dataesr/scientific-paragraphs-categorization huggingface dataset:
Inputs The input dataset is in tab-delimited format and can be either copied and pasted into a text field or directly uploaded from a file. The columns represent the different data vectors to be compared with the first row considered the column names. Empty cells are allowed, provided they do not account for more than 50% of a data vector; the first column may contain characters other than numbers, in which case it will be assumed to be the row names and ignored when running the job. A typical file could contain expression values for a set of genes (rows) in various biological samples (columns). Such a file can be seen from the example dataset, which the user can fetch and use as a demo dataset. Once uploaded, the dataset is checked for errors before the user is directed to the next step (analysis options settings).
Outputs and visualization tools Pairwise P-value graphs. The first panel focuses on pairwise significance evaluation of the different data vector combinations. Two types of R-generated, publicationquality graphs are displayed here, and dynamic filtering of the graphs (leftmost section) allows for quick retrieval of a particular comparison. Boxplot panels (Figure  2A , upper carousel) provide a per-vector overview of its similarity to each of the other vectors. The boxplots represent the distribution of the computed distances (after random permutation of the dataset) with which the original distance, displayed as a red star, can be compared. The reference data vector for a particular boxplot panel is specified on top of the graph. Using this view, the user may immediately identify the various data vectors that are similar to a particular one and evaluate to what extent this similarity is significant.
Integrated visualization tools The subsequent panels present the dataset from a more integrated standpoint. The adjacency matrix (Figure  2B ) displays the P-values of all the combinations in a color-coded matrix. Red squares indicate P-values that are lower than a user-specified threshold (significantly similar vectors given the specified metric), while blue squares indicate significantly different vectors over the same threshold (P-value > 1 -threshold). Entering 0. 5 as a threshold will enable all the squares to be displayed and can therefore be used to avoid hard filtering of the displayed dataset. All squares can be clicked to show the corresponding vector pair and its associated P-value. By default, the vector names are displayed in alphabetical order, and the user may reorder the dataset to match any of the 18 different clustering methods that have been pre-computed.
The network view (Figure  2C ) is complementary to the matrix view. It displays each data vector as a node, while pairwise similarities are represented by the edges. Only those edges matching user-specified criteria are displayed, and pre-computed clusters are mapped to the network by node color-coding. The SIF (Simple Interaction Format) file associated with the displayed network is available for use in more advanced network visualization software such as cytoscape or gephi. Dendrogram visualization of the dataset is also available (Figure  2D ), taking the computed P-values or the normalized distances, if available, as input. The corresponding R- generated Newick statement is shown along with the tree for use in third-party software. As a common way of displaying entity relationships, trees can be particularly meaningful in terms of visual clustering of the various elements to be compared as well as evaluation of inter-cluster relationships.
Downloadable files All the generated files and datasets are made available for download. They include in particular the normalized input dataset (if normalization was requested), the adjacency matrix and the distances file (both original distances and those after random permutation). These files are in tabdelimited format. Newick and SIF files can also be retrieved for more advanced tree construction and network analysis, respectively, and R-output graphs (histograms, boxplot panels and adjacency graph) can be obtained as a batch from the download panel as well.
In the latter case, additional adjustment functions can be employed to ensure compliance of the dataset with the requirements of subsequent analytical methods, such as correlation coefficient-like values by using the function x = 1 -2y that maps the interval [0,1] into the interval [1,  -1]. There are also some limitations to the use of R. S. WebTool. This, for instance, is the case when gene clusters are constitutively co-expressed, or when genes exhibit constitutive, nearly identical expression changes in response to numerous stimuli, but which relationship (e. g. inter-genes and inter-samples relationships, respectively) is not relevant to the biological question underlying the analysis. If these undesired dependencies account for a significant part of the dataset, Random Sampling-based analyses might produce low P-values that are not necessarily representative of a meaningful, biological interaction between the compared vectors with respect to the original question.
