dataesr/scientific-paragraphs-categorization huggingface dataset:
BRCA1 and BRCA2 mutation carriers Final analyses included 7,432 breast cancer cases and 7,104 unaffected BRCA1 mutation carriers, as well as 3,989 invasive breast cancer and 3,689 unaffected BRCA2 mutation carriers, all belonging to CIMBA. Supplementary specifications regarding inclusion profiles and studies belonging to CIMBA are available in the reports by Couch et al. [35]  and Gaudet et al. [36]. All analyses were conducted separately on CIMBA BRCA1 and BRCA2 mutation carriers (abbreviated pop1 and pop2, respectively). Eligible female carriers were aged 18 years or older and had a pathogenic mutation in BRCA1 and/or BRCA2. Women with both BRCA1 and BRCA2 mutations were included in downstream analyses. Data were available for year of birth, age at study recruitment, age at cancer diagnosis, BRCA1 and BRCA2 mutation description and self-reported ethnicity.
Genotyping and quality filtering Genotyping was conducted using the iCOGS custom Illumina Infinium array. Data from this array are available to the scientific community upon request. Please see  [37]  for more information. Genotypes were called using Illumina's proprietary GenCall algorithm. Genotyping and quality filtering were described previously  [35, 36]. Initially, 129 mitochondrial SNPs were genotyped for both BRCA1 and BRCA2 mutation carriers. SNPs fulfilling the following criteria were excluded from downstream analyses: monoallelic SNPs (minor allele frequency = 0), SNPs with more than 5% data missing, annotated as triallelic, or having probes cross-matching with the nuclear genome. Heterozygous genotypes were removed from analyses, and we further filtered out SNPs having more than 5% of heterozygous calls to limit the potential for heteroplasmy affecting our results.
Consequently, a unique haplogroup cannot confidently be assigned to each short haplotype. Therefore, each short haplotype was assigned the most recent common ancestor of all the haplogroups that share the same short haplotype. Once this matching was done, short haplotypes were reconstructed in the same way for each individual in our dataset and were assigned the corresponding haplogroup. The accuracy of the method used was assessed by application to a set of 630 mtDNA sequences of known European and Caucasian haplogroups (see Additional file 3).
Handling genetic dependency ALTree is used to perform homogeneity tests to detect differences in enrichment or depletion of affected or unaffected individuals between clades in the phylogenetic tree. This kind of test can be performed only on independent data. However, because some individuals in the CIMBA dataset belong to the same family, we constructed datasets with genetically independent data by randomly selecting one individual from among all those belonging to the same family and sharing the same short haplotype. To take into account the full variability of our data, we resampled 1,000 times. The results of the analysis pipeline are obtained for each resampling independently and then averaged over the 1,000 resamplings to obtain final results.
Localization of susceptibility sites ALTree also includes an algorithm used to identify which sites are the most likely ones to be involved in the association detected. For each short haplotype observed, the ALTree add-on altree-add-S adds to the short haplotype sequence a supplementary character called S, which represents the disease status associated with this short haplotype. Are individuals carrying this short haplotype more often affected or unaffected? S is calculated based on the affected and unaffected counts, the relative proportion of affected and unaffected in the whole dataset, and sensibility parameter ε. ε was set to its default value, which is 1. After S character computation, haplotypes including character S are reconstructed at ancestral nodes. Susceptibility site localization is achieved with ALTree by computing a correlated evolution index calculated between each change of each site and the changes of the character S in the two possible directions of change.
Statistical analysis We quantified the effect associated with enrichment discovered by applying ALTree by building a weighted Cox regression in which the outcome variable is the status (affected or non-affected) and the explicative variable is the inferred haplogroup. Analyses were stratified by country. Data were restricted to the clades of interest. The uncertainty in haplogroup inference was not taken into account in the model. The weighting method used takes into account breast cancer incidence rate as a function of age  [42]  and the gene containing the observed pathogenic mutation (that is, BRCA1 or BRCA2). Familial dependency was handled by using a robust sandwich estimate of variance (R package survival, cluster() function).
