dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Records All tables are available at figshare (Data Citation 1). Clinical and genomic information of the patients (Table  1 : Patient annotations, Data Citation 1) include age at diagnosis, disease stage, and overall and event-free survival, MYCN status and the presence of segmental aberrations of chromosome arms frequently affected in neuroblastoma, such as 1p loss and 2p gain. Normalized probe-level copy number data using genome build hg19 can be accessed through GEO, with accession number GSE103123 (Data Citation 2). An overview is provided of the processing steps that were performed on the collected data to obtain the data that is available on GEO (Table  2 : data processing steps, Data Citation 1), as well as segmented copy number data (Table  3 : Segmented copy number data, Data Citation 1), in which the column "annotation" reports on the presence of an aberration considering the platform-specific cutoffs (see Methods).
Usage Notes Probe-level data can be accessed at GEO with the accession number GSE103123 (Data Citation 2). Sample annotations are provided in the GEO series metadata and at Figshare (Table  1 : Patient annotations, Data Citation 1). Instead of downloading probe-level data, it is also possible to directly use the segmented data, also available at Figshare (Table  3 : Segmented copy number data, Data Citation 1). The segmented profiles have been manually inspected to ensure confidence of the called aberrations. The column "annotation" represents the presence of an aberration considering the platform-specific cutoffs (see Methods for details), but also the segment log2 ratios can be used. Data can be analyzed and visualized using either the R statistical program, the R2 web-based platform  10  , or a Shiny web application  17.
For R users we recommend the R package "copynumber"  18  , which provides functions for visualization, including frequency plots and heatmaps with chromosome ideograms. The R package CNTools  19  can be used to transform the segmented data into a window-or gene-based matrix. A script is provided on GitHub, including functions to select samples with an aberration for a certain gene/region (https://github. com/padpuydt/copynumber_HR_NB/). Using the R2 platform  10  , one can perform multiple data analysis procedures and create several types of plots. A few examples are plotting a gene's copy number in groups of samples, associations between sample annotations, survival analysis and viewing samples in the genome browser (Fig. 6 ). To create these plots, go to the dataset home page on R2 (https://hgserver1.
amc. nl/cgi-bin/r2/main. cgi? &dscope = NB556HR&option = about_dscope). The R2 genome browser is directly linked The other three analyses/plots can be found under "Other Analyses". There we selected "View a gene in groups", "Relate 2 tracks" and "Kaplan Meier by annotated parameter" in step 3 (Select type of analysis). In addition, R2 allows to perform correlation analysis between genes, find genes with differential copy number between groups, perform dimensionality reduction with t-SNE maps, or generate a heatmap of a specific (grouped) subset of samples (CGH Sample Sorter on dataset home page). More detailed documentation can be found on the R2 website (http://r2. amc. nl). Finally, we developed a Shiny web application  17  to interrogate the copy number status and association with survival of any gene or genomic region of interest.
Acknowledgements The authors thank  Robrecht Cannoodt ,  Inge M. Ambros ,  Peter F. Ambros ,  Shahab Asgharzadeh ,  Edward F. Attiyeh ,  Valérie Combaret ,  Raffaella Defferrari ,  Matthias Fischer ,  Barbara Hero ,  Michael D. Hogarty ,  Meredith S. Irwin ,  Susan Kreissman ,  Ruth Ladenstein ,  Eve Lapouble ,  Geneviève Laureys ,  Wendy B. London ,  Katia Mazzocco ,  Akira Nakagawara ,  Rosa Noguera ,  Miki Ohira ,  Julie R. Park ,  Ulrike Pötschger ,  Jessica Theissen ,  Gian Paolo Tonini ,  Dominique Valteau-Couanet ,  Luigi Varesio ,  Rogier Versteeg  and  John M. Maris  for their contributions to the  UHR  project.
