dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Mikhail Pachkov  from the  University of Basel Biozentrum  for advice regarding the use of CREMA software. We kindly thank  Eric Ortlund  ( Emory University ) for sharing his GR and MR-LBD bacterial expression constructs. the  Alfonso Martín Escudero Foundation (Spain) ; OP-A was funded by  EDGJID/2021/098  ( Generalitat Valenciana ) and by ' ESF Investing in your future. ". We thank  NuRCaMeIN  ( SAF2017-90604-REDT ) for support for dissemination. PP is a member of the Scientific Network on ´Strategies for therapeutic targeting of the  Aldosterone-Mineralocorticoid Receptor signaling pathway (ADMIRE network ) funded by the  German Research Foundation  ( DFG-ID 470188766 ). This work has benefited from the facilities and expertize of the high-throughput sequencing core facility of  I2BC (Centre de Recherche de Gif  -http://www.
FUNDING INFORMATION This research is part of the grants  PID2020-114652RB-I00  funded by  MCIN/AEI/ 10. 13039/501100011033  to PP, and  PDC2021-121688-I00  to EE-P. The author's work was also supported by  Inserm ,  Université Paris-Saclay. EC-Z was recipient of a postdoctoral fellowship from.
IXPE data are distributed in a lower level format (L1), where relevant information about event tracks are reported, and also in a higher level format (L2), where several corrections have been applied and only the main properties of the reconstructed events are reported. In particular, in the L2 the photon energy is obtained after corrections for temperature and gain effects. Further corrections for the gain effects are applied using the data from the on-board calibration sources acquired during the observation. The imaging information in L2 is obtained from the L1 after correcting for dithering of the spacecraft pointing and orbital thermally-induced motion of the 20/39 boom that separates the optics from the detectors. The L2 data were then screened and processed using the current version of the HEASOFT software and calibration files.
On the selected event ensemble, the last step is to normalize the measured response to polarization by the modulation factor. Analysis of polarization is carried out with two different approaches. The first one, based on the unbinned formalism presented in  28  , is implemented in the IXPE collaboration software suite IXPEBOSSIM  29. The other method relies on the procedure presented in  16  , and it is based on the generation of the Stokes spectra, which are then fitted with standard spectral-fitting software, like XSPEC  30. The proper instrument response functions are provided by the IXPE Team as a part of the IXPE calibration database released on 2022 March 14 and available in the HEASARC Calibration Database 1 All values reported below are based on the spectro-polarimetric fits of the Stokes spectra unless stated otherwise.
e. t n = t 0 + n × p spin as illustrated in Extended Data Figure  2. It is important to emphasize that no appreciable evolution of the pulse profile shape occurs during the observation as illustrated in Extended Data Figure  2  and expected on the basis of previous observations of the source at a similar phase of the precession cycle  38. This allows us to use all the available data and achieve a sufficient sensitivity also in the individual phase bins. The observed pulsed fraction in the 2-7 keV band, defined through the maximum and minimum fluxes as f = (F max -F min )/(F max + F min ), is ∼ 55%. Based on the available counting statistics and known instrument sensitivity, seven phase bins were then defined as shown in Fig. 3. The Stokes spectra (I/Q/U), and binned polarization cubes, were then extracted individually for each of the phase bins using IXPEOBSSIM package  29.
2/543 0. 86-1. 00 5. 5 ± 1. 2 48 ± 6 1. 286 ± 0. 004 11. 99 ± 0. 08 676. 5/543 values and uncertainties were estimated based on mcmc chains produced using the chain command in XSPEC and are reported in Extended Data Table  2. We verified the consistency of the spectro-polarimetric and binned analysis results for all bins and found no significant differences in the phase dependence of the PD and PA, therefore only the results of the spectro-polarimetric analysis are reported. The same procedure has been used to investigate the time dependence of the polarization properties over the observation. The full dataset was split into seven intervals separated by large gaps defined either by the instrumental good time intervals or by the eclipses of the source. For each interval, the Stokes spectra (I/Q/U) were extracted and jointly modeled using nthcomp and polconst models to estimate the PD and PA values.
