dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Two datasets were constructed, one for dogs and one for cats. For each dataset, subsets were defined as male, female, unknown; age ≤ 1 year, or > 1 year, or unknown; stray, surrendered; and one of the ten Canadian provinces; as well as combinations of these (e. g. , males of age ≤ 1 year). The overall prevalence of infection with any parasite species and the prevalence for each parasite species were calculated for the entire dataset and for each data subset. Prevalence was the number of positive samples/total number of samples x 100%. A 95% confidence interval (CI) was calculated using the formula for the binomial distribution. When there was at least one positive sample in a data subset, both the upper and lower confidence limits were nonzero. When there was no single positive sample, only the lower confidence limit was equal to zero, whereas the upper confidence limit was nonzero; this upper confidence limit increased as the sample size decreased.
Any nonoverlapping subsets of the dataset were compared using the Fisher-Exact test. The dataset was split into four regions and defined as follows: Atlantic (NL, NS, NB, PEI), East (QC, ON), West (MB, SK, AB) and Pacific (BC). For pairwise comparison of the four regions and to detect differences among provinces, a Bonferroni correction was applied to the p-values, in order to avoid inflation of type-I error. All calculations were carried out on the AH Development Biostatistics IT infrastructure, PC AHCHBS-L13418, using the software SAS®, Version 9. 2. 2.
To evaluate excretion traits, fresh and dry weight, water content, pH, nitrogen to phosphorus ratio from 0 to 23 days, and pH of gizzard and jejunum contents at 23 days were measured. A set of 3379 single nucleotide polymorphisms distributed on 28 Gallus gallus (GGA) autosomes, the Z chromosome and one unassigned linkage group was used for QTL detection. Results: Using the QTLMap software developed for linkage analyses by interval mapping, we detected 16 QTL for feed intake, 13 for feed efficiency, 49 for anatomy-related traits, seven for growth, six for body composition and ten for excretion. Nine of these QTL were genome-wide significant (four for feed intake on GGA1, one for feed efficiency on GGA2, and four for anatomy on GGA1, 2, 3 and 4). GGA16, 19, and 26 carried many QTL for different types of traits that co-localize at the same position.
Intestine density was calculated as the ratio of intestine weight to intestine length for the duodenum, jejunum, ileum and total intestine (DUD, JED, ILD, IND, in g cm -1 ). Since proventricular dilatation was observed in this population, the ratio of proventriculus to gizzard weight was also calculated (PRW/GW, g g -1 ). Gizzard, proventriculus and total intestine weights (raw data and values relative to body weight) as well as intestine length relative to body weight and intestine density were presented in Tran et al. [9]  and are not discussed further in this paper. Breast meat (Pectoralis major and Pectoralis minor) and abdominal fat were also removed and their weights relative to the body weight at 23 days were measured (BRY, AFY, in g g -1 ). Finally, since the digesta pH in the different compartments of the digestive tract influences activity and secretion of digestive enzymes, we also measured the pH in gizzard and jejunum contents, by collecting boluses at slaughter.
Markers and genotyping All F0, F1 and F2 birds were genotyped with a dedicated Illumina Infinium custom array including 6000 single nucleotide polymorphisms (SNPs) that were selected for their informativity in our design and distribution across the genome  [9]. SNPs that deviated from the Hardy-Weinberg equilibrium within families or that showed inconsistent genotyping data relative to pedigree or genetic map information and poor quality SNPs were discarded from the analysis in order to reduce the risk of erroneous results  [9]. Finally, 3379 SNPs were used. The genetic map was deduced from the physical position of the SNPs and from the genetic consensus reference map published by Groenen et al. [11]. This set of SNPs covers 3099. 1 cM.
QTL analysis QTL detection was carried out with the QTLMap software  [12]  using a half-sib model  [13, 14]  with interval mapping based on maximum likelihood estimations  [15]. All data were analyzed in a single model but QTL effects were estimated for each sire family. This model does not make assumptions on the number of QTL alleles segregating in the design. The traits were analyzed separately. Depending on a preliminary analysis of the variance, data were pre-corrected for fixed effects of batch (four levels), sex (two levels), rearing cell (three levels), cage row (three levels), slaughter time (morning or afternoon), experimenter at slaughter (i. e. the person in charge of cutting intestinal segments, seven levels). QTL analyses were performed by comparing the hypothesis of one QTL (H1) versus no QTL (H0) to test the segregation of a QTL on each linkage group.
