dataesr/scientific-paragraphs-categorization huggingface dataset:
The ANSI Smalltalk Standard The ANSI standard defines some elements of the Smalltalk language  [1]. It gives the definition "String literals define objects that represent sequences of characters. " However, there are few guidelines helpful with designing a string API. The ANSI standard defines the readableString protocol as conforming to the magnitude protocol (which supports the comparison of entities) and to the sequencedReadableCollection protocol, as shown in Figure  2   SequencedReadableCollection. The sequencedReadableCollection protocol conforms to the collection protocol; it provides behavior for reading an ordered collection of objects whose elements can be accessed using external integer keys between one and the number of elements in the collection. It specifies that the compiler should support the following messages -we add some of the argument names for clarity: Concatenation: , tail (the comma binary message).
To identify the idioms and smells, we rely mostly on code reading and the usual tools provided by the Smalltalk environment. This is necessary in the discovery stage, but it raised several questions: • How to document groups of methods that participate in a given idiom? As we say in Section 2, method protocols are not suitable: they partition methods by feature or theme, but idioms are overlapping patterns of code factorization and object interaction. • How to specify, check and enforce idioms in the code? This is related to architecture conformance techniques  [7]. Our goal is not to provide definitive solutions or recommendations, but rather to provide a starting point for discussion around the complexity of the design space, and towards more understandable, reusable, and robust APIs.
Conversion between strings A different set of conversion operations occurs between strings themselves. • typography and natural language: asLowercase, asUppercase, capitalized, asCamelCase, withFirstCharacterDownshifted, asPluralBasedOn:, translated, translatedIfCorresponds, translatedTo: • content formatting: asHTMLString, asHex, asSmalltalkComment, asUncommentedSmalltalkCode, • internal representation:.
Genome-wide association studies (GWAS) have successfully identified a number of common singlenucleotide polymorphisms (SNPs) influencing glioma risk. While these SNPs only explain a small proportion of the genetic risk it is unclear how much is left to be detected by other, yet to be identified, common SNPs. Therefore, we applied Genome-Wide Complex Trait Analysis (GCTA) to three GWAS datasets totalling 3,373 cases and 4,571 controls and performed a meta-analysis to estimate the heritability of glioma. Our results identify heritability estimates of 25% (95% CI: 20-31%, P = 1. 15 × 10 -17 ) for all forms of glioma -26% (95% CI: 17-35%, P = 1. 05 × 10 -8 ) for glioblastoma multiforme (GBM) and 25% (95% CI: 17-32%, P = 1. 26 × 10 -10 ) for non-GBM tumors. This is a substantial increase from the genetic variance identified by the currently identified GWAS risk loci (~6% of common heritability), indicating that most of the heritable risk attributable to common genetic variants remains to be identified.
Statistical analysis. Consistent with our previous analysis 9 , quantile-quantile (Q-Q) plots for the German and US series showed some evidence of inflation (inflation factor λ = 1. 15 and 1. 11, respectively, based on the 90% least significant SNPs), however after correcting for population substructure using the top 10 principal-components in Eigenstrat, λ for all studies was ≤ 1. 04 (Supplementary Figure  1 ). Therefore all heritability estimates were calculated adjusting for 10 principal components. Artefactual differences in allele frequencies between cases and controls can contribute to the estimation of spurious genetic variation therefore for the current analysis we imposed a number of additional quality control measures to the dataset as advocated by Lee et al. when estimating heritability  23.
Using PLINK software  24  we excluded SNPs in cases and controls that had a minor allele frequency (MAF) < 0. 01 or a HWE test with P < 0. 05 (per-study exclusions = 60,932-62,065). Performing a differential missingness test between cases and controls we excluded SNPs with P < 0. 05 (per-study exclusions = 25,002-60,235). In addition we excluded individuals having a relatedness score of > 0. 05. rs2736100 exhibited a HWE P-value of 0. 01 in the German dataset but was still included in order to estimate the heritability attributable to known loci. This filtering resulted in the use of 263,905 SNPs common to the three case-control series (Supplementary Table  2 ). A total of 93 samples in the French series, 137 samples in the German series and 78 samples in the USA series were removed during quality-control steps for reasons including a failure to genotype, unknown duplicates, and closely related individuals, leaving 3,373 cases and 4,571 controls for heritability analysis.