Discussion We employed an original phylogenetic analytic method, coupled with more classical molecular epidemiologic analyses, to detect mitochondrial haplogroups differentially enriched for affected BRCA1/2 mutation carriers. We successfully inferred haplogroups for more than 90% of individuals in our dataset. After haplogroup imputation, the ALTree method identified T1a1 in the T clade as differentially enriched in affected BRCA2 mutation carriers, whereas no enrichment difference was found for BRCA1 mutation carriers. The T subclade is present in 4% of African populations compared with 11% in Caucasian and Eastern European populations  [43]. In our data, the T subclade represented 9. 34% of BRCA1 mutation carriers and 9. 30% of BRCA2 carriers. The ALTree method also identified three potential breast cancer susceptibility loci in mtDNA.
Acknowledgements Collaborative Oncological Gene-environment Study (COGS): This study would not have been possible without the contributions of the following:  Per Hall  (COGS);  Kyriaki Michailidou ,  Manjeet K Bolla  and  Qin Wang  ( Breast Cancer Association Consortium (BCAC) );  Rosalind A Eeles ,  Ali Amin Al Olama ,  Zsofia Kote-Jarai  and  Sara Benlloch  (PRACTICAL);  Alison M Dunning ,  Craig Luccarini ,  Michael Lush  and the staff of the  Centre  for Cancer Genetic Epidemiology; Simard and  Daniel C Tessier ,  Francois Bacot ,  Daniel Vincent ,  Sylvie LaBoissière  and  Frederic Robidoux  and the staff of the  McGill University  and  Génome Québec Innovation Centre ; and  Julie M Cunningham ,  Sharon A Windebank ,  Christopher A Hilker ,  Jeffrey Meyer  and the staff of  Mayo Clinic Genotyping Core Facility.
740. 11. 0780 );The  Ohio State University Clinical Cancer Genetics (OSUCCG)  is supported by The  Ohio State University Comprehensive Cancer Center ; the  Pisa Breast Cancer Study  (PBCS) was supported by  Istituto Toscano Tumori (ITT)  grants  2011-2013 ;  Sheba Medical Center  was partially funded through a grant from the  Israel Cancer Association  and funding for the  Israeli Inherited Breast Cancer Consortium ; the  Swedish Breast Cancer  Study collaborators are supported by the  Swedish Cancer Society ;  The University of Chicago  is supported by  NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA125183 ),  R01 CA142996  and  1U01CA161032  and by the  Ralph and Marion Falk Medical Research Trust , the  Entertainment Industry Fund National Women's Cancer Research Alliance  and the  Breast Cancer Research Foundation ;  University of California, Los Angeles Jonsson Comprehensive Cancer Center Foundation: Breast Cancer Research Foundation ;  University of California, San Francisco Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center; UK Familial Ovarian Cancer Registry (UKFOCR)  was supported by a project grant from  Cancer Research UK  (to Paul DP Pharoah); the  University of Pennsylvania: National Institutes of Health (NIH)  grants  R01 CA102776  and  R01 CA083855 ; the  Breast Cancer Research Foundation ; the  Susan G Komen for the Cure, Basser Center for BRCA; Victorian Familial Cancer Trials Group (VFCTG) :  Victorian Cancer Agency ,  Cancer Australia, National Breast Cancer Foundation ; The  Women's Cancer Program (WCP) at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center , is funded by the  American Cancer Society Early Detection Professorship  ( SIOP-06-258-01-COUN ).
Modelling/design platform In this section we introduce the VerCors platform that we use for the modelling and behaviour analysis of our self-controlled components. Having a tool-supported methodology is first important for the design phase, when the designer builds his/her application, using legacy functional components as basic bricks, and assembling them following our SCC generic components. The toolsuite is also very useful for generating executable code containing the whole architecture description and the skeleton of the final application. We present the VerCors platform in Section 4. 1, then discuss how to use this tool-set for formal verification of the application behaviours in Section 4. 2.