Copy number SNP array analysis Molecular karyotyping using 50 ng of tumour DNA was performed with the Affymetrix CytoScan HD Array (Affymetrix, Santa Clara, CA) according to the manufacturer's recommendations. Bioinformatics was done with the Affymetrix Chromosome Analysis Suite 2. 0 software using annotation files version GRCh37/hg19. For analysis and interpretation copy number changes 100 kb were considered. For the detection of uniparental isodisomies, the sample was checked for regions of homozygosity with a minimal size of 10 Mb. Common CNVs registered in the Database of Genomic Variants (http://projects. tcag. ca/variation) and from the software-internal control cohort were excluded.
Methylation profiling Meythlation profiling was performed using the Illumina Infinium HumanMethylation450 BeadChip. DNA was extracted as described above and 500 ng (37. 3 ng/μl) processed according to the manufacturers protocol. The raw intensity values (two channels for each probe) were extracted from the Illumina files using the LUMI software package for R  [21]. It is known that the two different probe chemistries used in the assay give different distributions of signals. In addition, we noticed that the dynamic range of intensities varies from probe to probe, but consistently across samples. We used the reference signals generated by Bibikova et. al. [22]  available from the Illumina website to normalise each probe read-out to a value between 0 and 1, comparable to the commonly used beta value.
More details on data processing, the raw signals as well as the normalised values are available at NCBI in the Gene Expression Omnibus database under accession GSE55521. The same processing was applied to the raw intensity values of the HumanMethylation450 dataset GSE34387. For the clustering we extracted 16100 probes with variance exceeding 0. 1 across all samples. Based on these 16100 values, we clustered the samples using Pearson's correlation as distance between samples and complete linkage as distance between clusters.
Methylome of the sequenced tumour We probed the methylation levels of ˜450. 000 CpG sites using a BeadChip and compared our sample against the analysis published in  [9]  (GEO dataset GSE34387), where a clear difference between the methylation profiles of SDH-negative and -positive GIST was reported. The clustering displayed in Fig  4  shows that the methylation pattern matches that of a group of SDH positive kinase-mutant GIST and even that of healthy muscular tissue much better than the methylation pattern of most SDH deficient GISTs. Inspection of the probed CpG sites in the promoter and gene body regions of common tumour suppressor genes revealed no significant gene-level deviations from normal tissue (data not shown). Therefore, we can rule out that hyper-or hypomethylation has played a role in formation of the examined low-grade GIST with reasonable certainty.
Predisposing polymorphisms We hypothesized that there may be other genomic alterations besides the KIT mutation and loss of chromosome 14q that drove the formation of the studied tumour. In particular, we searched for germline polymorphisms known to confer increased risk of developing cancer. Checking all mutations affecting proteins against the latest ClinVar  [26]  database yielded 48 single-nucleotide variants that have been linked to cancer or other diseases, of which the KIT mutation is the only somatic one. We further checked the mutations against the latest version of the GET-evidence database (http://evidence. pgp-hms. org/about) of crowd-sourced variant annotations. Among the genes with germline mutations that are possibly pathogenic are AURKA, BUB1B, BRCA2, MTHFR, IRF1, TLR4, ZEB1, LIG4, ELAC2, FUT3 (all heterozygous) as well as RET, EVC, IL4R, IL7R (all homozygous).
Twelve non-silent single-nucleotide variants and one frameshift deletion remained. All sites that were double-checked using Sanger sequencing confirmed the listed mutation (data not shown). While KIT mutations are well-known drivers of GIST, tissue-specific genes such as the serine/threonine protein kinase TESK1, the myelin regulatory factor MYRF, as well as the 5-oxo-L-prolinase OPLAH or the zinc influx transporter SLC39A9 have not been associated with cancer before. Inter-species homology-based methods estimate the amino acid changes caused by these mutations and the ones found in DOPEY2, LAMA5, PCDHB11 and STS to be likely not damaging for protein function. We could not exclude a priori the possibility that any of the mutations in the relatively uncharacterised genes RNF146 or ZNF407 was linked to the formation of the tumor.