Acknowledgements This paper is based on the observations made by the  Imaging X-ray Polarimetry Explorer (IXPE) , a joint US and Italian mission. This research used data products provided by the  IXPE Team (MSFC, SSDC, INAF , and  INFN ) and distributed with additional software tools by the  High-Energy Astrophysics Science Archive Research Center (HEASARC) , at  NASA Goddard Space Flight Center (GSFC). The US contribution is supported by the  National Aeronautics and Space Administration (NASA)  and led and managed by its  Marshall Space Flight Center (MSFC) , with industry partner  Ball Aerospace  (contract  NNM15AA18C ). The Italian contribution is supported by the  Italian Space Agency (Agenzia Spaziale Italiana, ASI)  through contract  ASI-OHBI-2017-12-I. 0 ,agreements  ASI-INAF-2017-12-H0  and  ASI-INFN-2017.
Further research is needed to theoretically refine the concept and empirically study how the EU is using its knowledge power to address particular issues and challenges. Nevertheless, it is clear that knowledge power is something that the EU has and is learning to mobilize and exploit. Global challenges provide unconventional opportunities for global leadership, and we believe that the EU is particularly well-suited to exploit them. Notes 1. A search of the Scopus database for the keywords Europe AND Power within the article title and restricted to the category of social sciences produced 419 results. Results with at least 10 citations were manually searched for texts that focused on international relations and the type of power Europe has. Several additional texts were identified through a general literature review.
Temporal clustering To examine whether infants and caregivers produce clusters of vocalisations simultaneously, we performed the following analysis. For each vocalisation, we estimated the likelihood of another vocalisation occurring both before and after that vocalisation. See Figure  1a , which shows as an example the likelihood of a subsequent infant vocalisation occurring 1-3 min after an initial infant vocalisation. To compare the observed probabilities with chance we performed a control analysis in which we inserted random 'non-vocalisation' events into the data and repeated the analysis relative to these 'non-vocalisations', and compared the 'real' and 'control' datasets using a Mann-Whitney U test. We then repeated this analysis across multiple time windows from 20 min before the vocalisation to 20 min after.
9). For infant arousal to infant vocalisations, significant increases in arousal were observed from 16 min before to 16 min after the vocalisation (all ps <0. 05, after correction). Note that these findings are not affected by autocorrelation in the arousal data as this was removed (see Appendix 1 section 1. 6). For infant arousal to caregiver vocalisations, significant increases were observed from 16 min before to 18 min after each vocalisation; for caregiver arousal to infant vocalisations, significant increases were observed from 4 min before to 6 min after; for caregiver arousal to caregiver vocalisations, no significant difference from 0 was observed at Time 0, but a significant difference was observed for the period between 4 minutes to 2 minutes before each vocalisation. Overall, these results suggest that infant arousal levels are elevated around both infant and caregiver vocalisations, and that caregivers also show smaller but significant increases in arousal around infant vocalisations.
By contrast caregivers' vocalisations show little association with their own arousal, congruent with the hypothesis of heightened vocal flexibility in human adults as compared to infants. Analysis 2 -vocalisation likelihood around arousal peaks. Conversely, we also examined the likelihood of vocalisations occurring around peaks in arousal. We identified the moments when the infants' and the caregivers' z-scored arousal levels exceeded the top 10% most elevated values observed for that participant that day (Figure  2c  and e ). Appendix 1 section 2. 4 shows the same analysis repeated with different threshold levels (5% and 20%). We then examined the likelihood of vocalisations occurring during the time windows around arousal peaks, and compared this with control data generated in the same way as described in section 1.
Analysis 3 As a further test of whether arousal levels predict vocalisation likelihood differently in infants and adults, we employed a signal detection framework based on the ROC (see Figure  2d ). Each dataset was systematically thresholded at all possible values from its minimum to maximum value. At each threshold, each epoch was individually classified either as a True Positive (above-threshold arousal, vocalisation present) or a False Positive (above-threshold arousal, vocalisation absent). If the systematic thresholding produced as many false alarms as hits, then the feature dimension could not be said to aid in predicting vocalisation likelihood. Following calculation of the ROC curves, the Area Under the Curve (AUC) was calculated: a higher AUC indicates that the feature dimension is more predictive.
