dataesr/scientific-paragraphs-categorization huggingface dataset:
0  (Heirendt et al. , 2017). Simulations were performed using Cobra toolbox v3. 0  (Heirendt et al. , 2017)  and RAVEN 2. 0 suite  (Wang et al. , 2018). All the operations were performed in MATLAB 2017b (Mathworks, Inc. , Natick, MA, USA). By integrating NAFLD stage-specific whole tissue transcriptomics (RNASeq) data with iHepatocytes2322, we contextualized and developed hepatocyte-GEMs for different stages of NASH-associated fibrosis, i. e. , 'minimal' NAFL + NASH (F0-1) (n = 85); 'mild' NAFL + NASH (F0-2) (n = 138); 'clinically significant' non-cirrhotic fibrosis (NASH F2-3) (n = 107); and 'advanced' fibrosis (NASH F3-4) (n = 68). A similar approach was adapted to contextualize GEMs (iHepatocytes2322) for hepatocytes in the individuals exhibiting carriage of gene variants, PNPLA3 (GC, GG) (n = 69), TM6SF2 (CT, TT) (n = 13), HSD17B13 (-T, TT) (n = 21) and wild type (WTs) (n = 36) (Tables  1  and S3 ).
Partial least squares discriminant analysis Multiclass and two groups partial least squares discriminant analysis (PLS-DA) (Le  Cao et al. , 2011 ) models were developed across the NAFLD spectrum, and for the three major gene variants respectively; by using the 'plsda' function coded in the 'mixOmics v6. 3. 2 0 package, implemented in R statistical programming language (R Development Core Team, 2018). Variable Importance in Projection (VIP) scores  (Farres et al. , 2015)  of the features (subsystems) were estimated. PLS-DA models were cross-validated  (Westerhuis et al. , 2008)  by 7-fold cross-validation and models diagnostics were generated using 'perf' and 'auroc' functions. All features that passed (VIP scores >1) were listed as significantly altered between or among the groups.
ACKNOWLEDGMENTS We would like to thank the  EPoS (Elucidating Pathways of Steatohepatitis)  (epos-nafld. eu)investigators. This study has been supported by the  EPOS (Elucidating Pathways of Steatohepatitis)  project funded by the  Horizon 2020 Framework Program  of the  European Union  under Grant Agreement 634413 and the  LITMUS (Liver Investigation: Testing Marker Utility in Steatohepatitis)  project which has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking , under grant agreement No. 777377. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. Q. M. A. is a Newcastle  NIHR Biomedical Research Centre  investigator. T. H. and M. O. received funding from the  Novo Nordisk Foundation  (Grant no.
METHOD DETAILS Transcriptomics Transcriptomic (RNA-seq) data associated with this study was obtained from  (Govaere et al. , 2020) ; also available in the NCBI GEO repository: GSE135251. Differentially expressed genes between a paired conditions were either obtained  (Govaere et al. , 2020) , or estimated by a method stated in  (Govaere et al. , 2020). All genes that were differentially expressed between the case vs. control at (p-values adjusted for FDR < 0. 05) were included in the RM analysis. Moreover, the transcriptomic datasets were corrected for batch effect, gender and other confounding factors as stated in  (Govaere et al. , 2020).
To date, there are two PAC studies in infants. Both have focused on clinical applications, specifically the role of PAC in functional connectivity  (42, 43). For example, one study measuring PAC in sleeping newborns during active versus quiet sleep reported hierarchical nesting of oscillations in both sleep states  (42). Accordingly, current data suggests that PAC mechanisms in the infant brain may be online at birth. However, no study to date has looked at the functional relevance of infant PAC when processing sensory stimuli, leaving PAC during language processing yet to be explored. Here we assess infant cortical tracking of the temporal modulation information in IDS present in both delta-and theta-rate AM bands. We analysed cortical tracking of nursery rhymes, originally recorded being sung or chanted to an alert infant.
We also tentatively hypothesised that theta tracking may come online more slowly. 64-channel EEG was recorded from a sample of 60 infants as they watched and listened passively to a video of a range of nursery rhymes with different beat structures being sung or rhythmically chanted by a female "talking head". The video lasted approximately 20 minutes (see Figure  2a ), and was followed by a resting "silent state" recording. Prominent modulation spectrum peaks were identifiable in the nursery rhyme stimuli at rates corresponding to adult delta and theta neural signals (Supplementary Figures  S2, S3  and S4 ). The neural data presented here were recorded from the first half of infants recruited to an ongoing longitudinal study of 113 infants (122 infants recruited, 113 retained), who listened to nursery rhymes at the ages of 4-, 7and 11-months.
