dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter is howwww. the-cryosphere. net/9/1633/2015/ The Cryosphere, 9, 1633-1648, 2015 ever unlikely, since interglacial ice is usually harder to deform due to lower impurity content and larger crystal size  (Dahl-Jensen et al. , 2013). Finally, as demonstrated in de Angelis et al. (  2013 ), the detailed analysis of individual inclusions supports the occurrence of in situ bacterial activity. To our knowledge, it is not known so far if these might have potential impact on the δ 18 O atm of the neighbouring gas phase. It is however unlikely that it might be significant given the observed low CO 2 mixing ratio (Fig. 3a ), in line with atmospheric values at glacial times. Despite the very poor resolution of the dust record in our bottom ice the large variability of the data within the glacial range could also result from our increased relocation scheme.
Acknowledgements. This work is a contribution to the European Project  for Ice Coring in Antarctica (EPICA ), a joint  European Science Foundation/European Commission (EC)  scientific programme, funded by the  EU (EPICA-MIS ) and by national contributions from  Belgium, Denmark, France, Germany, Italy, The Netherlands, Norway, Sweden, Switzerland  and the  UK. The main logistic support at Dome C was provided by  IPEV  and  PNRA. The authors wish to warmly thank  B. Hubbard  and two anonymous referees for their constructive comments on the "Discussion" version of this manuscript, and  D. Raynaud  and  F. Parrenin  for valuable discussions. Edited by:  M. Schneebeli.
These can either be manually inspected or serve as the input for future detection systems. Our approach is to combine data sources that are external to BGP to draw conclusions about the legitimacy of subMOAS events. First, we use information from the RIPE database to infer business and management relationships between the IRR objects stored in the database. Such information can only be altered by entities with valid access credentials. Our assumption is that an attacker does not have these credentials. Second, we use a topology algorithm to reason whether an attacker targets subprefixes of his own upstream provider. This is highly unlikely as the victim would simply be able to filter out the malicious BGP updates. Third, we use data from Internet-wide scans of the SSL/TLS landscape to determine hosts whose public/private key combinations are unique and remain stable over a longer period of time.
Methodology Our methodology consists of four steps. First, we determine actual subMOAS events from BGP routing tables and update messages. Subsequent steps focus on eliminating subMOAS events with legitimate causes. To this end, we establish a filter chain. First, we use the RIPE IRR database to infer the ownership for certain so-called IRR resources. If we find that an alleged attacker actually is the legitimate owner of a resource or has been delegated authority over it, we consider such a subMOAS event as legitimate. Our filter is currently limited to the RIPE space, but can be extended to other IRR databases. The next filter is a topology-based reasoning algorithm: the idea is that an attacker is unlikely to hijack his own upstream provider as this provider could simply counter the attack by filtering out malicious BGP udpates.
Utilizing IRR databases All five Internet Routing Registries (IRR) maintain databases that contain information pertaining to the management of Internet resource holders. A recent study  [6]  matched prefixes and ASes observed in BGP and IRR by looking for appropriate database objects. We provide a generalized set of inference rules for benign subMOAS events, which take into account multiple origins observed in BGP as well as complex relationships between the affected prefixes and a suspicious origin AS. Our filter is designed for the RIPE database as RIPE provides daily snapshots with a precise data model and a certain amount of consistency enforced. Still, IRR databases are updated by individual resource holders and can thus be outdated or even hold conflicting information. Our filter accounts for this.
Note that filters for other IRR databases are easy to design; this is ongoing work. As of June, 2014, our database holds more than 4 million nodes and 5 million relations extracted from the RIPE database. Table  1  provides details for selected objects that are relevant for our approach. We can see that less than 50,000 MNTNER objects share more than 5 million incoming maintained_by references. Although optional, roughly 80,000 ORGANISATION objects are referenced by almost 200,000 other objects. Less than 30,000 AUT-NUM objects import routing policies from more than 220,000 other AUT-NUM objects. Nearly 250,000 ROUTE objects bind prefix announcements to less than 30,000 AUT-NUM objects. We will see that these figures allow our filter to be very effective. Infering resource ownership Recall that our fundamental assumption is that an attacker does not have the credentials to change the RIPE database in order to cover his attack.
