dataesr/scientific-paragraphs-categorization huggingface dataset:
VWF itself is a well-established endothelium-expressed gene (for a review see  (Lenting et al. , 2015) ) and involvement of VWF in modulating angiogenesis has been recently proposed  (Starke et al. , 2011). Using a classical gene-by-gene analysis of our transcriptomic data, ablation of neither one of the three CCM genes lead to VWF mRNA differential expression. GSEA analysis applied to the CCM data did not find any significant gene set in all three CCM invalidations using the MSigDB GO gene set database or the MSigDB curated gene set database (data not shown). Note also that the above mentioned three main biological processes present in the neighborhood of VWF did not achieve significant enrichment under any of the CCM invalidations according to GSEA. A targeted single enrichment test using a single manually compiled list of 56 known VWFrelated genes, however, detected significant deregulation of VWFrelated genes (hypergeometric test P-value ¼ 5 Ã 10 À5 ; for the list of 56 genes see file Supplement 4).
Discussion Despite continuous advances in omics data analysis methods, the interpretation of genome-wide measurements is still difficult in the face of a missing framework of knowledge in which the observed changes can be interpreted. As noted previously, single-gene level interpretation of transcriptomic datasets has a number of major limitations  (Subramanian et al. , 2005) : Reduced sensitivity due to multiple testing, lacking power to detect slight, but concerted changes within molecular pathways, and poor reproducibility of biological results. To overcome these limitations, methods such as GSEA focus on predefined biological pathways instead of single genes, which is thought to increase detection sensitivity and allow easier interpretation of results. Limiting the analysis to predefined pathways, however, precludes detection of novel, previously unknown functions.
Network-based models combine increased sensitivity with the possibility to discover novel functions. The interpretation of subnetworks returned by current network-based models, however, poses a number of challenges: Firstly, validating the involvement of a detected subnetwork in the mechanisms underlying a studied disease is rarely straightforward. Experimental validation typically requires experimental observation or yet better activation/suppression of its activity. Such validation is feasible for single genes and thus also for mechanistically well-understood biological pathways by using a known upstream gene as a proxy. For predicted subnetworks, however, selecting a proxy gene is not straightforward, given the large size of many such predictions (e. g. the 82 or more genes returned by jActiveModules on the CCM data).
The LEAN approach As other subnetwork-based models, and in contrast to classical gene set enrichment analysis, LEAN combines the ability to detect activity of previously defined pathways with the potential to find active local subnetworks representing biological functions not contained in pathway databases. Activity of known pathways can be detected by LEAN since functional interaction networks-especially when restricted to high-confidence interactions-represent similar knowledge as pathway databases, albeit in a different form. In contrast to other subnetwork-based models, however, the simple LEAN subnetwork model offers the center gene as an evident starting point for further exploration of statistical subnetwork dysregulation. Importantly for practical applications, the simplified local subnetwork model allows the computation of statistically valid P-values for all possible local subnetworks in a short time.
The application of LEAN to different public datasets and to transcriptome changes measured in the context of the genetic disease CCM indicate that, despite the simplification of the local subnetwork model, LEAN is capable of detecting previously described pathways within the functional interaction network. One reason for this may be that STRING represents a previously described pathway by adding evidence of functional interaction to all possible pairs of proteins contained therein. The diversity of evidence represented in functional interaction databases poses a challenge for any subnetwork method. With rapidly advancing technology, the number of known functional interactions across multiple contexts increases, making it unlikely that all known functional interactions of any given protein are involved in the interpretation of any given set of experimental data.
Dependence on pathway compactness The local subnetwork model is clearly a very restricted representation of biological pathways. This fact, however, does not appear to restrict LEAN's capability to detect important biological signals. LEAN outperformed a reference method using the common unconstrained subnetwork model on simulated pathways which had a significantly larger average graph radius (2. 68, with SEM of 0. 078) than the radius of 1 of the local subnetwork model. As expected, we detected a negative correlation between graph radius and LEAN performance in a more detailed subnetwork simulation study (see Supplementary Fig. S1 ). LEAN did, however, still perform well (average AUC of 0. 8) on simulated subnetworks with a radius of 4. The results obtained on publicly available datasets equally indicate that LEAN is capable of detecting biologically plausible pathways and we have no reasons to assume that these pathways represent subnetworks with exceedingly small network radii.
LEAN results on CCM data LEAN analysis, applied to CCM transcriptomic data, detected two consistently deregulated local subnetworks in all three CCM groups. One of these local subnetworks was centered on VWF, which was not shown to be deregulated itself at the transcription level using gene-by-gene analysis. The hypothesis of a higher-level dysfunction of the subnetwork around VWF was validated in vivo in our CCM mouse model. Indeed, while a punctate VWF staining was detected in cerebral and retinal vessels from control animals as well as in normal vessels of CCM2-ablated animals, an accumulation of ultralarge VWF (UL-VWF) multimers was observed on the surface of iCCM2 lesions. Observation of such ultra-large vWF multimers in vivo is very unusual since they are proteolyzed very rapidly in normal conditions.