Infant participants Data from the first half (N=60) of a larger longitudinal cohort (122 infants recruited, 113 retained) were used in all analyses. From this initial cohort, four participants withdrew without providing any data and one withdrew after the first session so was also excluded. The remaining sample of 55 infants provided data at ~4-months (115. 6 ± 5. 3 days), ~7-months (212. 2 ± 7. 2 days) and ~11-months (333. 1 ± 5. 6 days) [mean ± standard deviation (SD)]. Due to missed appointments (7-months N=2), technical issues with the data file (4-months, N=1) the final number of data points at each month was, ~4-months (N=54), ~7-months (N=53), ~11-months (N=55). Infants were recruited from a medium sized city in the United Kingdom and surrounding areas via multiple means including flyers in hospitals, schools and antenatal classes, research presentations at maternity classes and online advertising.
EEG data collection Infants were seated in a highchair, approximately one meter in front of their primary care giver, within a sound-proof acoustic chamber. EEG data was recorded at a sampling rate of 1000Hz using a GES 300 amplifier connected to a correctly sized 64 channel infant electrode net (Geodesic Sensor Net, Electrical Geodesics Inc. , Eugene, OR, USA). The infant was seated ~650mm away from the presentation screen and sounds were presented at 60dB (checked by sound level meter) from speakers (Q acoustics 2020i driven by a Cambridge Audio Topaz AM5 Stereo amplifier) placed either side of the screen. Whilst the infant attended to the screen, the 18 nursery rhyme videos played sequentially, each repeated 3 times (54 videos, with a presentation time of 20' 33'' in total). If the infant lost attention to the screen an attention grabber video was played at the end of that nursery rhyme.
EEG preprocessing All analyses were conducted with custom-made scripts in Matlab 2017a (The MathWorks, Inc. , Natick, MA) incorporating the EEGLab toolbox  (52). The four (channels 61, 62, 63 and 64) facial electrodes were excluded from all analyses because they do not exist on the infant-sized EGI Geodesic sensor nets. The EEG data, from the remaining 60 channels, was filtered (pop_eegfiltnew function of EEGLab toolbox) into a broadband signal (0. 5-45Hz for PSD and PAC) or the frequency band of interest (0. 5-4Hz, 4-8Hz or 8-12Hz for the mTRF analysis) using zero-phase bandpass Hamming windowed FIR filters (transition band widths of 2Hz with cutoff frequencies at -6 dB, 0-5Hz, 3-9Hz and 7-13Hz respectively). The EEG data was down sampled to 100Hz to reduce the computational load. Next, the clean_asr EEGLab function (  52 ) was used to clean noise artifacts from the data by identifying and removing bad principal components via a modified PCA procedure (see supplement for more details).
9 ± 13. 3 epochs; 7-month, 79. 4 ± 6. 4 epochs; 11-month, 80. 7 ± 5. 1 epochs [mean ± standard deviation (SD)]). If less than half (42 averaged trials) remained after preprocessing the file was removed from further analysis (minimum numbers at 4-month, 47 epochs; 7-month, 47 epochs and 11-month 65 epochs). The mTRF analysis was conducted using the multivariate temporal response function (mTRF) toolbox  (47)  through Matlab 2017a (The MathWorks, Inc. , Natick, MA). The backwards model can be expressed by the following formula in which the reconstructed stimulus envelope ŝ(t) is created by a linear decoder, g(τ, n), mapping the neural response, r(t,n), back to the stimulus, s(t). The TRF decoder was used to integrate the neural responses at multiple time lags, τ, between 0 and 250ms (τmin = 0ms, τmax = 250ms).
mTRF auditory stimuli preprocessing The envelope of the auditory signal was extracted by taking the absolute value of the analytic signal generated by the Hilbert transform (Matlab). As the envelope of the lower frequencies is linearly relatable to the EEG signal  (16, 17, 53)  the envelope of the stimuli was filtered between 0. 5Hz and 15Hz (lowpass; 6 th order Butterworth filter. Highpass; 9 th order Butterworth filter). The resultant envelopes were normalized using nt_normcol (Noisetools). Finally, the stimulus envelopes were down sampled to 100Hz to match the EEG signal.
