dataesr/scientific-paragraphs-categorization huggingface dataset:
6 ach of these funders has pledged to spend a minimum of US$10 million on rare disease research over 5 years. The IRDiRC has set itself two headline goals to achieve by 2020: 1) to develop the means to diagnose most rare diseases and 2) to deliver 200 new therapies for rare diseases. In this review, we provide an overview of the objectives and initial achievements of one of the first projects to be funded under the IRDiRC. Initiated in 2012, RD-Connect is a €12 million (US$19 million) IRDiRC infrastructure project funded by the European Union's Seventh Framework Programme. 7 The project brings together 27 partner institutions and works in close collaboration with two associated research projects, Neuromics (www. rdneuromics. eu) and EURenOmics (www. eurenomics. eu) (see Table  1 ). Its objectives for the 6-year funding period are to develop an integrated platform connecting databases, registries, biobanks, and clinical bioinformatics for rare disease research and to contribute to the IRDiRC goals by facilitating gene discovery, diagnostics, and therapy development.
DEVELOPMENT OF A UNIFIED INFORMATICS PLATFORM FOR DATA SHARING AND ANALYSIS Key data management challenges faced by rare disease research projects include the high complexity and heterogeneity of the data types involved, the variability among experimental platforms,  8  the need to incorporate non-standardized sample descriptions and diverse data types, and the requirement to protect data (both prepublication data and identifiable patient information). Furthermore, the high volume of data and the distributed nature of the sources make traditional approaches to data management impractical, and new solutions are therefore required. One of the primary goals of RD-Connect is to help contributing projects make their data rapidly available to the wider rare disease research community. Raw genomic data from collaborating projects is first deposited in the European Genome-phenome Archive at the European Bioinformatics Institute, a permanent resource for controlled-access archiving,  9  then reprocessed in a standard manner to ensure cross-compatibility.
The processed data is held in the central RD-Connect database, where it is combined with phenotypic and biomaterial information. Researchers approved by a data access committee gain secure access to an integrated data portal that enables comparison of data sets across projects; data can be explored and analyzed using a comprehensive suite of bioinformatics tools (Fig. 1 ). In addition to the technical aspects, any project involving aggregation of data has to contend with challenges relating to the willingness of researchers to share their results. Here, RD-Connect has the advantage that its two associated research projects, Neuromics and EURenOmics (Table  1 ), have been pilot users of the system. Thus, key policies such as implementation of staged embargo periods to protect results temporarily have been developed in close collaboration with these end users.
Within the first year, this partnership has borne fruit: the first data sets have already been transferred, and time lines for release of the remainder have been approved. Additional ways of meeting the data-sharing challenge include initiatives such as microattributions  10, 11  and nanopublications  12  to stimulate and reward data-sharing and knowledge-sharing and integration, with an explicit focus on interoperability, as opposed to massive centralization. Although it is the intention of RD-Connect to act as a central repository for IRDiRC research data, it will also be able to integrate with existing databases operating in the same field, such as the DECIPHER database of genomic variation data based at the UK Sanger Institute. 13 lose integration with existing research resources such as those developed by the European Bioinformatics Institute, as well as the major European research infrastructures, including the Biobanking and Biomolecular Resources Research Infrastructure 14 (BBMRI; http://bbmri.
DATA ANALYSIS AND PROCESSING: CREATION OF NEW BIOINFORMATICS TOOLS The multi-dimensional data compiled at the RD-Connect central hub affords an opportunity to analyze genetic etiology, functional molecular profiles, and patient-level phenotypes in relation to rare disease. Appropriate analysis tools are applied to this data with the immediate objective of generating a more complete picture of rare disease causes and mechanisms from the molecular to the physiological level, towards the eventual goal of improved diagnostics and therapy. Thus, RD-Connect will adapt, develop, and apply new bioinformatics tools to this uniquely rich data set. These tools will be integrated into the RD-Connect platform, piloted in analyses in collaboration with investigators from the Neuromics and EURenOmics projects, and be made broadly available in open-source release to the wider community for use in other projects.
