dataesr/scientific-paragraphs-categorization huggingface dataset:
Research on genetics and epigenetics will allow a better understanding of somatic variation in banana and the development of a system for early detection and control. Genomic and bioinformatic analyses will play an increasingly important role in understanding the genetic variation of banana diversity. Given the affordable cost of Next Generation Sequencing approaches, an increase of genomic studies with larger sampling is expected, ultimately leading to a full coverage with genomic data of the international genebank to maximize its use  (Halewood et al. 2017). Additional insights should also come from new high-quality reference sequences from the B and S genomes  (Belser et al. 2018; Wang et al. 2019). The Musa Germplasm Information System database is continuously expanding its comprehensive dataset on banana accessions held collections across the globe, and integrating phenotyping studies is a current priority, despite the challenging level of details and volume of information.
Users are interested in comprehensive information about the potential of accessions. The availability of such information should be facilitated by the information systems once curation has been performed by the appropriate plant phenotyping standards  (Papoutsoglou et al. 2020)  and Ontologies  (Shrestha et al. 2012). The use of proper accession numbers (such as the DOIs) enables data integration across studies in order to contribute efficiently to the conservation and sustainable use of crop diversity and benefit-sharing. The numerous activities encompassing the ITC generate enormous potential for greater collaboration and knowledge exchange, which is where MusaNet plays its part. Stronger linkages among researchers of different disciplines and regions are being forged in order to develop projects and guidelines that result in positive and lasting impacts on banana production systems and livelihoods.
CRYOSTEM was initiated in 2010 to create a multicenter biobank in the field of Hematopoietic Stem Cell Transplantation (HSCT). After initially concentrating on Graft-versus-Host Disease, CRYOSTEM has broadened its focus to all HSCT complications. Thanks to a network of 33 transplant units and 23 Biological Resources Centers, CRYOSTEM has established a standardized collection of high-quality biological samples associated with well-annotated clinical data from donors and patients pre-and post-HSCT. Plasma, dried pellets and viable cells in DMSO are isolated and cryopreserved from blood samples. Currently, the collection has reached almost 200,000 available samples coming from nearly 5,800 patients. Since 2015, CRYOSTEM has provided the national and international scientific community with these samples for largescale research to improve the knowledge of HSCT complications.
complications; cohort; biobanking network; biological resources Funding statement: CRYOSTEM has been funded by the French government's "National Investment Program" (call for proposals "COHORTES", agreement ANR-10-COHO-008) and has also received financial support from INCa (call for proposals "BCB", agreement 2013-192) and patient associations. (1) Bioresource Overview Project description HSCT is the only curative treatment for numerous blood diseases (leukemia, lymphoma, aplastic anemia…)  [1]. Its main complication is Graft-versus-Host Disease (GvHD), in which the donor's immune cells recognize the recipient (patient receiving the graft) as foreign. Due to several factors (growing recruitment of mismatch-related and unrelated donors and increasing recipients' age), current levels of GvHD incidence (60%) may rise over the next few years.
In patients cured of their hematological diseases, GvHD is the leading cause of death post-HSCT (10 to 20%)  [2]. Lack of knowledge in human GvHD physiopathology, along with limited relevant studies, explains the absence of curative treatment. In this context, CRYOSTEM was launched in 2010, promoted by the Francophone Society for Stem Cell Transplantation and Cell Therapy (SFGM-TC), to establish a prospective and standardized multicenter cohort. In 2011, CRYOSTEM was funded with the financial support of the French government through the "National Investment Program". Initially focused on GvHD, CRYOSTEM has enlarged its thematics since November 2016 to all HSCT complications. CRYOSTEM's initial main objectives were defined as follows: -Establishing a collection of biological samples and well-annotated clinical data from patients and donors pre-and post-allogeneic HSCT; -Supplying the national and international scientific community with these samples for large-scale research on HSCT complications and to improve biomedical knowledge in HSCT-related fields, such as oncology, hematology and immunology.
Temporal coverage for accessibility Non Applicable. ( Blood samples are collected pre-and post-HSCT from patients and donors in line with a simple sampling schedule (Figure  3 ). A unique form, CRYOSTEM liaison document, is used to record data at blood sampling (patient ID,  collected period, blood volume, date and hour…) and must be attached to samples. BRCs process blood samples in line with pre-analytical procedures and protocols, established in tandem with CRYOSTEM network, according to French standard NF S96-900. Moreover, CRYOSTEM governance provides key reagents to BRCs (RPMI, lysis solution, cell preparation tubes), so as to ensure a homogeneous treatment of blood samples. Sample data, such as patient (or donor) personal information, consents and clinical data are centralized in a unique secured database: CRYOSTEM MBioLims, developed by Modul-Bio (www.
modul-bio. com). CRYOSTEM MBioLims is accessible by all transplant units and BRCs personnel using unique personal user codes. Dedicated procedures have been set up to manage the database using, the data recording and centralisation. Regarding CRYOSTEM Quality System Management (QMS), all procedures and protocols are reviewed every two years or on request. Moreover, CRYOSTEM governance proceeds each year to internal audits of transplant units and BRCs. Finally, since 2015, CRYOSTEM has set up around thirty indicators to control its processes achievement and efficacy.
