dataesr/scientific-paragraphs-categorization huggingface dataset:
RLS The RLS database is a global dataset of underwater visual transects that has monitored the abundance and size distribution of temperate and tropical fish species globally. According to the RLS protocol available at https://www. reeflifesurvey. com, a survey consists of a 50 m transect line on which two methods are applied. Method 1 monitors fish composition, abundance and size classes observed in two five-metre-wide by five-metre-high bands. Method 2 monitors invertebrates and cryptic fishes in two one-metre-wide by two-metre-high bands on either side of the transect line. We restricted our analyses to warm-water reefs by selecting sites that experience minimum monthly sea surface temperatures of 17 °C  34. Although the present study focused on the tropics, whose sea surface temperature limit is usually set at 20 °C, we decided to use a broader definition of tropical oceans, thereby including locations where species of tropical affinity are present.
Daily mortality rates were estimated stochastically by calculating the probability of a fish dying, taking into account individual growth trajectories, size and age, and removing fish that were expected to die, using previously published predictive models and the rfishprod package  25, 36. We estimated standing biomass and biomass production at the transect level by summing both metrics for each transect, and we calculated biomass turnover, the ratio between biomass production and standing biomass (P/B ratio)  25, 26, 60, 61. Site-level information was obtained by averaging all metrics across all transects of a given site.
Modelling drivers of management classes To understand the potential drivers of these management classes, we examined how key environmental and socio-economic drivers were related to the probability that a site was in any of the management classes. To model management classes according to our nine selected drivers, we used a random forest classification algorithm using the ranger package (v. 0. 12. 1)  67. We tested our model performance using 100-fold cross-validation. The drivers' importance was estimated using permutation, which is calculated using the percent increase in the mean square error after randomly permuting a given predictor for every tree in the model, averaging the error of the models and then determining the difference relative to the accuracy of the original model. The model parameters were num.
Acknowledgements We thank the RLS divers and data management team for data provision. RLS data management is supported by  Australia 's Integrated Marine Observing System, which is enabled by the  National Collaborative Research Infrastructure Strategy. This research was partly funded through the 2017-2018  Belmont Forum and BiodivERsA REEF-FUTURES  project under the  BiodivScen ERA-Net COFUND  programme and with funding from  ANR ,  DFG ,  NSF ,  Royal Society ,  ERC  and  NSERC. E. M. was supported by an  Early Career Fellowship  from the  Leverhulme Trust.
Study design and population Data of 557 MSA patients who were enrolled at the Bordeaux and Toulouse sites of the French MSA Reference Centre between 2008 and December 2019 were analyzed. Diagnostic procedures and annual follow-up assessments (including UMSARS) of the cohort were described in detail elsewhere  [2]. All patients met current consensus criteria for a diagnosis of probable or possible MSA and were examined by a movement disorder specialist  [5]. All patients provided written informed consent and the cohort is registered with the CNIL (Commission Nationale Informatique et Liberté).
The most informative items were determined as those providing the highest information percentage at most UMSARS IV stages. We completed the analysis by estimating the minimum sample size required for treatment trials according to UMSARS I and II total scores or subscores derived from selected items by using Monte Carlo simulations and by assuming a linear mixed model for the statistical analysis of change. We fixed the power of detection for a difference between two treatment groups at 80% and the effect size at 30%. We considered UMSARS data every 6 months and accounted for the high risk of death by generating a dropout of 5% at each visit. We performed the calculation of the sample size estimates on the entire cohort and on an early disease subpopulation of our cohort relevant for disease-modification trials in MSA, i.
Data analysis At least 10 000 neutrophils are acquired and analysed using BD FACSDiva Software, as previously described (figure  1 ). Myeloperoxidase expression in the peripheral blood neutrophil population within an individual subject is expressed as RCV. 14 The intraindividual RCV is calculated as the robust SD divided by the median fluorescence Open access intensity. The robust SD is a function of the deviation of individual data points to the median of the study population. 14 Intraindividual RCV is expressed as percentage and reflects the variability in myeloperoxidase expression in the peripheral blood neutrophil population within an individual subject. 14.