Let O ⊆ N and D ⊆ N be a set of all evacuees' origins and safe exit destinations, respectively. We modelled the evacuation as a unified crowd flow, where each individual is seen as a unit element (particle) of that flow and the objective is to maximise the flow of demands (evacuation requests) with certain constraints. We considered travel time optimisation with path safety, envy-freeness (fairness), and agile paths. Route safety. Our objective is to safely evacuate as many evacuees as possible from all origins o ∈ O over the safest and the most efficient evacuation paths to any of the safe exits d ∈ D. Let us assume that safety status S a is given for each arc a ∈ A as a function of safety conditions that can be jeopardised by a hazard. Safety can be calculated from sensor data (e. g. , temperature, smoke, etc.
Acknowledgments: This work has been partially supported by the  Spanish Ministry of Economy and Competitiveness  through grant  TIN2015-65515-C4-X-R ("SURF" ), by the  Autonomous Region of Madrid  through grant  S2013/ICE-3019  (" MOSI-AGIL-CM ", co-funded by  EU Structural Funds FSE and FEDER) , and by  URJC-Santander  grant  30VCPIGI15. The authors would like to thank  Matteo Vasirani  for his contribution to the application for coordinating of traffic flows through intelligent intersections outlined in Section 3. 1.
Background: Recent cancer genome studies on many human cancer types have relied on multiple molecular highthroughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community. If used in their entirety and provided at a high structural level, these data can be directed into constantly increasing databases which bear an enormous potential to serve as a basis for machine learning technologies with the goal to support research and healthcare with predictions of clinically relevant traits. Results: We have developed the Cancer Systems Biology Database (CancerSysDB), a resource for highly flexible queries and analysis of cancer-related data across multiple data types and multiple studies.
The CancerSysDB can be adopted by any center for the organization of their locally acquired data and its integration with publicly available data from multiple studies. A publicly available main instance of the CancerSysDB can be used to obtain highly flexible queries across multiple data types as shown by highly relevant use cases. In addition, we demonstrate how the CancerSysDB can be used for predictive cancer classification based on whole-exome data from 9091 patients in The Cancer Genome Atlas (TCGA) research network. Conclusions: Our database bears the potential to be used for large-scale integrative queries and predictive analytics of clinically relevant traits.
Background Large-scale cancer genome studies based on Next-Generation Sequencing (NGS) technology have enabled extensive research on tumorigenesis and treatment rationales  [14]. The amount of data that has been generated and made available contrasts its limited accessibility to the research community. There is an increasing demand for customized queries to the data in a way that is accessible to scientists and physicians without any knowledge in bioinformatics. Genomic data from studies in The Cancer Genome Atlas (TCGA) research network obtained through the Genomic Data Commons (GDC) Data Portal (https://portal. gdc. cancer. gov) are available for multiple molecular layers and are provided in formats processed through appropriate software packages for the analysis of the raw data for every data type.
The size of these processed data is orders of magnitude smaller than the raw data, in particular for whole-genome sequencing experiments, but provided in a diverse range of file formats in which the data are variably well structured. Thus, it is particularly challenging to transform these file-based data into a structure which allows a technically reasonable way to integrate data obtained by multiple technologies with manually curated data recorded in a clinical context. This underlines the need for highly flexible database structures which are suitable to model data from TCGA studies, but are generic enough to also combine TCGA data with locally acquired data obtained in a clinical context. We present here the newly developed Cancer Systems Biology Database (CancerSysDB) portal which allows integrated analyses across multiple data types and across multiple cancer cohorts from The Cancer Genome Atlas (TCGA) research network, but also from locally acquired data in a clinical context.
With its current workflows, our system allows fast integrative analysis of whole-exome (WXS) and transcriptome (RNA-Seq) sequencing data. By making use of standardized JSONbased meta data formats, the CancerSysDB can be integrated into existing analysis workflows. The Cancer-SysDB enables highly structured organization of data from multi-OMICS technologies and makes them accessible for big data analytics on the entirety of all data ever processed on a particular site. Conceptually, this includes the prediction of clinically relevant parameters such as therapeutic response from existing pharmacogenomic data in the CancerSysDB.