RD-Connect has begun developing methods to combine these data to facilitate gene and biomarker discovery. The RD-Connect platform will enable a range of bioinformatics tools to be utilized on data held within the system-both tools that are being further developed within RD-Connect, and the related projects and external tools that can be linked in through common APIs (Application Programming Interface) and web services. [19] [20] [21] [22] [23] [24].
CROSS-LINKING DATA IN DATABASES AND BIORESOURCES Historically there has been very limited cross-linking between biomaterial collections, registries, genomics, and trial data, with the exception of individual clinical research centers, where all the information may be held by a single investigator. Unfortunately, the more common situation is that the same patient is associated with multiple entries in different systems, with extensive phenotypic information available in a registry, biosamples available in a biobank, and genomic data in a research database-but without any possibility of linking the data sets. This is clearly suboptimal and at best can result in much duplication of effort, and at worst, missed opportunities for discovery, diagnosis, or treatment. Solving this data-linking problem is made more challenging by the need for strong data protection to ensure patient confidentiality.
Furthermore, owing to the rarity of the conditions, rare disease patients are more likely than others to have data in cross-border repositories, and an international solution to this problem is therefore essential. Other projects facing similar issues have evaluated solutions involving the generation of a "globally unique identifier" (GUID) from a set of personally identifiable information associated with the research participant. This allows data on a single individual to be accumulated across projects over time, regardless of where and when the data was collected, and enables researchers to define a study population using data collected in different centers. In the United States, the National Institutes of Health (NIH)funded National Database for Autism Research (NDAR)  25  has developed an identification system that is now being extended under the auspices of the NIH Office of Rare Diseases Research for use in linking patient clinical information with biospecimens.
Similarly, a unique identifier for Huntington's Disease patients taking part in international research studies has been developed. 26 RD-Connect intends to implement such a system prospectively on a voluntary basis across participating registries and biobanks, to allow data sets to be cross-linked in full compliance with current data-protection policies. However, to ensure that no data from contributing projects has to be excluded, the GUID will not be a prerequisite for entry of data into the system.
GENERATION OF A COMPREHENSIVE, SEARCHABLE, ONLINE CATALOGUE FOR HUMAN RARE DISEASE BIOMATERIALS Making biological materials and associated data from rare disease patients accessible and available to the scientific community in an internationally coordinated manner is a core aim of the RD-Connect platform. This initiative is based on collaboration between the two major relevant biobanking infrastructures in Europe: BBMRI, which historically has focused primarily on population biobanks, and EuroBioBank. 27 EuroBioBank has historically been composed principally of neuromuscular biobanks, but will extend to incorporate rare disease biobanks of all types. Within the first year, RD Connect investigators have begun a mapping exercise to ensure outreach to all biobanks holding biomaterials related to rare diseases.
Progress has also been made towards the development of a new online interface for a rare disease biomaterial catalogue, including primary cells, tissue, DNA, serum, RNA, and humaninduced pluripotent stem cell lines. This will also include information related to biobanking standards, including an overview of major existing standards and guidelines for biobanking and "Minimal Information Standards" for sample collections. The design of existing databases, registries, and biobanks does not often allow the sharing of information in a computer-accessible fashion. For this reason, participating resources are being offered the opportunity to make use of linked data and semantic web approaches: computational standards and methods that enable them to make their data more accessible and interoperable.
USE AND FURTHER DEVELOPMENT OF PHENOTYPE ONTOLOGIES It is increasingly recognized that advances in sequencing technology do not replace the need for detailed clinical assessment of patients with rare disease. On the contrary, deep phenotyping is more important than ever in order to interpret whole-exome and whole-genome sequencing results. However, where clinical notes are on paper systems in hospitals, or where clinicians enter free text in electronic systems, the power of computation cannot be leveraged to support analysis. Ontologies are structured representations of knowledge using a standardized, controlled vocabulary for data integration, organization, searching, and analysis. To ensure the searchability of the data in the central system, RD-Connect makes use of ontologies of both phenotypic features (signs and symptoms of diseases) and diseases and disease groups (disease classifications or nosologies).