23 Depending on peripheral blood monocytosis, one or more supporting criteria are required among (1) dysplasia involving on or more myeloid lineages, (2) acquired clonal cytogenetic or molecular abnormality and (3) detection of increased peripheral blood classical monocytes (flow cytometry MO1 >94%). 23 ta collection, management, and confidentiality Data are collected prospectively by the investigator or a designated representative, using a standardised case report form. Recorded data are listed in box 1. Final data review will be performed, checking for validity, consistency, omission or any apparent discrepancies prior to locking the database. Access to the study data will be Open access restricted to clinical research assistants, investigators and data managers.
Statistical analysis A statistical analysis plan (SAP) will be developed prior to database lock and reviewed by the principal investigator and an independent statistician. 32 Statistical analysis will be performed by a statistician in accordance with the SAP. The analytical sample will consist of all patients who have been included in the study. A Standards for Reporting Diagnostic Accuracy (STARD) statement style flow-chart will present graphically patient flow throughout the study. 33 escriptive summary statistics will be used for reporting continuous (arithmetic mean and SD or median and 25th-75th percentiles) and categorical (numbers and percentages) variables. Baseline patient characteristics and peripheral blood markers will be compared according to MDS status, using the χ² test, replaced by the Fisher exact test where appropriate, for categorical variables and the Student t-test or non-parametric Wilcoxon test for continuous variables.
0%. For transparency purpose, the completeness of study data will be reported for baseline characteristics, index test and reference standard. Missing values for baseline characteristics will be imputed using multiple imputations in multivariable analysis. No formal interim analysis is planned in this study. Twotailed p values less than 0. 05 will be considered statistically significant. All statistical analyses will be performed using Stata Special Edition V. 16 or higher (Stata Corporation, College Station, TX, USA). Additional software may be used for the production of graphics and for statistical methodology not provided by this software package.
Acknowledgements The authors thank  Drs Claude Eric Bulabois ,  Clara Mariette ,  Martin Carré ,  Stéphane Courby ,  Anne Thiebaut-Bertrand ,  Rémy Gressin  and  Lysiane Molina  for being involved in patient recruitment. The authors are indebted to  Laure Dusset ,  Ghislaine Del-Vecchio ,  Richard Di Schiena ,  Claire Gasquez ,  Frédérique Martinez  and  Karine Nicolino  for technical assistance. Contributors TR, LS,  M-CJ  and JL developed the protocol and drafted the initial manuscript. NG provided technical and administrative support. SC, ST, BB, MM, GS, FG, CL, CP, CD-P, RM and SP contributed to critical revision of the manuscript. JL provided statistical expertise. All authors critically reviewed the protocol and approved submission of the final manuscript.
Intervention The patients are selected from the emergency department of each center. The diagnosis is established by the surgeon investigator on clinical data and imagery. All consecutive patients with generalized peritonitis due to perforated diverticulitis are routinely proposed inclusion in the study. The decision of a possible inclusion is made after surgeon-anesthesiologist consultation. It's the same if the patient's condition changes peroperatively (infarction for example), a new surgeon-anesthesiologist consultation may need to modify the attitude initially adopted to adapt to the new clinical state of the patient. The diagnosis of the non-tumor nature of the perforation is based on probability arguments and intraoperative findings. It isn't be possible to obtain histological certainty of benign lesions before the first therapeutic act.
Statistical analysis Primary and secondary endpoints will be analyzed on an intention to treat basis. Data will be described overall and by randomized intervention group (protective stoma or no protective stoma) using standard parameters, i. e. , mean, median, standard deviation, range and interquartile range for quantitative variables, and frequencies and percentages for categorical variables. The main outcome being dichotomous, i. e. , complication after surgery (yes/no), Pearson's chi-square test or Fisher's exact test will be used as appropriate to compare the two intervention arms 12 months postoperatively. Point and (exact if necessary) 95% confidence interval estimates of the proportions of complications in each arm will be calculated. Use of the unadjusted test will be complemented by an adjusted comparison using the logistic regression model in order to adjust for center and possible prognostic characteristics.