For adult vocalisations, 44% occurred while the infant was freely moving; 33% while free stationary; 10% while strapped sitting; 12% while carried. Overall when we examined how arousal levels differed by physical position we found no evidence that arousal increases around vocalisations are attributable to changes in physical position. The second possibility is that arousal increases around vocalisations may be attributable to the physical act of vocalising itself. This may seem unlikely given that we also observed increases in infant arousal relative to caregiver vocalisations (Figure  2b ). Yet, because we also observed that caregiver and infant vocalisations occur in clusters (Figure  1b ), it remained possible that vocalising itself increased infant arousal in these periods. To address this, we conducted a more fine-grained analysis on a different dataset in which we continuously recorded vocalisations and arousal in 11-month-old infants and their caregivers during two 5-min tabletop interactions (see Appendix 1 section 2.
Additional analysis: infants' vocal affects and caregivers' vocal types In Appendix 1 we present additional analyses to further investigate our second research question, which is to examine how different types of vocalisation play different roles in arousal co-regulation across the infant-caregiver dyad. To do this, we subdivided infant and caregiver vocalisations by vocalisation affect, intensity and type, on the basis of a manual rating of the data by trained coders (Appendix 1 sections 2. 5, 2. 6). Note that the vocalisations identified as negative mostly corresponded to cries (98%), and positive vocalisations mostly included protophones, with a mixture of quasi-resonant vowel (20%), fully-resonant vowel (21%), marginal syllable (9. 6%), and canonical syllable (33%). First, we examined infant vocal affects as a function of valenceFig S8a, S8c and intensity (see Appendix 1 section Supplementary analyses for part 2 -arousal by infant vocalisation affect and intensity).
Experimental participant details The project was approved by the Research Ethics Committee at the University of East London (Approval number: EXP 1617 04). Informed consent, and intent to publish, were obtained in the usual manner. Participants were recruited from the London, Essex, Hertfordshire and Cambridge regions of the UK. In total, 91 infant-caregiver dyads were recruited to participate in the study, of whom usable autonomic data were recorded from 82. Of these, usable paired autonomic data (from both caregiver and child) were obtained from 74 participants. Further details, including exclusion criteria, and detailed demographic details on the sample, are given in Appendix 1 section 1. 1. The sample size was selected following power calculations presented in the original funding application ES/N017560/1.
Post hoc trained coders identified the timings of these movements in the accelerometer data from each device independently. This information was used to synchronise the two recording devices. Quantification and statistical analysis Autonomic data parsing and calculation of the autonomic composite measure Further details on the parsing of the heart rate (Appendix 1 section 1. 2), heart rate variability (Appendix 1 section 1. 3), and actigraphy (Appendix 1 section 1. 4) are given in Appendix 1. In Appendix 1 section 1. 5 we present our motivation for collapsing these three measures into a single composite measure of autonomic arousal (Figure  S3 ). In section 1. 6 we present a description of how the autocorrelation was removed from the arousal data.
Vocal coding The microphone recorded a 5 s snapshot of the auditory environment every 60 s. Post hoc, trained coders identified samples in which the infant or caregiver was vocalising, and the following codings were applied. For each coding scheme, consistency of rating between coders was achieved through discussions and joint coding sessions based on an ersatz dataset, before the actual dataset were coded. All coders were blind to study design and hypothesised study outcome. Importantly, analyses conducted on a separate, continuous dataset (see Appendix 1, section S10) suggest that the temporal structure of our vocalisations was maintained despite this 'sparse sampling' approach. Furthermore, our analyses examine how arousal changes relative to observed vocalisations, and any arousal changes that we do observe time-locked to vocalisations would be weakened (not strengthened) by the fact that the vocalisation data were sparsely sampled (because power would have been reduced by missing vocalisations through the sparse sampling method, rather than increased).
