dataesr/scientific-paragraphs-categorization huggingface dataset:
The Transition From Manual to Automatic Testing The many steps involved in testing software are often repetitive, tedious, and prone to human errors. The most basic testing steps consist of running the program with a given set of instructions and verifying that the outputs that are generated are as expected. If the source code and/or example data are not available locally, testing also involves downloading of a series of files. Additionally, when the software depends on other programs or libraries, their installation is required (see section 5. 3) prior to the creation of the program executable from the source code. As the frequency of software updates increases, full testing can become a great consumer of developers' time and is therefore sometimes avoided, increasing the risk of releasing faulty source code.
Shells are the most fundamental way in which users can interact with the system. Actually, shells used to be the main tools for interactions with computers before the advent of GUIs. Each command entered in the shell results in an action performed and/or in text output. The success (or failure) of each action is then summarized by an integer number called exit code generated after each command execution, although it is kept hidden to the user unless specifically requested. A series of consecutive operations can therefore be easily automated by creating a text file containing the corresponding commands (i. e. , a shell script), and their respective success can be checked using exit codes. Hence, the automation of model testing can be accomplished if all the steps involved can be summarized in a series of simple commands and included in a script.
Despite a few years of programming experience gained in the development of the RAPID source code, the author was not familiar with the many command line tools allowing such automation. However, after overcoming the initial learning curve associated with the discovery and use of a series of these tools, it was found that many of the steps involved in the testing of RAPID can be performed directly from the shell using existing programs (see the Appendix B for examples on Linux). The few tasks that could not be directly performed with off-the-shelf tools were specifically related to the outputs of RAPID and necessitated the creation of ad hoc programs. As mentioned earlier (section 2), two main modes of usage currently exist for RAPID: the first consists in performing flow simulations, and the second is dedicated to parameter optimization.
The programs that were created for the automatic reproduction of the first and second RAPID papers together combine 138 tests  (23 and 115, respectively). The number of tests allowed by these programs is about an order of magnitude greater than what was used when performing manual testing and approximately an order of magnitude faster. Such a stricter testing procedure allowed for both increased robustness of the software and for temporal savings. Here again, the value of these tests goes beyond their use for development work, as they provide examples of model usage as well as a means for users to check their installation (or modifications) of RAPID. The tests corresponding to the first RAPID paper were actually run successfully by 25 attendees of the second RAPID training (section 4. 3). Finally, the automatic reproduction of existing studies fully document the provenance of all simulations performed in the corresponding studies.
Despite demonstrating the portability of the RAPID source code through automatic building of the software, one of the direct benefits of hosted continuous integration was to fully trace the software dependencies and environment variables that are needed to build RAPID. A successful build on a CI server guarantees that all necessary software were properly described and therefore that the installation procedure was fully documented. Setting up continuous integration for RAPID also proved to be immediately valuable as it allowed for illumination of some dangerous programming practices present in the source code (e. g. , lack of variable initialization and implicit data type conversions). These weaknesses were exposed because the compiler collection used in the CI server provided different warnings than that of the development machine.
Code and data sharing are particularly valuable once a small community of users exists. Training courses allow for the evaluation of software usability and are also an integral part of user engagement. Exposing software empowers users through eased access and usability and hence contributes to the growth of the users community while simultaneously saving developer time therefore making the The continuous integration of RAPID is currently running on fifteen separate Travis CI workers. Note that the build time provided is the sum of the run times of all workers and that the wall clock time is shorter as several workers run concurrently. Earth and Space Science 10. 1002/2015EA000142 sharing process more sustainable. Finally, this phase demonstrates a certain level of maturity in the software and thus helps attract more users including potential industry partners.
g. , array sizes) by compilers. This advantage comes at a price: the source code needs be rebuilt every time new instructions are used. As the software matures, it is therefore good practice to combine all possible instructions in a single text file that is read at runtime. The instruction file then enables multiple simulations to be run without rebuilding the source code, which greatly eases day-to-day operations and automatic testing. Some files create a link between the source code and the data of an Earth system model. Examples include the data downloading script (which contains the names of all input and output files) and the instruction file (which can also contain their respective sizes). In the process of sharing source code and data, one may Earth and Space Science 10. 1002/2015EA000142 therefore wonder which of the associated repositories is the most appropriate to store these specific files.