Accordingly, we look for legitimate relationships between the parties involved in a subMOAS event to disprove an attack. Given a routing change that results in a subMOAS, we map the affected AS numbers and prefixes to AUT-NUM and INETNUM objects in our graph database. We then traverse the graph along a path of legitimizing relations. We look for paths between a) the two affected AS or b) the more specific prefix and its origin AS. If we succeed with a), we can infer a valid business relation between the victim and the suspected attacker. If we succeed with b), the suspected attacker holds ownership rights for the more specific prefix and is thus authorized to originate it from his AS. Legitimizing paths are formed by one or more of the following relations: import, origin, maintained_by and org.
In addition, we have nearly ten times more ROUTE objects and import relations than AUT-NUM objects. It is therefore promising to look for objects with common references to these objects. Note that our approach does not require the RIPE database to be complete, and not even to be conflict-free. Our inference rules are solely based on legitimate objects. In case of absent or conflicting database objects, we are unable to establish a legitimizing path-we cannot wrongly legitimate a subMOAS event this way.
Figure2shows entities and relations in the RIPE database that are of significance for our work. We use a graph database to store the extracted data using the same schema as in the figure. We also track all changes over time. The RIPE database models access rights with MNTNER objects. Only maintainers with valid credentials can modify or delete objects. For any object, this is expressed by adding a maintained_by reference pointing to the respective MNTNER object. ORGANISATION objects are optional and mainly used to provide administrative contact details. The RIPE snapshots remove details for privacy reasons but preserve the references to the objects themselves. INETNUM objects represent allocated or assigned IPv4 prefixes managed by RIPE. ROUTE objects are created by resource holders and are used to document or confirm intended prefix announcements by specific ASes.
To create a ROUTE object, a resource holder needs to provide valid maintainer credentials for both the INETNUM and the AUT-NUM object. The corresponding maps_to relation is computed by our parsing algorithm. AUT-NUM objects represent AS numbers and may be referenced as the origin of ROUTE objects. Our parsing algorithm also deduces import relations from free-text description fields, which are often used to model routing policies in the so-called Routing Policy Specification Language (RPSL). When resources are deleted from the RIPE database, RPSL definitions may still reference (now) nonexisting ASes. We account for this by tracking such orphaned import relations.
Conclusions and outlook We introduced a methodology that allows us to reliably identify subMOAS events with legitimate causes. Our method combines data from several sources and proves promising: although coverage for the entire Internet can be improved, our individual filter techniques are highly effective. Our findings show that both IRR databases and active scans are useful tools to reason about routing anomalies indepth. Moreover, we outlined straightforward steps to increase coverage, which puts manual inspection of the remaining subMOAS events within reach. Finally, we intend to grow our framework into a service that makes its data publicly available on a continuous and permanent basis. This framework promises to be greatly beneficial for future systems to detect subprefix hijacking. We invite the research community to participate in this effort.
Methods Our research uses peer-reviewed academic literature, other literature including news stories, and examination of social network sites. Phase 1: Understanding the Extent to Which Social Networks Related to Health Are Documented and Evidence of the Prevalence of These Networks Related to Health The following databases were searched: Medline, Web of Science, Embase, and the Applied Social Sciences Index and Abstract (ASSIA) using the keywords: lay, volunteer Ã , lobby Ã , pressure group Ã , interest group Ã , self help group Ã , social media, digital media, digital communication, web 2. 0, internet, blog Ã , twitter, facebook, tweet, forum Ã , crowdsourcing, wiki, email, health, healthcare, medicine, medical. This retrieved 3,154 references after de-duplication. For this scoping review, we rapidly sorted this literature based on title to exclude irrelevant articles and to exclude, for example, reports of health professionals using social networking as an intervention, or the use of social networking within closed support groups.