To rule out a contribution of RNF146 or ZNF407 we screened a panel of 21 frozen tumour samples for the presence of mutations in the fifth exon (in this splice variant) of RNF146. However, all these samples were devoid of mutations. We therefore decided to stop the mutation screening for this gene and concentrate instead on the ZNF407 gene to sequence all its coding exons (altogether 6. 8 kb). Screening of 52 GIST tumour samples of various grades uncovered 117 point mutations at 20 different locations, most of which are known polymorphisms (SNPs). In addition to the ZNF407 mutation listed in Table  1 , we found one novel (as of the time of writing) germline mutation. Based on the minor allele frequency of each known polymorphism as estimated from the 1000 Genomes project data, only the first polymorphism in Table  2  was observed a significantly higher number of times than expected (data not shown).
RNF146 is expressed in the brain where it protects neurons from programmed cell death  [35]  which raises the question whether the same holds true for Cajal cells. A recent study  [36]  correlates RNF146 overexpression with invasiveness of non-small cell lung cancer. In the light of these findings it is uncertain whether the frameshift deletion we found contributed to tumour formation or rather hindered it. Moreover, it seems that RNF146 mutations are not commonly associated with GIST since the studied tumour remains the only example among a panel of 21 samples of various GIST types. ZNF407 is a member of the large C2H2 (Krüppel) type zinc finger protein subfamily whose members have previously been implicated in cancer development  [37, 38]. Depending on the stringency of motif, protein databases predict between 8 and 22 zinc fingers in ZNF407.
The study focused on expression level and copy number variation and is therefore not directly comparable to our analyses. The minimum we can say is that our sample harbours only known SNPs in these genes and only SNYNE2 is affected by the 14q loss. Another recent paper  [55]  focuses on wildtype GIST and sets their gene expression pattern apart from those of kinase-mutant tumours. The complete datasets from these studies have not been published yet whence we can not draw further conclusions, e. g. about the expression of RNF146 in GISTs. The genetic and epigenetic analysis of a low-risk GIST presented here suggests that a typical activating KIT mutation together with loss of one of chromosomes 14 might indeed be sufficient to trigger formation of clinically symptomatic tumours. We can not, however, exclude the possibility that germline polymorphisms in the mitotic checkpoint genes AURKA and BUB1B, despite being heterozygous, facilitated tumour formation.
Planktonic Foraminifera are a major contributor to the deep carbonate flux and their microfossil deposits form one of the richest databases for reconstructing paleoenvironments, particularly through changes in their taxonomic and shell composition. Using an empirically based planktonic foraminifer model that incorporates three known major physiological drivers of their biogeography -temperature, food and light -we investigate (i) the global redistribution of planktonic Foraminifera under anthropogenic climate change and (ii) the alteration of the carbonate chemistry of foraminiferal habitat with ocean acidification. The present-day and future (2090-2100) 3-D distributions of Foraminifera are simulated using temperature, plankton biomass and light from an Earth system model forced with a historical and a future (IPCC A2) high CO 2 emission scenario.
Foraminiferal assemblage and calcite saturation data For model evaluation we use two independent data sets: (i) the surface abundances from global plankton tows  (Bé and Tolderlund, 1971 ) and (ii) the relative abundances from sediment top cores. The observed relative abundances of Foraminifera in sediment cores (Fig. S2a  in the Supplement) are compiled from the MARGO (Multiproxy Approach for the Reconstruction of the Glacial Ocean surface) database  (Barrows and Juggins, 2005; Hayes et al. , 2005; Kucera et al. , 2004). Although the key focus of the MARGO database is the reconstruction of sea surface temperatures, the relative abundances are also available. We compiled all the available relative abundances in the MARGO database from the top cores and recalculated the relative abundances based on only the eight species used in this study.
