dataesr/scientific-paragraphs-categorization huggingface dataset:
Baseline data From the 759 patients recorded in our REDCap database at the time of data extraction, we selected 354 patients with two disease-causing mutations in ABCB11. From these patients, we selected 138 patients with a BSEP1/1 (n = 31), BSEP1/3 (n = 30) or BSEP3/3 (n = 77) genotype (Fig. 1 , Fig. S1 , Table  S1 ). Nine of the 85 patients in whom this information was available had originally presented with a BRIC (benign recurrent intrahepatic cholestasis) phenotype (i. e. , episodic cholestasis and/or transient pruritus and indications for hepatocellular damage), but this had developed into a PFIC phenotype during follow-up (i. e. , continuous cholestasis and/or pruritus and continuous hepatocellular damage). Individuals with an initial BRIC phenotype were fairly equally distributed across the groups (2/15 (13%) in BSEP1/3, 3/ 22 (14%) in BSEP 1/1, and 4/48 (8%) in BSEP3/3).
The 10-year (pre-transplant) mortality was lowest in the BSEP1/3 group (0%), compared to 14% in the BSEP1/1 and 21% in the BSEP3/3 group (p = 0. 11, Fig. S3 ). Cause of death was related to liver disease in all cases. Association between specific type of BSEP1 mutation in BSEP1/3 group (p. D482G or p. E297G) and NLS The unique size of the NAPPED database previously allowed us to determine differences in the natural history of patients with the   p. D482G or the p. E297G mutation. 5 This analysis showed that patients with at least one p. E297G mutation usually had a slightly more severe phenotype. Therefore, we assessed possible differences between the p. D482G and p. E297G within the BSEP1/ 3 genotype, thus when combined with a PPTM. The NLS of the p. D482G -PPTM patients was consistently lower than that of p.
D482G and p. E297G mutations. 2] [23]  The difference between the natural history in p. D482G-PPTM and p. E297G-PPTM BSEP deficiency could also possibly be due to different levels of dominant negative interference. At this moment, however, neither of these two hypotheses can be proven. Future studies in other combinations of residual function mutations and PPTM might provide more clarity, as discussed above. Our study shows that the phenotype of patients with p. D482G-PPTM and p. E297G-PPTM genotypes is among the most severe category of BSEP genotypes, i. e. similar to patients with two PPTM mutations. It is generally known that patients with two PPTMs have a poor prognosis. 3, 5 In the NAPPED database these patients currently make up about 22% of all BSEP deficiency cases with two confirmed ABCB11 mutations (Fig.
The absolute number of patients in the BSEP subgroups is still limited and we did encounter missing data, as indicated. Nevertheless, we believe that our numbers were sufficient to draw the present conclusions. We are aware that center bias could play a role: treatment strategies, including the application of siEHC and the indications for LTx, have likely been different among the contributing NAPPED centers. However, sensitivity analyses towards centers or global regions did not impact the main outcomes of our study. Due to its (largely) retrospective nature, our cohort included patients that were treated over an extended period, which could have led to a chronological bias. Finally, some patients had been or still were on UDCA treatment. Unfortunately, we do not have more precise data on actual use or dosage of UDCA.
We did not see significant differences between patients that had been or still were on UDCA treatment vs. those that never had used it (Table  1 ). Our data did not enable a more detailed assessment of possible associations between UDCA use and disease course. In conclusion, our global NAPPED database has enabled us to further address genotype-phenotype relationships for individuals with genetically confirmed BSEP deficiency, or PFIC2. We identified a subgroup, representing 8% of all individuals with BSEP deficiency, with an unexpectedly severe disease phenotype, both at initial presentation and during follow-up, including a poor response to siEHC and a low NLS rate. Even beyond the clinical implications, the results of this study will allow for a better prognostication of patients when confronted with a genetic diagnosis of p.
, 2022b). Finally, the public availability of data also influenced the choices of model factors and countries in the tropical and subtropical mountains of Africa, Asia, and Latin America. The abovementioned inputs led to the specification of seven types of variables for the FSS model (Table  1 ). These were as follows: average farm size, association with global region, Gross Domestic Product (GDP) per capita (district scale), distance from major city, crop type (for foodproducing crops), aridity, elevation, and the variation of topography (ruggedness). Four of these variables represent socioeconomic and political factors (farm size, association with global region, GDP per capita, and distance from major city). The final three variables are environmental factors. Crop type was distinct since its role in FSS reflects both socioeconomic and political factors that determine seed-sector development, plant-level traits associated with FSS (e.