mTRF Linear mixed effects model A linear mixed effects model (LMEM), as described in the main text, was conducted in SPSS (IBM SPSS statistics 24), submitting data type (real r values, random permutation r values), frequency band (delta, theta or alpha) and age (4-, 7-or 11-months) along with interactions between data type by frequency band, data type by age and age by frequency band, to the LMEM. Due to the sensitivity of LMEM to outliers in the sample, outlier analysis was conducted to exclude data more than 1. 5 interquartile ranges above the upper quartile or below the lower quartile separately per band and per age group, leaving, delta (4-months N=53, 7months N=53 and 11-months N=51), theta (4-months N=53, 7-months N=52 and 11-months N=55) and alpha (4-months N=53, 7-months N=52 and 11-months N=52) data sets included in the LMEM (Table  2  and Figure  3 ).
PAC Linear mixed effects model (LMEM) A LMEM was conducted using SPSS (IBM SPSS statistics 24), to investigate whether the MI values (Canolty's modulation index)  (10)  were significantly different when either delta or theta was the low-frequency carrier phase, when either beta or gamma was the high-frequency amplitude and if this relationship was affected by the age of the infants. Due to the sensitivity of LMEMs to outliers in the sample, outlier analysis was conducted to exclude data more than 1. 5 interquartile ranges above the upper quartile or below the lower quartile, leaving, 4-months N=54, 7-months N=50 and 11-months N=54 data sets included in the LMEM Table  3  and Figure  4. PACij = β0j +β1j AGEij + β2 LFPij + β3HFAij + β4(AGE x LFP)ij + β5(AGE x HFA)ij + eij The LMEM can be described by the equation above, in which LFP denotes the band of the low-frequency phase (delta; 2-4Hz or theta; 4-8Hz), HFA is the band of the high-frequency amplitude (beta; 15-30Hz or gamma; 30-45Hz) and age is the age grouping of the infant (~4months, ~7-months or ~11-months).
Spectral analysis (periodogram PSD estimate) The same concatenated data sets created for the PAC analysis were also used for the periodogram Power Spectral Density (PSD) estimates. A one-sided PSD estimate was conducted separately for each electrode channel using the periodogram function (Matlab). The length of the participants data was zero padded to ensure the size of the rectangular window used was equal in length to the number of discrete Fourier transform (DFT) points, ensuring the correct FFT across participants. This resulted in 52,834 equal spaced frequency bins from 0 to 50Hz. The periodogram can be defined by the following formula. In which the EEG signal, xn , is sampled at 100Hz, with Δt as the sampling interval. To achieve the one-sided periodogram output reported in Figure  1 , values at all frequencies (except 0 and the Nyquist, 1/2Δt), were multiplied by two to conserve the total power.
Results: Both delta and theta EEG frequency bands respond to nursery rhymes Prior to investigating cortical tracking and PAC, the distribution of low-frequency neural signals within our data was established using spectral decomposition of the signal, achieved using the periodogram power spectral density (PSD) estimate. After preprocessing, PSD was obtained for each of the remaining 60 electrodes in response to audio-visually presented nursery rhymes and during a five minute period of silence. A grand average across channels and ages revealed two frequency peaks centered around 2. 18Hz and 4. 4 Hz for the nursery rhyme period (Supplementary Figure  S1 ). These peak responses corresponded to the frequency peaks found in the acoustic envelope of the nursery rhymes (Supplementary Figures  S2, S3  and S4 ).
LMEM estimates of fixed effects for Pearson correlation (r) values The mTRF stimulus reconstruction data show that delta and theta EEG responses track the acoustic envelope of sung and chanted speech from the age of 4-months, supporting our hypothesis that delta-band cortical tracking is developmentally important. Furthermore, 4-month infants showed significantly greater tracking than older infants, and this was strongly influenced by the strength of the delta-band response. This could either reflect bottom-up infant use of acoustic landmarks at a delta-band rate in the sung and chanted speech, facilitating attentional mechanisms such as grouping, or could reflect the fact the speech was sung and therefore rhythmic  (54) , or both. Theta-band neural signals also tracked the speech envelope, but at a significantly lower level.
To investigate whether the MI values were significantly different when either delta or theta was the low-frequency carrier phase plus when either beta or gamma were the highfrequency amplitudes, and if this relationship was affected by the age of the infants, a LMEM was conducted. Fixed effect factors of low-frequency phase (delta or theta), high-frequency amplitude (beta or gamma) and age (4-, 7-or 11-months) were inputted to the LMEM. Theta, gamma and 4-months data were defined as the respective base cases. The maximum MI value from the four PAC band pairings (delta/beta, delta/gamma, theta/beta and theta/gamma), from pre-defined frequency band groupings (delta 2-4Hz, theta 4-8Hz, beta 15-30Hz and gamma 30-45Hz) were extracted per participant and submitted to the model. Interactions of low-frequency phase by age and high-frequency amplitude by age were also included in the model, with a random intercept (AR1) per participant included to account for individual differences across the 3 ages of recording (see methods).