For these very specific scenarios, one could simplify the dataset via Principal Components Analysis or any multivariate and multidimensional data analysis method, and use the generated eigenvectors (or eigengenes  (25) ) in subsequent Monte Carlo analyses. Many gene function prediction algorithms heavily rely on expression profile similarity, which is almost always computed using a correlation coefficient  (7, 23). The possibility of evaluating the significance of a similarity value given by any distance metric, some examples of which have been shown to perform better than correlation coefficients in multiple cases (1-4), will increase the flexibility of the comparison methods as well as the accuracy of the prediction while still allowing one to make use of the available clustering/gene function prediction software that requires correlation coefficient-like data.
(C) Weighted network representation of pairwise distance value significance. Pre-computed clusters are mapped by color-coding the nodes (entities), and dyadic significance values above a user-provided threshold are represented by edge thickness. (D) Dendrogram representation of the adjacency matrix. This graph highlights potential clusters as well as the degree of similarity between them. (E) P-value-distance plot. If requested at the time the analysis has been launched, the adjacency function (red curve) for mapping the space of possible distances [0,∞] to the interval [0,1] of P-values is computed from the plot. The equation of the deduced non-linear model and the coefficient of determination R 2 are provided. The presented screenshots are from various datasets, including those from the demo pages of R.
FUNDING National Science Foundation  [ IOS1025837  to J. M. K. ];  Agence National de la Recherche  [ ANR-10-BLAN-1524  to O. B. ,  ANR-12-BIME-0005  to O. B. ,  ANR-12-JCJC  to O. B. ];  Institut National de la Recherche Agronomique [FU-GAL  to O. B. ]. Funding for open access charge:  Institute for Basic Science, Republic of Korea.
Introduction Since their introduction in  [20] , actor languages have evolved as a powerful computational model for defining distributed and concurrent systems  [2, 3]. Languages based on actors have been also designed for modelling embedded systems  [23, 24] , wireless sensor networks  [11, 31] , multi-core programming  [22] , and web services  [9, 10]. The underlying concurrent model of actor languages also forms the basis of the programming languages Erlang  [4]  and Scala  [19]  that have recently gained in popularity, in part due to their support for scalable concurrency. In actor languages  [2, 20, 32] , actors use a queue for storing the invocations to their methods in a FIFO manner. The queued invocations are processed sequentially by executing the corresponding method bodies. The encapsulated memory of an actor is represented by a finite number of fields that can be read and set by its methods and as such exist throughout its life time.
A. open(server , cToken, aToken) = (server ← server ). (ctoken ← cToken). (atoken ← aToken) A. authenticate(u, p, cToken) = [ctoken = cT oken] server!succeeds(atoken); server!fails(atoken) The main program that instantiates the client, the server and the authProvider is as follows: let client = new C in let server = new S in let authProvider = new A in client!init(server ). server !init(authProvider ) Example 2. 3. We illustrate the modeling of a register that stores natural numbers and supports the operations of increment and decrement, when its value is positive. In the absence of data-types in our nominal actor language, we model a register by an actor and implement the value of the register by the number of invocations of the method item stored in its queue. Below we describe the corresponding class R, assuming a class Ctrl which encodes the control of the register machine.
We will use a reduction technique from a Turing-complete model to our actor model. In particular, the Turing-complete models we consider are the 2 Counter Machines (2CMs)  [29]  and a faulty variation of them. A 2CM is a machine with two registers R 1 and R 2 holding arbitrary large natural numbers and a program P consisting of a finite sequence of numbered instructions of the following type: • j : Inc(R i ): increments R i and goes to the instruction j + 1; • j : DecJump(R i , l): if the content of R i is not zero, then decreases it by 1 and goes to the instruction j + 1, otherwise jumps to the instruction l; • j : Halt: stops the computation and returns the value in the register R 1. A state of the machine is given by a tuple (i, v 1 , v 2 ) where i indicates the next instruction to execute (the program counter) and v 1 and v 2 are the contents of the two registers.
