dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiarc least square estimation filter Juno's Doppler data have been fitted using a multi-arc least square estimation filter. Data acquired by Juno during a pericenter pass forms an observation arc. For each arc, we solve for a set of local parameters: Juno's initial position and velocity, the velocity change during Earth repointing turns (they may occur after the perijove pass, before the orbit trim maneuver), and normal modes amplitudes. Global parameters (common to all arcs) are Jupiter's gravitational parameter (GM), zonal harmonic coefficients to degree 30, non-zonal coefficients of degree-2, tidal Love numbers up to degree and order 4, Jupiter's spin axis initial position, and polar moment of inertia factor, the mass of the GRS dipole, and a scale factor for the solar radiation pressure.
Except for normal modes amplitudes, no a priori uncertainty constraint has been imposed on any parameter. The reader should refer to the dedicated Method's section for more details on Juno's dynamical model. Concerning normal modes, it is impossible to fit the data by assuming phase coherence of the oscillations across all observation arcs, given the large gap between the pericenter passes and the uncertainty in the frequency of the modes. For a given solution on our search grid, we, therefore, estimate the amplitude and phase (the amplitudes of a cosine and sine term) of each mode as local parameters (pertaining to a single observation arc). Furthermore, we include only those modes having: (1) a spherical harmonic degree up to 8, since higher order harmonics are mutually highly correlated due to Juno's eccentric orbit and would not be accessible, and (2) a period larger than 10 min (that is, a radial order up to 7 at most) since Juno's 60 s Doppler data would not provide sufficiently dense measurements to distinguish higher-frequency modes.
Our choice is driven by two factors: the noise in the Doppler data (higher at shorter integration times) and the need to avoid overparameterization of Juno's dynamical model. The signal coming from the neglected (i. e. , truncated) modes is, per unit amplitude, smaller than the signal from lower frequency, lower order modes. Then, we estimate a mode only if the amplitude of the spherical harmonic coefficient is larger than ~10 -11 : smaller values would not be large enough to affect Juno's orbit in any detectable way (e. g. , with the current data set, Juno's sensitivity to static J 2 is ~2 × 10 -10 ). For this reason, the number of modes included in each solution can vary according to model parameters. The maximum number of modes is 56, which is reached for v min , v max larger than ~3 mm/s.
Model selection: AIC We performed the task of model selection using the AIC  18. This wellestablished statistical criterion is based on the use of entropy as a measure of information and provides a quantitative value of the information loss when a given data fitting model is selected. It selects models using their likelihood L in fitting the Doppler data while favoring low complexity models, expressed by the associated regression-effective degree of freedom k: AIC = 2 k À 2 log L ð7Þ AIC consists of a trade-off between the goodness of fit and the simplicity of the model itself. Its minimization requires the maximization of the (log-) likelihood function of the model to the data, while keeping the solution's degrees of freedom to minimum (low value of k). See dedicated subsection in Methods for how to compute the number of degrees of freedom of a given dynamical solution.
In the case of a least square estimation filter, by substituting the corresponding likelihood function, the AIC can be computed as: AIC = 2 k + n obs log RSS obs + C ð8Þ With n obs being the number of Doppler data points, RSS obs their rootsum-square, normalized with the noise standard deviation, and C a constant which depends only on the number of data points (which is unchanged in the different solutions we aim to compare). Given the possibility of incurring into a solution with a large difference between estimated and model amplitudes for the normal modes, the likelihood is constructed as the product of the data-driven and amplitude-based likelihoods. The additional, amplitude-based, likelihood term is: L m = 1 2πσ 2 n modes =2 exp À 1 2 RSS modes ð9Þ here n modes is the number of modes estimated (for all the arcs), RSS modes the root-sum-square of the difference between the model and estimated amplitudes of the modes, normalized to the uncertainty of the mode amplitude, and σ = RSS modes /n modes is the maximum likelihood estimate of the variance of a model's residuals distribution.