Utility measure QALY will be derived from the EQ5D-5L questionnaire. The EQ-5D-5Luestionnaire will be filled at 6, 12, 26 and 52 weeks after primary surgery. For patients benefiting from a continuity restoration, analyses will be performed on the cumulative data of the two interventions. To avoid overestimating or underestimating patients' utility, it will be estimated that this utility evolves in a linear way taking into account the time between each visit for each patient in the two groups. Therefore, QALYs will be calculated as the utility × time interval between measures.
Data collection and monitoring Data of each patient are collected via an electronic Case Report Form (eCRF) (base line, perioperative and longterm data), and all eCRFs are gathered by the coordinating center, Rouen University hospital. After the initial surgery, all patients will be examined at 6, 12, and 26 weeks, in the surgical department where they were operated; a final study visit will be carried out 12 months (evaluation of primary endpoint) after surgery. The parameters explored at medical examinations are: occurrence of complications and quality of life assessment. The entry of data are performed using Ennov Clinical ® software (Ennov Group, 251 rue du Faubourg Saint Martin in Paris 75010-France). The database will be analyzed by the Biostatistics Unit of the University Hospital of Rouen.
Acknowledgements. This research was supported by the  Research Council of Norway  through its  Centres of Excellence scheme , project number  262622 , and through grants of computing time from the  Programme for Supercomputing. We acknowledge funding support by the  European Research Council  under  ERC Syngergy  grant agreement No. 810218  ( Whole Sun ). We also acknowledge the use of Visualization and  Analysis Platform for Ocean, Atmosphere , and  Solar Researchers  (VAPOR version 3. 6. 1)for field tracing and heat rendering figures. VAPOR is a product of the  Computational Information Systems Laboratory  at the  National Center for Atmospheric Research. R. A. R. acknowledges the insightful and motivating conversations with Pascal Démoulin and  Étienne Pariat  on the matter of inverse helicity cascade.
■ ACKNOWLEDGMENTS This work received financial support from  Agence Nationale de la Recherche  ( dehalofluidX  project  ANR-17-CE07-0009 , which also funded E. G. 's PhD thesis). Performed within Interdisciplinary Thematic Institute "IMCBio" (ITI 2021-2028 program  of  University of Strasbourg, CNRS and Inserm ), this work was also supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ),  SFRI-STRAT ' US  project ( ANR-20-SFRI0012 ),  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program , the previous  LabEx NetRNA  ( ANR-10-LABX-0036 ), and was also supported by  Centre National de la Recherche Scientifique (CNRS) ,  Université de Strasbourg, and its Initiative of Excellence (IdEx).
We used variational inference with a gaussian prior for the mean with a precision equal to 10 5 to enforce a zero-mean constraint for all components of the mixtures, and a Dirichlet process prior for the weights with concentration 1 divided by number of components, using the model BayesianGaussianMixture of the package scikit-learn  [Pedregosa et al. , 2011]. Since the inference and resampling processes are susceptible to finite-size fluctuations, for the DMS task in Fig. 3  we complemented the clustering with some retraining of the covariance matrices found for each component. For this we developed a class of Gaussian mixture, low-rank RNNs, in which the covariance structure of each population is trainable. Directly training the covariance matrices is difficult given that they need to be symmetric definite positive; we therefore used a trick akin to the reparametrization trick used in variational auto-encoders  [Kingma and Welling, 2013] : the set of input, connectivity and readout vectors were defined as a linear transformation of a basis of i.
Acknowledgements The project was supported by the  ANR  project  MORSE  ( ANR-16-CE37-0016 ), the  CRCNS  project  PIND , the program " Ecoles Universitaires de Recherche " launched by the  French Government  and implemented by the  ANR , with the reference  ANR-17-EURE-0017. There are no competing interests. SO thanks  Joshua Johansen  and  Bijan Pesaran  for fruitful discussions.
This results in effectively two-dimensional data plots, with the detected signal energy defining one parameter axis, stacked according to the excitation energy. FLN spectra were also measured using resonant excitation by the different emission lines of a continuous wave Ar ion laser (465. 8 nm, 476. 5 nm, 488 nm, 514. 5 nm). For this purpose, the samples were mounted in the variable temperature insert of a helium bath cryostat. The measurements were performed at T = 1. 6 K. The scattered light was detected in backscattering geometry, dispersed with a double monochromator equipped with a liquid nitrogen-cooled CCD camera. The PL decay curves were measured at temperatures ranging from 4 K to 60 K using a ST-500-Attocube cryostat supplied with a temperature controller. For excitation, we used a picosecond-pulsed semiconductor laser PILAS 405 nm (Advanced Laser Systems) with an average excitation power of 100 nW measured before the cryostat window.