This initial sift identified 133 potential articles. These were read in full and data extracted on the identity of the studied social network, the research approach used, and a summary of results. News items on social networks were identified using individual newspaper search systems. We excluded non-English language literature. Phase 2: Describing the Characteristics of Documented Networks and How They Vary From reading the literature and discussion within the research team, we drafted a framework for characterizing the social networks identified in phase 1. We then examined and compared each social network to refine this framework (Table  1 ). We then re-examined each social network and summarized its characteristics within this framework (Table  2 ). Distinct network elements, such as blogs, discussion forums, and multimedia, were easily discernible from the homepage of networks.
Case Study 1: PatientsLikeMe Two brothers founded PatientsLikeMe in 2004 as a result of their experience supporting a close family member suffering from ALS. Their belief was that by creating a network or platform for individuals to share their experiences, patients would gain support and researchers could use contributed data to accelerate the development of treatments. PatientsLikeMe was restricted to only those with ALS until it expanded in 2011 to individuals experiencing any condition. Currently, it claims over 250,000 unique users covering over 2,000 different conditions. The user interface is of high quality. To participate within the network, users create personal profiles highlighting their health conditions and any symptoms they have been feeling. Once a profile has been created, the network automatically links users (via a chart that aggregates data) to others who are experiencing similar problems.
Site members can observe how similar or different their experience is from others with a similar health condition. 1 The aggregate data are continuously updated based on symptoms reported by users each day. Patients-LikeMe advocates for open sharing of health data for speeding up the development of treatment development. It suggests that it can play a role in emerging "patient experiments" where patients initiate studies, monitor their diseaserelated symptoms, and pool their data  (Frost, Okun, Vaughan, Heywood, & Wicks, 2011; Wicks, Vaughan, & Heywood, 2014). It claims that over 50 published research studies have used information generated through the network. Patient-sLikeMe finances its operational costs through the selling of data to its partners, which include pharmaceutical companies and medical device makers.
Two case study sites (PatientsLikeMe and Mumsnet) claimed to have been established as a response to the difficult experiences of the founders, and are based on a desire for support in their situation. These have become established sites. My Pro Ana claims to support those with anorexia, although its content (similar across other pro-eating disorder social networks) seems to promote the condition  (Haas et al. , 2011; Rouleau & von Ranson, 2011). It is relatively new and is a small, condition-specific site. On three case study sites, there is evidence of more general activity that aims to change health systems (TAC, Mumsnet, and PatientsLikeMe). The level of control by the digital platform owners over what issues are identified for campaigns and how campaigns are supported varies. PatientsLikeMe keeps complete control as they aim to change health care through selling data for research.
ABSTRACT: Analyzing the type and frequency of patientspecific mutations that give rise to Duchenne muscular dystrophy (DMD) is an invaluable tool for diagnostics, basic scientific research, trial planning, and improved clinical care. Locus-specific databases allow for the collection, organization, storage, and analysis of genetic variants of disease. Here, we describe the development and analysis of the TREAT-NMD DMD Global database (http://umd. be/TREAT_DMD/). We analyzed genetic data for 7,149 DMD mutations held within the database. A total of 5,682 large mutations were observed (80% of total mutations), of which 4,894 (86%) were deletions (1 exon or larger) and 784 (14%) were duplications (1 exon or larger). There were 1,445 small mutations (smaller than 1 exon, 20% of all mutations), of which 358 (25%) were small deletions and 132 (9%) small insertions and 199 (14%) affected the splice sites.
Point mutations totalled 756 (52% of small mutations) with 726 (50%) nonsense mutations and 30 (2%) missense mutations. Finally, 22 (0. 3%) mid-intronic mutations were observed. In addition, mutations were identified within the database that would potentially benefit from novel genetic therapies for DMD including stop codon read-through therapies (10% of total mutations) and exon skipping therapy (80% of deletions and 55% of total mutations).