The user has to provide the initial state of the machine. The transition relation of the 2CM will be denoted by =⇒. The faulty variation of the 2CM we use are the "two Faulty Towers Machine" (2FTM, for short)  [12]. These machines have two faulty registers R 1 and R 2 holding either arbitrary large natural numbers or the faulty value ⊥. The program of a 2FTM is a finite sequence of numbered instructions that are the same of those in 2CMs. However, in contrast with 2CMs, 2FTMs from a state (i, v 1 , v 2 ) may nondeterministically evolve into a faulty state (i, ⊥, v 2 ) or (i, v 1 , ⊥) or (i, ⊥, ⊥). If an instruction i is an Inc/DecJump that refers to the register R 1 (respectively, R 2 ) then (i, ⊥, v) (respectively, (i, v, ⊥)) evolves to (0, ⊥, v) (respectively, (0, v, ⊥)). In 2FTMs, the instruction numbered 0 is always assumed to be Halt.
3. 1. The language Actor ba. We encode the value n stored in a register as n messages (of the same type) that are enqueued in an actor -see Figure  1. Namely, let R 1 and R 2 be two actors of class R and let the number of messages item in R 1 and R 2 be their value. The instruction Inc is implemented by inserting one item message in the queue of the corresponding register. In our formalism, this is done by invoking the method item whose execution has two possible outcomes: (i) the invocation is enqueued again; (ii) the invocation is discarded because we are in the presence of a residual of a DecJump operation, as described next. R // R has fields dec, ctr, loop and stop R. item(tt, ff ) = [stop = ff ] [dec = ff ]this!item(tt, ff );(dec ← ff ) R. inc(pc, tt, ff ) = [stop = ff ](loop ← ff ).
3. 2. The language Actor ro. We show that Actor ro is Turing-complete by means of an encoding of a 2CM -see Figure  2. In this encoding the two registers are represented by two disjoint stacks of actors linked by the next field. The top elements of the two stacks are passed as parameters r 1 and r 2 of the run method of the controller. As before, this actor encodes the control of the 2CM. The instruction Inc is implemented by pushing an element on top of the corresponding stack. This element is an actor of class R storing in its field the old pointer of the stack. The new pointer, i. e. the new actor name, is passed to the next invocation of the run method. The instruction DecJump is implemented by popping the corresponding stack. In particular, the method run of the controller is invoked with the field next of the register being decreased.
Microarray experiments 100 ng of total RNA was profiled at the Institut Jules Bordet using the Affymetrix® HG-U133 Plus 2. 0 Arrays (Affymetrix, Santa Clara, California), following the manufacturer's instructions. Briefly, the RNA was first reversetranscribed into double-stranded cDNA. This cDNA was transcribed in vitro. After purification of the aRNA, 12. 5 μg were fragmented and labeled prior to hybridization to the chips. Quality control (QC) for each chip was performed following the recommendations posted on  [44]. Following QC, the probe level intensities were background adjusted and quantile normalized using the Frozen Robust Multiarray Analysis (fRMA) method  [45]  as implemented in the R/Bioconductor package fmra  [46]. Probeset level annotations were obtained from R/Bioconductor package jetset  [47]  and complemented with BioMart  [48] ; when multiple probesets mapped to the same Entrez Gene ID, the probeset with the highest jetset score was selected.
The cDNA fragments were end repaired with the addition of a single ' A' base and the ligation of adapters. The products were purified using the AMPure XP beads and enriched with PCR (15 cycles) to create the final cDNA library followed by purification using the AMPure XP beads. Library quality control and quantification were performed using the Agilent Bioanalyser 2100 and qRT-PCR. The libraries were then pooled (4 libraries/pool). Clusters were generated in a cBot Cluster Generation System using the Paired-End Cluster Generation Kit v2-HS and sequenced on the Illumina® HiSeq 2000 platform with a 2x50 base-pairs paired-end mode. Base calls were made using the Illumina CASAVA 1. 5 pipeline. Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG  [49] , under study's accession number EGAS00001000495 and dataset's accession number EGAD00001000626.
