dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, most PLWRDs have common needs, as reported by Eurordis  [5]  which include: • Lack of access to appropriate diagnostic approaches; • Diagnostic odyssey (prolonged time to diagnosis); • Insufficient scientific knowledge on the disease, including clinical presentation and natural history; • Heavy social and psychological consequences of diagnosis; • Lack of appropriate quality healthcare once diagnosis is reached; • Inequities across all these aspects These hurdles do cause an heavy demand of care to HSs, which might be however ineffective in meeting the patients' needs since of the lack of diagnosis. relevant data from this study will be made available upon study completion. Funding: S4C has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 101034427.
The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: none. Abbreviations: AI, Artificial Intelligence; DBS, dried blood spots; EHR, electronic health records; ELST, Ethical, Legal and Safety Team; ERN, European Reference Network; EU, European Union; FAIR, Findable, Accessible, Interoperable, Reusable; GDPR, General Data Protection Regulation; gNBS, genetic newborn screening; HCP, Healthcare Provider; ML, Machine Learning; PAB, Patient Advisory Board; PLWRD, persons leaving with rare diseases; R&D, Research and Development; RD, Rare Disease; S4C, Screen4Care; WGS, whole genome sequencing; WP, Workpackage.
To comply with ethics and legal requirements, we develop a Data Protection Impact Assessment Framework. The application of the framework ensures that the data of individuals is protected from a legal and technical point of view and the framework lays the foundations to ensure comprehensive data safety and security. In addition, we develop a Code of Ethical Practice to serve as guiding document for ethically sharing and accessing personal data throughout the project. Pillar 1 is working closely with the internal Ethics, Legal, and Safety Team (ELST) and the Independent Ethics Advisor in order to address all ethical and legal issues properly. To be mentioned that he ELST and the independent Ethics advisor are cross-cutting through the whole project such as the Patient Advisory Board (PAB), the multistakeholder board (MSEB) or the transversal innovation group.
Pillar 2 (WP2): Federated meta-data repository. Medical data is scattered around the globe in different hospitals, specialized public or private databases and research institutes in so-called data silos. The data are often stored in incompatible formats, meaning that the information of one dataset cannot easily, if at all, be combined with the information of another dataset. This hampers research and the application of modern machine learning tools which require large amount of data to be trained sufficiently; and the problem worsens when considering rare diseases with consequentially low incidence numbers per data silo. This renders the application of machine learning for rare diseases difficult, if no solution to this data sparsity is found. The obvious solution of consolidating the data into a single cloud fails due to regulatory restrictions and privacy considerations.
Pillar (Work package) 2 plans to tackle these issues by creating a FAIR (Findable, Accessible, Interoperable, Reusable) research environment. Firstly, to make data findable, we will develop a metadata repository, which does not store the actual data, but information about the data (metadata) which remains in the data silos. Therefore, with the metadata repository, we can identify where data is located, for example which databases contain data of patients with a disease of interest and identify compatible datasets. Secondly, to ensure interoperability and reusability, we are working on a Screen4Care Implementation Guide for Rare Disease Documentation (IGRDD). The S4C IGRDD has two goals: it serves as a guideline for action in the preparation of existing data from care and research for secondary research use, and it serves as a guideline for the design of new RD documentation in clinical department information systems, RD electronic data capture systems and RD registers.
This approach will serve to analyse and use common data elements of the European Rare Disease Registry Infrastructure, as those carried on by European Reference networks (ERNs), therefore building upon existing standards to ensure maximal compatibility with existing data sources. When all relevant and compatible data sources are identified, to ensure that they can be accessible to train and test artificial intelligence (AI) algorithms, we will develop new algorithms, tailored for low incidence classes in an innovative federated fashion: the data remains at the data silos, but the machine learning comes to the data. These local machine models will help to identify undiagnosed RD patients, whom phenotype is represented in the datasets. Importantly for the legal and ethical aspects, at no point do the sensitive medical data ever leave the site of storage, and continuous data protection assessment will be carried out.
