dataesr/scientific-paragraphs-categorization huggingface dataset:
We also considered 18 commonly altered genes in breast cancer (ERBB2, ESR1, TP53, RB1, MYC, JUN, CDKN2A, BCL2, APOBEC3B, PTEN, MDM2, USP7, UBE3A, SPDYE7P, PLK1, BAX, MET, FOXM1)  [56]. In addition, 23 genes related to ploidy alteration were also included (TP73, LATS2, MAPK14, CDKN1A, CHEK1, AURKB, AURKA, BRCA1, BRCA2, DUSP5, MST1, PPP1R13L, BIRC3, TGFA, ETS1, ETS2, HIF1A, LDHA, FOXO1, NDRG1, PPP2R1A, PPP2R2A, CCNE1)  [38, 40]. Genomic properties of ohnolog genes in vertebrates. The genomic properties susceptible to be associated with the retention of 'ohnolog' gene duplicates (as well as SSD and CNV duplicates) in the human genome have been obtained from various resources: • Cancer mutations. Cancer mutation profiles for all the protein coding genes were obtained from the COSMIC database  [34].
We counted all the non-synonymous mutations per unit length in all the available samples, and partitioned the 18,538 genes with available mutation information into three equal frequency bins (S1 Data). • Disease genes. Human disease genes were collected from OMIM, GeneCards  [57] , and from published curated lists  [44, 58]  and combined to give a total of 7,171 disease genes. • Recessive vs dominant genes. Based on the inheritance information from Online Mendelian Inheritance in Man (OMIM) database, we could obtain 981 and 952 genes that were described as autosomal dominant and autosomal recessive genes respectively. • Autoinhibition. Genes with autoinhibitory protein folds were obtained from search and manual curation in PubMed and in various databases (OMIM, SwissProt, NCBI Gene and GeneCards).
Additional autoinhibitory candidates with the domains known to be frequently implicated in autoinhibition (e. g. SH3, DH, PH, CH, Drf and Eth domains) were obtained based on the domains identified using HMMER search  [59]  against Pfam database  [60]. This led to a total of 881 genes with autoinhibitory protein folds (S1 Data). • Essentiality. A total of 6,436 1-to-1 mouse orthologs obtained using BioMart and tested for lethality or infertility phenotypes on loss-of-function or knockout mutations in mouse were obtained from the Mouse Genome Informatics database  [32]. 2,729 [resp. 3,227] of these 6,436 genes were found to be essential [resp. non-essential] genes in mouse. • Protein complex. A total of 6,119 genes involved in protein complex formation were obtained by combining the protein complexes from Human Protein Reference Database  [61] , CORUM database  [62] , the human soluble protein complex census  [63] , and the human genes belonging to the Gene Ontology term "protein complex" under Cellular Component.
These genomic properties susceptible to be associated with the retention of 'ohnolog', SSD and CNV gene duplicates are provided as S1 Data. For each genomic property or combination of properties for which a number of samples presents missing data, multivariate information, such as I(X; Y|{A i }), are computed on the number of available samples N a without missing data for X, Y and {A i } variables (N a < N). Finite size corrections are then estimated based on N a instead of N samples (S1 File). This assumes that the missing data is missing completely at random.
Methodology Ancestral graphs. The miic software reconstructs Markov equivalent models of the broad class of 'ancestral graphs'  [11]  which can contain three types of edges, undirected (-), directed (!) and bidirected ($) edges, but: 1. no directed cycles (i. e. X ! ! Á Á Á ! ! Y with X Y) 2. no almost directed cycles (i. e. X ! ! Á Á Á ! ! Y with X $ Y) 3. no arrowheads pointing to an undirected edge (i. e. ! -or $ -) Multivariate information and most likely information contributors. The miic algorithm is an information-theoretic method that learns graphical models by progressively uncovering the information contributions of indirect paths in terms of multivariate information. The multivariate information between p variables, I(X 1 ; Á Á Á; X p ), is defined through alternating (inclusion-exclusion) sums of multivariate entropies H({X i }) = -∑ {x i } p({x i })log p({x i }) over all subsets of variables {X i } {X 1 ,Á Á Á,X p } as  [15] [16] [17] , IðX 1 ; Á Á Á ; X p Þ ¼ X i HðX i Þ À X i<j HðX i ; X j Þ þ X i<j<k HðX i ; X j ; X k Þ À Á Á Á ðÀ 1Þ kÀ 1 X i 1 <ÁÁÁ<i k HðX i 1 ; Á Á Á ; X i k Þ þ Á Á Á ðÀ 1Þ pÀ 1 HðX 1 ; Á Á Á ; X p Þ ð3Þ In particular, for p = 2 and 3 variables, it yields, IðX; YÞ ¼ HðXÞ þ HðYÞ À HðX; YÞ ð4Þ IðX; Y; AÞ ¼ HðXÞ þ HðYÞ þ HðAÞ À HðX; YÞ À HðX; AÞ À HðY; AÞ þ HðX; Y; AÞ ð5Þ where the 3-point information, I(X; Y; A), can be positive or negative unlike the 2-point (mutual) information, I(X; Y), which is always positive  [20].