RNA-Seq analysis After trimming the poor quality bases, the reads were mapped to the human reference genome hg19 with TopHat2 (version 2. 0. 0) [53] , and gene expression was quantified with Cufflinks (version 2. 0. 0) [53]. The annotation file (GTF file) used for both alignment and gene quantification was downloaded from Ensembl (on 26 July, 2012). To match the log scale of gene expression measurements from the microarray data, the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values computed by Cufflinks were logtransformed using the following formula: X' ¼ log2 Xþ1 ð Þ where X represents the FPKM value computed by Cufflinks, and X' is the log-transformed expression value.
Gene expression signatures Our study focused on the following gene expression signatures: (1) six prognostic signatures (GENE70  [14] , GENE21  [15] , Genomic Grade Index (GGI  [16] ), Risk of Relapse-Score (ROR-S  [12] ), ENDOPREDICT  [17] , and CIN70  [18] ); (2) two immune signatures (STAT1-MODULE  [7]  and IRMODULE  [19] ); (3) three stromarelated signatures (PLAUMODULE  [7] , DCN  [20] , and STROMACD10  [21] ); and (4) ten pathway related signatures (PIK3CA-GS  [22] , PTEN loss  [23] , IGF1  [24] , AKT/mTOR  [25] , MAPK  [26] , SRC, RAS, MYC, E2F3, and beta-catenin  [27] ). We also evaluated six subtype classifiers: SCMOD1  [7] , SCMOD2  [8] , SCMGENE  [9] , SSP2003  [10] , SSP2006  [11] , and PAM50  [12]. In addition to these multivariate subtyping models, we evaluated the three individual breast cancer clinically relevant genes: ER, PgR, and HER2.
We used the original signature algorithms for GENE70, GENE21, GGI, ROR-S, ENDOPREDICT, CIN70, STAT1-MODULE, PLAUMODULE, DCN and STROMACD10 as implemented in the Bioconductor package genefu  [54]. For the remaining gene expression signatures, we computed the signature scores following the approach used in Ignatiadis et al. [55]. The scores were computed from the list of genes in their respective signatures (as listed in GeneSigDB  [56] ) as the weighted average using the following formula: s ¼ X i⊂n w i x i X i⊂n w i j j where s is the signature score, n is the number of genes in the signature of interest, x i is the expression of the gene, and the gene-specific weight w i ∈ {-1,1} is the sign of the coefficient defined in the original publication. Only genes that could be mapped to EntrezGene IDs were used.
To compare the correlation of gene expression over the whole transcriptome between each pair of data type from a given sample, we used Spearman's rank-based correlation, the null distribution of which was established as the range of coefficients observed from all possible combinations of the 57 pairs excluding self-self pairs. This is efficiently computed from the cross correlation matrix minus the diagonal elements. The analyses performed in this study are fully reproducible and comply with proposed guidelines in terms of availability of the code and data  [57]. The R scripts developed for the analysis are available upon request.
Data availability The raw Affymetrix CEL files are available from the NCBI's Gene Expression Omnibus under accession number GSE43358. The data can be accessed through this link: http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? token=trmzbecoaqyugtc&acc=GSE43358. Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG  [49] , under study's accession number EGAS00001000495 and dataset's accession number EGAD00001000626.
Results Gene-wise comparison of expression levels using Affymetrix microarray and Illumina RNA-Seq platforms A subset of 16,097 genes were defined as common to the two platforms and retained for downstream analysis. Gene identifiers did not perfectly overlap due to differences in the annotation systems: jetset matched the Affymetrix probesets to the NCBI RefSeq human cDNA database, while the RNA-Seq analysis pipeline used Ensembl gene annotations (see Methods for more detail). When comparing the expression levels of the genes retained after selection of the best Affymetrix probeset, we found that although the scale of expression values differs due to different technology and normalization procedures, their rank is well conserved with 52%, 34%, and 11% of these genes having Spearman's rank-based correlation greater than 0.