We calculated Cochran's Q statistic to test for heterogeneity and the I 2 statistic to quantify the proportion of the total variation that was caused by heterogeneity  32. GWAS array SNPs were functionally annotated using SeattleSeq Annotation 138  33  , making use of genomic evolutionary rate profiling (GERP)  34  conservation metrics and combined annotation dependent depletion (CADD)  35  scores. Conserved transcription factor bindings sites were predicted by the 'tfb-sCons' track from the UCSC Genome Browser  36  , which searches for conserved elements in TransFac Matrix Database v4 after alignment in Human/Rat/Mouse.
Impact of known risk loci on variance. To determine the additive impact of the known GWAS risk loci on the heritability of glioma we estimated heritability by including the risk SNP as a covariate. The total estimated heritability of glioma explained by the seven risk loci is 1. 6% (± 2. 6%), 1. 3% (± 3. 9%) and 1. 7% (± 3. 3%) for all glioma, GBM and non-GBM tumors respectively (Table  3 ). These estimates are substantially lower than the genetic variance associated captured by all the SNPs on the array. These data therefore suggest a large proportion of the heritability in glioma remains unaccounted for by currently identified risk SNPs.
e. confounding) between genetic and environmental risk factors. A strength of our study is that we were able to make use of data from three independent GWAS to derive combined heritability estimates, therefore minimising the possibility of biases in individual studies affecting study findings. Additionally we endeavoured to protect against differences in population structure between cases and controls affecting estimates by adjusting for 10 principal components in each of the three GWAS datasets. In the German dataset the GBM heritability estimate was significantly larger than in the other datasets hence we cannot entirely exclude the possibility of residual population structure differences affecting estimates. Previous estimates of the heritability for glioma from segregation analysis, which have been based on analyses of pedigrees of 297 and 639 glioma probands have reported values of 0.
2-1. 4), collectively they underscore less than 5% of the entire genetic variance in risk of all forms of glioma. It is, therefore, likely that additional common low risk variants remain to be discovered and should be eminently harvestable in new larger GWAS or through further pooling of additional existing datasets. However, most novel risk variants yet to be discovered are likely to have more modest effect on glioma risk than those which have been so far discovered. It is possible that some disease-causing variants which are very rare have a substantive effect on glioma risk but there is no reason to believe that much of the genetic variance is solely explained by a restricted number of high-risk mutations. Additional ongoing GWAS are therefore likely to be informative in refining estimates of heritability.
In this study we did however seek to address the impact of genetic variation on risk of glioma subtype by analysis of GBM and non-GBM glioma. A combined estimate of the genetic correlation between GBM and non-GBM in our datasets was 0. 87, albeit with wide confidence limits (± 0. 22), suggesting that a significant complement of the heritable risk of glioma may be generic. In summary, we report the first study to show that a large proportion of the heritability of developing glioma can be ascribed to common genetic variation. Moreover, it is the first to show biologically and unequivocally that the risk of glioma is highly polygenic. Our findings imply that very large sample sizes will be needed to detect novel loci with genome-wide significance and that the majority of additive genetic variation for glioma is not explained by rare variants that are not in LD with common SNPs.
Acknowledgements We are grateful to all the patients and individuals for their participation and we would also like to thank the clinicians and other hospital staff, cancer registries and study staff in respective centers who contributed to the blood sample and data collection. MD Anderson acknowledges the work on the  USA GWA study of Phyllis Adatto ,  Fabian Morice ,  Hui Zhang ,  Victor Levin , Alfred W. K. Yung ,  Mark Gilbert ,  Raymond Sawaya ,  Vinay Puduvalli ,  Charles Conrad ,  Fredrick Lang  and  Jeffrey Weinberg  from the  Brain and Spine Center. For the French study , we are indebted to  A. Rahimian  (Onconeurotek),  A. M. Lekieffre  and  M Brandel  for help in collecting data, and  Y Marie  for database support. For the German GWA study, we are indebted to  B. Harzheim  ( Bonn ) and  Dr A.