Conditional multivariate information, I(X 1 ; Á Á Á; X p |A), are defined similarly as multivariate information, I(X 1 ; Á Á Á; X p ), but in terms of conditional multivariate entropies  [18] , H({X i }|A). In particular, conditional mutual information is defined as, IðX; YjAÞ HðXjAÞ þ HðYjAÞ À HðX; YjAÞ ¼ À HðAÞ þ HðX; AÞ þ HðY; AÞ À HðX; Y; AÞ ð6Þ using the definition of conditional entropy  [20] , H(X|A) = H(X,A) -H(A). Then combining the expressions of I(X; Y|A) and I(X; Y; A) yields a generic decomposition rule relative to a variable A or a set of variables {A i } m = {A 1 ,A 2 ,Á Á Á,A m } as, IðX; YÞ ¼ IðX; YjAÞ þ IðX; Y; AÞ ð7Þ IðX; YÞ ¼ IðX; YjfA i g m Þ þ IðX; Y; fA i g m Þ ð8Þ and conditioning Eq 7 on {A i } n-1 and setting A A n yields, IðX; YjfA i g nÀ 1 Þ ¼ IðX; YjfA i g n Þ þ IðX; Y; A n jfA i g nÀ 1 Þ ð9Þ which can be combined with Eq 8, setting {A i } m = {A i } n-1 or {A i } n , to yield the following iterative scheme on the contribution increment of the collected set {A i } n (see Results), IðX; Y; fA i g n Þ ¼ IðX; Y; fA i g nÀ 1 Þ þ IðX; Y; A n jfA i g nÀ 1 Þ ð10Þ As explained in S1 File, only positive information terms, I(X; Y; A n |{A i } n-1 )>0, contribute to the global mutual information between X and Y through the iterative decomposition of Eq 9, IðX; YÞ ¼ IðX; Y; A 1 Þ þ IðX; Y; A 2 jA 1 Þ þ Á Á Á þ IðX; Y; A n jfA i g nÀ 1 Þ þ IðX; YjfA i g n Þ ð11Þ where the most likely contributors A n after collecting the first n-1 contributors {A i } n-1 is chosen by maximizing I(X; Y; A n |{A i } n-1 ) > 0, while taking into account the finite size N of the dataset (S1 File).
• Step 1: Learning skeleton taking into account latent variables Starting from a fully connected undirected graph, miic iteratively removes all dispensable edges after collecting one-by-one their most likely contributors {A i } based on the edge ranking order, R(XY; A n |{A i } n-1 ) (Eq. S20 in S1 File), and using the following pseudocode, Repeat: take the top edge XY with highest rank R(XY; A n |{A i } n-1 ): -Update its contributor list: Once a first skeleton has been obtained using Step 1, the confidence on each retained edge can be estimated through an edge specific confidence ratio C XY based on the probability P XY exp(-NI(X; Y|{A i })) to remove a directed edge X ! Y from the graph G (S1 File), {A i } n {A i } n-1 + A n -If I(X; Y|{A i } n ) is C XY ¼ P XY hP rand XY i ð12Þ where hP rand XY i is the average of the probability to remove the XY edge after randomly permutating the dataset for each variable.
e. partial anti-correlations (S1 File). Algorithmic performance on benchmark networks with latent variables. The performance of the information-theoretic method miic was tested on benchmark ancestral graphs with latent variables using partially observed real-life networks (i. e. considering some variables as hidden) as well as random networks generated with the causal modeling tool Tetrad V (http://www. phil. cmu. edu/tetrad). Reconstructed networks are compared to partial ancestral graphs (PAGs)  [23] , which are the representatives of the Markov equivalent class of all ancestral graphs consistent with the conditional independences in the available data. In practice, benchmark PAGs have been derived by hiding some variables in benchmark directed acyclic graphs (DAG) using the dag2pag function of the pcalg package with slight modifications  [25, 26].
e. arrowhead (>) tail or undefined (-/) endpoint marks) as the PAG reference, TP misorient , leading to the orientation-dependent definitions TP 0 = TP -TP misorient and FP 0 = FP + TP misorient with the corresponding PAG Precision, Recall and Fscores taking into account arrowhead endpoint marks. The alternative inference methods used for comparison with miic are the FCI algorithm  [9]  and its recent approximate variant RFCI  [10]  implemented in the pcalg package  [25, 26]. The results obtained with FCI and RFCI are in fact very similar and we only present here comparisons with the more recent RFCI algorithm  [10]. RFCI's results are shown for an adjustable significance level α = 0. 01 and using the stable implementation of the skeleton learning algorithm, as well as the majority rule for the orientation and propagation steps  [27] , which give overall the best results.