Acknowledgments This work was supported by the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  and by the  University of California Office of the President Multicampus Research Programs and Initiatives ; both institutions are gratefully acknowledged. This research was also partially supported by a  Microsoft Azure for Research grant  from  Microsoft Research. The practical application of this study was enabled by the netCDF, MPICH, PETSc, and TAO scientific libraries that were all built with the GNU Compiler Collection installed on a Community Enterprise Operating System (CentOS). Version control was performed using git. This study was also made possible using the following free online services: GitHub (code repository), Zenodo, (data repository), and  Travis CI  (Continuous Integration).
Comments from the Editor, Associate Editor, and two anonymous reviewers on earlier versions of this manuscript are gratefully acknowledged. The authors are thankful to  Yolanda Gil  for stressing the importance of software licenses and for continuous support throughout the research presented in this paper, to an anonymous reviewer of a National Science Foundation review panel for suggesting the use of GitHub, to  Luke A. Winslow  for demonstrating the power of Continuous Integration and for mentioning Travis CI, and to  Lars Holm Nielsen of Zenodo  for support on hosting the RAPID input and output files. Thank you to the  EarthCube OntoSoft leadership team  and the advisory committee members for enlightening discussions. The two data sets used in this paper [David et al. , 2011b[David et al.
, , 2011c] ] are openly available through their respective digital object identifiers (DOI). The piece of software used herein includes the scripts to reproduce all numerical experiments performed in this paper (hence describing provenance of results) and is openly available at https://github. com/c-h-david/rapid/tree/20150906; it will be assigned a DOI [e. g. , David, 2010David, , 2011aDavid, , 2011bDavid, , 2013aDavid, , 2013b] ] pending publication of this study.
Since SPKMG values are real numbers, we will be able to employ statistical techniques to unleash the structure and functional relationship of these numbers for a population. SPKMG values are calculated within a software which we have named OncoTrack. We hypothesized that if nucleotide count alterations due to structural variations in the genome are truly associated with a tumour state, then we will be able to capture these and find some meaningful phenotypic associations through SPKMG. For this, we looked at the SPKMG measure from two perspectives, case/control comparative analysis, and control-free analyses using correlation and mutual information. Our goal was to assess the usefulness of SPKMG as an improved way of looking for population level patterns for tracking and predicting molecular changes in tumours.
In this paper we explore the efficacy of SPKMG as a more efficient quantification technique for precision medicine and for tracking cancer progression. To establish our theory, we have used total 69 exome datasets that comprise of 11 non-BRCA1/2 familial breast cancer (BC)  27  , 19 Esophageal Squamous Cell Carcinoma (ESCC) tumour samples (T) with 19 ESCC matching germline normal (N) samples  28  , and 20 healthy (control) samples (H)  27, 29. The OncoTrack analysis pipeline architecture is shown in Fig. 1.
Breast Cancer Comparative Analysis (Case/Control). We performed the comparative analysis of the breast cancer population and the control population (Supplementary Table  2 ) using edgeR. We selected the most statistically significant genes with p-values ≤ 1. 0e-20 (or adjusted p-values ≤ 1. 0e-17). This gave us 28 genes with adjusted p-values ranging from 6. 3E-063 to 5. 5E-018 (Table  1 ). An analysis of the COSMIC  32  database showed that all these 28 genes have been discovered in multiple cancer studies, with study counts ranging from 6 for KIR3DL1 to 43 for FLG. All these genes have been widely researched with the number of PubMed citations ranging from 8 for SPRRD2 to 92 for FLG (in the References section of COSMIC database). We also performed a manual bibliomic analysis to verify the role of these 28 genes in cancer progression; the results are listed in Supplementary Information 1.