Sampling design: NC was first stratified in 5 areas [Noumea has 39. 7% inhabitants of NC, the greater Noumea area (26. 9%), East Coast (12. 9%), West Coast (13. 4%) and Loyalty Islands (7. 1%)]. At the first stage, a randomized selection of medical centers and GPs was made. At the second stage, eligible individuals were included in the study by systematic sampling. Data on socio-demographic characteristics and history of travel were collected using a standardized questionnaire. A blood sample was collected by venous puncture at the end of the interview. For children under the age of 10y/o, antigenic detection was performed only on blood samples collected for other reasons. After collection, samples were stored and then shipped at -20°C to the Louis Malarde Institute in Tahiti (FP) where they were stored at -20°C until processing.
Findings A total of 1035 individuals were included in the study. Melanesians were over-represented (48. 3%) and Europeans underrepresented (22. 9%) in the sample compared to the general population (p < 0. 0001). The overall LF antigenic prevalence (after considering the sample plan) was 0. 62% (CI 95% [0. 60-0. 63]). The ICT filariasis test was positive for 7 patients. Prevalence of antigenemia in the different NC areas and locations of the ICT positive patients is shown in Figure  1. For the 7 ICT positive, all the data recorded through the medical questionnaire and their laboratory data are given in Table  1. The median age of those patients was 35 y/o (ranging from 28 to 64 y/o), the sex ratio (F/M) was 1. 33, 5 lived in Noumea, 1 in Belep Islands and 1 in a village on the West Coast. Two were Melanesians and five were Europeans, none of them were from the Tahitian population living in NC.
Acknowledgements We are grateful to all the hospitals, medical centers, GPs and people who participated in this study. We thank  Dr Xavier Deparis  and the team of the  French Army's Epidemiology and Public Health Center  for their assistance during the finalization of the protocol, and the  Institute Pasteur of New Caledonia  and the  Koumac Hospital  for their microscopic filarial detection. We also thank  Dr Michèle John  for her proofreading and wise remarks and  Laurent Guillaumot  for his valuable advices about entomology. Finally, we thank  Mrs Sheryl Mellor  from the  Secretariat of the Pacific Community  for her help in English translation.
In IPSec, the security of communication links is defined in terms of security associations (SAs) that should be established between two peers. In fact, a SA allows the establishment of an agreement (between peers) to define all security parameters that should be respected during next exchanges. In addition, IPSec used IKEv2 protocol [5] in order to ensure a dynamic management of security associations. It is based on two databases: Security Association Database (SAD) and Security Policy Database (SPD), to store all security associations and policies for each device and it also proposes four pairs of messages to negotiate security association between peers. These requirements make a big challenge to implement the IKEv2 on constrained environments by considering energy and bandwidth limitation.
To store all security associations and policies, two databases Security Association Database (SAD) and Security Policy Database (SPD) have to be added to each device (see Figure  6 ). In IKEv2, all communications consist of pairs of messages which are called "request/response pairs. " To maintain a security association, two phases are required by IKEv2. Phase 1 in Figure  4  performs mutual authentication between two parts and establishes an IKE SA. At this stage, a secret is shared between peers, which is used for further IKEv2 exchanges to perform encryption and integrity protection. They will agree with each other on the following parameters of their IKE SA: (ii) SKEYSEED: it is the secret keys from which all keys are derived for IKE SA (SKe: encryption key to ensure confidentiality, SKa: authentication key to ensure integrity, and SKd: derivation key master secret to compute further CHILD SAs keys); (iii) IKE SPI standing for IKE Security Parameter Index and uniquely identifying an IKE SA; (iv) lifetime: duration of IKE SAs; (v) nonce: they are INITIATOR nonce (Ni) and RESPONDER nonce (Nr).