These estimates were then used to construct FSS as a response variable in hypothesized relations to the socioeconomic, political, and ecological predictors. The surveys and reports, as well as other data described in the following, were used to estimate the values of FSS utilization and the seven variables at the levels of the subnational administrative units referred to here as districts. Subnational districts were defined at one or two geographic levels below the national level and depended on actual census units. The estimation of FSS utilization was calculated as the proportion of farm households in a district utilizing sources other than the ones providing seed recognized as certified, hybrid, improved, and modern varieties seed-all major examples of the formal seed system  (Almekinders et al.
, 1994; Almekinders and Louwaars, 2002; Sperling and McGuire, 2010; Louwaars and De Boef, 2012; McGuire and Sperling, 2012). FSS utilization was then specified as 100 minus the percent households responding they used at least some seed recognized as sourced through the formal system or as improved varieties. This estimation provided a lower-level estimate of FSS utilization since, as mentioned above, the seed types recognized as certified and hybrid as well as improved or modern varieties can in fact be a part of FSS. Also, if any such seed types were utilized, then the household was characterized as non-FSS. The tendency in our methods to create a lower-level estimate of FSS was consistent with a focus on FSS functions that contain local and agrobiodiverse seed. Overall FSS utilization for each subnational administrative unit was calculated by incorporating the data across all crop types and taking the weighted average of the proportion of FSS for each specific crop.
The   (1991, 1996, 2003)  3 weighted average was calculated as the proportion of farms growing the specific crop in the subnational district. Mean arable farm area (Ha) was estimated as a predictive factor (detailed conceptualization of this factor and others in Table  1 ) using the national surveys by dividing the estimate of arable farm area across the surveyed district by the number of surveyed farms. Additional spatial, socioeconomic, political, and ecological predictors were estimated for each of the districts in the 11-country sample. Per-capita GDP of districts was estimated in US$ as mean purchasing power parity (PPP) using data from the Organisation for Economic Co-operation and Development, World Bank, and national sources. These were adjusted to 2011 value as a standardized time point representing the mean date of the agricultural survey and census data, by using the annual trends in national-level, percapita GDP  (World Bank, 2016).
We assessed distance from the nearest large city by spatially determining the centroids of district polygons in a Universal Transverse Mercator projection using QGIS open-source software and then calculating the Euclidean distance in kilometer to the nearest city with a population greater than 500,000 persons. Mean elevation and mean topographic ruggedness index  (Riley et al. , 1999)  were also calculated over each district polygon using QGIS software and the NASA Shuttle Radar Topography Mission digital elevation model data at 500-m resolution. Aridity index (AI), an integrated measure of rainfall and evapotranspiration potential of climate that assesses drought stress on agriculture, land use, and natural vegetation, was assessed as the average value for each district polygon using the CGIAR Global Aridity and Global Potential Evapotransporation (PET) databases (https://cgiarcsi.
community/data/globalaridity-and-pet-database/). Multiple linear regression was used as the principal approach to analyze the relationship of FSS utilization to hypothesized predictor variables (Table  1 ). Methodologically similar approaches to the spatial-demographic analysis of environmental and agricultural outcome variables include Hird and Reese (1998),  Hubal et al. (2022), and Zheng et al. (2021). We used this approach to assess the potential relation of FSS utilization at the district level to mean farm area as a factor emphasized in the thematic and case study-based literature (Table  1 ), while controlling for the district-level effects of per-capita GDP, distance to larger cities, aridity, elevation, and ruggedness. Added variable plots  (Gallup, 2020) , also called partial regression plots, were created for these geographic and climate covariates to examine their effects on FSS utilization while  controlling for the other predictors in the model.
The first regression analysis was conducted using survey data on the combination of all crops. A second analysis was completed using only the surveys with crop-specific data (Table  2 ) to examine whether there were any significant differences in FSS utilization among globally common staple food crops. Continuous predictors were assessed for significance using P values, while differences among categories such as crop types were assessed using Tukey tests for multiple simultaneous comparisons among all levels. Analysis focused on linear relationships as a first estimation of the relationships between potential drivers and FSS utilization. The effect size of these linear trends was also assessed using standardized b coefficients with a heuristic for the effect size of b coefficients  (Acock, 2014 ): b* < 0.
a A separate regression analysis was conducted using only the Andean countries in the database to relate FSS utilization to the mean elevation of subnational districts. to display a log scale to visualize the low values below 1. 0 hectares and between 1. 0 and 5. 0 hectares. FSS utilization declined by 52. 5% across the range of mean farm areas (Table  4 ), while the rate of this decline was just under 18% with each log 10 (Ha) change of farm area (Table  4 ). Even districts with values of mean farm areas that categorized as medium (1. 0-5. 0 hectares) demonstrated the predominant utilization of FSS. The prevalence of FSS is also notable, where mean farm areas are slightly larger than 5. 0 hectares (Figure  1a ). As a single predictor omitting other factors in the regression model, mean farm area accounted for approximately 22% of the variability of FSS utilization across the data set (R 2 ¼ 0.