For dried pellets, a 1. 90 mean OD ratio was obtained, confirming high DNA purity. Results also highlighted the high reproducibility of the parameters tested since 2014. Moreover, an extensive monitoring of biological and clinical data entered into CRYOSTEM MBioLims database is ensured weekly by an external clinical data technician. Liaison documents and database filling quality is reported and used as indicators in CRYOSTEM QMS and presented at each annual board meeting. Finally, with a view of continuous improvement, CRYOSTEM MBioLims software is tailored to the biobank evolution and is regularly updated, thus resulting in homogenization of samples labelling, traceability and real-time monitoring of operations (consents import, reagents follow-up…).
Source of associated data The liaison document attached to blood samples enables to collect several types of data: -Socio-demographic: directly related to patients identity (anonymised). These data are collected initially at patients' inclusion and checked at each new sampling. Regulatory files were submitted to all official authorities and all authorizations were obtained within a year. Since then, all regulatory authorizations have been regularly updated as the network has expanded and the project has developed further (Figure  4 ). The authorizations duration corresponds to the time of the project accomplishment. At the moment of recording in CRYOSTEM MBioLims database, patient identity is made anonymous and a unique ID is assigned to each patient, in the same way as for each blood sample and aliquot.
Size of the bioresource Since its beginning, a high proportion of transplanted patients have been included in CRYOSTEM protocol due to the great diligence of the medical community. In only 6 years, more than 5,700 patients and 2,300 donors have been included with an annual rhythm of almost 1,100 patients between 2013 and 2017 (as of May 31, 2019, Figure  6 ). In 2017, the inclusion rate was almost 82% (as a percentage of patients transplanted in CRYOSTEM network transplant units, data from the Agence de la Biomédecine, https://www. agence-biomedecine. fr/). Since January 1, 2018, CRYOSTEM plenary committee has voted a restriction of inclusions to answer budgetary constraints and to optimize cohort longevity.
The file specifies also the selection criteria of patients and samples required for the project and the number of aliquots. The number of aliquots needed is not limited but has to be justified and should be consistent with the project. Applications are first pre-assessed by CRYOSTEM Scientific Board, and then forwarded to international experts for review. The Scientific Board is responsible for the final selection based on the scores awarded by the reviewers. Once selected, investigators must comply with the general terms of the call for projects by signing an agreement form. Following interviews with investigators to determine sample type, number and characteristics, sample provision is coordinated by CRYOSTEM project managers. Access costs to the collection are reviewed annually and included in the general terms for each call for projects, defining the financial conditions under which access to biological samples and clinical data can be obtained.
These costs include pre-analytical processing fees, sample selection, destocking, associated clinical data export, and collection durability. They allow for BRCs to be compensated for sample retrieval and control, transplanting units for blood sample management, SFGM-TC for clinical data management, and CRYOSTEM for sample selection and centralization. General terms and application file of the ongoing call for projects are available and downloadable on CRYOSTEM website (www. cryostem. org). (4) Reuse potential Investigators could have access CRYOSTEM biological resources to lead fundamental or translational researches directly in the field of HSCT complications or in fields related to HSCT (hematology, cancerology, immunology, infectiology…). CRYOSTEM samples and data could be used for retrospective studies: for example to validate preliminary results obtained in smaller and/or local cohorts or in murine models, to evaluate efficacy of drugs under development….
Once selected, investigators have access to CRYOSTEM biological resources according to their selection criteria and the availability. Biological samples and associated clinical data have to be used within the scope for which CRYOSTEM has given its approval. Investigators could, within a period of 36 months following the end of their projects, ask for authorization from CRYOSTEM to use the biological resources for another research project. The new project must be submitted beforehand to CRYOSTEM Scientific Committee, who decides to reuse the biological resources for an additional scope. Since 2015, and as of May 2019, almost 6,600 aliquots (around 4% of the collection) have been provided to researchers for the implementation of 9 national and international projects.
Acknowledgements We would like to acknowledge all partners working in CRYOSTEM consortium transplant units and BRCs, including nurses, clinical research assistants, physicians, technicians, BRC managers. We would like to thank  CRYOSTEM  scientific and advisory boards' members. Furthermore, we thank SFGM-TC president, members and database managers. We also acknowledge the  Agence de la Biomédecine. Finally, we acknowledge CRYOSTEM funders: the  Health and Biotechnologies Direction at French Government Agency , the  French National Cancer Institute (INCa)  and the patient associations ( Association Laurette FUGAIN, IRGHET, Cent pour Sang la Vie, Vaincre la Leucémie ).