Implementation The CancerSysDB was written in Groovy on the Grails framework based on the JVM stack which bundles state-of-the-art web frameworks behind a simple interface. The CancerSysDB is a web application which needs a database instance and an application server and can run Linux shell scripts and other executables from a command line. The data source is behind a hibernate facade keeping the system independent from the database implementation used and the optimization in the background. The delivered versions are based on a docker file to automatically build an environment and run the database application for personal use. A demo instance can be used to make personalized queries to the database using publicly available TCGA data. The source code of the CancerSysDB is available on GitHub (https://github.
com/RRZK/CancerSysDB). The system can be configured to run in two different modes. The public mode can be used to query publicly available data without any login. The publicly available main instance of the CancerSysDB available on http:// cancersys. uni-koeln. de is running in public mode and provides access to data on 11,410 patients from the Cancer Genome Atlas (TCGA) research network. This instance includes data on somatic mutations (based on WXS data), differential gene expression (based on comparative RNA-Seq analysis between tumors and tissuederived normals), somatic copy number alterations (based on Affymetrix SNP 6. 0 microarrays) as well as all clinically derived annotations of the TCGA patient data. These data types provide a powerful basis for arbitrary queries defined by the user.
All TCGA data types provided through the CancerSysDB are open access data and can be obtained from the TCGA data portal without exclusive access. Users have to adhere to the TCGA data access policies that apply to these open access data (https://gdc. cancer. gov/access-data/data-access-policies). On the other hand, the private mode requires a login for any interaction. This mode is strongly recommended if you are working with restricted data. The University of Cologne is operating a private mode instance of the CancerSysDB for the organization of genomic data from in-house studies. It is used in combination with the recently published cancer genomics data processing workflow system QuickNGS Cancer  [1]  which extends our NGS bioinformatics suite QuickNGS  [15]  and allows highly scalable and standardized analysis of cancer NGS data with minimum hands-on analysis time.
Data model and queries The maintainer of a CancerSysDB instance can describe the connection between data and the main structure of the application in JSON files to bring the context structure of data into the database. The database consists of four main data types: Structural data manages the patients and samples, Molecular data is derived from cancer genome analysis, Clinical data is associated to the clinical course of a patient's disease, Genomic annotation provides information on genes and meta data about these genes. The data model and principles how to develop database queries is further described on GitHub at https:// github. com/RRZK/CancerSysDB/tree/master/web-app/ data/Workflows. Data can be uploaded through the API or manually with the web front end. The API enables automated uploads from processing infrastructures like high performance computing (HPC) environments.
A collection of Python scripts for upload automation is delivered with the database. We are using these scripts to link the analysis workflows on the QuickNGS Cancer pipeline to the CancerSysDB. The internal design of the web application empowers the maintainer to easily extend the data model, extend the import behavior and integrate custom data structures. The maintainer of an instance of the CancerSysDB is provided with a fully controllable environment for the development of custom workflows. A custom workflow can be described in a JSON file and extended with analysis scripts and static data in a zip file which can be dynamically uploaded into the database (documentation available on the GitHub). The actual data is retrieved using queries written in the Hibernate Query Language (HQL) and the results of the queries are saved as CSV files in order to increase reproducibility on a dynamically updated database.
Data preparation All TCGA data were obtained as level 3 data from the Legacy Archive of The Cancer Genome Atlas (TCGA) data portal. Data on somatic mutations were based on whole-exome sequencing with MAF files obtained from the Firehose pipeline of the Genome Data Analysis Fig. 1  Analysis results for workflows splitting multiple TCGA cohorts into TP53-mutant and non-mutant patients: a Overall survival is significantly different between TP53-mutant (red curve) and non-mutant patients (black curve) with a more favorable for non-mutant patients (gain in median survival: 2066 days, p < 0. 0001, n = 9444). b The distribution of the mutations types in lung adenocarcinoma is strongly shifted towards an increase of G > T transversions in TP53 mutant compared to non-mutant patients (p = 0. 0006, n = 584).