The empirical relationships between foraminiferal growth rates and abundances in the FORAMCLIM model were originally calibrated against a compilation of multinet plankton tow data  (Watkins et al. , 1996 (Watkins et al. , , 1998;; Schiebel et al. , 2001 Schiebel et al. , , 2004;; Field, 2004; Kuroyanagi and Kawahata, 2004) , which is why we cannot use this database to evaluate the model. We use the sampling sites from this same data set to characterize the carbonate chemistry of the present-day potential habitat of foraminiferal species. Here we "sample" the observed calcite saturation state, C , at the same locations (latitude, longitude, depth; Fig. S2b ) where Foraminifera have been collected in multinet plankton tows  (Field, 2004; Kuroyanagi and Kawahata, 2004). C is calculated based on the GLODAP (Global Ocean Data Analysis Project)  (Key et al.
Modelling planktonic Foraminifera: strengths and limitations The two most established approaches currently used to simulate the biodiversity of Foraminifera are the ecophysiological approach used in this study, FORAMCLIM  (Lombard et al. , 2011) , and the ecosystem approach developed specifically to capture dynamic changes in planktonic foraminiferal populations, PLAFOM  (Fraile et al. , 2008). All approaches Table  2. Assessment of the simulated distribution of foraminiferal species (percentage of area with model-data agreement) using the plankton tow data of  Bé and Tolderlund (1971). The RMSE of diversity and relative abundance are assessed against the MARGO top-core data. used for projecting climate impacts on marine biogeography have their unique set of strengths and weaknesses  (Pereira et al.
A database of time series of abundance and assemblage data and concomitant environmental measurements of temperature, food and light would be an invaluable resource for the evaluation of the drivers of the simulated changes in the distributions of foraminiferal abundance. The simulated response of foraminiferal diversity to climate change may differ significantly if all extant species could be included in the model. For example, tropical species present in low concentrations, but excluded in this analysis, could flourish as temperatures increase. Also, the vertical stratification of large regions of the global ocean should increase with climate change  (Sarmiento et al. , 2004) , which can increase vertical niche separation, thereby allowing a greater diversity of species to subsist  (Al-Saboui et al.
The QCD background is characterized by jets that are misidentified as an e, µ, or τ h lepton. The QCD multijet process is an appreciable background only for the τ h τ h final states. There are negligible contributions from background processes such as single-top and VBF production of a Higgs or Z boson. These background yields are taken from simulation. Simulated samples of signal and background events are generated using Monte Carlo (MC) event generators. The signal event samples are generated with the MADGRAPH program (v5. 1. 5) [33] , considering pair production of χ ± 1 and χ 0 2 gauginos ( χ ± 1 χ ± 1 , χ ± 1 χ ∓ 1 , χ ± 1 χ 0 2 , and χ 0 2 χ 0 2 ) with two associated partons. The signal events are generated requiring a pseudorapidity gap |∆η| > 4. 2 between the two partons, with p T > 30 GeV for each parton.
Background event samples with a Higgs boson produced through VBF processes, and single top are generated with the POWHEG program (v1. 0r1380)  [34]. The MADGRAPH generator (v5. 1. 3) is used to describe Z+jets, W+jets, tt, diboson, and VBF Z boson production. The MC background and signal yields are normalized to the integrated luminosity of the data. The tt background is normalized to the next-to-next-to-leading-logarithm level using the cal-culations of Refs. [35, 36]. The Z+jets and W+jets processes are normalized to next-to-next-toleading-order using the results from the FEWZ v2. 1  [37]  generator. The diboson background processes are normalized to next-to-leading-order using the MCFM v5. 8  [38]  generator, while the VBF Z boson events are normalized to next-to-leading order using the VBFNLO (v2.
6)  [39?  ] program. The single-top and VBF Higgs boson background yields are taken from the POWHEG program, where the next-to-leading order effects are incorporated. Signal cross sections are calculated at leading order using the MADGRAPH generator. All MC samples incorporate the CTEQ6L1  [40]  or CTEQ6M  [41]  parton distribution functions (PDF). The corresponding evaluation of uncertainties in the signal cross sections is discussed in Section 7. The range of signal cross sections is 50-1 fb for χ 0 2 = χ ± 1 masses of 100-400 GeV. The POWHEG and MADGRAPH generators are interfaced with the PYTHIA (v6. 4. 22)  [42]  program, which is used to describe the parton shower and hadronisation processes. The decays of τ leptons are described using the TAUOLA  (27. 1215)   [43]  program. The background samples are processed with a detailed simulation of the CMS apparatus using the GEANT4 package  [44] , while the response for signal samples is modeled with the CMS fast simulation program  [45].