For this reason, the introduction of an oblate NC shape as a fit parameter in the FLN/PLE data is not convincing. Below, we show that the similar ZPL signal in zb-and wz-CdSe NCs can be modeled if one considers a dependence of the ±1 L bright exciton oscillator strength on the NC shape and a shape dispersion in an ensemble of nominally spherical zb-CdSe NCs. Let us consider an ensemble of zb-CdSe NCs with a normal distribution of the NC shape anisotropy: g(µ) = 1 σ √ 2π exp - (µ -µ 0 ) 2 2σ 2 , ( 4 ) where µ is the anisotropy parameter µ = c/b -1, µ 0 is the mean anisotropy parameter for NCs of a given radius, and σ is the standard deviation. The ZPL in FLN studies of CdSe NCs is the result of the dark exciton emission after resonant excitation of the ±1 L bright exciton. We write the oscillator strengths of the ±1 L,U states, measured in units of the oscillator strength of the 0 U state using Equation (28) from Ref.
LOFAR The LOFAR HBA data was taken during a targeted observation of 3C 295 (PI Sweijen; project code COM_010, L693723) on January 25, 2019. It was observed from 01:41:00 to 09:40:59 UT, for a total of 8 hours on source and was book-ended by two 15 minute calibrator observations. A typical observing setup was used where the data was recorded at 3. 05 kHz frequency resolution and 1 s time resolution. The data were then archived to the Long Term Archive (LTA) after they were averaged in frequency to a resolution of 12. 21 kHz and RFI was excised using AOFlagger  Offringa (2010). The frequency coverage ranged from 120 MHz to 168 MHz for a total of 48 MHz of bandwidth. 51 stations in total (24 core stations (CS), 14 remote stations (RS) and 13 international stations) participated in the observation, all of which provided usable data except for the remote station RS508.
First the prefactor calibrator pipeline was run on one of the calibrator observations to obtain corrections for polarisation alignment, find the station bandpasses and correct for clock offsets  (de Gasperin et al. 2019). After this all core stations were combined into a single "super" station, ST001 to significantly reduce the field of view. Finally, the data were averaged to a time resolution of 4 s and a frequency resolution of 48. 82 kHz. The data were then self-calibrated. We used PyBDSF  (Mohan & Rafferty 2015)  to generate a starting model from a VLA A-configuration image at 8. 561 GHz shown in  Fig. A. 1 (Gilbert et al. 2004 ). Self-calibration then followed a strategy similar to that presented in van  Weeren et al. (2020). First 5 cycles of phase-only calibration were performed at the resolution of the data.
Subsequently the calibration switched to 5 cycles of phase-only calibration followed by amplitude calibration and a frequency interval of 195. 28 kHz and a time interval of 15 minutes. Such short intervals were possible due to the exceptionally high signal-to-noise ratio present on this class of source. Calibration was restricted to baselines 40 kλ or longer to help stabilise calibration. After each iteration a new image was made and > 7 σ rms emission was used to update the model for the next iteration, where σ rms is the local root-meansquare noise in the image. Imaging was done using multifrequency synthesis, a robust -1 weighting and multi-scale clean. WSClean  (Offringa et al. 2014 ) was used for imaging and DP3  (van Diepen et al. 2018)  was used for calibration. Finally, we note that international LOFAR data can be impacted by large dispersive delays, which are challenging to remove for low signal to noise sources where large bandwidths are necessary to find good solutions.