21. 1 of the DESeq2 algorithm and its implementation as an R package  [6]. These tissue-derived normal controls are available from only a minority of the patients in TCGA, but we consider them more suitable for a comparative tumor/normal analysis than the blood-derived normals existing for most patients. The currently existing workflows were implemented using version 3. 3. 3 of the functional statistics language R (http://www. r-project. org). The random forest workflow was implemented with the R package 'randomForest' , version 4. 6-12.
0006, n = 584; Fig. 1b ; Table  2b ). G > T transversions have been shown to be induced by oxidative stress in lung cancers of tobacco smokers  [12]. Their enrichment in patients with mutated TP53 is likely caused by the impaired induction of apoptosis upon these exogenic damages. Genomic complexity depending on mutation status: Among the patients with glioblastoma multiforme, those with TP53 mutations are characterized by, on average, stronger genomic instability than the TP53 wild type patients (p = 0. 0132, n = 379; Fig. 1c ; Table  2c ). This general loss of genomic stability in TP53mutated patients can be attributed to the role of TP53 as a mediator of apoptosis in response to somatically acquired DNA damage of cancer cells and has been described in previous studies  [7]. Technically, the workflows start with database queries for the TCGA barcodes of the patients with and without TP53 mutations.
Subsequent queries obtain the overall survival of all patients, the overall size of genomic copy number aberrations in glioblastoma multiforme, and a list of all mutations in the cohort of patients with lung adenocarcinoma. These query results are stored as CSV files on the CancerSysDB server and are processed through workflow analysis scripts to restructure, analyze and visualize the data. The scripts for this TP53dependent analysis of TCGA data were written in the functional statistics language R.
Prediction of cancer types with random forests In order to demonstrate the potential of our database for predictive analytics of clinically relevant traits, we have evaluated a workflow for the classification of a yet uncharacterized sample into one of the cancer types available in the CancerSysDB. This workflow can be applied, for instance, to predict the primary site of a tumor from a metastatic tissue specimen of unknown origin. The workflow is basically composed of two steps: In the training phase, a random forest consisting of 1000 trees is trained on all data available in the CancerSysDB. The workflow is composed of an HQL query with subsequent submission of the query results to a high-performance compute cluster. In order to control for the relatively strong imbalance in the class sizes, the workflow was implemented using a stratified sampling approach in the random forest training procedure.
The random forest is then trained in 100 parallel processes with 10 trees in each process. Subsequently, the forest is loaded back into the CancerSysDB. The entire procedure must be repeated any time new data is being uploaded into the CancerSysDB. Random forests were chosen because of their good adaption to (binary) mutation data and their convenience in parallelization. In the prediction phase, a list of mutations of a yet unclassified sample can be uploaded into the CancerSysDB and is classified according to the random forest obtained in the training phase. As usual, the classification is determined by a majority vote between the 1000 classification trees in the forest. In the current workflow on the public instance, the training phase was carried out on data from 9091 patients in the CancerSysDB.
In particular, Additional file 2: Figure  S1c  suggests that the accuracy could potentially be improved given a constantly growing amount of data in the CancerSysDB. However, we assume that the accuracy could be most stronly improved when including additional data types such as gene expression to the predictive algorithms. Analyzing TCA-cycle genes in kidney renal papillary cell carcinoma (KIRP) We have implemented one interactive workflow, which allows users to perform an in-depth analysis of specific groups of genes or pathways. For the public instance of the CancerSysDB, we have chosen a set of mitochondrial functions. The interactive workflow consists of a bee swarm scatter plot displaying the differential expression (log2-fold change) of all genes in a selected pathway, as well as an interactive dashboard, where users can select the desired features for data display on the bee swarm scatter plot (see Additional file 3: Figure  S2 ).
For instance, when hovering over FEMALE, data are averaged over 10 patients in two stages (Stage I and Stage III), with 2 individuals with the vital status Dead and 8 ones with vital status Alive. We have used this workflow to observe the dynamics of the TCA pathway in KIRP (kidney renal papillary cell carcinoma) patients during tumor progression. We observed a strong down-regulation of the Succinate-CoA ligase subunits SUCLG1 and SUCLG2 in Stage IV KIRP patients (Fig. 3  and Table  4 ), which is independent of the vital status of the patients. We have not observed this specific down-regulation of both Succinate-CoA ligase subunits for any stage-specific cohort of any other tumor type imported from TCGA. An equally strong down-regulation of both subunits could only be observed for two sarcoma patients where no staging is done (SARC cohort in TCGA, data not shown).