The VerCors modelling tool-set VerCors  [26]  is a platform for the specification, analysis, verification, and validation of GCM-based applications. The principle of the tool is illustrated in Fig. 2. First, a user specifies the architecture of a GCM-based application, the signature of its interfaces, and the behaviour of the primitive components using VerCors Component Editor (VCE). Here a first validation is performed, concerning all structural coherency aspects of the application model. This check guarantees static validity of the model, and ensures also that the code generation will terminate correctly, and that the generated code will not fail during deployment of the application components. Then, from these descriptions the behavioural model of an application is generated (ADL2N) in a form of a dedicated behavioural semantic model: a parameterised networks of synchronised automata (pNets).
This semantic model is transformed by abstraction functions (ABS*), until reaching a finite model suitable for model-checking. Finally, the Model Checker, in our case the CADP tool  [27] , verifies the correctness of the model with respect to a set of temporal logic properties (user requirements) and in the case errors are detected it provides their description. Once the requirements have been proven correct on the VCE specification, the user can generate the set of files allowing the deployment of the application (Architecture Description Language (ADL) for the Architecture Description, IDL for the Interface definition in the form of Java interfaces). Then naturally, the user has to provide java classes implementing the service methods of the primitive components. These files are processed by the GCM component factory to build an executable application that is executed within the GCM/ProActive execution environment.
Behaviour specification and verification In order to generate a behavioural model in the VerCors platform, and ultimately ensure by model-checking that the overall component assembly behaves as expected (e. g. does not deadlock), the application architect should provide: 1) an architectural description, in terms of a Fig. 2  Principles of the VerCors platform composition of components, with their interfaces and bindings; and 2) for each service method of a primitive components, an abstract specification of its behaviour, e. g. in the form of a Unified Modeling Language (UML) statemachine, encoding the data-and control-flow of the component assembly. The transitions of the state-machines represent the communication events between components, including whatever data that is significant for the modelling.
To implement this application, our partners proposed a PaaS which can be described as an open platform for cloud software engineering, accessible to cloud architects and developers, deployed on multi-IaaS through a self-service portal. Our work has allowed us to define Springoo using mostly SCC components, with their In-and Out-monitors and their QoSControl component. We have defined the architecture described below. First, all the main components of Springoo (Composition, Apache-SCC and Jonas-SCC) are generically defined. They are all of the self-control and stateless type, as described in Section 3. 2. To keep the example simple enough, we do not describe here the details of the Jonas component. Recall that an SCC component includes an input monitor, an output monitor and a service quality control component (QoSControl).
Springoo is therefore composed of a set of primitive and composite components, all of SCC type. The only exception is the frontal component, that receives the client requests, transmits them to Apache, and waits for a return value from Jonas. The frontal awaits for answers, either from Apache if the Http components detect an error (typically when the query syntax is incorrect -code 400 -not shown in the figure for lack of space), or from Jonas when the request is fully processed. In both cases, the frontal returns the request answer (error or response value) to the client, through the InOut-Monitor of the Springoo component. As a consequence, the ClientRequest service method, on the external Springoo service interface, but also on the Frontal service interface, has a significant ReturnType.
Finally, 3 interfaces have been defined to communicate with the outside:  This structure has been specified, and validated by the VCE Editor. Here "validated" means structural validity, staticaly checked by VCE, including correctness of the architecture, interfaces, and namings (full definitions in  [32] ). This guarantees that the generated ADL will be valid, and the application will be built without runtime error by the component factory. The generated ADL code gives the description of the whole Springoo Application. It encompasses functional and nonfunctional components, associated classes, interfaces, and bindings.
Experiments We have partially implemented 1  the architecture from Fig. 10  using the GCM/ProActive middleware to provide the Monitors, QoSControl, and MAPE components. GCM/ProActive comes with all the software infrastructure necessary to simply define interceptor components and easily integrate monitoring components in the composition. The implementation provides a MAPE loop that monitors the response time of both Apache and Jonas components and is able to trigger QoS decisions on them. Two criteria are implemented: (1) enable/disable properties like caching on Apache components, which can improve the response time, but may be more costly in storage; (2) whenever the average response time of the Jonas component increases over T secs, the QoSControl component notifies it through the OutOfContract interface; then, the MAPE triggers an AddCpt action through the ExecScript interface, to add a Jonas worker inside the Jonas component.