For each sample size (N = 10 to 50,000) and fraction of hidden variables (0% to 20%), miic and RFCI inference methods have been tested on 20 combinations of hidden variables and 50 dataset replicates each. S1, S2 and S3 Figs give the average results over these multiple combinations of latent variables and dataset replicates and compare the reconstructed networks including orientations (solid lines) or without orientation (i. e. skeleton, dashed lines) to the theoretical PAG (or its skeleton) of the benchmark network. Algorithmic performance on undirected benchmark networks. The performance of miic was also tested on non-causal benchmark networks reconstructed from Monte Carlo sampling of Ising-like interacting systems. To this end, real-life causal networks, such as Alarm and Insurance, have been transformed into non-causal Ising-like networks (with binary spin variables x i = ±1) by setting pairwise interacting parameters k ij between connected variables X i and X j , so as to approximately reproduce the pairwise conditional mutual information I(X i ; X j |A X i Xj ) of the original real-life causal network.
This leads, however, to significant correlations between successive accepted configurations with a roughly exponential decay between n distant samples, C(n) ' C(0)exp(-n/R) = C(0)α n , where CðnÞ ¼ Cðk À 'Þ ¼ h P i dx ð'Þ i dx ðkÞ i i is the average autocorrelation with lag between the kth and ℓth samples (with n = k-ℓ), where dx ðkÞ i ¼ x ðkÞ i À " x i. The effective number of independent samples N Ã eff can then be estimated through the apparent increase of variance between the N partially correlated samples as  [67] , V N ¼ 1 N 2 X k X ' h X i dx ðkÞ i dx ð'Þ i i ¼ 1 N 2 X k X ' Cðk À 'Þ ¼ 1 N Cð0Þ þ 2 1 À 1 N Cð1Þ þ 2 1 À 2 N Cð2Þ þ Á Á Á þ 2 N CðN À 1Þ ! ð13Þ leads for a first order Markov process with C(n) = C(0)α n to, V N ¼ Cð0Þ N 1 þ 2 1 À 1 N a þ 2 1 À 2 N a 2 þ Á Á Á þ 2 N a NÀ 1 ! ' Cð0Þ N 1 þ a 1 À a ¼ Cð0Þ N Ã eff ð14Þ yielding a smaller effective number of samples N Ã eff < N for correlated datasets (α > 0) as, N Ã eff ¼ N 1 À a 1 þ a ð15Þ This estimate suggests to use N Ã eff , instead of N, to compute the finite size corrections of the miic approach, in order to correct for the correlations between successive samples generated through Monte Carlo sampling.
e. N Ã eff > 300. These results demonstrate that the theoretical estimate of N Ã eff , Eq 15, yields the best compromise between over-fitting and under-fitting graphical models given the finite and partially correlated available datasets. They underline also miic accuracy to discard spurious causality in observational data, even from relatively small effective numbers of independent samples, i. e. N Ã eff > 300 in S6 Fig and for network skeleton (dashed lines) and oriented network CPDAG (solid lines) for a decreasing edge-specific confidence filtering, C XY = 1 (no filtering) 0. 01, 0. 001 and 0. 0001. For sample size >100, confidence filtering of individual edges improves the precision (at the expense of recall) not only for the skeleton (dashed lines), as expected, but also for the oriented networks (solid lines).
deg. 2. 5, 509 parameters], Insurance [27 nodes, avg. deg. 3. 9, 984 parameters] and Barley [48 nodes, avg. deg. 3. 5, 114,005 parameters]. (F) Computation times of miic (warm colors) compared to RFCI (cold colors). Inserts: computation times in log scale showing a linear scaling (solid bar) in the limit of large datasets, τ cpu * N 1±0. 1 , with miic, and a close to quadratic scaling (dashed bar), τ cpu * N 1. 8±0. 3 , with RFCI. https://doi. org/10. 1371/journal. pcbi. 1005662. g001.