The order of trials was randomized with the restriction that each number could not appear more than two times in a row. Each trial started with an eye fixation cross presented for 300 ms. Subsequently, the number appeared and was presented until the participants responded or for a maximum duration of 2 s. The next trial started after 500 ms. A standard, portable, MS Windows compatible computer (15. 4 in. ) running DMDX software  (Forster & Forster, 2003)  was used to present stimuli and collect responses. We also administered Advanced Raven Matrices (see  Raven, Court, & Raven, 1983 , Polish adaptation by  Jaworowska & Szustrowa, 1991)  in order to control for fluid intelligence.
g. , to perform analytics, generate statistics, anonymise or encrypt, as required for 'Big Data', and other software is restricted to access only one such item, see §II, §IV. Here we propose two-component tags to represent the concern of data and a specifier for an item of that kind, for example medical, bob for Bob's medical record. We have implemented this in FlowK2 (FlowK 2-component). Such a tag model more closely reflects the policy maker's intent than previous models, and current atomic tags can easily be expressed in this way, such as location, EU or regulation, EU 111. We have experience of collaboration in healthcare record management  [6] ,  [7]  and monitoring  [8]  and take examples from this domain to demonstrate this approach. The main contribution of this paper is this enhancement of standard IFC tags to make IFC feasible for 'Big Data' processing in the cloud and to allow better expression and maintenance of policy.
We describe in §III the model we use in FlowK that follows this general idea. Details of implementations of IFC at language  [14] , language library and operating system level are given in  [2] ,  [15]. She et al. [16] , use run-time dependency and information flow control to track information within and between services in a cloud service composition chain. Roy et al. [17]  and Akoush et al. [18]  propose to implement IFC in a MapReduce framework to prevent the computation provider leaking data from the data provider. Airavat  [17]  is limited in the expressiveness of the policy due to its reliance on SELinux, as acknowledged by the author "While [DIFC] would provide far greater flexibility [. ] only prototype DIFC operating systems exist". MrLazy  [18]  only verifies IFC policy at sink-points, forcing the entire data-processing to be re-done or data discarded.
B. The need for IFC in Cloud Computing IFC controls the use of data beyond applications' access control points, throughout its whole life cycle. Therefore users and applications can safely share and exchange data, if they are willing to trust the cloud provider as a policy-enforcing thirdparty. This is not unreasonable, given the existing contractual relationships between tenants and cloud providers and the role of regulators that operate in many jurisdictions. IFC allows the nature of the shared data usage to be defined precisely and enforced  [20]  (e. g. data labelled as medical can only flow to applications that have been authorised and labelled to manipulate medical data). The trust relationship is greatly simplified as only a common, well-defined party needs to be trusted (i. e. the cloud provider).
C. Role Based Access Control RBAC is a mature, widely used access control scheme with a large literature and existing standards  [21] ,  [22]. 1  Role definitions in RBAC tend to be functional in their scope, being application-or organisation-specific. Administrative roles are also included to capture the need to manage RBAC itself. In work on RBAC by ourselves  [7]  and others  [23] ,  [24] , parametrised roles were found to provide elegant expression of policy and avoid explosion in the number of roles required. For example, certain company software such as "Payroll" may need to access all employees' data whereas each employee can access only their own data record. To achieve this, a company either creates a role per employee e. g. employee smith or parametrises a single role, for example employee(smith) etc.
III. ATOMIC TAG MODEL IFC augments authorisation by enforcing dynamically that only permitted flows of information can occur, end-to-end, across applications. Entities to which IFC constraints are applied include cloud web applications  [2] , a web worker instance  [18] , a file, a database entry  [25] , etc. IFC is applied continuously, typically on every system call for an IFC-enabled OS. IFC policy should therefore be as simple as possible, to allow verification, human understanding and to minimise runtime overhead.