In our dataset, the correlation of gene expression profiles for the whole transcriptome measured by microarray and RNA-Seq was statistically significant for all except three patients (Additional file 4: Figure  S3 ). Definition of ER, PgR, and HER2 status according to IHC, microarray, and RNA-Seq Among the genes retained for analysis, we focused our attention on three that are clinically relevant for breast cancer: ER, PgR, and HER2. Measuring them precisely is of utmost importance to clinical practice as these are presently the only validated breast cancer predictive biomarkers available, and they are routinely used to make decisions about patient treatment  [6, 59]. When comparing the expression levels of these three genes as defined by microarray and RNA-Seq, we found excellent Spearman correlation coefficients: 0.
Correlation between technologies for the definition of breast cancer subtype classifiers Two different gene expression approaches have been developed to prospectively classify breast cancers into molecular subtypes: Subtype Classification Models (SCMs)  [7] [8] [9]  and Single Sample Predictors (SSPs)  [10] [11] [12] , which include PAM50. In the current dataset, B: and C: Box plots showing median level of gene expression for both Affymetrix and RNA-Seq for the genes with low (<0. 7) and high (≥0. 7) correlation. Genes highly correlated between the two platforms showed higher levels of expression than those with low correlation. our subtype classifier SCMOD2  [8]  showed the highest correlation between microarray and RNA-Seq technologies (κ = 0. 975; Figure  3A , Additional file 1: Table  S3 ), which was significantly higher than the other classifiers (100 bootstrap replicates, corrected p-value <0.
Correlation between technologies: immune, stroma and pathway related gene expression signatures After a first wave of prognostic signatures, which essentially captured tumor proliferation signals, a new generation of "biological" signatures were developed that focused on determining the prognostic and/or predictive role of host-tumor immune interplay, tumor microenvironment and pathway activation signaling. We evaluated the correlation between microarray and RNA-Seq platforms in measuring the following: 1) two immune signatures (STAT1MODULE  [7] , IRMODULE  [19] ); 2) three stroma signatures (PLAUMODULE  [7] , DCN  [20] , STROMACD10  [21] ); and 3) ten pathway signatures (PIK3CA-GS  [22] , PTEN loss  [23] , IGF1  [24] , AKT/mTOR  [25] , MAPK  [26] , SRC, RAS, MYC, E2F3, beta-catenin  [27] ).
Hence, it is imperative to determine the reliability of transitioning from microarray to sequencing platforms in the clinical setting. To our knowledge, our is the first report investigating the correlation between the expression level of single clinically relevant genes and gene expression signatures obtained with the most commonly used microarray and RNA-Seq platforms, Affymetrix and Illumina respectively, in a selected dataset of breast cancer patients. When focusing on the genes in common between the two platforms, our analysis showed that the expression of more than half of them had a Spearman correlation of 0. 7. This was the case simply by selecting the most specific Affymetrix probeset and correlating it to gene log2 transformed FPKM obtained at sequencing, without any further computation.
Conclusions In our study, we have demonstrated that RNA-Seq can reliably be used to evaluate both the expression of clinically relevant single genes and well established gene expression signatures originally defined with microarray technology. Considering the advantages that RNA-Seq offers over microarray, such as its ability to explore larger sets of genes, it is envisaged that its application to wider datasets could even provide information relevant for de-novo classification of breast cancer or for the development of new prognostic and predictive signatures. With the cost of RNA-Seq experiments decreasing continuously and with well-established analytical tools increasingly available  [71] [72] [73] [74] [75] , RNA-Seq is becoming an accessible tool that is superseding microarray in the research setting.
The PpIX fluoresces in the 630-700 nm wavelength range and is excited throughout the entire optical region. However, the wavelengths of 405, 532, and 632. 8 nm are usually used as excitation wavelengths during laserinduced FD  [6, 7]. The long wavelength irradiation is of special interest due to its deep penetration into biological tissue, whereas the short-wavelength irradiation can provide higher resolution. In this work the investigation was conducted in two regions, the red ( = 632. 8 nm) and green ( = 532 nm) regions of the spectrum. By means of fluorescent diagnosis surgeon decides to reject or to leave tissue, so it is necessary to know from which area or depth all the data were collected to leave the healthy tissue and to remove tumor which accumulate photosensitiser. The aim of the work was the estimation of the optical probing depth dependence on the distance between source and detector, allowing for an interpretation of FD results.