Data set The current analysis extends the data set used in Durante et al. 4  to also include Juno's gravity-dedicated passes up to PJ33, for a total of 22 passes and 12299 Doppler points (at an integration time of 60 s). In a typical pass, the ground station tracks the spacecraft in a dual link (Xand Ka-band, 7. 2-8. 4 GHz and 34-32 GHz) two-way mode for 6-8 h around perijove; the outbound pass, whose primary scope is to monitor an orbit trimming maneuver (OTM), is collected by a different station. The data set used in the analysis includes Doppler data from the pericenter pass and the outbound pass, until when the OTM maneuver is executed. In most of the passes, the links are established by DSS-25 antenna of NASA's Deep Space Network, located in Goldstone, California, in a X-up/X-down and Ka-up/Ka-down configuration.
Combining X and Ka-band data allows removal of up to 75% of the noise due to charged particles. Only in a few passes (PJ01, PJ13, PJ27, PJ33) the Ka-band uplink signal was not available, and a X-up/X-down/ Ka-down configuration was used, which only allow plasma calibration on the downlink leg. The dual-link configuration is extremely important due to the presence of the Io Plasma Torus, which introduces a delay and a Doppler shift in the data, thus potentially biasing the gravity estimation, if left uncalibrated. Moreover, the multi-link configuration reduces the total noise on Juno's Doppler data by 10% on average. The open-loop Doppler data have been compressed to 60 s, allowing sufficient sampling of the gravity signal of interest. The data have been calibrated for wet tropospheric noise with the Advanced Water Vapor Radiometer (AWVR), when available.
Acknowledgements The work of D. D. and L. I. at  Sapienza, University of Rome , M. Z. and L. G. C. at the  University of Bologna , and G. L. at the  University of Pisa  has been supported in part by the  Italian Space Agency. T. G. and S. M. acknowledge funding from the  Centre national d'études spatiales  and from the  Programme national de planétologie. All the authors would like to thank the  Juno Interior Working Group  for stimulating discussions and exchange of ideas.
A glance at the scientific literature A systematic literature search in Scopus database recovered 645 articles that include "regenerative agriculture" (29 articles) or "agroecology" (616 articles) in the article title. Text mining of terms from "Title, Abstract and Keywords" of these articles (methodology described in Supplementary Material-II) revealed four classes of terms based on Reinert's top-down hierarchical classification (Figure  2A ). Each class relates to a specific dimension, such as "knowledge" (Class 1), "framework and definitions" (Class 2), "social engagement" (Class 3), and "practices" (Class 4). More than 70% of the variability in the cloud of terms was explained by a post-hoc correspondence factor analysis (Figure  2B ). Globally, the analyses showed that each class differs in the textual information provided.
The green cluster centred in "soil", one of the central goals of RA, reveals terms related to effects of RA on soil improvement, soil quality/health drivers, and other pivotal natural resources. The turquoise cluster includes terms related with food production, a second goal of RA. The orange cluster, including the terms evaluation, participatory and monitoring is perhaps the closest to what could be defined as a social dimension of RA. These observations correspond to a few articles from a PhD Thesis on "Participatory monitoring and evaluation of regenerative agriculture in South West Spain"  [Luján Soto et al. (2021) -cf. Box 1]. The co-occurrence network analysis of terms based on keywords reported in 101 articles recovered from Scopus database including the term "regenerative agriculture" in title, abstract and keywords, revealed the connection of RA with other approaches (Figure  4A ).
The blue cluster is centred around regenerative agriculture, but also includes terms such as sustainable agriculture, organic agriculture, permaculture and holistic management. The yellow cluster reveals the connection of RA with agroecology, alternative agriculture, and soil quality. The green cluster is centred in agriculture and on its connection with soil (microbiology), environmental protection, and ecosystem. The red cluster links RA with agroforestry and holistic and strategic approaches, and highlights the relevance of land management and land use in nitrogen, carbon and carbon sequestration, and in provision of ecosystem services. The violet cluster links RA to the food industry, food supply and sustainable development. Finally, the co-occurrence network analysis based on keywords reported in 4426 articles recovered from Scopus database including the term "agroecology" in title, abstract and keywords, revealed four main clusters (Figure  4B ).