Applicability to other types of genomic data Here, we studied the application of LEAN to transcriptomic data, but we believe that LEAN could equally well be applied to genomic measurements of other biological states, as we see no reason to believe that the local subnetwork model represents an important facet of pathway activity exclusively on the transcriptomic level. Interesting application scenarios would include levels of genetic variants (identification of mutational 'network hotspots'), proteomics ('hotspots of proteomic activity') or integrated datasets comprising multiple data sources. We note that LEAN also applies to incomplete data, as typical in the case of shotgun proteomics: Network nodes without information can just be ignored in the computation of the LEAN statistics.
Acknowledgments We would like to thank the  'IGBMC Microarray and Sequencing platform' in Strasbourg  for carrying out sample hybridization to and intensity read-outs of the microarrays. We are grateful to  Coralie De Luca  for excellent technical help. We furthermore would like to thank  Robin Friedman  for pointing out the relationship of LEAN with gene set enrichment scoring. We deeply thank  Agnes Veyradier  and  Peter Lenting  for VWF-related helpful discussions.
Funding This work was supported by the  European consortium ERA-net NEURON ; supported the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  ( ANR-10-LABX-62-IBEID ), the  French CCM patients association 'ACC', Institut national de la santeet de la recherche medicale (INSERM)  and  Paris Diderot University-Paris 7. Oriol Guitart-Pla  was supported by the  National Institute of General Medical Sciences (NIGMS)  under grant  P41 GM103504.
The use of appropriate growth standards/references is of significant clinical importance in assessing the height of children with short stature as it may determine eligibility for appropriate therapy. The aim of this study was to determine the impact of using World Health Organization (WHO) instead of national growth standards/references on height assessment in short children. Data were collected from routine clinical practice (1998-2014) from nine European countries that have available national growth references and were enrolled in NordiNet 1 International Outcome Study (IOS) (NCT00960128), a large-scale, non-interventional, multinational study. The patient cohort consisted of 5996 short pediatric patients diagnosed with growth hormone deficiency (GHD), Turner syndrome (TS) or born small for gestational age (SGA).
The WHO growth standards provide lower cut-offs for short stature than many of these references, which are generally newer. As a consequence, the assumption that universal WHO growth standards can be used globally to accurately screen for linear growth disorders is challenged. Moreover, the WHO growth references for children aged 5-19 years are based on re-analyzed data from the USA between 1960 and 1980, and may also provide lower cut-offs due to the age of the data and because children in the USA historically have been shorter than their European counterparts. The aim of the present analysis was therefore to compare potential differences between the WHO growth standards/references and available national growth references in the Czech Republic, Denmark, France, Germany, Netherlands, Norway, Sweden, Switzerland and the UK on height assessment in children with short stature enrolled in NordiNet 1 International Outcome Study (IOS).
NordiNet 1 IOS (NCT00960128) is a large-scale, non-interventional, observational study designed to gather long-term data on the safety and effectiveness of Norditropin 1 (recombinant human growth hormone [GH], somatropin; Novo Nordisk A/S, Denmark) as prescribed by treating physicians in everyday clinical practice, and to provide insight into the diseases of the specific endocrine patient populations treated with GH  [21]. The implications of using various references and standards in clinical contexts are discussed.
Ethics statement NordiNet 1 IOS is conducted in accordance with the Declaration of Helsinki; all patients provide written informed consent for data collection. All data collected within NordiNet 1 IOS are anonymized. Approval of the local ethics committee or institutional review board at the individual center level in accordance with country-specific rules is a prerequisite for each center's inclusion in NordiNet 1 IOS. The data are completely anonymized prior to access for analysis. It is mandatory for the parents or guardians of the pediatric patients to provide written informed consent for the minors' data to be collected.
Statistical analysis Data are presented as means with standard deviation (SD) and percentages. Height standard deviation scores (SDS) at GH treatment start were calculated using the most recent national growth chart references (Czech Republic  [23] ; Denmark  [6] ; France  [9] ; Germany  [24] ; Netherlands  [7] ; Norway  [25] ; Sweden  [26] ; Switzerland  [27] ; UK  [28]  and WHO growth standards  [10]  or WHO references  [12] ). The proportions of children aged !5 and <5 years with heights below clinical cut-off values (height below -2 SDS for GHD and TS; -2. 5 SDS for children born SGA) were calculated on country level based on national growth references and WHO growth standards/references. The difference in the proportion of patients classified with short stature (sensitivity) according to national or WHO growth standards/references was calculated by the following equation: difference in sensitivity ¼ n n À n w N where n n = number of children with height below clinical cut-off values (national reference); n w = number of children with height below clinical cut-off values (WHO reference); N = total.