A consultative co-design process will be adopted by involving user different groups to addressing the needs of RDs patients and it supports efforts across the entire project, especially pillar 4 and 4, see below, by including regular involvement of co-creation participants at each stage of project research, development and testing. Pillar 3 (WP3): Genetic newborn screening for RDs and early diagnosis in young symptomatic infants. Pillar 3 aims at exploring the use of gNBS in newborn as gateway to shorten the diagnostic path and offer the opportunity for early treatment. This pillar is composed of different tasks. Preference studies will be carried out on expecting couples, PLWRD and caregivers, healthcare professionals and the general public, to provide input to the gNBS design, data flow, and compliance.
who. int/iris/handle/10665/63910, 1997). However, when infants present with early symptomatic disease, those who tested negative on the TREAT (and/or ACT) panel will be offered optional Whole Genome Sequencing (WGS) to recognize known gNBS-escaped RDs and novel genes/phenotypes. In addition, communications with ERNs (https://webgate. ec. europa. eu/ern/) will help map out post genetic report diagnosis work up and referrals, and correct therapies addressing. Pillar 3 will also collect some data on stakeholder preference and economic impact with the objective to help shape an ethical and sustainable gNBS ecosystem in Europe. Genetic strategies are described in Fig  4. Pillar 4 (WP4): Harnessing artificial intelligence to leverage electronic health records. WP4 will develop AI-based algorithms to scan electronic health records (EHR) and generate "symptom checker apps", which we intend to make available as tools for clinicians, genetic counsellors, patients, and the general public, to support patients in the early stages of disease by enabling faster diagnosis, supporting symptom-based diagnosis at any age, also beyond the neonatal period.
Metadata repository will be used to identify cohorts, which are scattered around several data silos and form the basis for the training of the artificial intelligence algorithms. The trained ML models will be carefully evaluated and tested for their practical applicability. Pilot studies, including analysis of effects of EHR-based diagnostics on pilot participants, as well as a cost-effectiveness analysis, will enable to recognize and highlight ethical, legal, socio-economic and safety aspects that may become relevant to users especially patients, healthcare providers and society. Another part of Pillar 4 is dedicated to the phenotypic characterisation of neuromuscular diseases, with the aim of identifying new biomarkers based on imaging parameters, such as high-resolution multiphoton imaging and multispectral optoacoustic tomography.
Conclusions and perspectives The clinical and genetic heterogeneity of RDs require many and diverse information for correctly identifying RDs patients so we believe that the impact of S4C technological developments and pipeline design will have a profound transformative effect in EU (and not only) on how we can utilize the data and pave the way for future endeavors in RD, as well as in genomic medicine in general  [14]. gNBS pilot studies (TREAT and ACT) will involve the largest EU population never studied until now (about 25. 000 infants) providing a unique dataset which will be stored in the S4C platform and open to be further shared in other EU contexts. The datasets generated by S4C include population genetic data (genotypes of genes included in TREAT and ACT panel), genome data from early symptomatic babies, phenotype data defined via AI and aggregate phenomics defined by the new developed algorithms.
In addition, more focused datasets on muscle imaging or specific phenotypes derived from EHRs' analysis will be collected. These unique datasets will grant the possibility to have a variety of information about different EU population, with the outstanding opportunity to correlate genotypes with phenotypes. S4C dataset will greatly improve the understanding about RD profiles in EU thus will be helping in designing a variety of health and therapy policies related to RDs, from population polymorphisms to very rare variants, and associated phenomics  [15, 16]. Although it is difficult to predict the opt-in rate from participating expecting parents (and preference studies will also serve to address eventual criticisms) it is extremely unlikely that gNBS enrolment will be insufficient, based on regional birth rates and an estimated opt-in rate of 10% on overall births is more than achievable.