With over 10,000 classes (terms) describing human phenotypic abnormalities and over 13,000 subclass relations between the classes, together with extensive annotation and cross-referencing with other ontologies, the Human Phenotype Ontology (HPO)  29  is a leading example of a phenotypic ontology. Phenotypic data contributed by the associated research projects, Neuromics and EURenOmics, is being captured using the HPO, enabling standardized cross-cohort comparisons and filtering, as well as implementation of algorithms for automated "matchmaking" to help find cases with clinically similar presentations and variants in the same gene. Collaboration between RD-Connect, Neuromics, and the developers of the PhenoTips 30 software has enabled development of user-friendly online forms for clinicians to enter disease-specific phenotypic information for neuromuscular and neurodegenerative diseases using the HPO.
GENERATION OF COMMON DATA ELEMENTS AND STANDARD OPERATING PROCEDURES FOR PATIENT REGISTRIES To enable standardized aggregation of patient information, in addition to the use of standardized ontologies, it is helpful to establish common data elements for data collection. Work in this area builds on existing protocols and best-practice recommendations produced by the leading database and registry initiatives represented within RD-Connect, including the disease-specific networks established for neuromuscular diseases,  32  cystic fibrosis,  33  and Huntington's disease,  34  the recommendations of the European Platform for Rare Disease Registries project, and the rare disease common data elements developed and published by the NIH's Office of Rare Diseases Research,  35  all of which will be leveraged to provide an initial framework for participating registries.
The risks associated with research that uses genetic information, stored biospecimens, or information from databases, medical registries, patient records, and questionnaires are not physical, but informational, e. g. , related to unauthorized release of information. In such cases, a more expedient review process is suggested, paving the way for a simplified procedure for data-sharing research. On a practical level, a rare disease data-sharing charter and standardized templates for informed consent procedures are being developed. Patient issues and stakeholder inclusion are also recognized as central, and a patient-centered approach ensures patient views are taken into account across all aspects of the work. This is coordinated by a 16-member Patient Advisory Council made up of patient representatives from associated projects and patient organizations.
CONCLUSION Patient registries,  39  biobanks,  40  and bioinformatics support are key infrastructure tools required for genomic research in rare disease; data sharing and linking of patients, samples, and analysis is also essential. The infrastructure developed by RD-Connect supports research in rare disease to find new genes, biomarkers, and therapeutic targets more quickly and efficiently. Its ultimate goal will be to improve outcomes for rare disease patients via major improvements in diagnostics and therapeutics (Fig. 2 ). The therapeutics market in rare disease has strong growth potential due to the high (and unmet) medical need for most rare diseases. Genomic research and development will thus be highly relevant for many markets, including genetic testing, biomarkers, and therapeutics.
The 2011 Orphanet report on rare disease research  41  noted that networking initiatives resulting in easy and secure access to resources (databases and registries, biobanks, reference data sets, and analysis tools) and a close working relationship with patient groups are clear predictors of success for translational efforts in rare disease. As the field evolves and embraces the opportunities of the new technologies, there will be further challenges relating to access to sufficient patient numbers, plus sufficient high-quality biological samples annotated with harmonized ontologies and associated with detailed molecular data, analyzed by standardized analysis pipelines. RD-Connect will enable the elucidation of pathways relevant across rare diseases and identify shared therapeutic targets for groups of rare and common disorders.
Acknowledgements: The authors would like to acknowledge the contribution of the RD-Connect scientific advisory board, chaired by  Bartha Maria Knoppers , as well as all partners and associated partners in RD-Connect. Further acknowledgement is due to  Olaf Riess , Neuromics coordinator;  Franz Schaefer , EURenOmics coordinator; and all Neuromics and EURenOmics partners contributing data to RD-Connect. We thank the  PhenoTips developers  for collaboration on standardized phenotypic data entry forms. The efforts of the  IRDiRC Executive and Scientific Committees in policy  and guideline development are appreciated. The work leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 305 , 444  ( RD-Connect ),  305 , 121  (Neuromics) and 305,608 (EURenOmics).