The equality of the proportions classified with short stature according to national or WHO growth references was evaluated at country level using the McNemar test for the GHD, TS and SGA population of children aged !5 years. Due to the low number of patients aged <5 years in most of the countries, the McNemar test could not be applied at country level for all countries in this population. Hence, the McNemar test was also performed on pooled data of the countries that had an overall higher short stature classification rate based on the national growth references than with the WHO growth standards. For consistency a pooled analysis on patients aged !5 years was also applied.
Children aged <5 years As WHO growth standards are designed for use in children aged <5 years, this age group was analyzed for differences in height SDS between national growth charts and WHO growth standards. Despite low numbers of children aged <5 years in some countries, a similar pattern of proportionally more patients with height SDS below -2 (or -2. 5 for SGA) was observed using national growth references compared with WHO growth standards across all indications, except in France and the UK (Table  3 ). When pooling data from the seven countries with higher classification rates with national references vs. WHO growth standards, a significantly greater proportion of children with GHD had height SDS below -2 with national references (8%; P<0. 0001). Among the children aged <5 years with TS, analysis of pooled data from the seven selected countries with higher classification rates revealed a significantly greater proportion of children with height below -2 SDS according to national growth references than with WHO growth standards (12%; P = 0.
NordiNet1 IOS is funded by  Novo Nordisk Healthcare AG, Zurich, Switzerland. Novo Nordisk  is responsible for conducting the study and performing the data analysis. The decision to publish, results' interpretation and manuscript development is the outcome of  Novo Nordisk collaboration  with the members of the  International Study Committee (ISC)  who are co-authoring this manuscript. The NordiNet1 IOS ISC acts as the custodian of the database and provides governance with respect to the data analyses and the publication plan of the study. Writing and editorial assistance from  Watermeadow Medical  was funded by  Novo Nordisk Healthcare AG, Zurich ,  Switzerland. Henrik Thybo Christesen  has received honoraria from  Novo Nordisk. Birgitte Tønnes Pedersen  and  Effie Pournara  are employees of  Novo Nordisk.
Germany: Regional Courts -Infringement We collected data on infringement actions directly from the three regional courts that deal with the majority of patent infringement cases in Germany: the Regional Courts of Düsseldorf, Mannheim, and Munich I. We obtained detailed information on proceedings filed during the time period 2000 to 2008. This provides us with a nine-year window but also minimizes the number of cases that were still pending during the data collection. 30 he information extracted for each case concerns procedural aspects, the identity of the litigants and their legal representatives, and the patents at issue. The data include information on the names and addresses of the plaintiffs and defendants, which allowed us to match corporate litigants to firm-level databases, including Bureau van Dijk's ORBIS, Compustat and THOMSON One.
U. K. : PHC and PCC/IPEC -Infringement and Invalidity For the U. K. , we have detailed data on the population of cases at the PHC, and the PCC/IPEC. Data for the PHC are available for the period 2000-2013, whereas no IPEC court records are available for cases filed prior to 2007. 32 The PHC and IPEC data for the period 2007-2013 were collected directly from physical PHC and IPEC court records/files in 2014 and 2015. 33 The PHC data for the earlier 2000-2006 period were collected from a range of online sources. 34 The U. K. court records contain detailed information on both infringement claims and invalidity counterclaims, as well as case outcomes. As for the German data, we matched the names of the parties involved to firm-level databases (Bureau van Dijk's FAME, Amadeus, and Compustat) and retrieved detailed information from Patstat on the litigated patents.
EPO and DPMA -Opposition We have data on any prior or parallel opposition of the patents involved in an infringement action. For DE patents we have information on the opposition proceeding, i. e. the opposition's filing and end dates as well as outcome, from the register of the DPMA. We constructed data on oppositions at the EPO based on legal status information from Patstat covering 1981 to 2012. In contrast to the data from the DPMA, the data for oppositions at the EPO have information on the identity of the opponent, that is, the party filing the opposition. We added information on any opposition to the patents involved in an infringement proceeding to identify parallel invalidity proceedings in form of oppositions and to construct each patent's history of validity challenges.
Germany The patent litigation actions collected at the Regional Courts of Mannheim, Düsseldorf, and Munich cover around 90% of all patent infringement cases during the period 2000 to 2008. 35 We drop cases from our dataset that involve a patent, but that are not  32  Estimates based on anonymized data for 2005/06 suggest that cases at the PCC accounted for only around 20% of all patent cases in the U. K. during the 2000-2006 period (Cremers et al. , 2016). Also, case counts at the PCC were very low in absolute terms compared to the period 2007-2013 for which we have data. 33 The data collection and assembly is described in detail in Helmers et al. (2015). 34 To ensure completeness of our records, we relied on the Court Diary which lists all cases scheduled for a hearing or application, including a case management conference which is usually the first step in the litigation process.