Fortunately, 3C295 has a high enough flux density that we can perform self-calibration per channel, removing the need to solve for these dispersive delays separately. This data was further self-calibrated using the Wirtinger pack software  (Smirnov & Tasse 2015; Tasse et al. 2018) , which consists of the DDFacet imager and killMS calibration software. We used small intervals in time and frequency (8 seconds and 4 channels, respectively) so as to maximise our tracking of underlying changes in the antenna gains, solving for full-Jones matrices in each interval. Intervals of increased ionospheric activity (corresponding to noisier measurements and thus worse S/N for our gain solutions) were corrected for by using the quality-based weighting scheme developed in  Bonnassieux et al. (2018). This was key in maximising the relative contribution of the international baselines to the final image.
MERLIN The archival MERLIN observation was taken on 26 May 1998, with a central observing frequency of 1. 658 GHz and a total bandwidth of 26 MHz. The observation included 6 antennas. The data were extracted from the MERLIN archive, and flux-calibrated with 3C286. We then performed 8 passes of self-calibration using killMS and DDFacet to improve the calibration, followed by a final re-imaging using wsclean2. 1. 0, using exactly the parameters and initial model as for our LOFAR data. The shortest baseline available in our MERLIN observation is 25 kλ, which corresponds to an angular scale of 8. 25. It therefore does not lead to loss of flux, as 3C295 has a LAS of 6. 1. Using the same imaging procedure as for LOFAR ensures homogeneous uv-coverage for the angular scales of interest. The final noise level achieved was 25 mJy beam -1 , leading to a dynamic range of about 200 in the final science image.
VLA Our VLA data were acquired from the NRAO Science Data Archive. We restricted our search to observations from A-configuration and longer than two hours in duration to maximise our uv-coverage. We used two observations from the NRAO Science Data Archive, each in A-configuration and longer than two hours in duration to maximise our uvcoverage and each comprising two frequency bands, taken as part of project AP135 (PI. R. Perley) on 12 September 1987. The central frequencies of each observation were 4. 76 GHz and 14. 975 GHz, with bandwidths of 50 MHz. Both use the full 27 VLA antennas. This configuration leads to shortest baselines of 9. 3kλ at 5 GHz and 35kλ at 15. 5 GHz, corresponding to LASs of ∼ 22. 2 and ∼ 3 , respectively. Because the latter is smaller than the LAS of 3C295, we added a calibrated C-configurationd dataset from the VLA science data archive in order to properly sample 3C295 at 15GHz.
The archival data sets contained uncalibrated visibilities. We therefore used the Wirtinger pack for our selfcalibration, with the exact same procedure and initial model as for LOFAR. We then imaged our final visibilities using wsclean to match the imaging parameters used at all other frequencies. This converged quickly for both frequencies, allowing us to acquire science images with rms noise levels of 15 mJy beam -1 and 670 µJy beam -1 at 5 and 15 GHz respectively (corresponding to dynamic ranges of 165 and 754, respectively). The hotspot positions were used to align the final image with the LOFAR images, as for the final eMERLIN image. This offset was ∼ 0. 2.
These fits are then used as the initial parameters of our Markov-chain Monte-Carlo (MCMC) fitting method. We employed an 'affine invariant' MCMC ensemble sampler  (Goodman & Weare 2010)  as implemented by the emcee package (Foreman-Mackey et al. 2013), creating 500 'walkers' to fit the data, with the 1σ confidence interval for each of our fits used as a final error estimate. The points and the best-fit curves are all shown in Fig. 5. The best-fit parameter values and their errors are summarised in Table  3. Fig. 5  shows the spectral behaviour of our 4 components along with the spectral model best-fit curves. We see that the SSA and FFA models both seem give good fits to the data. However, only the LOFAR data point lies below the turnover frequency, which means that we lack the low-frequency constraints to discriminate between FFA and SSA.
Acknowledgements. The authors thank  F. Massaro  for fruitful discussions over the course of this work, which helped improve the form and contents of this paper. E. Bonnassieux ,  A. Bonafede ,  M. Brienza ,  C. J. Riseley  acknowledge support from the  ERC-Stg  grant  DRA-NOEL , n. 714245. K. Rajpurohit  and  M. Brienza  acknowledge financial support from the  ERC   Starting Grant  "MAGCOW", no. 714196. LKM is grateful for support from the  UKRI   Future Leaders Fellowship (grant   MR/T042842/1 ). LOFAR (van Haarlem et al. 2013) is the  Low Frequency Array designed  and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy.