dataesr/scientific-paragraphs-categorization huggingface dataset:
Pairwise correlations and variance inflation factors for the entire dataset did not show any collinearity concerns (all pairwise correlations were below 0. 6 and VIF were below 1. 5). Subsetting the data revealed some interdependencies among covariates in some subsets. For example, in reserve sites, reserve size was correlated with sampling area such that larger reserves had larger sampling areas (Pearson's correlation=0. 56). In remote reefs, atolls had lower ocean productivity compared to non-atolls (Pearson's correlation=0. 96). These interdependencies did not impact model convergence, probably because effect sizes from covariates correlated in some subsets were informed by other subsets where correlations were much weaker. Next, we tested the utility of two alternate models of varying complexity in capturing the structure of our data by following a "Principled Bayesian workflow" on each model (56; Supplementary information 2).
Site-specific reference points and status Assuming that sampled reefs are representative of the conditions at the scale of population closure, we assessed the biomass status of sites open to extraction (i. e. , excluding marine reserves and remote reefs used to estimate sustainable reference points) by comparing a site's biomass (corrected for methodological effects) to the site-specific estimated BMMSY value. Site-specific reference points (BMMSY and MMSY) for every site in our data were estimated using the estimated posterior unfished biomass and community growth rate for average environmental and sampling and our sites' available environmental information. Note that for sites that did not have available coral cover information we assumed that coral cover was at its mean level in the database:  0, =  log ( 0 )+ 1  _, + 2  , + 3  , + 4  ,   = ( 0, * )/ (  22 ) 𝐵 𝑀𝑀𝑆𝑌𝑠 = 𝐵 0,𝑠 /𝑒 (23) To make biomass estimates comparable to reference points, standing stock observed biomass estimates were corrected for methodological covariates (Habitat type, Depth, Census method, and Sampling area) using the posterior effect sizes from the reference point model and calculating the marginalized biomass (i.
We used catch volumes instead of effort because (i) catch estimates are directly available from global catch databases (e. g. ,  20, 21) , and for data-poor coral reef regions catch volume reporting tends to be more consistent than effort (e. g. , number of boats, number of fishers, number of certain gear types;  [46] [47] [48]. Similarly, a jurisdiction's biomass status was defined relative to BMMSY: whether or not its estimated biomass (weighted or not, t/km 2 ) was below its jurisdiction-specific BMMSY estimates from the Gompertz-Fox model  (25). A jurisdiction's fishing and biomass status was calculated as:  , = (    ℎ, )/  ,  , =   /    (34)  These are distributions, but in the main manuscript we report median and 0. 9 quantiles. Finally, for jurisdictions with both catch and biomass data available, we characterized a jurisdiction as "in good condition" if weighted biomass was above its jurisdiction-specific BMMSY and the total catch was below the jurisdiction's MMSY value; "unsustainable" if it was simultaneously catching above its estimated surplus production curve and had biomass estimates below BMMSY; "warning" if biomass was above BMMSY but catching above MMSY (i.
Trade-offs between production and ecosystem metrics To assess the trade-offs between production and other ecosystem metrics on fished reefs, we evaluated the relationship between reef fish biomass and four ecosystem metrics thought to be important for ecosystem functioning  (1, 29). Ecosystem metrics were: mean fish length (i. e. , average observed length for species in the community; L; n= 1763), the probability of observing top predators (i. e. , presence/absence (PA, n=1763) of fish from the following families: Carcharhinidae, Ginglymostomatidae, Heterodontidae, Sphyrnidae, and Carangidae, Lutjanidae, Serranidae and Sphyraenidae above 50 cm), parrotfish scraping potential (P; n=1116), and estimated total fish species richness (R; n=1753). Note that sample sizes vary depending on whether or not that metric was provided by data providers at that scale.
When reconstructed non-spatial catch data (including the industrial sector or not) was used the percentage of jurisdictions catching above MMSY remained above 40% (48% and 42%, respectively), still yielding >50% (53% and 51%, respectively) of jurisdictions classified as "conservation concern". When only reported data was used (which is likely an underestimate of reef fish catch; 21), the percentage of jurisdictions catching above MMSY decreased (33% or 27% when the industrial sector was excluded) but jurisdictions of "conservation concern" remained at ~45% (Supplementary information 1). Individual intrinsic growth rates vs. community biomass specific growth rates: Here we estimated community biomass growth rates for coral reef-fish assemblages, which, to our knowledge, does not have a straightforward relationship between the average of individual species intrinsic growth rates.
Acknowledgments The authors would like to thank anonymous referees for their constructive comments on the manuscript,  Andreas Dietzel  for providing constructive comments on reef area estimates,  Eva Maire  for providing travel time estimations,  Gordon Tsui  for providing catch data cell ID's,  Michel Kulbicki  and  Tim McClanahan  for providing data and constructive comments on the manuscript, and  Pascale Chabanet ,  Eran Brokovich ,  Marah Hardt ,  Juan Cruz ,  Laurent Vigliola ,  Mark Tupper , and  Stuart Sandin  for providing data. We also thank all the data collectors. Funding: JZM, SRC, AH and JEC thank the  Australian Research Council  for funding support. MAM was supported by an  NSERC Canada Research Chair.
We study the impact of tiered settlement on relative intraday liquidity use (liquidity consumption) for settlement banks in TARGET2. We estimate a panel data model employing transaction-level data from 2010 to 2019 which shows that a higher share of tiered payments from client banks reduces relative liquidity consumption by settlement banks. Metrics on timing, delay, and payment priorities suggest that settlement banks can make use of more leeway in settling tiered payments from client banks compared to their own payments. Payment timing as a proxy for external delay suggests that tiered payments are used to smooth settlement banks' liquidity positions. Results on payment delay within the system show no clear dynamic over time, whereas payment priorities are consistently lower for tiered payments.
Assuming liquidity is costly, settlement banks exhibiting lower values of liquidity consumption settle payments in a more cost-efficient manner. Tiering generally decreases liquidity needs as payments offset when concentrated among fewer direct participants. However, little is known about the effect of tiering at the participant level and how tiering factors into banks' liquidity management. To shed some light on this question, we employ a panel data model and identify channels via which tiering affects liquidity consumption. Using transaction-level data from TARGET2, we find that higher shares of tiered payments reduce relative liquidity needs for settlement banks. The results are robust controlling for pooling effects via bank fixed effects, payment activity and other factors. The main driver appears to be that banks have more discretion in settling tiered payments.
Given their roles, failures of settlement banks would lead to substantial welfare losses in terms of operational risks and loss of information. From a central bank perspective, monitoring payment system activity is crucial for risk mitigation. A variety of approaches are available to identify different risks. Berndsen and Heijmans (2020)  develop a traffic light approach, based on different indicators to identify credit, liquidity and operational risk in TARGET2. Triepels, Daniels, and Heijmans (2018)  apply an unsupervised learning method to detect anomalies in RTGS systems. Sabetti and Heijmans (2021)  apply a similar approach to Canadian LVPS data and discuss how deep-learning methods could be implemented by operators. Rubio, Barucca, Gage, Arroyoa, and Morales-Resendiz (2020) built on their work to asses deep networks to detect anomalies in the largest systemically important payment system in Ecuador.
Underlying business reasons for large-value or urgent transfers are manifold, including for example payments for goods and services, the purchase or sale of securities, loan payments or transactions based in the real economy. The measures in section 4 are constructed using TARGET2 transaction data from 2010 to the end of 2019. We focus on transactions of commercial banks as participants of TARGET2. Transaction-level data is filtered for central bank operations, participants' liquidity transfers between their own accounts and technical transfers in order to focus on business-related payments that affect settlement banks' liquidity position during the day and to exclude payments that serve the purpose of liquidity management. In addition, we disregard start-of-day balances that banks hold, in part, for fulfilling minimum reserve requirements.
Model of liquidity consumption To test the effect of tiering and explain the outcomes in terms of liquidity consumption on the direct participant level, we estimate a panel data model using bank and time fixedeffects. We prefer fixed effects over random effects as the latter assume the unobserved bank-level effects are uncorrelated with the independent variables. As the level of tiering and size of settlement banks probably factor into the unobserved effects, we prefer fixed effects here. However, the results are robust to employing random effects. Liquidity consumption is calculated daily across direct participants. As the independent variable of interest, the share of tiered payments is included. Note: The share of tiered payments is calculated as the value of tiered payments sent relative to all payments sent by a participant, the log value sent is the log-transformed value of overall payments sent, the time difference is the difference in average timing between all received and sent payments, the concentration is measured by the Gini coefficient for outgoing and incoming payments, the priority of payments is the average priority of payments (values between 1 and 3), the money market rate is expressed as a percentage (calculated via loans identified from TARGET2 data), and log-transformed overall liquidity measured in millions of euro (ECB data).
Higher concentration of payments inhibits participants from shuffling payments. As controls for the cost of liquidity and the overall levels of liquidity, the overnight interbank money market rate and overall liquidity 18  are included. The money market rate is calculated using an algorithm proposed by  Furfine (1999) , applied to TAR-GET2 data following  Arciero, Heijmans, Heuver, Massarenti, Picillo, and Vacirca (2016)  and  Frutos, Garcia-de Andoain, Heider, and Papsdorf (2016). We use a modified version of the latter to calculate the euro money market rate. 19  The algorithm identifies interbank loans by matching payments with plausible repayments the next business day. We estimate the model using fixed effects for direct participants and time effects on a yearly basis to account for changes over time.
Discussion The analysis is limited to information from systems data focusing on payments once they enter TARGET2. Information on settlement banks' internal procedures and contractual arrangements with client banks is obtained only implicitly. Importantly, TARGET2 is only one part of banks' overall liquidity position. Other systems, bilateral relationships and exposures may play a significant role for some banks' liquidity disposal. These limitations of the study point to the need to shift the focus of policy to participants, rather than focusing solely on systems as a whole. Importantly, agreements between direct and indirect participants cannot be observed. Taking a more holistic, participant-centric view could be highly beneficial. Gathering more data on participants and their internal procedures for settlement, could shed more light on questions of potential free-riding, postponement of payments and exposures.
The indication for surgery was based on cytology in 79 patients (Bethesda IV in 19 patients, Bethesda V in 32 patients, and Bethesda VI in 28 patients), symptoms and/or signs of compression caused by the goiter in 35 cases, an autonomously functioning nodule causing hyperthyroidism in 5 patients, and patients' wish in 4 cases. Final diagnoses were, in all cases, obtained by histological examination of the surgical samples. Relevant patient data appear in Table  1. Representative parts of each US video recording were presented to seven investigators (see later), who were blinded to the outcome data. The video recordings are available for the reader (http://thyrosite. com/case_ studies/section08/consecutively_operated. php). Written consent has been obtained from each patient after a full explanation of the purpose and nature of all procedures used.
The reason being that we deemed it superfluous to test the US diameter measurement capability of expert US users. Two patients with thyroid malignancy other than thyroid cancer were also among the studied nodules, as by definition, consecutive cases were included. Strengths of our study include only evaluating surgically removed thyroid nodules, the US investigator team consisting of highly skilled physicians with extensive US experience, and the use of videos rather than still pictures thereby resembling the realworld situation. While the participation of experienced investigators might have positively affected sensitivity and specificity, the true extent and direction of this influence for the interpretation of our data remains unclarified and awaits testing in a number of different settings.
2-20 arcsec, with an average of 17 arcsec. At the assumed 16. 5 Mpc distance to Virgo, the 15/8-arcsec beam/spaxels of the VERTICO data correspond to a 1200/650 pc physical resolution. Signal masks were created for the CO(2-1) and H datacubes as described in  Brown et al. (2021, but  see also  Sun et al. 2018 and Leroy et al. 2021) , and moment 0 maps created for each galaxy. The H mass in each spaxel was calculated assuming optically thin emission (e. g. ,  Meyer et al. , 2017) , M H [M ] = 2. 35 × 10 5 D 2 L (1 + z) -2 S H , (1) where D L is the luminosity distance to the object (assumed to be 16. 5 Mpc for all VERTICO galaxies), z is the redshift, and S H is the velocity-integrated spectral flux density (in Jy km s -1 ). Unless otherwise specified, we do not correct our H masses for the Helium abundance.