Modulation Spectrum of nursery rhyme sound files All analysis was computed using MATLAB 2016a. The modulation spectrum extraction was based on an approach described by Plomp, et al. ,  (1)  and developed by  Leong, et al. (2). First, the sound files were down sampled to 14. 7 kHz and then band-pass filtered using a series of adjacent FIR filters, into five bands: 100-300 Hz, 300-700 Hz, 700-1750 Hz, 1750-3900 Hz, and 3900-7250 Hz. Next, the Hilbert envelope was extracted from each of the five sub-band signals. The five envelopes were down sampled to 1050 Hz then filtered through a modulation filter bank. This modulation filter bank comprised 24 channels logarithmically spaced between 0. 9-40 Hz. In Figure  S2 , the RMS difference power was averaged across 18 nursery rhymes and determined for each spectral band.
Data Sharing agreement The analyses were conducted by using publicly available MATLAB toolboxes that can be downloaded at http://audition. ens. fr/adc/NoiseTools/, https://sccn. ucsd. edu/eeglab/download. php, https://github. com/mickcrosse/mTRF-Toolbox. The custom code used for the mTRF, PSD and PAC and analysis can be downloaded from https://github. com/adamattaheri/BabyRhythm. Due to infant confidentiality the raw data is held on a secure server but can be made available upon request.
Data collection and screening Studies were identified based on a literature review aimed at identifying original research relevant for use in informing a human health threshold value for MPs in drinking water. Peer-reviewed studies were identified through an original literature search which included search results from Gouin et al. [42]  and was expanded to include additional studies published through June 1, 2021. Studies were identified using the ProQuest database, Google Scholar, and PubMed search engines using the keywords 'microplastic(s)' AND 'toxicity'. Additional studies were identified through the reference list in the United Kingdom Health and Safety Executive report summarizing hazards and risks of fibers  (Burdett and Bard 2007)   [15] , and literature review by  Rahman et al. (2021)   [92]  and  Wright and Kelly (2017)   [122].
While every effort way made to identify all relevant studies, we acknowledge that this approach may not be comprehensive and that some studies could have been missed based on how the literature search was performed, specifically due to the keyword 'microplastics' only being in use since ~ 2004  (Thompson et al. [108] ). Information from studies was extracted manually and compiled in an interactive webbased RShiny application database "ToMEx" (Thornton Hampton L, Lowman H, Coffin S, Darin E, De Frond H, Hermabessiere L, et al: A Living Tool for the Continued Exploration of Microplastic Toxicity, In Press), from which summary statistics are reported and evaluation scores against various quality assurance and quality control (QA/QC) criteria can be obtained  (Gouin et al. ,  [42] ). Both in vivo and in vitro studies were identified.
g. , benchmark doses [BMDs]). BMDs are doses (or concentrations) that elicit a predetermined change in response of an adverse effect based on a modelled dose-response curve, and is the preferred method for assessing risks by many regulatory agencies such as the US EPA and the European Food Safety Authority (EFSA Scientific Committee et al. ,  [35] ). The BMD of endpoints, along with their respective upper and lower 95% confidence intervals were estimated based on modelled output using the US EPA benchmark dose software (BMDS) (US EPA  [36] ), which was further compared and interpreted using the RIVM PROAST tool  (Slob, [101] ). Figure  1  provides a schematic illustration of the process adopted.
Benchmark dose modelling Knowledge obtained from the external experts was considered by workshop participants, with the output of discussions collated and used as the basis for identifying endpoints for use in estimating their statistical BMD which (if BMD model is reliable) is used as a point-ofdeparture (Fig. 1 ). Benchmark dose modelling software fits mathematical models to data and determines the exposure concentration (benchmark dose or BMD) that corresponds to a predetermined response level (benchmark response or BMR). All analyzed data were continuous, and a BMR of one standard deviation from the control mean was used for all endpoints in accordance with US EPA guidance (US EPA  [36] ). BMDs were derived using both the US EPA BMDS (EPA,  [36] ) and the RIVM PROAST  (Slob [101] ) to examine the uncertainty of different BMD modelling methods.