Our approach is harmonized with the EU health data space initiative [20] just launched. Collecting health data in a single data space is intuitively very important to allow aggregate data analysis and to ensure interoperability of the various datasets. Lastly, meta-symptom checker digital task will empower Health Care Providers (HCPs) and patients to facilitate assessing the appropriate pathway of diagnosis. To do that and to build the metasymptom checker app, by horizon-scan of existing algorithms, we will assess the benefit of the Virtual Platform, again following the directives of the EU to improving health digitalization. The possibility to design innovative gNBS is due to the fantastic work made and results obtained by the metabolic NBS initiatives now fully translated and embedded into the health systems.
Satellite remote sensing is commonly used to observe the hydrologic cycle at spatial scales ranging from river basins to the globe. Yet it remains difficult to obtain a balanced water budget using remote sensing data, which highlights the errors and uncertainties in earth observation (EO) data. Various methods have been proposed to correct EO datasets to make them more coherent, so that they result in a more balanced water budget. This study aimed to improve estimates of water budget components (precipitation, evapotranspiration, runoff, and total water storage change) at the global scale using the methods of optimal interpolation (OI) and neural network (NN) modeling. We trained a set of NNs on a set of 1,358 river basins and validated them on an independent set of 340 basins and in-situ observations of evapotranspiration and river discharge.
We extended the models to make pixel-scale predictions in 0. 5°grid cells for near-global coverage. Calibrated datasets result in lower water budget residuals in validation basins: the mean and standard deviation of the imbalance is 11 ± 44 mm/mo when calculated with uncorrected EO data and 0. 03 ± 24 mm/mo after calibration by the NN models. This study suggests to data producers where corrections should be made to the EO datasets, and demonstrates the benefits of physically-driven NN models for studying the hydrologic cycle at the global scale.
Abstract Satellite remote sensing is commonly used to observe the hydrologic cycle at spatial scales ranging from river basins to the globe. Yet it remains difficult to obtain a balanced water budget using remote sensing data, which highlights the errors and uncertainties in earth observation (EO) data. Various methods have been proposed to correct EO datasets to make them more coherent, so that they result in a more balanced water budget. This study aimed to improve estimates of water budget components (precipitation, evapotranspiration, runoff, and total water storage change) at the global scale using the methods of optimal interpolation (OI) and neural network (NN) modeling. We trained a set of NNs on a set of 1,358 river basins and validated them on an independent set of 340 basins and in-situ observations of evapotranspiration and river discharge.
Plain Language Summary Today, satellite remote sensing can measure all the major flows in the water cycle. This includes precipitation, evaporation, and changes in the amount of water stored underground and in lakes and reservoirs. These flows are related to one another via the water cycle: flows into and out of any region should be balanced. Yet, we cannot calculate a balanced water budget with satellite data. This shows that further improvements to these data are possible. We used a method called optimal interpolation to make corrections to satellite datasets, so that they better balance the water budget. However, this method only works at the scale of river basins, where river discharge data are available. To extend our findings to other locations, we created a statistical model based on machine learning.
Introduction The water cycle, or hydrologic cycle (HC), is an important field of study for earth scientists -changes to the HC have broad societal implications, with effects on drought, flooding, agriculture, and water supply. And while enormous progress has been made in monitoring the water cycle via remote sensing, capturing a complete picture from space remains a difficult goal. The usefulness of earth observation (EO) datasets has not been fully achieved because of "incoherence" among various data products -studies have demonstrated that the water budget cannot be closed using remote sensing data without significant errors  (Hegerl et al. , 2015; Rodell et al. , 2015; McCabe et al. , 2017). This leads to the conclusion that satellite datasets still suffer from systematic bias or random errors.