5, E7. 75 and E8. 25), Fig 2A-2C and S10 Fig. The cells extracted from E8. 25 were also divided by the authors in two different pools: potential endothelial precursors and potential hematopoietic precursors based on the expression of the Runx1 hematopoietic marker. Gene expression was collected using single cell qRT-PCR and binarized by the authors, leading to two-state (on / off) expression levels in the available dataset. Pooling all cells together regardless of their developmental timing (from day E7. 0 to E8. 25), we first analyzed their population heterogeneity using.
(TIFF) S10 Fig. Gene expression distribution in 3,934 single cells from mouse embryos. Expression data on the 33 TFs are obtained from [24]. Percentage of samples with expressed genes (red) and non-expressed genes (gray). (TIFF) S11 Fig. Unfiltered network skeleton for hematopoiesis differentiation data. Hematopoietic / endothelial gene expression data in 3,934 single cells from mouse embryos [24]. 7 out of 82 edges (8. 5%) with C XY > 10 -3 have been filtered in Fig 2D (blue edges correspond to anti-correlations). (TIFF) S12 Fig. Expression alterations in 807 samples of breast tumor data from COSMIC database [34]. Percentage of samples with normalized over-expression (red), normalized underexpression (blue) and unchanged normalized expression (gray) for each gene based on COSMIC. (TIFF) S13 Fig.
Mutations in 807 samples of breast tumor data from COSMIC database [34]. Percentage of mutated samples (red) for each gene. (TIFF) S14 Fig. Unfiltered network skeleton for breast tumor ploidy-mutation-expression data from COSMIC database [34]. Due to the limited numbers of samples (N = 807) and recurrent gene mutants (Figure -figure supplement 2), most gene mutations (yellow) are not confidently linked to any altered expression levels (green) and have been filtered in the high confidence network Fig 3D (C XY < 10 -3 ), with the notable exceptions of TP53 and RB1 mutations, which have a significant impact on gene expressions, Fig 3D, see main text (blue edges correspond to anti-correlations). (TIFF) S15 Fig. Unfiltered network skeleton for ohnolog retention data in human. Genomic data for the 20,415 human coding genes is provided in S1 Data.
The only edge with confidence ratio C XY > 10 -3 is RecDominance -ProteinComplex with C XY = 0. 25 (blue edges correspond to anti-correlations). (TIFF) S1 Software. Software and tools. miic is provided in two formats: an R-package to be used in the R environment, and miic and FCI executables, which were used for all benchmarks included in the paper. (ZIP) S1 Data. Dataset of human genomic properties. This dataset contains all genomic data for the 20,415 human genes analyzed in Fig 4. (XLS) S1 Table.
Sustainability The first BTM systems comprised specific elements, both for the hardware (RTUs) and for the software. In particular the software was usually created on a case-by-case basis by services companies, and the project teams were sometimes dissolved even before the BTM was put into production, due to delays. All of this has vastly evolved in part because of the standardization of the SCADA protocols  [8] , making equipment interchangeable, and in part because of SCADA and BTM software whose sustainability is largely greater than that of specific software development. Nonetheless, today's BTM systems still suffer from major lack of sustainability:  Configuration files, which contain the data describing the points and their organization, sometimes use a proprietary format, and sometimes are not even stored on the machines that comprise the BTM system.
Replacement of Vector Data by Bitmap Data Up to the 80s, there were two types of user terminals: the "alphanumeric" terminals, and the "graphical" terminals, using random scan mode only. The second were uncommon due to their high price. In the case of SCADA systems, a third type of display time was often used; custom-built overview displays on control room walls using lamps and electromechanical displays driven by the central computer using a custom-built RTU. The 90s will see wide-spread use of graphical card using "bitmap" memory, which will replace both the "alphanumeric" and "graphical" screens. However the BTM continue to use vector graphics because CAD software used by architects produces vector graphics. As we will see later, there is no longer any reason to use vector graphics in BTM software.
Changes in Professional Software All wide-spread graphical software tools are able to produce files in Web bitmap format, JPEG or PDF. This is true in particular of software used by the workers of the construction and equipment production fields, that is to say software used by architects, sensor providers, and RTU or PLC providers. In the case of BTM systems, millimeter level precision of a 3D vector format such as the DXF format is useless, a nice artist's rendering or a bitmap plan are preferred when the human interface of the BTM is using web technologies. Given the definition of the points, the XLS format is ideal. In particular, all spreadsheet software can product CSV format files, which all database software can then import, for instance using PHP scripts.