HERACLES control sample We extracted a control sample of galaxies from the HERA d CO(2-1) Legacy Survey (HERACLES,  Leroy et al. , 2009). HERACLES targeted the CO(2-1) emission line in 33 galaxies from The H Nearby Galaxy Survey (THINGS,  Walter et al. , 2008) , a sample of nearby (D < 15 Mpc), late-type galaxies spanning from dwarfs to massive spirals. After excluding nondetections and five galaxies that overlap with VERTICO, there are 25 HERACLES galaxies with public data (for more details see  Brown et al. , 2021). Physical quantities for HERACLES galaxies were calculated using the same methods and sources as described for VERTICO, and B-band R 25 were drawn from  Walter et al. (2008). We did not compute log Σ H for HER-ACLES galaxies as the R H was nearly always outside R 25. The HERACLES native resolution datacubes have a synthesised beam-width of 13 arcsec, while the THINGS data are more varied depending on the VLA configuration.
Before calculating physical quantities, the HERACLES and THINGS data were smoothed using the routine of CASA to match the physical spaxel size and resolution of the VERTICO data, and circularise the synthesised beam. As the distances to HERACLES sample galaxies vary, this left 11 galaxies that could be matched to a 1200-pc physical resolution in both CO(2-1) and H. To ensure that our HERACLES control sample is a reasonable representation of star-forming galaxies, we restricted this sample to galaxies more star-forming than 1σ below the star-forming main-sequence using the parametrisation from the z0MGS sample (σ = 0. 36, with star-formation rates (SFR) from the same catalogue,  Leroy et al. , 2019). After the stellar mass and inclination selection cuts, our final HERACLES control sample consists of eight galaxies, listed in Table  1.
We compared the integrated properties of the final control sample galaxies to a subset of star-forming galaxies from the xCOLD GASS e  (Saintonge et al. , 2017)  sample, selected using the same SFR threshold. While the HERACLES galaxies lie on or slightly above (< 1σ) the locus of the star-forming main-sequence, they span the same range in log M H /M and log M MG /M as the xCOLD GASS sample, indicating that there is no bias toward H -or molecule-rich systems in our control sample. Last, we briefly mention the detection limits of each dataset after the homogenisation of the spatial resolutions and the masking procedures. The HERACLES:THINGS data are shallower than the VERTICO:VIVA data in both H (Σ H ≥ 1 -3 M pc -2 within B-band R 25 compared to Σ H ≥ 1 M pc -2 ) and molecular gas (Σ mol ≥ 1 -3 M pc -2 compared to Σ mol ≥ 0.
4 -1 M pc -2 ). Given that we are interested in environmentdriven reductions in both H and molecular gas surface densities, the fact that the VERTICO:VIVA data are deeper ensures that our findings are not due to differences in sensitivity between the two datasets. The shallower nature of HERACLES means that it can have an increased fraction of non-detected spaxels near the edge of the disc, and we account for this in our analysis.
38 ± 0. 03 dex expected and 0. 41 ± 0. 05 dex observed. We also can exclude that both def-H samples are drawn from the same parent distribution as their respective mock control samples (p < 10 -4 ). Thus, in VERTICO galaxies it is not Σ gas that sets Σ mol /Σ H , and explaining the observed Σ mol /Σ H requires a reduction in their molecular gas content that is not related to insufficient fuel or an inefficient conversion of H into molecular gas. We must also consider if there are any systematic differences between how molecular gas is traced in VERTICO and HERACLES. As the data have been homogenised and both use the same constant R 21 and α CO to convert CO(2-1) luminosity to molecular gas mass, this scenario is unlikely. Although R 21 and α CO have their own uncertainties, this homogenisation means that our results are actually re-scaled differences between the observed CO(2-1) emission in our samples.