ROC analyses In order to assess the selection of visual features we employed a signal detection framework based on the Receiver Operator Characteristic (ROC). This analyses the degree to which arousal levels predict the timings of vocalisations relative to the timings of randomly sampled comparison samples, epoch by epoch. See Results section and  (Fawcett, 2006)  for more details. Figure  8. Schematic illustrating the auto-and cross-correlation analyses. Arousal data were downsampled to 1 min epochs (corresponding to the sampling frequency of the microphone data). The windowed auto-and crosscorrelation was then calculated, using a window size of 10 epochs, which shifted 5 epochs between windows. The average change in auto-and cross-correlation relative to vocalisations was then calculated.
Moving window analyses To estimate how stability and coupling changed relative to vocalisations, we used a moving window analysis (see Figure  8 ). Arousal data were downsampled to 1-min epochs (0. 016 Hz) (which was the sampling frequency of our microphone data). The size of the moving window was set arbitrarily at 10 epochs, with a shift of 5 epochs between windows. We excerpted the stability and coupling values around each individual vocalisation, and averaged these across all vocalisations.
Arousal composite Previous research has shown significant patterns of tonic and phasic covariation between different autonomic measures collected from infants  (Wass et al. , 2016; Wass et al. , 2015). Here, we include plots showing that the present dataset replicated and extended these results. The plots only show the sections of the data when participants were at home, comparing sections in which the infants were awake and asleep. Figure  S2a  shows cross-correlation plots examining the relationship between heart rate and movement. In both waking and sleeping sections the zero-lag correlation is 0. 5. Figure  S2c  shows how these zero-lagged correlations vary on a per-participant basis. S2b shows an illustrative sample from a single participant. Sleeping sections show very low movement levels and lower heart rate.
g. Feldman et al. , 1999; Feldman et al. , 2011; Jaffe et al. , 2001; Suveg et al. , 2016  for similar approaches). The residual values (shown in Figure  1 ) were converted into z-scored values. These z-scored values were then used for all analyses. The only exception to this is the analyses specifically examining changes in autocorrelation relative to vocalisations, for which the raw uncorrected data were used. 1. 7 Home/awake coding 1. 7. 1 Home/not home Coding of when participants were at home was performed using the GPS monitors built into the recording devices. The position of the participant's home was calculated based on the postcode data that they supplied, and any GPS samples within a 50 m area of that location were treated as Home (corresponding to the accuracy of the GPS devices that we were using).
Sleeping/waking To identify samples in which infants were sleeping, parents were asked to fill in a logbook identifying the times of infants' naps during the day. This information was manually verified by visually examining the actigraphy and ECG data collected, on a participant by participant basis. Actigraphy, in particular, shows marked differences between sleeping and waking samples (see Figure  1  in main text), which allowed us to verify the parental reports with a high degree of accuracy. N=4 of the participants recorded did not sleep during the day that we were recording.
Video coding of physical positions while vocalising In order to ascertain the physical position of our participants during the vocalisations we also performed an additional analysis based on the data recorded by the wearable Narrative Clip 2 Cameras worn by participants. These cameras were positioned in a breast pocket on the T-shirts worn by the infants (see Figure  1  in main text). Like the microphones, they were programmed to record a 5-s video every minute. A trained coder watched each video one by one and categorised then into the following four categories: Free moving (camera is moving, and infant is self-generating movement); free stationary (camera is stationary and the baby is stationary but not strapped in); strapped sitting (child is strapped in to a feeding chair, pushchair, sling, car seat, shopping trolley etc); carried (camera is moving and the child id being freely carried in an adult's arms).
Permutation-based clustering analyses To estimate the significance of the time-series relationships in the results, a permutation-based temporal clustering approach was used. This method examines temporally contiguous patterns of change in instances where the centre-point of the expected response window is unknown, or unimportant  (Maris and Oostenveld, 2007). In each case, the test statistic (always specified in the text) was calculated independently for each time window. Series of significant effects across contiguous time windows were identified using an alpha level of. 05. 1000 random datasets were then generated with the same dimensions as the original input data. To ensure that the same level of autocorrelation was present in the simulated data as in the original datasets, multivariate autoregressive models were fitted to each sample included in the original dataset using the Matlab function ARfit.
m  (Neumaier and Schneider, 2001) , and the matching AR parameters were used to generate each of the random datasets using the Matlab function ARsim. m  (Neumaier and Schneider, 2001). Then, the same sequence of analyses was repeated, and the longest series of significant effects across contiguous time windows was identified. The results obtained from the random datasets were used to generate a histogram, and the likelihood of observed results have been obtained by chance was calculated by comparing the observed values with the randomly generated values using a standard bootstrapping procedure. Thus, a p value of<. 01 indicates that an equivalent pattern of temporally contiguous group differences was observed in 10 or fewer of the 1000 simulated datasets created. Another possibility is that the arousal increases around vocalisations that we observed may be because the physical act of vocalising causes increases in arousal.