, 2006; Tuffery-Giraud et al. , 2009]. Furthermore, while the location of the breakpoints in introns will differ for patients with a deletion of exon 48-50, these deletions will give rise to identical transcripts. Therefore, the skipping of certain exons would be applied to relatively large numbers of patients. Understanding the type and frequency of patient-specific mutations that give rise to DMD-associated phenotypes is an invaluable tool for genetic diagnosis, basic scientific research, and improved clinical care, potentially leading to new treatments for the disease. Currently, the TREAT-NMD DMD Global database contains over 7,000 (7,149 as of November 2013) mutations (http://umd. be/TREAT_DMD/). Locus-specific databases (LSDBs) allow for the collection, organization, storage, and analysis of genetic variants of disease.
LSDBs collect all published and unpublished mutations for a specific gene along with complete clinical and phenotypic information. Additional confidence exists in these data sets due in part to the role of experts or "curators. " Curators validate the data held within the database and significantly reduce error rates  [Béroud et al. , 2005; Cotton et al. , 2008]. In the case of LSDBs for DMD, a number of databases exist including, the Leiden muscular dystrophy pages (http://www. dmd. nl/) in the Netherlands  [Aartsma-Rus et al. , 2006] , and the UMD-DMD (http://www. umd. be/DMD/) in France  [Cotton et al. , 2008]. We here report a new global mutation database for DMD, and outline how this can be used for genetic analysis and development of genetic therapies.
Methods A new global database for DMD (TREAT-NMD DMD Global database) based on the French UMD-DMD system has been developed with TREAT-NMD collaboration. TREAT-NMD was initially established as an EU-funded "network of excellence" with the remit of "reshaping the research environment" in the neuromuscular field ([http://www. treat-nmd. eu/], 2013;  Bushby et al. , 2009). Standardized mutation (DMD mutations) specific data based on TREAT-NMD mandatory and highly encouraged items from the national TREAT-NMD DMD registries  [Bladen et al. , 2013]  were.
Results The TREAT-NMD DMD Global database currently contains 7,149 DMD mutations. There were 5,684 large mutations (80%), of which 4,894 (86%) were deletions (1 exon or larger) and 784 (14%) were duplications (1 exon or larger) (Table  1 ). There were 1,445 small mutations (20% of all mutations), of which 358 (25%) were deletions (smaller than 1 exon) and 132 (9%) were duplications (smaller than one exon); 199 (14%) splice site mutations were recorded. Point mutations totalled 756 (10% of all mutations, 52% of small mutations) with 726 nonsense mutations (10% of all mutations, 50% of small mutations) and three missense mutations (<1% of all mutations, 2% of small mutations). Finally, 22 (less than 1% of all mutations, 0. 3% of small mutations) mid-intronic mutations were observed (Table  1 ).
Large deletions Four thousand eight hundred ninety four large deletions were reported and accounted for 68% of total mutations. Figure  2a  highlights the ten most commonly reported (observed more than 100 times) large deletions within the database. The most common large deletion was a deletion of exon 45, which was recorded 316 times in the database (4% of deletions). Distribution of large deletions was nonrandom with the majority of large deletions (80%) covering either the distal region mutation hot spot of the dystrophin gene (exons 45-55) or the proximal region (exons 2-20) (Fig. 3 ).
Single exon deletions and duplications The five most frequent single exon deletions recorded in the database (all reported more than 100 times) were deletion of exon 45 (4%), 51 (3%), 44 (3%), 52 (3%), and 50 (2%). Single exon duplications occurred less frequently than single exon deletions. Duplication of exon 2 was the most frequent and was reported 50 times, while the second most frequent single exon duplication was duplication of exon 17 and was reported 11 times in the database.
Small Mutations The database contained 1,445 small lesions and included deletions (smaller than 1 exon; 355, 25%), duplications (smaller than one exon; 132, 9%), 199 (14%) splice site mutations and 756 (52%) point mutations, 726 (50%) nonsense mutations, 30 (2%) missense mutations, and 22 (0. 3%) mid-intronic mutations (Table  1 ). Small deletions and mutations ranged in size from two nucleotides (occurring 85 times) to 111 nucleotides (occurring twice).