Communication Costs. First of all, we determined the communication energy cost at one constrained node (initiator) in order to bring out the difference between IKEv2 and CKES protocols on one node type which presents 40% of the network. The energy model is based on the reception and the transmission costs to compute the communication energy during the IKE INIT and IKE AUTH steps. Table  5  shows the communication energy costs of IKEv2, the proposed CKES, and the sent and received bytes. The energy communication cost is reduced by 22 percent with the use of CKES. These results show the efficiency of our CKES system especially at the constrained node level. Secondly, we compared the energy consumed for the data communication at network level. Thus, it is important to take into consideration the communication cost added to route packets from the initiator to the responder.
Conclusion and Future Work This paper has presented a Cooperative Key Exchange System (CKES) based on the concept of CRT. The proposed approach is an adaptation of the IKEv2 in IP based WSN. We have modified it in order to provide more balanced energy consumption and longer lifetime comparing to a basic IKEv2 implementation. We have presented the details of the design and implementation of CKES in NS2. We have compared this with the IKE and implemented the main functionalities that can be used in WSNs. The improvement of key exchange system in WSNs which we have proposed with the help of this new module can offer a better lifetime of network. Our future work would explore the possibility to add a trust management system which could play an important role to make decisions in the collaborative system.
Background. Most genetic disorders are caused by single nucleotide variations (SNVs) or small insertion/deletions (indels). High throughput sequencing has broadened the catalogue of human variation, including common polymorphisms, rare variations or disease causing mutations. However, identifying one variation among hundreds or thousands of others is still a complex task for biologists, geneticists and clinicians. Results. We have developed VaRank, a command-line tool for the ranking of genetic variants detected by high-throughput sequencing. VaRank scores and prioritizes variants annotated either by Alamut Batch or SnpEff. A barcode allows users to quickly view the presence/absence of variants (with homozygote/heterozygote status) in analyzed samples. VaRank supports the commonly used VCF input format for variants analysis thus allowing it to be easily integrated into NGS bioinformatics analysis pipelines.
, 2014)  or interaction data  (Smedley et al. , 2014)  to enhance the analysis. However, molecular biologists still require simple tools in the variant filtering and ranking process to identify causal mutations among a large pool of rare variants existing in each individual. Here we propose a new simple and powerful tool named VaRank (http://www. lbgi. fr/VaRank) for human variant ranking which provides a comprehensive workflow for annotating and ranking SNVs and indels. Four modules create the strength of this workflow (Fig. 2 ): (i) Data integration with variant call quality summary, to filter out false positive calls, depending on the sequencing technology and the analysis pipeline; (ii) Variant annotation to integrate genetic and predictive information (functional impact, putative effects in protein coding regions, population frequency, phenotypic features.
IMPLEMENTATION VaRank is written in Tcl/Tk and runs on all Unix platforms with a standard Tcl/Tk 8. 5 installation and one of the compatible annotation engines (Alamut Batch, a commercial software developed by Interactive Biosoftware, Rouen, France, or SnpEff  (Cingolani et al. , 2012) ). PolyPhen-2  (Adzhubei et al. , 2010)  can also be installed locally and results can be automatically integrated into VaRank.
Input and output To run VaRank, the user specifies the input files and chosen options with a single command. In case of wrong commands, VaRank will print a description of the options and defaults settings. VaRank reads SNV and indel variant descriptions from a VCF file (variant call format  Danecek et al. , 2011) , the reference format for genomic variants. The VCF file can be gzip compressed. The program can take any combination of a single VCF file with multiple patients and/or multiple VCF files with single patient's data as input (Fig. 2 ). Each variant is checked for consistency (genotype, depth of coverage, variant call especially for indels) and VaRank prints warnings if appropriate (i. e. , patients redundancy based on the sample identifiers in the VCFs), information from each analysis step (including status and running time), as well as statistics on the submitted data such as total number of variants and number of patients.