| Preprocessing The ROBEX  (Iglesias et al. , 2011)  brain extraction tool was used to extract the brain mask before any other preprocessing step for the T1-weighted (T1w) images. The mask was subsequently used during the co-registration step. Noise removal from the T1w image was performed using the nonlocal means algorithm from the Dipy package  (Descoteaux et al. , 2008)  followed by a bias field correction using the N4BiasFieldCorrection algorithm from the Advanced Normalisation Tools (ANTs)  (Tustison et al. , 2010). Noise and Gibbs ringing artefacts were removed from the DWI series using dwidenoise and mrdegibbs, respectively. Both tools can be found in the MRtrix3 suite  (Tournier et al. , 2019). The eddy_openmp algorithm from FSL  (Andersson & Sotiropoulos, 2016)  was used to correct for eddy currents and subject movement.
| Voxel-based analysis statistical tests and filtering The registered parameter or parameter-sensitivity maps can then be used for VBA in the common co-ordinate system. The values for each parameter map at corresponding locations through the patient and control database can then be rigorously compared. These maps were compared using SPM12 (rev. 7771)  (Frackowiak et al. , 1997)  on MATLAB R2014a which assumes a linear relationship between the individual's status (i. e. PD vs. HC) and the value of the metrics, subject to Gaussian noise. We applied Gaussian smoothing (std. 8 mm isotropic) followed by spatial statistical correction with a family-wise error (FWE) rate of 1% and a minimum cluster size of 256 mm 3. In order to determine the quality of our approach, we compared it to voxel-based diffusion analysis using the same patient database and registration procedures described in Sections 3.
However, more research would need to be done to ensure that the sensitivity maps for tensor orientation can be meaningfully aggregated (i. e. meaningfully combined in a population-wise manner) and interpreted. The approach may also be improved through the use of more advanced diffusion protocols that have been developed since the PPMI data set was launched. However, any improvement in the protocol may be offset by the more limited supply of training data at least until new, large databases are constructed. Interestingly, for traditionally CNN-based classification, it appears that the white matter areas in the cerebellum and brain stem are particularly indicative of PD, more so than the cerebrum which provides additional evidence of the cerebellar role in PD  (Mirdamadi, 2016).
ACKNOWLEDGMENTS Alfonso Estudillo Romero  is supported through the  Fondation Recherche Médicale (FRM)   DIC20161236441 , the  SAD Région Bretagne programme , and the  Institut des Neurosciences Cliniques de Rennes (INCR). John S. H. Baxter  is supported by the  Institut national de la santé et de la recherche médicale (INSERM).
Trial results will be disseminated at conferences, through relevant patient groups and published in peer-reviewed journals. Trial registration number NCT05195385. ⇒ The CASCADE study will answer important preliminary questions by exploring practical methods for CT readings before an organised large-scale lung cancer screening is implemented. ⇒ The study will validate the single reading of lowdose CT scans by non-expert radiologists trained in lung cancer screening. ⇒ The study will provide a prospective evaluation of artificial intelligence in lung cancer screening based on current low-dose CT technology. ⇒ The results of this study regarding adherence to screening, its psychological consequences and its effect on smoking cessation will be based only on French participants, with the limitation that the results may not be generalisable to other countries.
Methods: data collection, management and analysis Data collection methods Clinical data will be collected in each centre during the inclusion and end visits by the investigator or by a Open access clinical research technician, supervised by the investigator. Deidentified data will be collected on an electronic form, using the cleanweb software. Reminders by telephone, post and email will be used to schedule appointments in order to collect the data from all participants. If the participant is lost to follow-up, the contact details of the participants' general practitioner will be used in order to collect the information of a cancer diagnosis at 2 years. Anonymised CT images and AI reports will be transferred via secure connections to a dedicated Picture Archiving and Communicating System (SPHERE CASCADE), developed for the study.
Statistical analysis The statistical analysis will be carried out at Cochin Hospital Clinical Research Unit using R and/or SAS software V. 9. 3. A statistical analysis plan will be produced and validated by the study steering committee before freezing and analysing the data. Data analysis and reporting will follow the STARD statement recommendations (http:// www. equator-network. org). The analysis will be carried out on all the participants included in the protocol. Quantitative variables will be described as mean and SD or median and IQRs depending on the data distribution. Qualitative variables will be described as numbers and percentages. Diagnostic performance (sensitivity, specificity, negative and positive predictive values, positive and negative likelihood ratios) will be calculated as usual.