Breast Cancer Control-free Analysis using Mutual Information. The breast cancer control-free Mutual Information (MI) network was constructed by using 0. 05 as threshold for removing the weakest edge of each triple of nodes in parmigene (using ARACNe)  40, 41  and removing all gene-pairs with MI coefficients less than 1. This gave us 1405 genes with high MI. We then did graph theoretic analysis using igraph package  42. The MI network of the breast cancer population is shown in Fig. 4(a). We selected the top 18 genes that had highest degrees (8 and above). All these genes are responsible for multiple carcinomas. These genes are PPP1R15B (30 tissues), TTC21B (35 tissues), XIRP2 (47 tissues), KIAA1143 (16 tissues), BCHE (36 tissues), GLRB (33 tissues), JAKMIP2 (40 tissues), OR13C8 (19 tissues), ZNF790 (29 tissues), POF1B (38 tissues), ARHGAP24 (26 tissues), DACH1 (40tissues), LIPI (38 tissues), SPAM1 (34 tissues), DIAPH3 (38 tissues), CSMD3 (49 tissues), DCC (44 tissues), and FLRT3 (22 tissues).
The tissue numbers are taken from the COSMIC database. We then looked at the properties of the breast cancer MI network and how it compares with other biological networks. The MI network of 1405 genes had 1172 edges. The network had a diameter of 13 and a clustering coefficient of 0. 446. Metabolic networks have a clustering coefficient of 0. 67  43. The degree distribution, P(k), gives the probability that a randomly selected node will have k links. We found that the MI network followed a power law degree distribution, P(k)~k -α as shown in Fig. 4(c ) with an α (Alpha) of 2. 44. Typically the value of α is 2 < α < 3 with occasional outliers for natural scale-free networks. Biological networks such as protein interaction networks and metabolic networks have similar values of alpha (2. 4, and 2.
We selected the clusters that have 10 or more members for further analysis. Figure  4(b)  shows the subnetwork of genes from the clusters with 10 or more genes. We combined the resulting 9 clusters and top 19 highest degree genes to get 264 unique genes. We looked at the COSMIC database for these 264 genes. Out of these 264 genes, only 4 genes were not present in the COSMIC database as cancer genes. This implies that 98. 5% of the genes that form clusters with high degree of connectedness within the MI network are already known to be associated with cancer. We looked at the functional enrichment of these 264 genes in DAVID  50, 51. The enrichment analysis returned the terms 'Cell-cell adhesion' , 'Cell adhesion' and 'Biological adhesion' , with adjusted p-values less than 0. 01 (Supplementary Information 2).
Discussion Lack of combinatory tools and technologies, have for long, prohibited looking at DNA for quantitative molecular pathology. In this paper, we have presented a novel technique called SPKMG for quantitative DNA analysis. The SPKMG values are similar to RPKM values, applied in the exonic context. We have used various types of analytics to study properties of the SPKMG measure within OncoTrack software, and have found that the results from the different analyses show high levels of concordance. We have used cancer patients' exome data to measure SPKMG and showed that it shows a normative pattern, and can be used as a tracking mechanism for a complex heterogeneous disease like cancer. This technique identifies common patterns across populations which can be used to define and track the phenotypic properties of cancer at a population level and provide interesting insights into the state of the cancer and cancer development.
This is particularly interesting considering the observation that in a high proportion of patients diagnosed with early stage breast cancer, the tumour has already metastasized  68. The superior ability of OncoTrack to identify these states shows promise for providing a better metric for identifying the actual state of tumours. On the other hand, the case-control analysis for the ESCC tumour showed primary differences in cell adhesion and tumour development and metastasis related signals. Therefore using these analyses we were able to more accurately characterize the genetic state of the tumour. In addition, we were able to extract some interesting insights into the ESCC tumour states using the SPKMG values for the germline dataset. OncoTrack was able to discover similarities and dissimilarities between tumour and the matching tissues based on their SPKMG values.
Methods Architecture of OncoTrack. The OncoTrack analysis pipeline architecture is shown in Fig. 1. We identified suitable publicly available exome datasets from SRA. We included datasets which were sequenced using Illumina HiSeq with the random Agilent sure-select library preparation protocol. This type of library preparation is required for being able to measure the SPKMG statistic. The SRA files were downloaded and converted to FASTQ format before further processing. The data were pre-processed, quality checked and cleaned with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc), followed by alignment to hg19 reference genome (http://hgdownload. cse. ucsc. edu/downloads. html) using BowTie  76  to create SAM/BAM files. This was followed by duplicate marking and indel-realignment, using Picard (http://picard.