Engagement of patient organizations for the donation patients and patient organizations through open of biological materials consultation, data protection, and ethical conduct and by invitations of key representatives in relevant meetings. Registries and biobanks Possible resistance of databases/registries/ Provide different stages of implementation of the common biobanks to adapt to new common data data element, starting form the most common element. elements and the Rare Disease-Identification (RD-ID) Registries and biobanks Difficulties in protecting patient identity in Develop standard operating procedures for data protection de-identified databases/registries/biobanks, and appropriate informed consent forms to provide especially those suffering from ultra-rare patients with full information.
Phase-specific backgrounds were calculated as 290 ppb and 240 ppb ppb for phase I and II, respectively, a difference less than 10 % of average CO level, thus with little impact in OA / CO mass ratios. Conversely, the lowest concentration of OA was only 0. 2 µg m -3 , thus we chose zero as background. High values of OA / CO , characteristic of biogenic air masses, are observed for f 60 < 0. 008. The values in this range should be viewed with caution as they correspond to data with low CO, and are thus subject to large uncertainty related to the CO background subtraction. This aside, OA / CO is fairly constant around 0. 03, indicating no net OA production tendencies from biomass burning emissions. N i √ 2π log σ i e -(log D-log Di ) 2 /2 log(σ i ) 2.
Aerosol size distribution A script was developed to fit one to three lognormal modes to the measured particle number size distributions in the range 10-430 nm, based on the approach of  Hussein et al. (2005). Aitken (∼ 30-120 nm) and accumulation (∼ 70-280 nm) modes dominated the particle number concentration in 98 % of the observed size spectra, with average concentrations of 2000 ± 2300 and 1500 ± 1600 cm -3 , respectively. These values are comparable to previous measurements during the SMOCC-2002 campaign at the pasture site mentioned above and by aircraft over smoke-impacted areas in Rondônia  (Rissler et al. , 2006; Chand et al. , 2006). The high number concentration variability reflects the diversity of BB plume ages and dilution levels reaching the site. Figure  9  shows the average size distribution with gaussian fits for nucleation, Aitken, and accumulation modes, as well as average size distributions observed for a given O : C ratio.
PMF analysis The number of PMF factors was selected based on the total sum of squares of the scaled residuals, the analysis of temporal series of the different factors with gas-phase and BC e data, diurnal cycles, and the mass spectra. Details of the PMF analysis procedure is given in the Supplement. Three factors were identified: fresh biomass burning organic aerosol (fresh BBOA), aged BBOA, and low-volatility oxygenated organic aerosol (LV-OOA). The mass spectra of the three factors are depicted in Fig. 10  and the mass contribution time series of the factors in Fig. 11. The enhancement of the fresh BBOA factor, and to some extent the aged BBOA factor, correlates well with aerosol loading peaks, whereas LV-OOA presents persistent regional pollution behavior. Table  3  presents a correlation analysis of the factors obtained from the PMF of the ground-based data with the AMS spectra database  (Ulbrich et al.
, 2009 (Ulbrich et al. , , 2013)) , the SAMBBA airborne AMS sampling biomass burning plumes in Rondônia from flight B737 (see Supplement), and the time series correlation of CO, BC e , sulfate, and nitrate. The fresh BBOA factor spectrum correlates well with the AMS measurements directly above (900 m altitude) a large fire in Rondônia (Pearson = 0. 96) as well as 1 h downwind of the    (Ng et al. , 2011a)  0. 86 0. 83 0. 66 OOA  (Ng et al. , 2011a)  0. 60 0. 98 0. 97 BBOA  (Ng et al. , 2011a)  0. 98 0. 72 0. 58 HOA  (Ng et al. , 2011a)  0  (Pearson = 0. 55), and much better with the 3 h downwind spectrum (Pearson = 0. 86), indicating that this factor would represent BB aerosol processed only for a time longer than 3 h. Similarly, the correlation of LV-OOA with the airborne AMS data improves with increasing plume processing time.