TE mapping data provide evidence of an active ping-pong amplification cycle in An. gambiae ovaries In the ping-pong amplification cycle, antisense piRNAs are considered to be almost exclusively derived from piRNA clusters and are proposed to be primarily responsible for TE silencing through the piRNA pathway  [2]. In the An. gambiae dataset, we identify a strong bias toward antisense piRNAs. About 69 % of the piRNAs identified as TE-derived were found in the opposite orientation with regard to TEs identified by the "overlap" method. This finding holds consistent across each of the arms and Fig. 3  TE-derived piRNAs in An. gambiae identified by two different approaches. The percentage of TE-derived piRNAs mapping to the consensus sequences of annotated TEs with up to three mismatches in TE families (a) and TE classes (b).
These data suggest that AGAP012494 is derived from an ancient AgaP8MITE2450 element. All transcripts' piRNAs, which also map to TEs when allowing 0-3 mismatches, equal 14,350 reads (859. 7 RPM). About half of these piR-NAs, 49. 6 %, map to RepBase reference TEs from Anopheles without any mismatches, suggesting a rather recent origin of the corresponding sequences within proteincoding gene transcripts. The piRNAs map to many types of TEs: SINEs, LINEs, Gypsy-like, BEL-like and DNA transposons (Additional file 14: Table  S7 ). Still, a large number of these piRNAs, 5346 reads, map to SINEX-1_ AG, a SINE element. We then removed the piRNAs that map to both gene transcripts and TEs, and used the Fig. 7  The An. gambiae genes with substantial piRNA enrichment. The top 25 genes with piRNA mapping are shown.
3-fold between 0 and 10-day-old adult female mosquitoes  [54]. Exclusion of AGAP003387 from the analysis results in 2. 1 % of the total piRNA pool being generated from transcripts of the remaining gene dataset (Fig. 2b ). Nearly 58 % of the unique piRNAs that mapped to genes were located within the 3′ UTR of gene transcripts. Approximately 800 genes with any piRNA mapping had piRNA reads entirely derived from the 3′ UTR. piRNAs have been mapped primarily to the sense strand of various genes in D. melanogaster  [19, 20]  and M. musculus  [55] , including Traffic jam, brat, and Klp10. We saw a similar phenomenon in An. gambiae, where 81 % of genome-unique piRNAs matching protein-coding transcripts were derived from the sense strand. This, in concordance with previous reports  [21] [22] [23] , likely suggests that in mosquitoes, there is a mechanism that generates piRNAs from various non-TE-related sequences, protein-coding genes in particular.
Identification of piRNA clusters To identify the genomic loci responsible for piRNA generation in An. gambiae, data were restricted to sequences that mapped uniquely across the reference genome. All 24-29 nt reads were initially mapped to the An. gambiae PEST AGamP4 genome assembly (without repeat masking) using NucBase  [81] , a short-read mapper designed to align short sequence reads from large nucleic acid databases to genomes or input sequences. After the initial mapping, genome-unique reads, i. e. , mapping only once in the An. gambiae genome, were retained, and reads present more than once in the library were reduced to one occurrence, resulting in a non-duplicate (collapsed) library of unique mapping reads that was then re-plotted with NucBase. The final mapping run resulted in 568,080 small RNA sequences that mapped to the five chromosomal arms (2R, 2L, 3R, 3L, and X), as well as to the nonassembled chromosome UNKN.
Initial short-read mapping was completed on 5-kb windows for each chromosome arm of the An. gambiae AgamP4 assembly. A Perl script was written to identify consecutive windows containing a minimum of ten unique piRNAs that held a single constraint. This constraint allowed the inclusion of gaps of up to four contiguous windows (or 20 kb) containing less than ten piRNAs each that could continue the cluster. The ten piRNA cutoff value used in the Ae. aegypti study  [22]  was necessary to improve cluster detection when working with such a large dataset. To be considered a cluster, 0. 05 % of the subset of 568,080 uniquely mapping piRNAs, or 284 piRNAs, was the minimum total piRNAs within a given region. We ran our piRNA cluster analysis pipeline using the w 1118 piRNA library (sample GSM327620)  [44]  on the Dmel_R6.