To test our model, we used exome data from studies on two types of cancers viz. , breast cancer  27  and esophageal squamous cell carcinoma (ESCC)  28. The first cancer dataset consists of exome data from 11 breast cancer patients with familial Non-BRCA1/ BRCA2 breast cancer from France, Italy, the Netherlands, Australia, and Spain. The dataset comprises of patients from seven families having at least 6 breast cancer cases (between 6 and 10). None of the patients had familial BRCA1/BRCA2 pathogenic mutations and were diagnosed with breast cancer before the age of 60. No woman was affected with ovarian cancer in these families  27. The second cancer dataset is from Esophageal Squamous Cell Carcinoma (ESCC) patients with > 70% malignant cells and matched germline tissues (adjacent esophageal epithelial tissue five centimetres away from the border of surgical area) collected from Cancer Institute/Hospital, Chinese Academy of Medical Sciences (CAMS) and Linxian Cancer Hospital  28.
We included 19 tumour tissues and 19 germline tissues from this whole exome dataset. For healthy control data, we created a healthy population from the combination of healthy reference data from two independent studies. These were 13 independent normal healthy HapMap exome data from Comino-Méndez I. et al. 29  and 7 normal HapMap data from another independent study  27. The datasets and their details are: 1. Exome data from 11 familial non-BRCA1/BRCA2 breast cancer patients, having NCBI accession numbers ERR166303, ERR166304, ERR166307, ERR166308, ERR166310, ERR166312, ERR166315, ERR166330, ERR166333, ERR166335, ERR166336. In this paper they are referred to as BC1, BC2, BC3, BC4, BC5, BC6, BC7, BC8, BC9, BC10, and BC11 respectively. Megabase of the mappable Genome. Intuitively, SPKMG is similar to RPKM 78 or FPKM  79  , adapted for exome data with normalizations at the gene and genome level.
The formula for SPKMG is as follows: = * * SPKMG R/(E G/(1000 1000000)) (1) where, R = Sequence reads aligned in exonic regions of a gene (after removal of PCR duplicates and low quality reads) E = Length of genic region that includes only the exonic regions of this gene G = Total number of reads that aligned to the reference genome The SPKMG measures of breast cancer patients are presented as an example in Supplementary Table  1. We plotted scatterplot matrices for the SPKMG values to examine the characteristics of the algorithm. The 11 breast cancer SPKMG values exhibited a linear correlation between any two samples. For the ESCC samples, the gene TTN had very high SPKMG measures (outlier) in both tumour and germline datasets and had to be removed for the analysis. The gene TTN has been previously mathematically associated with cancer  80.
Comparative Case/Control Analysis. In case/control analysis, we computed the comparative SPKMG for pairs of populations using edgeR. For this analysis, we used cancer data (11 breast cancer and 19+ 19 ESCC samples) as case and 20 healthy samples (13 H samples + 7 NA samples) as control. We then took these samples and examined how they clustered in MDS (Multi-dimensional scaling) plots using edgeR, along with hierarchical clustering for the samples. We identified genes that were highly significant, and analysed them to identify their roles in cancer. We took 168 KEGG cancer related pathways from Broad Institute 33 and used the XomPathways R script  34  , followed by network analysis, for the functional analysis of these genes. We also used DAVID, for further annotation.
ARACNe 41 uses MI to predict potential functional associations between genes by identifying statistical dependencies between gene expressions. We used parmigene  40  , an R statistical package with ARACNe to measure the MI between each pair of genes, to reconstruct the gene-gene interaction network with core genes. The BC (19358 × 11) SPKMG matrix of 11 breast cancer patients and the N and T (19358 × 19) ESCC SPKMG data were used to obtain 19358 × 19358 respective matrices of MI. We used appropriate thresholds for removing the weakest edge of each triple of nodes in parmigene and all gene-pairs with low MI coefficients to obtain the final adjacency matrices. The interaction networks constructed from these adjacency matrices were called the MI networks. The MI networks were then analysed for network properties.