Cost analysis The cost analysis is based on a non-comparative study undertaken from a health system and payer perspective over a 2-year time time frame. One expected outcome of the cost analysis is to advise at the national level on the need for the use of AI in LCS. The other reported cost data include the average screening costs with scenario analyses on screening uptake, the costs per cancer detected and the costs associated with the workup of thoracic lesions detected by screening. These will be collected prospectively at the participant level only via the study case report form, administrative data will not be queried, partly due to regulatory difficulties but mainly because it cannot differentiate workup/cancer costs from other costs. Screening programme costs include: ► The fixed costs of screening invitation such as those involved if the programme is implemented (printing invitation letters and additional postage costs), retrieved from the billing systems of the regional cancer screening organisations.
Acknowledgements We would like to thank the  Regional Cancer Screening Coordination Centers  for their collaboration ( Dr J Nicolet CRCDC-IDF ,  Dr Forzy CRCDC-HDF ,  Dr Exbrayat CRCDC-AURA ,  Dr E Robert CRCDC-BRETAGNE ) Contributors M-PR, HA, MW and ID-Z constructed the protocol and design. M-PR made the first draft of this manuscript. HA contributed with statistical advice and study design. M-PR, HA, MW, GC, EC and ID-Z contributed with a thorough evaluation of the design, method and manuscript. All authors accepted the final manuscript version.
Bathymetric surveys of the upper canyon were conducted in 2005 and 2019 using Kongsberg EM120 and EM122 instruments. Survey design and water depths resulted in optimum gridding of these datasets at 25 m and 5 m, respectively (Methods). The con-trasting dataset gridding prevents small-scale changes (<25 m) from being confidently resolved. However, large-scale changes to canyon morphology can be assessed confidently. Comparison of these datasets shows significant morphological change has occurred over ~14 years (Fig. 2 ). These data show that a large landslide occurred on the southern canyon flank at 1,200 m water depth (Fig. 2b ). The landslide headscarp has a perimeter of 2. 5 km, a maximum width  of 1 km and height of 390 m. The length of the associated deposit is ~800 m. Overall, the landslide is 1.
Methods The results presented in this paper are based on analysis of bathymetry data of the upper Congo Canyon collected during cruises JC187 of the RRS James Cook (2019) and OPTIC-CONGO2 in 2005 on board the FS Beautemps-Beaupré. JC187 used a Kongsberg EM122 system, and OPTIC-CONGO2 used a Kongsberg EM120. Both operated at 12 kHz with a vertical sounding accuracy of 0. 2% of water depth. The beam aperture was 0. 5° × 0. 5° for JC187 and 1° × 1° for OPTIC-CONGO2. Data were processed to correct for differences in sound velocity in the water column (using data from sound-velocity profilers) and the ship's motion. JC187 data were processed in CARIS HIPS and SIPS software and gridded with a resolution of 5 m. JC187 data were able to be gridded at 5 m due to the high number of soundings that were retrieved due to the survey design.
The canyon was surveyed using along-canyon lines crossed by traverse lines perpendicular to the along-canyon lines to minimize potential shadowing or reflection due to steep canyon walls. The survey paths were also designed to ensure the entire survey area was covered by the central third of the swath width to maximize the number of soundings. The OPTIC-CONGO2 survey used along-canyon lines. OPTIC-CONGO2 data were processed and gridded at 25 m in CARAIBES. Inter-survey positioning was validated by matching features in common areas of relatively stable seafloor. Erosion and deposition of sediment between 2005 and 2019 were calculated by first resampling the JC187 5 m data to 25 m so as to be comparable with the OPTIC-CONGO2 data. A bathymetric difference map produced in ArcGIS was subsequently used to determine volumes of eroded and deposited sediment.
Where canyon-flank landslides were observed, their volumes were calculated using depth changes associated with their headwalls. This was necessary because the emplaced deposit was often buried, and no seismic data penetrating the landslide deposits were available, preventing direct assessment of the deposit volume. The calculated landslide volumes are therefore probably only minimum volumes. The bathymetric surveys were used to construct along-canyon profiles. The position of submarine canyon and channel thalwegs shift as they evolve, so down-canyon profiles were measured along the position of the thalweg in each survey. These profiles were subsequently normalized for comparison in the section of the canyon impacted by the canyon-flank landslide and infill. Normalized distances along canyon were calculated by taking a starting point (Fig.
