S4. First, lower cord blood DNAm at cg24815001 was associated with longer actigraphy-estimated sleep duration among 577 children (p = 3.
Discussion This meta-analysis did not identify associations between DNAm at birth and parent-reported sleep duration among 3,658 children. Similarly, we found no associations between DNAm at birth and parent-reported sleep initiation or fragmentation problems. Actigraphy can provide a more objective and potentially more sensitive measure of child sleep duration compared to parental reports  [55, 56]. Actigraphy can also be used to measure sleep onset latency, an indicator of sleep initiation problems, and wake-after-sleep-onset duration, which can increase when the number of sleep fragments or the average time spent awake between fragments (or both) increases. However, collecting actigraphy data is often relatively cumbersome, compared to questionnaire-based data. The current meta-analysis brought together three cohorts that have both DNAm at birth and actigraphy-estimated child sleep data.
Our study also has limitations. First, while this is the largest study so far, it is possible we are still underpowered to identify subtle associations between DNAm and sleep. Further, differences between cohorts, relating for example to age at assessment and cultural and genetic differences between populations, can obscure associations that are specific to certain subgroups, and differences in how sleep was measured (e. g. phrasing of questionnaires) could introduce noise. Second, we combined the 450 k and EPIC array data sets, which could also introduce some noise; however, correlations between these arrays are high for DNAm in blood  [29]. Third, we used parent-reported sleep measures as primary outcomes because of comparability across studies, large sample size, and previously reported associations with genetic and early-life environmental factors.
mRNA Levels in Human Liver Cells and the Functional Response of Hepatocytes to IL-10RB Axis Components We used single-cell RNA-sequencing data for the human liver obtained by integrating publicly available datasets for 28 individuals, to study the abundance of IL10RB-related transcripts in the basal state (Fig. 4A )  [65]. IL10RB mRNA is detected in most of the cell types present in the human liver, including hepatocytes, cholangiocytes, endothelial cells, macrophages/monocytes, and lymphoid cells. By contrast, IL10RA mRNA is mostly restricted to macrophages/monocytes, plasmacytoid dendritic cells (pDC), and lymphoid cells, although it is also detected in a small number of other cell types. IL22RA1 is detected only in a small number of hepatocytes and cholangiocytes. Cholangiocytes are the main source of IL20RA mRNA in the liver, whereas the transcript of IFNLR1, like that of IL10RB, is detected in most liver cell types, but in smaller amounts.
Single-Cell RNA-Sequencing Analysis Data were compiled as described in  [65]. Briefly, cells of high quality (> 249 transcripts and < 30% mitochondrial expression) were clustered and identified after the integration of five different publicly available single-cell RNA-seq datasets. Violin plots were constructed from the expression levels of IL10RB, IL10RA, IL10, IL22RA1, IL22, IL20RA, IL26, IFNLR1, and IFNL1 and binned by cell type. All data were analyzed with the Seurat (v 3. 2. 2) library on R (v. 4. 0. 2).
Acknowledgements We thank the patient and the family for participating in this study. We thank  Bénédicte Neven  and  Anne Puel  for helpful discussion. We thank the  Genomics Resource Center at the Rockefeller University  and several members of the laboratory:  Yelena Nemirovskaya ,  Dominick Papandrea ,  Mark Woollett , and  Cécile Patissier  for administrative assistance.
RFID Instrumentation In february 2020, a portion of the landslide was equipped with a RFID system consisting of 32 battery-assisted passive tags and an acquisition station located near the landslide scar (see Figure  1 ). These tags can last about a decade without maintenance or replacement, in the present real-time monitoring scenario. The station includes 4 antennas, an interrogator (Impinj SR420), a micro-computer (RPI-3B), and a modem to send the data automatically to a remote server, as described by (patent pending FR-17/53739). It is powered by a photovoltaic module and a wind turbine. The station collects RFID data during 3 minutes every 20 minutes from every tag and every antenna. The data includes the Phase of Arrival (that we will call phase) measured at 865. 7 Hz, the Received Signal Strength Indication and the tag temperature.