In this study, we seek to simultaneously optimize multiple satellite-estimated datasets of hydrologic fluxes, reconciling them to create a balanced water budget. A simplified water budget for any land area (e. g. , river basin, grid cell) includes the four main fluxes or HC components: precipitation, P , evapotranspiration E, total water storage change (TWSC in the text and ∆S in equations), and runoff, R. By conservation of mass, the water budget can be stated: manuscript submitted to Water Resources Research P -E -∆S -R = 0 (1) Conventional optimization methods have focused on calibrating individual components of the HC one at a time (for example fitting P or E to ground-based observations). This approach is fundamental, but as one author wrote, it "ignores the interdependencies and relationships inherent in observed responses"  (McCabe et al.
Rather than seeking to optimize the datasets, the authors looked for the best combination of inputs, and reduced the imbalance through "cancellation of errors in poor estimates of water budget components. "  Aires (2014)  introduced an integration method called optimal interpolation (OI). This closed-form analytical solution imposes a HC budget closure constraint. It forces the imbalance to zero, and modifies each of the HC components by an amount inversely proportional to its uncertainty. Aires showed that this constraint improves the estimation of the HC components in some places and times. In a related paper Munier et al. (2014) applied OI over the 3 million km 2 Mississippi River basin, revising satellite estimates for P , E, R, and ∆S. Later,  Munier and Aires (2018)  applied OI over 11 large river basins.
OI has also been shown to work well in optimizing satellite observations of the hydrologic cycle over river basins in the Mediterranean  (Pellet et al. , 2018) , South Asia  (Pellet, Aires, Papa, et al. , 2019) , and the Amazon  (Pellet et al. , 2021). A major limitation of OI is that it can only be used at the basin scale, where observations of river discharge are available. Yet, the vast majority of the world's rivers and streams are ungaged, and gage data are particularly sparse in less-developed countries. Furthermore, to make truly global predictions, a model must be able to make predictions at the pixel scale. Munier and Aires (2018)  extrapolated the balanced solution from OI to the global scale with the help of auxiliary environmental information. However, environmental datasets were used in a rather simple way; the authors did not use them as explanatory variables, but rather to divide basins into classes based on climate regime.
The NN output is a set of calibrated monthly estimates for P , E, R, and ∆S in each basin. Again, these are not calibrated in the conventional sense of fitting to in situ observations, but by combining information from multiple remote sensing datasets while seeking to satisfy the HC closure constraint (Equation  1 ). We used environmental data  (elevation, slope, vegetation, etc. )  as inputs to the model, hypothesizing that these additional inputs will help the NN to find an optimal solution under varying conditions. This study has two main objectives. First, to optimize hydrologic EO datasets and to calculate a balanced water budget at the river basin scale from these data. Second, to train a NN model based on these results to make improved estimates at the pixel scale. A third, stretch goal for the study was to test the model's ability to estimate missing data via inference.
Datasets We created a database of earth observations (EO) based on satellite remote sensing, with datasets that quantify each of the four major fluxes in Equation  1. The EO datasets are summarized in Table  1. All fluxes are expressed in area-normalized units of depth per time in millimeters per month (mm/mo). Our database covers the 20-year time period from January 2000 to December 2019, a total of 240 months. This time period was chosen to overlap with the availability of observations of TWSC from the GRACE satellites, launched in 2002. As can be seen in Table  1 , the EO datasets vary in terms of their spatial and temporal resolution, posing a challenge to their integration. We put all EO data into the same 0. 5°equirectangular grid, based on latitude and longitude, rescaling and projecting as necessary.
Total Water Storage Change Information on total water storage (TWS), comes from the GRACE (Gravity Recovery and Climate Experiment) satellites. The first pair of satellites were in operation from 2002-2017, and a follow-on mission began in 2018. GRACE makes detailed measurements in changes to the Earth's gravity field over time. Most short-term changes are due to the movement of water on land and underground  (Tapley et al. , 2004). GRACE data have been used in groundbreaking studies to analyze the terrestrial water budget, drought, climate change, and water management (see e. g. ,  Famiglietti et al. , 2011; Richey et al. , 2015; McCabe et al. , 2017; Rodell et al. , 2018). GRACE provides the monthly TWS anomaly, expressed as a liquid water equivalent thickness, in units of cm or mm. GRACE does not estimate the total volume or mass of water in a region, but rather its change with respect to a historical baseline.