Comparison of SPKMG with CNV and SNP. We used GATK  25  and XHMM  26  protocols for calling SNVs and CNVs respectively. For the point mutation analysis, we used GATK haplotype caller on the recalibrated BAM files, followed by joint genotyping, variant quality score recalibration (VQSR) and variant annotation. We annotated the variants with the ClinVar database and 1000Genome allele frequencies and the SNPEff 89,90 impact. Finally, the variants considered for analysis passed the VQSR filter, had minor allele frequency less than 0. 01 in at least one 1000Genome population, and had a moderate/high impact as annotated by SNPEff. For the CNV calls we followed the steps as suggested in the XHMM tutorial (https://atgu. mgh. harvard. edu/ xhmm/tutorial. shtml). We calculated the read depths across the reference genome for all samples and removed the regions with outlier GC content.
Dataset containing exome data from 19 tumour tissues. These data have accession numbers SRR1044337 (ESCC-D1T), SRR1044333 (ESCC-D2T), SRR1044331 (ESCC-D3T), SRR1044329 (ESCC-D4T), SRR1044327 (ESCC-D5T), SRR1044325 (ESCC-D7T), SRR1044323 (ESCC-D8T), SRR1044321 (ESCC-D9T), SRR1044357 (ESCC-D10T), SRR1044355 (ESCC-D11T), SRR1044353 (ESCC-D12T), SRR1044351 SPKMG Computation. OncoTrack computed the SPKMG values as the Sequence Per Kilobase of exon, per.
2. 2 Dataset containing exome data from 19 tumour tissues. These data have accession numbers SRR1044337 (ESCC-D1T), SRR1044333 (ESCC-D2T), SRR1044331 (ESCC-D3T), SRR1044329 (ESCC-D4T), SRR1044327 (ESCC-D5T), SRR1044325 (ESCC-D7T), SRR1044323 (ESCC-D8T), SRR1044321 (ESCC-D9T), SRR1044357 (ESCC-D10T), SRR1044355 (ESCC-D11T), SRR1044353 (ESCC-D12T), SRR1044351 SPKMG Computation. OncoTrack computed the SPKMG values as the Sequence Per Kilobase of exon, per.
Introduction The present study was carried out in the framework of a collaboration between CERFACS, SCHAPI and LNHE (EDF). It deals with the simulation of river hydrodynamics, with a focus on environmental risk assessment related to flooding. It relies on the use of the MASCARET software that solves the mono-dimensional shallow water equations and is used by SCHAPI (Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations) and SPCs (Services de Prévision des Crues) to build models for catchments of interest in France. In spite of the advances in numerical modelling and the expertise invested in the development and the use of MASCARET, the capacity for real-time anticipation of extreme flood events remains limited because of several sources of uncertainties in hydraulic models.
The implementation of an ensemble-based DA algorithm thus appears as the next step towards a physically consistent DA system for flood forecasting. The structure of the paper is as follows: Section 2 provides a brief description of the hydraulic model equations and application to the Adour catchment. The ensemble-based approach is presented in Sect. 3 as well as the elements for the implementation of this approach with a coupling software and MASCARET. In Sect. 4, the DA results are presented for a synthetic test case as well as over a set of selected real flood events. Conclusions and perspectives for this work are given in Sect. 5.
Even though all these emission processes are now included in many CTMs, the emitted species have different chemistry and lifetimes, and models often address some specific applications and thus specific spatial areas. It was the case of the CHIMERE model, extensively described in  Menut et al. (2013b)  for its 2013 version. Originally, CHIMERE was designed for urban areas. It was extended later to western Europe, and then to the northern part of Africa by including mineral dust emissions, but was able to run over these areas only, due to limitations in forcing sources available data (such as the anthropogenic emissions). In the new version presented in this paper, CHIMERE-2016, the new developments made since the CHIMERE-2013 version presented in  Menut et al. (2013b)  focus on input databases and model grid management in order to build a CTM able to run over a hemispheric domain as well as for smaller regions anywhere in the world.