Acknowledgments This work was carried out within Expert Tasks from the  Field of Plant Protection , a program funded by the  Ministry of Agriculture, Forestry, and Food -Administration for Food Safety, Veterinary Sector, and Plant Protection , and within  Horticulture  ( P4-0013-0481 ), a program funded by the  Slovenian Research Agency. Special thanks to  Juliette Pijnakker  for the photo of Euseius gallicus, to  Johan Witters  for the photo of Neoseiulus barkeri, and to  Institut Agro Montpellier  for the photos of Amblyseius andersoni,  Neoseiulus cucumeris , and Neoseiulus californicus.
, 2019). The crest heights were measured manually from the top of the crest to the first myofibril top layer using Fiji software and the SSM number was quantified for each crest. The sarcomere lengths were measured similarly on the first myofibril layer below the CM surface crests. To analyze the interlateral space between two CMs, orientation of CMs in the longitudinal axis is mandatory. The selected area for quantification of the lateral space must not go through the crest architecture, so that the interlateral space appears regular and almost straight (in control conditions). The interlateral space is then photographed at high magnification (×5000) and five independent measurements were conducted. Myofibril numbers were quantified on the longitudinal axis of the CM at the level of their anchorage on an entire intercalated disk from top to bottom of the cell, thus allowing better individual counting of the myofibrils.
Hemodynamic measurements of left ventricular pressure Catheterization was performed before sacrifice under isoflurane anesthesia (2%). The animals were monitored with ECG leads and body temperatures were carefully monitored and maintained as close to 37°C as possible during the entire procedure. Hemodynamic parameters were measured using a Science pressure catheter (Transonic) connected to the PowerLab 8/35 data acquisition system (AD Instruments). The catheter was inserted into the right carotid artery, and after stabilization for 5 min, arterial blood pressure was recorded. After that, the catheter was advanced into the ascending aorta and then into the left ventricle under pressure control. After stabilization for 5 min, signals were continuously recorded. Trace analysis was then performed using the LabChart Pro software system (AD Instruments) to determine systolic and diastolic blood pressure, LV end-diastolic pressure (LVEDP), the maximum slope of the LV systolic pressure increment (dP/dtmax), and diastolic pressure decrement (dP/dtmin), and the relaxation time constant (Tau) was calculated from the LV pressure trace.
Exercise exhaustion test The maximum stress test with measurement of gas exchange was carried out according to the method described by  Kemi et al. , 2002  using the TSE CaloTreadmill system, a fully computerized, electronically controlled system to investigate exercise calorimetry in mice and rats. CaloTreadmills are enclosed by an air-tight transparent housing that can be connected to a fully automated indirect gas calorimetry system operated by PhenoMaster software. After 2 days of acclimatization to the treadmill, the stress test was carried out in mice from the two experimental groups, respecting heterogeneity between the two populations on each run. The mice warmed-up on an inclined treadmill (25°) at a speed of 5 cm. s-1 for 5 min. Then, the mice ran at 15 cm. s-1 for 5 min, and the speed was increased every 5 min by 5 cm.
Then regions of interest with a fixed size of 500 × 35 pixels (corresponding to an area of 50 μm × 3. 5 μm) were manually drawn and duplicated from the image. On these regions, the quantitative TT power measurement was performed with a Python script, written in a Jupyter Notebook. At first, a two-dimensional Fast Fourier Transformation (FFT2D) is performed. From the FFT2D, the power spectrum P_2D is calculated as: P_2D = |〖FFT〗_2D |^2/N with N the total number of pixels. From the power spectrum image, a one-dimensional power spectrum P_1D is retrieved by extracting the middle horizontal line. Finally, TT power (indicative of the regular organization of TT system) was measured on P_1D as the amplitude of a Gaussian curve fitted on a peak located between 0. 45 μm-1 and 0. 7 μm-1 and frequency indicative of as the center of the Gaussian curve.
600 ng of Cy3-labeled cRNA were hybridized on the microarray slides following the manufacturer's instructions. Immediately after washing, the slides were scanned on Agilent G2505C Microarray Scanner using the Agilent Scan Control A. 8. 5. 1 software and fluorescence signal extracted using Agilent Feature Extraction software v10. 10. 1. 1 with default parameters. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE196257 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE196257).