The data collection is described in detail in Helmers and McDonagh (2012). 35 We estimate that roughly half of the remaining 10% of cases are spread over the other nine regional courts. However, these courts are of minor importance and reputation. The possibility to choose a litigation venue might create incentives for forum shopping (Gaessler, 2016). In our setting forum shopping directly concerned with infringement. 36 We also drop cases involving utility models because the bifurcation principle only applies to invention patents. Furthermore, to avoid misinterpretation of case outcomes, we also remove a small number of negative declaratory actions and cases where the court decides only on issues regarding the enforcement of a previous judgment (e. g. the amount of damages granted). The resulting sample contains 3,279 patent infringement cases.
U. K. For the U. K. , our data cover the population of patent cases between 2007 and 2013. For 2000-2006 we estimate that our data contain around 80% of disputes. As in the German data, we drop cases that are not directly concerned with infringement as well as those on negative declaratory actions. We also drop cases that are withdrawn or settled within 90 days counting from the filing date of the claim form as these cases are not relevant for our analysis. 39 The resulting sample contains 299 patent infringement cases involving 377 different patents. As several actions are filed on the basis of more than one patent, our sample contains 461 patent-case observations.
In contrast, there is no evidence that the size of the plaintiff in the infringement proceedings, that is the patent holder, plays any role in the decision to challenge the patent's validity. This supports the view (and Prediction 2 of our model) that the decision not to file a parallel action at the BPatG is at least partly determined by resource constraints on the alleged infringer's side. Next, we turn to our cross-jurisdictional comparison of the incidence of a separate invalidity action at the BPatG in Germany with that of an invalidity counterclaim in the U. K. Columns 1 and 2 in Table  4  show the results for Germany and the U. K. individually (the specification differs slightly form that used in Table  3  because a number of variables are not available in the U. K. dataset). 44 The estimates for the U.
If several companies from the same business group appeared as co-plaintiffs or co-defendants, we allocated the entire business group into the size category of its largest member. -Residence: we identified a company's origin using information available in the court records, Bureau van Dijk's FAME and Amadeus databases, as well as web searchers. We then allocated companies into three categories: domestic (Germany/U. K. ), Europe, and rest of the world. -Top legal representative: For Germany, we identified top law firms in patent litigation according to a ranking of leading law firms published in 2009 (Top 50 law firms in patent litigation and patent applications in Germany) by the legal professional journal JUVE Rechtsmarkt. For the U. K. we relied on the ranking published by Chambers and Partners, so-called Band 1 and Band 2 law firms in London and outside London.
-Non-practicing entity (NPE): we identified NPEs in the German and U. K. data by identifying the patent holder in each case and we then determined manually, using web searches, news reports, court filings, and the existing academic literature on NPEs and PAEs whether a patent holder was an NPE at the time of the court case. For more details see Love et al. (2016). -Number of parties: we counted the number of independent parties that are accused of infringement. -Multinational group: we created a binary variable that indicated whether alleged infringers with different residences belong to the same multinational group.
Cason et al. (2012) consider a behavioral spillover to exist when observed behavior in a game is di¤erent depending on whether that game is played together with other games or in isolation and acknowledge that learning e¤ects can be a source of such spillovers. 4 th the objective of analyzing such learning e¤ects, we study data from 403 penny auctions conducted by a telecommunications operator between June and December 2011. 5 Our database of-fers distinct advantages to study the role of learning through participation in simultaneous auctions. The telecommunications operator auctions o¤ essentially …ve types of items: mobile phones, tablet PCs, laptops, mobile services (e. g. , games, etc. ) and USB pens for mobile internet access. Whilst certainly di¤erentiated within each type, these items belong to a relatively homogeneous category of products: devices and/or services which may enhance users'bene…ts from their current mobile subscriptions.
In that sense, these auctions may attract a relatively homogeneous set of potential bidders and this appears to be adequate to study learning e¤ects. By comparison, Swoopo or other major penny auction websites sell a wider variety of items to certainly more heterogeneous bidders. We …nd that the telecommunications operator has an average pro…t margin of 219%: it obtains an average revenue of e1,224 per auctioned item, whose average retail price is e370. There is signi…cant dispersion in pro…t margins (including large negative pro…t margins), and its distribution exhibits positive skewness and is, thus, right-tailed. Our detailed bid-level dataset allows us to investigate drivers of bidding behavior. First, we analyze the decision to participate in a penny auction and …nd that the probability of participation by an individual bidder is decreasing with the number of simultaneous auctions and bidder experience -evidence of both vertical and horizontal learning e¤ects.