Acknowledgements The crews and shipboard parties of the  RRS James Cook and FS Beatutemps-Beaupré  are thanked for their help collecting the data. We thank our partners  Angola Cables  and others involved in requesting and granting permissions to work in Angolan territorial waters. E. L. P. was supported by a  Leverhulme Trust   Early Career Fellowship  ( ECF-2018-267 ). D. R. P. acknowledges funding from  EU  grant no. 725955 -GEOSTICK. C. J. H. and M. S. H. acknowledge funding from  EU  grant no 721403 -ITN Slate. M. J. B. C. was supported by a  Royal Society   Research Fellowship  ( DHF\R1\180166 ). S. H. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 899546. We acknowledge  NERC  funding ( NE/R001952/1 ,  NE/S010068/1 ,  NE/V004387/1 ).
Data availability We do not own the rights to make the JC187 or OPTIC-CONGO2 datasets available. However, access to each dataset can be requested following these instructions. Requests for the multibeam data from JC187 should be made to the British Oceanographic Data Centre (BODC; https://www. bodc. ac. uk/). Access or requests for the multibeam data compiled on the Congo Fan, including the OPTIC-CONGO2 cruise, should be made through the Sextant Portal at Ifremer (https://doi. org/10. 12770/d1076601-82a9-42a2-9d30-d0be8b2481a7).
Louis, MO, USA). Next, several washing steps were performed with acidified water. Finally, excess water was removed and sections were dehydrated further cleared in xylene, and mounted with a cover slip. Images were taken under polarized light microscopy in an Olympus IX83 inverted microscope (Olympus, Westborough, MA, USA) using the cellSens Dimension Desktop software v2. 2 (Olympus, Westborough, MA, USA).
The Osteogenic RT 2 Profiler PCR arrays (PARN-026Z) and the RT 2 SYBR Green Mastermix were purchased from Qiagen (Qiagen GmbH, Hilden, Germany). PCR assays were performed in a CFX 96 Real-Time System thermocycler (Bio-Rad, Hercules, CA, USA) following the instructions of the manufacturer. The melting curves displayed a well-defined single distinct peak for each amplified gene, indicating that the amplified PCR products are single discrete species. Ct values were exported to an Excel file following Qiagen's instructions. This table was then uploaded to Qiagen's data analysis web portal at http://www. qiagen. com/geneglobe, accessed on 6 June 2021. Samples were assigned to control (native tissue) and test groups (ED, TC, S-AT, and S-MT). Ct values were normalized against the arithmetic mean of the Ct values from the references genes Ldha and Rplp1 included in the array set up.
Statistical Analysis The statistical analysis was performed using the GraphPad Prism 8. 3. 0 Software (Graph-Pad Software, San Diego, CA, USA). Measurable data are displayed as the mean ± SD. For mechanical testing, the data on calculated mechanical properties are displayed through box plots (median, 1st, 3rd quartile ranges and outliers > 1. 5 × IQR + Q 1/3). Statistical significance was determined by Kruskal-Wallis test and Dunn's multiple comparisons test, with a p value < 0. 05 indicating statistical significance. The µ-CT data were analyzed using one-way analysis of variance (df = 4; F = 4. 139) followed by Dunnett's multiple comparison test, with a p value < 0. 05 indicating statistical significance.
Contributions: C. J. P. S. , investigation, formal analysis, and writing-original draft. S. A. M. , N. Q. and P. S. P. P. , investigation, formal analysis, and validation. C. M. (Carla Mayer), investigation. A. M. and C. M. (Claudio Migliaresi), methodology and investigation. M. J. C. , investigation. C. H. E. , conceptualization, methodology, validation, and resources. E. R. B. , conceptualization, methodology, validation, and writing-review and editing. M. v. G. , conceptualization, methodology, validation, writing-review and editing, and supervision. All authors have read and agreed to the published version of the manuscript. Funding: C. H. E. 's research is partly funded by the John and Posy Krehbiel Professorship in Orthopedics. This work was supported by the Province of Limburg, Limburg Invests in its Knowledge Economy (LINK).