Changes in Graphics Software The two leading pieces of software from the Adobe corporations, Photoshop and Illustrator, are, in terms of software development costs, colossal. It is entirely impossible to produce as many features and as much power when a SCADA or BTM piece of software, when the developer of the overview is only a small part of the overall cost of the product. Moreover, these very powerful programs have a readily available large amount of trained users. Finally the web provides a nearly unlimited library of graphics. To create simple animations for the overviews, the creation of an animated GIF file is much cheaper than that of a script for a SCADA or BTM application. All wide-spread graphical software tools are able to produce files in Web bitmap format, JPEG or PDF. This is true in particular of software used by the workers of the construction and equipment production fields, that is to say software used by architects, sensor providers, and RTU or PLC providers.
Changes in DBMS The relational data model is considered to have appeared in 1970, the year when Edgar Frank Codd published a seminal article entitled A Relational Model of Data for Large Shared Data Banks. In 1979, the Relation Software Inc. company (now Oracle Corporation) begins the commercialization of the first version of SQL. In 1987, ISO published the international standard ISO/CEI 9075 -Information technologies -Database languages -SQL. During 15 years or so, the costs of relational DBMS were very steep. Commercial practices of forced sales of expensive intellectual services (training and support) were common. Finally, as hardware support for the DMBS, it was necessary to acquire and maintain powerful and costly computers. For all these financial reasons, the editors of BTM programs were forced to keep their strictly proprietary point management systems, although the technical advantages of a relational model were increasingly obvious.
Changes in the Controllers Since the 80s, the significant reduction in processor and RAM memory costs have led to both a lowering of the costs of the RTUs, but also to an increase in their capabilities. From simple telecommunications hub, they have become genuine computers, that is to say with storage, data processing, and broad communications capabilities. Moreover, the protocols used have become standard, such as MODBUS/TCP. Modern controllers, in addition to their processing capabilities, can handle so-called "fieldbus" protocols, while talking to the central system through a single unified protocol (MODBUS/TCP). The server software architecture is simplified, and the communication between the controllers and the server can occur through a WAN (Internet). Moreover the controllers are able to directly feed DBMS (remote SQL client).
Consequences of these Changes in BTM It doesn't seem necessary to lead the reader and detail point by point the obvious advantages of the technical changes with regards to the previously outlined new challenges. We limit ourselves to underline the follow-on fact: many professional SCADA and BTM software has become obsolete:  The functionality at the heart of these professional applications can be better provided by widely available cheap or free programs.  Conversely, major issues such as the data exchange with the cloud, and security policy aspects are not at all taken into account.
Software Architecture The fixed workstations are two Windows HP-Z1 PCs; the only software they need is a Web browser. The BTM server runs CentOS; it rust the Apache Web Server, PHP, and the MySQL relational DBMS. The remote workstations are Windows computers, tablets, or smartphones, the only required software being a web browser. The WAGO controllers use the MODBUS/TCP protocol as well as a MySQL client which lets them directly use the MySQL DBMS. The software for the controllers is written using an automation language by the WAGO company. The application layer of the BTM comprises two distinct applications. An administrative application is used to configure the BTM: number of floors, on zone, description of the 24,000 points. This description is stored in the DBMS; it can be used simultaneously by the administrative application, but also by the second application which is the supervision workstation.
HP-Z1 ADSL Overall Working (Figure  3 ) 1) All of the state changes which must raise an alert are all detected on the controllers which use their SQL client to directly perform a write in the DBMS (remote SQL UPDATE). 2) The values of the energy meters are stored in the DBMS every 10 minutes. The server performs the MODBUS/TCP read requests to the controllers, one after the other, which requires around 30 seconds for around 3,000 measurement points. 3) When a user workstation displays a zone map, the HTML/JavaScript human interface performs periodic polling to the WAGO controllers shown on the map, typically around 200 values. The human interface performs the read requests by MODBUS/TCP to the controllers in charge of the displayed points. 4) When a user workstation performs a command request, the human interface performs a MODBUS/TCP write directly to the relevant WAGO controller.
The human interface also performs DBMS request which logs the request in order to maintain a history of the commands which were run. 5) Periodically, each connected TV runs an HTTP GET request on the server which dynamically generates a new HTML page (PHP script). Figure  3 : Overall Working 1 The alerts are directly sent by the controllers using remote SQL/TCP 2 The values of the meters are polled using MODBUS/TCP 3 A user workstation polls the values for the displayed zone using MODBUS/TCP 4 A user workstation performs a "manual" command using MODBUS/TCP 5 A screen read the values to display using an HTTP GET request When software updates must be performed on the WAGO controllers, this is done using a Windows PC dedicated to this task and running an application provided by WAGO. This workstation is normally in the same room as the two fixed HP-Z1 stations.