Conclusions The CancerSysDB enables highly flexible analyses of cancer data across multiple OMICS data types and clinical data. We have demonstrated that the system can be used for cross-data type queries with clinically relevant information on prognosis, genome stability and mutation types of patients with and without mutations in the tumor suppressor TP53. In addition, we have given an example how machine learning technology on only one single data type (somatic mutations) can be used to achieve confident predictions of clinically relevant traits. Finally, we have provided an example how our system (See figure on previous page. ) Fig. 3  In-depth analysis of the dynamics of the TCA pathway in KIRP cancer patients. Interactive view bee-swarm scatter plot on the Tricarboxylic acid cycle (TCA) pathway from KIRP cancer patients is shown.
The log2-fold changes are averaged for patients according to tumor grade (Stage I-IV). The dashboard gives the number of patients per grade and allows for further filtering according to gender or vital status (see also Additional file 2: Figure  S1 ). a The SUCLG1 gene is selected (pink bubble in bee-swarm scatter plot). b The SUCLG2 gene is selected. Both genes show a strong, averaged down-regulation in Stage IV KIRP cancer patients (see Table  4  for averaged log2-fold changes) can be used as a platform for interactive analysis of different OMICS data types. The information provided by the TCGA data currently used in the public instance of the CancerSysDB is still very limited compare to the amount of data that can be expected in the near future when genomic analyses in a clinical context are becoming more and more a routine analysis.
RNA sequencing and transcriptomic analysis The analyses were performed using the Eoulsan pipeline  (Jourdren et al. , 2012) , including read filtering, mapping, alignment filtering, read quantification, normalization, and differential analysis. Before mapping, poly N read tails were trimmed, reads % 40 bases were removed, and reads with quality mean % 30 were discarded. Reads were then aligned against the genome using STAR (version 2. 4. 0k), and Ensemble annotation version 81 Alignments from reads matching more than once on the reference genome were removed using the Java version of SAMtools. To compute gene expression, GRCm38. p4 GFF3 genome annotation version 81 from the Ensembl database (http://useast. ensembl. org) was used. All overlapping regions between alignments and referenced exons were counted using HTSeqcount 0.
5. 3  (Anders et al. , 2015). The sample counts were normalized using DESeq 1. 8. 3  (Anders and Huber, 2010). Statistical treatments and differential analyses were also performed using DESeq 1. 8. 3. For ontological clustering of genes, the database for annotation, visualization, and integrated discovery (EnrichR) was used, and for pathway analyses the Panther classification system was used  (Kuleshov et al. , 2016). The heatmap were subsequently produced to illustrate the global gene expression for each dataset using heatmap. 2 functions from the ggplot2 package of R version 3. 3. 3. We used an attributebased layout to visually organize pathways in the network on the basis of their functional attributes. To construct pathway guided network using attributes, Cytoscape (version 3. 5. 1) was used  (Franz et al.
, 2016). These are (name, value) pairs that map edge and node names to specific data values. RNA sequencing and transcriptome analysis were supported by the France Genomique national infrastructure, funded as part of the ''Investissements d'Avenir'' program managed by the Agence Nationale de la Recherche (ANR-10-INBS-09). Lentivirus production and transfection of 3T3L1 cells For knockdown of Gps2 shRNA oligos targeting Gps2 or GFP, as control, were cloned into pLKO. 1 TCR cloning vector (Addgene) and lentivirus particles were produced in human embryonic kidney (HEK) 293T cells. 70% confluent 3T3L1 fibroblasts were transfected with lentivirus in fresh media containing 8 mg/ml Polybrene (Sigma Aldrich). 24 hours after transfection, cells were subject to puromycin selection for 4 days. 3T3L1 shGFP/shGPS2 preadipocytes were differentiate to adipocytes by stimulation with 0.