Potential DMD therapies Nonsense stop codon read-through therapy has obtained conditional marketing authorization  [Howard et al. , 2000; Wagner et al. , 2001; Hirawat et al. , 2007; Welch et al. , 2007]. This treatment selectively induces ribosomal read-through of premature stop codons but not normal stop codons. Mutations were identified within the database that would potentially benefit from this therapy. These included 317 mutations (4% of overall mutations) with a premature TGA stop codon, 215 (3%) with a TAG stop codon, and 194 (3%) with a TAA stop codon. Exon skipping technology takes advantage of the fact that internally deleted dystrophins, often seen in BMD, can be partially functional. Mutations were identified within the database that would potentially benefit from exon skipping therapy.
CpG sites Substitutions involving a CpG dinucleotide accounted for 31% (233/756) of point mutations within the database. The CpG dinucleotide has been shown to undergo oxidative deamination of 5methyl cytosine resulting in a mutational "hot spot" and mutation rates an order of magnitude higher than normally expected  [Akalin et al. , 1994; Krawczak et al. , 1998; Flanigan et al. , 2009].
Databases Resources existed for DMD prior to the creation of the TREAT-NMD DMD Global database, the Leiden muscular dystrophy pages (http://www. dmd. nl/) in the Netherlands  [Aartsma-Rus et al. , 2006] , and the UMD-DMD (http://www. umd. be/DMD/) in France  [Cotton et al. , 2008]. For both the previously existing databases, bias of recorded mutations has been an inherent problem due in part to the method used to determine the mutation. Historically, detection of deletions and duplications was easier than detection of point mutations and other small rearrangements leading to an overrepresentation of such mutations in the literature and indeed a possible underrepresentation of point mutations and other small rearrangements. However, this bias is becoming less of an issue due to current diagnostic techniques that are widely available  [Prior and Bridgeman, 2005; Flanigan et al.
, 2009]. Also, while initially, the most commonly occurring mutations were recorded; now there is potentially a bias toward only novel mutations being recorded in the Leiden database. In addition to this, the UMD-DMD database is specific to France and could potentially include a bias for mutations observed with higher or lower frequencies only in France. The new TREAT-NMD DMD Global database houses what we believe to be the single largest cohort of verified DMD mutations in the world and was established to collect and compare and molecular mutations found within this patient group. Mutational analysis of the database illustrates the allelic heterogeneity of the DMD gene. Indeed, one-third of all DMD mutations occur de novo  [Laing, 1993].
Analysis and Comparisons Analysis of the TREAT-NMD DMD Global database revealed that large deletions were the most prevalent genetic mutation recorded and accounted for 68% of the total mutations analyzed, deletion of exon 45 being the single most common large deletion (reported 316 times). These results are similar to both the French UMD database  [Tuffery-Giraud et al. , 2009]  with 62% of the mutations being large deletions and the Leiden database  [Aartsma-Rus et al. , 2006] , where 72% of the mutations are large deletions. In the Leiden database and the TREAT-NMD DMD Global databases, deletion of exon 45 was the most common deletion, making up 4% of the mutations in the TREAT-NMD DMD Global database and 2% of the Leiden database. Large duplications accounted for 11% of mutations in the TREAT-NMD DMD Global database compared to 13% in the French UMD database and 8% in the Leiden database.
The most commonly occurring large duplication was duplication of exon 2 in all three databases. Small rearrangements accounted for 20% of the TREAT-NMD DMD Global database that was similar to the French UMD database (26%) and the Leiden database (20%). Point mutations and nonsense mutation were the most prevalent small rearrangements with nonsense mutations accounting for 50% of the small rearrangements in the TREAT-NMD DMD Global database, compared to 40% in the French UMD database and 50% in the Leiden database. Large deletions and duplications follow a nonrandom distribution with 78% of them including either the proximal or distal mutation hot spots  [Koenig et al. , 1989; Prior and Bridgeman, 2005; Aartsma-Rus et al. , 2006].