The US EPA Exposure Decision Tree Approach was used to derive a deterministic RSC, which considers availability of exposure data and specific subpopulations of concern (EPA,  [110] ). An age-specific DWI is estimated using methods described by OEHHA  [83] , from which data  (1)  Screeninglevel(mg∕L) = RfD(mg∕kgday)xRSC(unitlessfraction) DWI(L∕kgday) obtained from a nationwide survey of food and beverage intake for approximately 20,000 people (US Department of Agriculture's Continuing Survey of Food Intake of Individuals 1994-1996, 1998 dataset) were used and normalized to body weight. The upper 95th percentile for the 70-y lifetime weighted average of 0. 053 L•kg -1 •d -1 was used as the default value (see derivation in supplemental information) (OEHHA  [83] ).
For the POD, the upper and lower 95% confidence interval values (BMDU and BMDL, respectively) can be obtained and used as a quantitative estimate of uncertainty. To arrive at the reference dose, the POD is typically divided by UFs used to account for variabilities and uncertainties unless data is available (EPA  [36] , OEHHA  [83] ). The UFs consider a number of factors, such as: extrapolating a POD between animals and humans (interspecies; UF = 10); differences in response within the population (intraspecies; UF = 10); adjustments required for converting LOAELs to NOAELs (UF = 10); and to account for database deficiencies (UF = √ 10 ). UFs are multiplied and rounded down to derive the composite UF. Accordingly, for cases in which BMDs or NOAELs are used as PODs, the maximum composite UF would be 300 (i.
4  a A composite uncertainty factor of 1 was used for derivation of these reference doses. The default composite UF that OEHHA would use in this case would be 300 Reference dose a = 0. 024 mg•kg introduced to the screening level due to the use of BMD modelling software is assessed here by considering the BMD and associated 95% confidence intervals (i. e. , BMDL and BMDU). The relative sensitivity of the variability associated with BMDs varies by each POD, ranging from an absolute percentage difference of 9% to 315%, with an average variability of 54% (Fig. 4 ). The BMD for the lowest POD (AMH; Hou et al. [48] ), for instance, has consistent variability between the upper and lower 95% confidence intervals (-29% to 311%) relative to all other BMDs modelled based on the lower and upper 95 th percentiles, respectively), which is due to the variability in the relative uncertainties estimated using BMDS.
5•10 3 to 1. 4•10 9 polystyrene particles• L -1. Finally, considering that in many instances a reference dose might be estimated when a POD is used in supporting risk assessment (whereby a composite UF value of 300 might be used based on inter-species variability, intraspecies variability, and database deficiencies) a non-regulatory screening level value based on default DWI and RSC parameters (i. e. , 0. 053 L•kg -1 •d -1 and 0. 20, respectively), and derived using the BMDL for AMH reduction (i. e. , 0. 024 mg • kg -1 • d -1 ), would equate to 0. 3 µg • L -1 or 5. 7•10 5 particles• L -1 for 0. 5 μm polystyrene spheres using Eq. 1. This value thus represents the most conservative estimate derived from the mammalian effects data considered in this study towards characterizing a potential human-health effect, which we suggest should only be used for helping to guide monitoring activities.
Acknowledgements The authors would like to thank the outside reviewers  Aimen Farraj ,  Ruben Gandia ,  Jodi Flaws ,  Leon Earl Gray , and  Jonathan Powell  for providing valuable insights into the reliability and relevance of toxicity studies, members of the  Damdimopoulou laboratory  for evaluating the reliability of studies using SciRAP, and  Darrin Polhemus  for providing guidance on interpreting California's drinking water risk management framework.
Sample Collection for Metabarcoding and Morphological Assessment Tuna stomach samples of 55 skipjack (Katsuwonus pelamis) (TL ± S. D. = 520 ± 79. 53 mm) and 41 yellowfin (Thunnus albacares) (TL ± S. D. = 523 ± 85. 37 mm) tuna captured from the Pacific Ocean during 2015-2017 were accessed from the Pacific Marine Specimen Bank (https://www. spc. int/ofp/PacificSpecimenBank). This biobank was established to facilitate research to support the sustainable harvest of tuna in the Pacific Ocean and samples are accessioned into the biobank opportunistically. For this preliminary study samples were selected for analysis based on their associated metadata that document where (latitude and longitude) and when (date and time) specimens were collected during different ENSO events in the Pacific. Tuna specimens were captured using either longline, pole-and-line, purse seine or trolling methods.