g. ,  Genius et al. , 2014). Our analyses are based on survey data from 1029 wheat growers eligible to participate in the large-scale pesticidefree production program in Switzerland, a country with strong institutional support, well-functioning extension services, and farmers with a high average level of education and relatively high environmental awareness. Our results confirm the importance of accounting for varying information links within farmer networks, which distinguishes our study from studies that rely only on spatial proximity in addressing the role of social interactions in the adoption of agricultural innovations (e. g. ,  Krishnan & Patnam, 2014; Sampson & Perry, 2019; Ward & Pede, 2015). The remainder of this article is structured as follows: Section 2 provides background of the pesticide-free wheat production system; Section 3 introduces the conceptual background for our analysis; Section 4 presents the empirical strategy; Section 5 describes the data; Section 6 presents the results, followed by discussions; Section 7 concludes.
Except for herbicide resistance and organic farming density, which is measured at the municipality level, all other variables are measured at the farm level. In addition, we match average Extenso wheat yield over the past 10 years at the postcode level to proxy the local production potential. The dataset is publicly available and described in more detail in  Möhring and Finger (2022a). Since extension services and various layers of farmer exchange are organized at the cantonal level, we assume social interactions primarily take place in farmer networks within the same canton (see detailed discussion in the Empirical Strategy section). To ensure a sufficient number of peers in a farmer's network, we restrict the sample to cantons with at least 20 respondents, which results in a sample of 1036 farms.
A C K N O W L E D G M E N T S The authors thank the editor  Awudu Abdulai , three anonymous reviewers,  Michael S. Delgado ,  Viviana Garcia ,  Chun Song , and  David Wuepper  for helpful comments. Robert Finger  acknowledges support of the  Swiss National Science Foundation , under the project ' Evidence-based Transformation in Pesticide Governance'  (Grant  193762 ). Data collection was conducted in the PestiFreeWheat project, supported by the  ETH Foundation  and  IP-SUISSE  and Jowa. We are grateful to all farmers for their participation in the survey. Open access funding provided by  Eidgenossische Technische Hochschule Zurich.
The reality, as mentioned above, is that within each ERN there is often a 'focal' disease or group of diseases which has attracted a relatively large amount of research attention (predating the ERNs' foundation), and/or is better understood and supported in terms of diagnosis and care (although many ERNs are seeking to address this, for instance by ensuring their registries collect data on all diseases covered by the Network). 3  But nonetheless, the inherently egalitarian nature of the ERN model is, in itself, a strong step in the right direct of casting much-needed light on the many thousands of so-called neglected diseases which have traditionally lacked any research interest.
At the same time, the European Platform on Rare Diseases Registration, initiated in 2013 by the European Commission's Joint Research Centre (JRC) in collaboration with DG SANTE is building tools to facilitate access and re-use of RD registry data, via its European Rare Disease Registry Infrastructure (ERDRI) (Fig. 1 ). The creation of a European platform to increase the reuse potential of precious rare disease data is of major importance when one considers that over 800 rare disease registries exist in Europe (or are fed by European centres or actors)  [32]. However, the creation of ERN registries-or platforms to link new ERN registries with historical or possibly new disease-specific registries-holds major potential for advancing knowledge and better care, but also naturally for stimulating and advancing research.
Supported by projects like the EJP RD and ERICA, attempts are being made to ensure a certain level of interoperability in terms of the data collected in these new ERN registries. For instance, the Common Data Elements issued by the EU RD Platform have been turned into a richer data dictionary under the EJP RD  [33] : this is just one example of efforts to make registry data FAIR (Findable, Accessible, Interoperable, and Reusable). Greater value will come with the advance of individual ERNs agreeing and standardising domain-specific datasets  [34]. The summaries above are far from exhaustive, but illustrate why ERNs hold such significant potential to advance rare disease research. However, to-date, the research activity of the ERNs has been limited, for several reasons.
These Working Groups were met twice, remotely, for 1. 5 h each time, and worked via Sharepoint documents. A range of distinct types of collaboration emerged. Together4RD was able to distil these insights and practices into tables, one for each Working Group, to show clearly and comprehensively. Firstly the different sorts of collaborations possible with Industry; on the 'Registries' side, these include accessing registry data to elucidate natural history, to conduct postmarketing surveillance, to serve regulatory purposes as Real-World Evidence (RWE), and collaborating on the definition of datasets. In terms of broader 'Clinical Research' , activities ranged from strategic fora to advance research to creating opportunities for researchers to pitch ideas to companies: and from creating or improving biobanks to diagnosing patients through electronic health records.