In a near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i. e. compute spatial derivatives of the Sea Surface Height (SSH) measurements, the small-scale noise expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first, second, and third-order derivatives of SSH.
The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the Western Mediterranean Sea, from a 1/60 • simulation and the SWOT simulator. Based on Root Mean Square Error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths the noise level is reduced by 10 4 and 10 3 for Summer and Winter respectively. This is encouraging for the processing of the future SWOT data.
Introduction The Surface Water Ocean Topography (SWOT)  [1]  mission will provide an unprecedented two-dimensional view of ocean surface topography at a pixel resolution of 2 km. The launch is scheduled for 2021. SWOT's wide-swath altimeter, based upon SAR interferometry technology, will measure Sea Surface Height (SSH) over a 120-km wide swath with a 20-km gap at the nadir. The satellite will also carry a conventional nadir altimeter. SWOT will evolve on two different orbits: the first 3 months of scientific data production will be dedicated to a fast-sampling phase, where the repeat cycle will be of 1 day. Then, the satellite will be moved to its nominal orbit with a 20. 86-day repeat cycle. SWOT is a multi-disciplinary hydrology and oceanography mission, and here we focus on the latter.
The SWOT mission objectives will be reached if we can accurately estimate gridded maps of at least the first and second-order horizontal derivatives of SSH. Altimetry describes the upper ocean dynamics through geostrophy, which involves the horizontal SSH gradients. Geostrophy is a fairly good approximation of mesoscale dynamics, i. e. at scales larger than the for first Rossby deformation radius (about 10-15 km in our region  [12] ), for which Rossby numbers are typically smaller than 1. Kinetic energy dissipation is driven by the horizontal strain rates of the ocean surface flow [e. g. 13]. Complete, gridded maps of SSH derivatives are required for climate studies and short-term operational applications. One way to make gridded maps from incomplete SSH observations (including SWOT, but not only) is to assimilate those data into dynamical models.
The assimilation of SWOT is expected to be challenging because of the spatially correlated noise, and promising solutions to this rely upon the joint assimilation of SSH and its derivatives  [14, 15]. All these considerations compel the scientific community to strive for getting accurate estimates of SSH derivatives. Unfortunately, SWOT data will very likely be contaminated by small-scale noise that prevents the direct computation of SSH derivatives. The noise expected to contaminate SWOT measurements gathers several components with different spatial coherences and different amplitudes. Details are provided in the SWOT mission performance and error budget document  [16]. To be prepared to exploit the future SWOT data, the SWOT simulator for ocean science has been developed to simulate realistic realizations of SWOT uncertainties  [17].
Formulation of the image de-noising problem The primary purpose of image de-noising here is to allow the computation of first and secondorder SSH spatial derivatives of SWOT data as accurately as possible. The two reasons, already mentioned in the introduction, are: (i) these quantities represent geostrophic velocities and relative vorticity, respectively, whose estimation is central to the success of SWOT mission; and (ii) these quantities can be needed to draw maximum benefits from the assimilation of SWOT data into ocean circulation models  [14, 15]. We therefore propose a method that explicitly constrains these derivatives. The proposed de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization. The de-noised SWOT image h is searched for by minimizing the following cost function: J(h) = 1 2 m • (h -h obs ) 2 + λ 1 2 ∇h 2 + λ 2 2 ∆h 2 + λ 3 2 ∇∆h 2 (1) where represents the L 2 -norm, h obs is the original noisy image (i.
Simulated SWOT dataset The input of our database is a 15-month North Atlantic simulation at a resolution of 1/60 The horizontal resolution is between 0. 8 and 1. 6 km (depending on latitude), and the grid has 300 vertical levels. This NEMO model configuration is referred to as NAtl60 and the source files and codes are available in  [25]. The particular simulation used herein has been described in  [26, 27, 28]. Lastly, the simulation time span is from mid-June 2012 to October 2013. [29]  The SWOT simulator for Ocean Science (version 2. 21)  [17]  is run to generate pseudo-SWOT scenes from the NAtl60 simulation. The SWOT simulator first builds the SWOT observation grid, based on the provided satellite orbit. In this study, SWOT grid resolution is fixed at 1 km. After this work started, the resolution of the basic SWOT level 2 SSH data products has been fixed to 2 km, but this small mismatch does not modify the general approach.