TE, gene ontology, and expression data analysis The TE identification was performed by using the RepeatMasker tool to search against all available annotated elements. We elected to use subphylum Hexapoda TEs when performing RepeatMasker searches, as the complete annotation of TEs in An. gambiae is still lacking. For "overlap" identification, the genome was masked using the Hexapoda subphylum from RepBase  [42]  in RepeatMasker  [45]. Masked regions (correspondent to TEs) were then mapped by piRNA sequences to identify "overlapping" piRNA-TE sequences and positions, while allowing no mismatches. These positions were then marked to identify the associated TE family/class. We used the -k = 1 option in BowTie2, which reports a single valid alignment for each read in the library. This setting allows the report to identify mapping to TE sequences without over-reporting hits from identical, repetitive sequences.
Acknowledgements This work is supported by the  Fralin Life Science Institute , the  USDA National Institute of Food and Agriculture Hatch  project  223822 , by  NIH  grant  R21AI094289  to IVS and  Ligue Nationale  contre le cancer to CV. PG received the  Chateaubriand Fellowship  from the  French Embassy  in the USA. We thank  Melissa Wade  for editing the manuscript.
ORCHIDEE land surface model ORCHIDEE is a global dynamic vegetation model that simulates the exchange of energy, water and carbon between the atmosphere and the land surface. It is the land surface model of the IPSL-CM5 Earth system model  (Dufresne et al. , 2013; Krinner et al. , 2005). The processes and equations of the SPITFIRE fire model  (Thonicke et al. , 2010)  were implemented in ORCHIDEE, with some modifications being described in  Yue et al. (2014). There, the model was evaluated against different satellite observations for simulated burned areas and fire regimes. The SPITFIRE module simulates burned area and fire consequences (e. g. emissions, plant mortality) in a mostly mechanistic way. The central underlying engine is the Rothermel fire spread model  (Rothermel, 1972; Pyne et al.
Simulations and input data sets To evaluate the role of fires in the global terrestrial carbon balance, two parallel simulations were conducted: fireON and fireOFF, with SPITFIRE being switched on or off, respectively. In both simulations, the dynamic vegetation module of ORCHIDEE was de-activated, and a currentday vegetation distribution map (converted into the 13-PFT map in ORCHIDEE based on the IGBP 1 km vegetation map, http://webmap. ornl. gov/wcsdown/dataset. jsp? ds_id=930) was used as the static land cover. Here, firevegetation-climate feedback was not included because the relative fractions of different PFTs remain the same over the simulation period. It means not only that fires associated with land-cover change (deforestation fires) are not included, but also that wildfires are not affected by changing PFTs.
The climate data used for 1901-2012 are 6-hourly CRUNCEP data (http://dods. extra. cea. fr/ store/p529viov/cruncep/V4_1901_2012/readme. htm). During the period 1850-1900 when CRUNCEP climate data were not available, the data of 1901-1910 were used and cycled. Lightning data were retrieved from the High Resolution Monthly Climatology of lightning flashes by the Lightning Imaging Sensor-Optical Transient Detector (LIS/OTD) (http://gcmd. nasa. gov/records/GCMD_ lohrmc. html). The LIS/OTD data set provides mean monthly flash rates over the period of 1995-2000 on a 0. 5 • grid, which were cycled each year throughout the simulation. The annual historical population density data were retrieved from the Netherlands Environmental Assessment Agency (http://themasites. pbl. nl/tridion/en/themasites/hyde/ download/index-2.
html). Please refer to  Yue et al. (2014)  for the detailed information on these input data sets. For the fireON simulation, after the second spin-up, there is a global carbon sink of 0. 19 Pg C yr -1 over the last 50 years prior to the transient simulation due to the notfully complete equilibrium of slow soil carbon pools. We verified that this sink has a negligible trend (annual trend of 0. 003 Pg C yr -1 ). For the fireOFF simulation, the residual sink before the transient simulation is 0. 17 Pg C yr -1 (with a negligible annual trend of -0. 001 Pg C yr -1 ). Because the ORCHIDEE version used here is computationally expensive, we did not run the model until a complete carbon saturation state. The simulated annual global total net biome production (NBP) during 1901-2012 was bias-corrected for this incomplete spin-up by subtracting the remaining positive NBP over the last 50 years of the second spin-up.