Each of these output files is also available in two versions: one contains all submitted variants ("AllVariants" files) while the other one is prefiltered ("filteredVariants" files). The default filters remove variants: (i) called with a read depth <= 10, (ii) with a supporting read count <= 10, (iii) with a ratio of supporting reads <= 15%, (iv) with a validated status annotation in the dbSNP  (Sherry et al. , 2001)  database (based on at least two supporting evidences) that are not pathogenic (based on the ClinicalSignificance field), and (v) with an allele frequency >1% (extracted from the dbSNP database or the Exome Variant Server). All these parameters can be modified by the user. Finally, a short report of counts (homozygous, heterozygous and total counts) for each of the variant categories (5 ′ and 3 ′ UTR, upstream, downstream, frameshift, in-frame, nonsense, splice site, start loss, stop loss, missense, synonymous, intronic, not annotated) is generated for each sample and for the whole submitted dataset.
Variant annotation The annotation of variants is performed by the annotation engine (Fig. 2 ). It is composed of several parts including the main annotation software, which can be either Alamut Batch or SnpEff, the barcode annotation (see Fig. 3  and corresponding section) and optionally PolyPhen-2 predictions. As an example, Alamut Batch collects among others (Table  2 ): the gene symbol, the OMIM ID, the transcript ID (i. e. , RefSeq), the protein IDs (RefSeq and UniProt), the HGVS nomenclature (genomic, cDNA and proteic), information from public variation databases such as dbSNP and the Exome Variant Server Samples that are homozygous for the reference allele are represented using "0," heterozygous variants are represented using "1" and homozygous variants are represented with "2. " (B) Selected annotations from the VaRank output representing 3 variants from a single patient.
When available, known mutations are highlighted by extracting either reported SNVs/indels flagged as "probably-pathogenic"/"pathogenic" in the field "Clinical significance" introduced since dbSNP134 or using the HGMD database  (Stenson et al. , 2014). The choice of transcript is a critical task that can lead to misannotation  (McCarthy et al. , 2014). To avoid underestimating variant effects, they are annotated on all transcripts available (i. e. , one variant can be either intronic or exonic depending on the isoform) and the most pathogenic effect is retained. Given that VaRank is compatible with 2 annotation software there are small differences. Using Alamut Batch, each variant is scored for each transcript. By default we report the longest transcript for each gene except if any variation is more pathogenic in another transcript.
In addition when using Alamut Batch, each variant is assessed for any potential effect on the nearest splice site. Following the guidelines from  Houdayer et al. (2012)  and our own tests (data not shown), we selected three assessment programs: MaxEntScan  (Yeo & Burge, 2004) , NNSplice  (Reese et al. , 1997)  and Splice Site Finder (based on  Shapiro & Senapathy, 1987). A variant is considered to affect splicing when at least two out of the three programs indicate a significant score change between the wild type and the mutated sequences (respectively -10%, -5% and -15%). A VaRank score is then attributed depending on the following three splice site categories (score into parenthesis): (i) essential splice site: two.
Barcode Comparing variations of several individuals, related and/or unrelated, affected or not, has proven to be a very effective strategy for distinguishing polymorphisms from variants causing or increasing the likelihood of disease  (Ng et al. , 2010; Ng et al. , 2009). In order to take advantage of this, VaRank introduces a barcode that allows a quick overview of the presence/absence status of each variant within all samples and their zygosity status ("0" representing homozygous wild type, "1" heterozygous and "2" homozygous for the variant) (Fig. 3A ). In Fig. 3B , three variants are reported for one specific patient out of a cohort of 32 samples analyzed together. For example, the third variation c. 601G>A in TTC21B is heterozygous for this patient. In light of the presented barcode, one can   immediately notice that the variant is also present in 28 other samples from the cohort, of which in total 12 are homozygous and 17 heterozygous.
Indeed, in rare diseases such as the Bardet-Biedl syndrome (BBS, OMIM# 209900), mutations are often private (i. e. , one mutation found only in one family)  (Muller et al. , 2010)  meaning that their frequency in the population is very low. Counts can be used to estimate the frequency of a known variant in the user cohort and add significant value to variants not yet reported in any public variant database but for which a frequency can be estimated based on the user's cohort. As an example, looking at 2,888 non redundant SNVs observed in 107 patients with moderate to severe intellectual disability, 979 did not have any frequency information in the dbSNP and EVS databases. Such information could be directly retrieved from the VaRank output. The observed frequency of variants in public databases but also in private cohorts can be a powerful filtering strategy.