After building the grid, the simulator reads SSH data from NAtl60 and linearly interpolates them from model to SWOT grid (rendering the variable SSH model). In a last step, it computes random realizations of observation errors and adds them to the interpolated SSH data (rendering SSH obs). Observation errors considered at the moment are KaRIn errors, roll errors, phase errors, baseline dilation errors, timing errors, and errors due to signal alternation by atmospheric humidity. Among these errors, only the KaRIn noise is expected to be spatially uncorrelated. Technically, the SWOT simulator provides simulations of the noise-free SSH observed by SWOT, and of the noisy data that SWOT will actually yield (sum of the former and the noise: SSH obs = SSH model + noises/errors). For the evaluation of image de-noising methods, it thus provides "true", noise-free images (h true ) along with the realistic SWOT data 88) passes.
Discussion and Conclusions Several objectives of the SWOT mission will be met only if the small-scale noise affecting the data can be efficiently removed. Small-scale noise, in particular the spatially uncorrelated KaRIn instrument noise, prevents the computation of horizontal SSH derivatives. This limits both the direct estimation of relevant oceanic variables on the SWOT swath, and the use of SWOT data to build gridded products of altimetry. To remove the small-scale SWOT noise, we propose a de-noising method that performs better than conventional convolution-based methods both in terms of RMSE (physical space diagnostic) and spectra. The method, which originates from image processing applications, is based on the regularization of the SWOT SSH data by the penalization of its derivatives of orders 1 to 3 in a variational, optimization framework.
The method should also benefit from additional developments to reconstruct more realistic fields of relative vorticity on the SWOT swath, and could ultimately lead to the estimation of vertical velocities. The de-noising process inevitably smoothes out the very fine-scale, elongated structures usually visible in surface relative vorticity fields  [10, e. g. ]. Restoring these structures should be investigated, perhaps using appropriate image processing techniques  [37, 38] , or methods already developed in the oceanographic community such as Lagrangian advection  [39, 40]. Dynamical models could also be used in a data assimilation framework. To conclude, the de-noising method opens the way to several relevant applications using the SWOT data, possibly including SWOT data validation, assimilation, and SSH mapping.
We mention SWOT data validation due to the in-painting capability of the variational de-noising method, i. e. the fact that the process naturally fills the 20-km gap of the SWOT swath (the gap is in-painted, and emptied again after de-noising to restore SWOT data in the original shape). In other words, the SWOT KaRIn data are interpolated on the track of the SWOT nadir altimeter. This is obviously relevant for data comparison and validation. De-noising is also interesting to pre-process the SWOT data before their assimilation in ocean circulation models. This actually was a primary motivation for the method development. Computing spatial derivatives of the SWOT data allows the implementation of data assimilation methods that account for SWOT error correlations  [14, 15]. Alternatively, the relative vorticity derived from the de-noising can be directly assimilated.
, 2009) , a series of community epidemiological surveys that assessed lifetime prevalence of a wide range of both untreated and treated mental disorders in a representative household sample across a range of nations. The study aimed to examine pair-wise temporally-ordered lifetime comorbidity between all logically possible pairs of DSM-IV disorders (24 types) assessed in the WMH surveys and to generate age-and sex-specific HRs and CIPs for each of these pairs. Our study is observational, and thus we do not make any claim that prior mental disorders cause later mental disorders. Our guiding hypothesis is that comorbidity within mental disorders is pervasive. The data are available in an interactive data visualisation webpage (https://holtzy. github. io/Como-in-World-Heath-Survey/index. html).
Samples and procedures Data came from 29 coordinated WMH Surveys conducted in 27 countries. Spain and Colombia have separate national and regional surveys. A total of 19 of these surveys were nationally representative, and several focused on selected urban or metropolitan areas within the country. These surveys included six lower-middle-income countries, seven upper-middle-income countries, and 16 high-income countries, according to the World Bank classification at the time the survey was conducted. In most countries, internal subsampling was used to reduce respondent burden and average interview time by dividing the interview into two parts (see Supplement for additional details).
These were stratified by sex and age of onset of the prior disorder (<20, 20-40, >40 years; consistent with the analysis by Plana-Ripoll et al. (2019b)). Respondents were followed until they developed later disorder or until the time at an interview (censored). This allowed us to estimate the absolute risk of later disorder after developing the prior disorder. All analyses were performed using SAS 9. 4. The Supplementary Material, and all the data management and analysis codes can be found on https://github. com/clim072/NB-COMO. An interactive webpage was also created to display all the results from this study https://holtzy. github. io/Como-in-World-Heath-Survey/index. html.
, 2007)  and broad socioeconomic and cultural factors impact on the profiles of some types of mental disorders (e. g. substance use disorders are less common in some countries). The current study included surveys from 27 nations, and analyses were adjusted by the site in order to focus on cross-national findings. We believe that the estimates from the current study are more generalisable across sites. We wish to draw attention to several limitations. Despite the relatively large sample, for many disorder pairs we lacked sufficient power to generate reliable estimates, and (in contrast to the Danish study) were not able to generate age-and sex-specific estimates for many of the disorder pairs. Survey data relies on the respondent's memory and recall bias may result in a systematic bias against the recall of temporally distant events.