To address this, we conducted a more fine-grained analysis on a different dataset in which we continuously recorded vocalisations and arousal in a cohort of 11-month-old infants (mean (std): 10. 86 (1. 23) years) during two 5-min infant-caregiver tabletop interaction. Timings of the onsets of vocalisations were recorded to an accuracy of 20 Hz (i. e. 50ms). Dual EEG were also recorded during these interactions, but these data are not presented here. These analyses were based on heart rate data alone, since the other measures included in the composite arousal measure were not available for this dataset. In total, 2270 adult and 623 infant vocalisations were recorded, which was a mean ( st. err. ) of 66. 8 (3. 3) adult and 18. 3 (2. 6) infant vocalisations per dyad. Mean ( st. err. ) vocalisation duration was 2.
Acknowledgements This research was funded by  ESRC  grant number  ES/N017560/1 , by  ERC  grant number  ONACSA 853251 , by Project Grant  RPG-2018-281  from the  Leverhulme Trust  and by an  ERC   Marie Curie Fellowship   JDIL 845859. Thanks to  Kaili Clackson  and  Farhan Mirza  for help with data collection; to  Caitlin Gibbs ,  Emma Bruce-Gardyne ,  Florian Andrey-Csolm ,  Joan Eitzenberger ,  Leanne Barnes ,  Louise Stubbs ,  Deborah Scnatlebury  and  Anne Hepworth  for help with data coding. Thanks to members of the  UEL BabyDev Lab  for comments and discussions on earlier drafts of this manuscript, and to all participating children and caregivers.
org/10. 5061/dryad. 612jm6473. The code used to conduct the analyses is available here: https://doi. org/10. 5281/zenodo. 7409281. A readme file in the Data folder explains how to run the analyses to reproduce the results that we report in the paper. For any queries, please contact the lead author s. v. wass@uel. ac. uk. The following datasets were generated:.
Results At the end of the five-week exposure period, 69% of the controls and 67% of the PAH-contaminated fish were found to be infected with the intestinal parasite Pomphorhynchus sp. Regarding infected chub, the parasite load ranged from two to nine parasites per individual and the pools of infected chub used for molecular analyses had an average parasite burden of between 3. 0 and 6. 3 (Table  S1 ). The number of intestinal parasites did not differ among controls and PAH-exposed chub (Mann Whitney Wilcoxon test, p > 0. 05). Processing of 16S rRNA sequencing data. The sequencing of the amplicon libraries resulted in 3,020,539 raw sequences (Table  S2 ). After quality filtering, denoising and merging of paired-end reads, 63. 8% of the raw dataset were retained, for a total of 1,898,430 high quality non-chimeric reads with a median of 78,355 reads per sample (Table  S2 ).
, 2013). Paired-end sequencing was performed at MrDNA Molecular Research LP (Shallowater; TX, USA) using the Illumina MiSeq workflow. Bioinformatics and statistical analyses. Raw 16S rRNA sequencing reads were quality filtered using the dada2 R package (v 1. 16. 0) and its associated pipeline  61. Low-quality base pairs were removed using the 'filterAndTrim' function by setting the maximum expected errors parameter (maxEE) to 2 for both paired reads and truncating the forward and reverse reads at 250 bp and 210 bp, respectively. Exact biological sequences present in the sample (ASVs) were defined and taxonomies were assigned in accordance with the dada2 pipeline from the SILVA rRNA reference database (v. 138) using the Ribosomal Database Project naive Bayesian classifier method  62. All subsequent analyses were then carried out in the R environment (v.