Nevertheless, the observations encompass water in all its forms and "represent the full magnitude of land hydrology and land ice"  (Landerer, 2021). We obtained three different GRACE products (see Table  1 ). Each is based on the mass concentration solution developed by the Jet Propulsion Laboratory (JPL), known as mascon. This technique employs a gravity field basis function to separate the contributions in the signal from unequal distribution of the earth's mass from other factors such as water storage variations. We calculated the month-over-month rate of change in water storage to provide the flux in mm/mo. This converts the TWS anomaly to a flux, TWSC or ∆S, and creating the link between GRACE data and the other variables in the HC. There are several methods for calculating the rate of change, but most researchers in this field use simple finite difference methods (see e.
g. ,  Landerer & Swenson, 2012; Biancamaria et al. , 2019). We used the backwards finite difference method. ∆S ∆t = S t -S t-1 t -(t -1) (2) The results we obtained from more complex methods such as fitting a cubic spline or using an "equivalent smoothing filter" (see e. g. ,  Landerer et al. , 2010)  were comparable to those obtained with the simpler methods but often resulted in more missing observations, therefore we used the simple method in Equation  2. Modeled TWSC -As a source of validation data, we also collected predictions from a recent modeling study that reconstructed GRACE-like TWSC via other water cycle components. to in situ observations, then using a constrained Kalman filter to merge the datasets based on their error information, with a goal of minimizing the imbalance. This study produced global gridded datasets at 0.
Precipitation We obtained data from three sources (Table  1 ). GPCP -The Global Precipitation Climatology Project (GPCP) has the longest time record, beginning in 1979  (Adler et al. , 2018). It also has the coarsest spatial resolution, at 2. 5°. This dataset, produced by an international consortium of researchers, is based on multiple satellite observations that are merged to estimate precipitation at the global scale. We used version 2. 3 of this dataset, which was updated in 2018. GPM-Imerg -GPM-Imerg is the multi-satellite precipitation product from NASA. IMERG combines data from multiple low-earth orbit satellites and geosynchronous orbiting infrared satellites, using morphing techniques and a Kalman filter, to provide accurate satellite-based precipitation estimates, supplemented by precipitation gauge analyses.
, 2017; Miralles et al. , 2011; Hersbach et al. , 2018). The authors used an empirical relationship, the Priestley-Taylor equation, to calculate potential E based on satellite observations of surface net radiation and near-surface air temperature. GLEAM version 3. 5a used reanalysis rather than satellite observation, and covers 1980 to present. The updated version 3. 5b relies more on remote sensing data, and has a more limited temporal coverage of 2003 to present. ERA5 -We also included a dataset that is not a purely remote-sensing based product, but based on the assimilation model ERA5, from the European Centre for Medium-Range Weather Forecasts (ECMWF). The model combines historical estimates (from both remote sensing and in situ observations) using an advanced modeling and assimilation system.
ERA5 produces many variables describing the atmosphere, land, and ocean, at a resolution of up to a 30 km grid  (Guillory, 2022). ERA5 estimates of E have been used in many recent hydroclimatic studies (see e. g. ,  Tarek et al. , 2020; Singer et al. , 2021; Lu et al. , 2021). manuscript submitted to Water Resources Research Observed evapotranspiration at flux towers -In order to validate our results, we obtained in situ measurements of E from flux towers, where latent heat flux and other measurements are obtained via eddy covariance methods. We selected data for 117 towers from the FluxNet2015 dataset, which compiles data from 212 global towers  (Pastorello et al. , 2020). The majority of selected towers are in Europe (51 towers) or North America (  46 ), with fewer in Africa (2), Asia (6), Australia (9), and South America (3).