One key area of Industry engagement concerns patient registries. TREAT-NMD links numerous registries (and developed core and expanded datasets to standardise data in these standalone registries) to facilitate the identification of specific patient groups and boost patient recruitment. It also coordinates global patient registries for several NMDs. The inter-connected registries provide a wealth of information and can be queried by academic sites (free) or by Companies (for a fee). In the days before it became a legal entity, contracting was performed by the institution coordinating the network, namely Newcastle University in the UK. This was not always straightforward, but procedures were honed over the years to govern the collaborations with Industry, based on policies agreed by the whole consortium (composed of mainly academic institutions and patient organisations).
A typical activity entailed registry data being sought by a company, for instance to assess the feasibility of conducting a clinical trial in a given neuromuscular disease. The registries associated with TREAT-NMD were largely national, standalone autonomous registries for conditions such as Duchenne Muscular Dystrophy, which had agreed to collect a common data set defined by the TREAT-NMD consortium. TREAT-NMD established an advisory board composed of the curators of these autonomous registries, called the TREAT-NMD Global Database Oversight Committee or TGDOC. A request from a company would be reviewed by this TGDOC and if favourably reviewed, the team at Newcastle University would negotiate a contract with the company. Aggregate data would be collected by the national curators of each registry, by disseminating a questionnaire via their registries (or simply providing the aggregate data themselves) -a typical query might be 'how many patients with an X deletion of Y neuromuscular disease are enrolled in your registry, between the ages of 5 and 10, and what proportion are still ambulant?'.
), to support better and earlier diagnostics, elucidate natural history, monitor longitudinal outcomes, and support research and regulatory goals, will require significant resources-human, financial and technological. Even thinking purely of registry data entry, Together4RD's research has illustrated the extent of data entry challenges (although here at least, possible strategies have been identified. For instance the ERK-REG registry appears particularly successful at getting sites within the rare renal ERN to provide data on their patients,   [46]. Given patient preferences to control consent over what happens to their rare disease data  [25] , governance issues are crucial, and data access should be maximised, to allow it to serve as many purposes as possible. It is also important to avoid setting-up entirely new registries when options exist to expand or connect existing structures: where a new registry is unavoidable, particular care should be given to interoperability with other key resources.
400 clinical sites (including around 50 international sites in approximately 20 countries). Crucially, the RDCRN has been able to leverage private funding as well as public support, and as a result has been able to conduct cutting-edge rare diseases research, including gene editing and gene therapy trials. The learnings from this may be very valuable for the European setting  [11]. What could then be achieved by the ERNs-which arguably offer even greater potential  12  -if similarly unbridled in the research sphere? The European-level will to unlock the potential of precious rare disease data is greater than ever, and with momentum growing around the EHDSwith the accompanying availability of technical, legal and organisational solutions and assets-there is finally real cause to hope that ambition will translate into reality.
Acknowledgements This work was supported by discussions which took place in meetings of the Together4RD Steering Group, and smaller working groups dedicated to Registries and to Clinical Research, respectively, throughout 2022 and 2023. All members of the  Together4RD Steering Group  contributed to discussions around the scope and content of this Position Statement (initially drafted as a White Paper). The members of the Together4RD Steering Group are as follows (as of January 2023): ERN coordinators:  Alexis Arzimanoglou -Coordinator of the  ERN for Rare and Complex Epilepsies (ERN EpiCARE) , Chair of the  ERN Coordinators Group, Lyon University Hospital (France) ;  Hélène Dollfus- Coordinator of the  ERN for Rare Eye Diseases (ERN-EYE)  and Co-Chair of the  ERN Coordinators Group, Strasbourg University Hospital (France) ;  Dorothée Leroux -Project Manager,  ERN-EYE ,  Strasbourg University Hospital (France );  Maurizio Scarpa- Coordinator of the  ERN for Hereditary Metabolic Disorders (MetabERN) ,  University of Padova (Italy) ;  Franz Schaefer -Coordinator of the  ERN for Rare Kidney Diseases (ERKNet) ,  Universitätsklinikum Heidelberg (Germany) ;  Alain Verloes -Coordinator of the ERN for congenital malformations and rare intellectual disability (ERN ITHACA),  Hopitaux de Paris ,  Hopital Robert Debre (France).
In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.
Limited Generalization: A recent survey showed that about 90% of studies use the same dataset (i. e. , ADNI dataset) to evaluate their model performance which limits our knowledge of CNN performance on other databases  [14]. Moreover, most of the studies mentioned above used the same dataset for training and testing. Such validation framework is known to over-estimate method performance. Indeed, in-domain validation is dangerous as methods showing high performance on a single dataset might just better capture the particular characteristics of that dataset and might poorly perform in another dataset  [35]. As a consequence, current DL literature offers limited knowledge about the generalization capability of DL methods on external datasets. This limitation does not only apply to AD classification application but also to other diseases (e.