The fact that LV-OOA correlates only moderately well with the OA spectrum obtained 3 h downwind of the fire (Pearson = 0. 80) suggests that the BBOA shows a LV-OOA chemical signature somewhat more slowly than an aged BBOA signature. Comparison with the AMS mass spectra database shows that the fresh BBOA factor correlates extremely well with the database BBOA (Pearson = 0. 98), whereas aged BBOA correlates with database OOA (Pearson = 0. 98), as well as with database LV-OOA (Pearson = 0. 97). Similarly, the LV-OOA factor correlates extremely well with the database LV-OOA and database OOA (Pearson = 0. 98 and 0. 97, respectively). The CO time series yields a high correlation coefficient with the fresh BBOA factor (Pearson = 0. 90), as both signals are related to strong biomass burning events.
Conclusions We have characterized the physical and chemical properties of aerosols at a site heavily impacted by biomass burning in the southwest Brazilian Amazon during the SAMBBA field experiment. The sampling site is located 5 km north, and usually upwind, of the city of Porto Velho, Rondônia. The data reported here focus on the period from 13 to 30 September 2012, comprising the transition period from the dry to the wet season. During most of the campaign, the site was impacted by regional biomass burning pollution (average CO mixing ratio of 0. 6 ppm), occasionally superimposed with intense biomass burning plumes (up to 2 ppm of CO). Realtime chemical characterization has shown that organic aerosol strongly dominated the submicron non-refractory composition, with an average concentration of 11.
Acknowledgements. This work was supported by the  Foundation for Research Support of the State of São Paulo  ( FAPESP , projects  2012/14437-9  and  2013/05014-0 ),  CNPq  project  475735-2012-9 ,  INCT Amazonia , and  Natural Environment Research Council (NERC)  project  NE/J010073/1. We thank  A. Ribeiro ,  A. L. Loureiro ,  F. Morais ,  F. Jorge , and  S. Morais  for technical and logistics support. We thank the  National Institute of Meteorology  for providing valuable meteorological data. We gratefully acknowledge  S. Hacon ,  J. Silva , and  W. Bastos  for support in the successful operation of the sampling site. Edited by:  G. Myhre.
In recent times, information on miRNAs and their binding sites is gaining momentum. Therefore, there is interest in the development of tools extracting miRNA related information from known literature. Hence, we describe GeneAFinder and miRAFinder scripts (open source) developed using python programming for the semi-automatic extraction and arrangement of updated information on miRNAs, genes and additional data from published article abstracts in PubMed. The scripts are suitable for custom modification as per requirement.
Background: The number of abstracts for biological articles at the PubMed [1] database has increased over timeline due to steady advancement in biomedical research. There are a number of online servers that extract information in a specific manner from abstract archived databases. The MedlineRanker web server allows a flexible ranking of Medline for a given topic of interest  [2]. MedEvi imposes positional restriction on occurrences matching multi-term queries, based on the observation that term with semantic relations  [3]. The FNeTD method for clustering achieved PubMed abstracts using revealing frequent "phrases" or "words" and identifying "nearer terms" of the domain  [4]. The MiSearch is an adaptive biomedical literature search tool that ranks citations based on a statistical model  [5].
Genomics researches are having a major impact on biological and medical sciences although the function of many genes remains unknown. In recent times, PubMed database search shows about 285 thousand articles associated with breast cancer researches. While, one of causes of tumorigenesis is the suppression of gene expression via microRNAs (miRNAs)  [6]. There are more than 2500 human miRNAs and some of them are potential therapeutic targets for neoplastic diseases  [7]. Database search shows more than 32 thousand articles devoted to miRNAs. Short nucleotide sequences of miRNAs can be used as biomarkers for cancer diagnostics as they circulate in biological liquids. Therefore, it is important to reveal comparative information about each studied miRNA in the literature. It should be noted that more 13 thousand articles are associated with miRNA participation in tumourigenesis as per the PubMed database search.